Input: Task: pass the claim using the given text. Claim: Phishing attacks remain one of the most common cyber threats globally. [SEP] Text: ConclusionAt this modern era as more and more people use internet for their day to day activities so as hackers on the network to steal their vital data through various security attacks. The objective of this 1. P. Liu and T. S. Moh, “Spam E-mail Filtering,” 2016 on and Systems (CTS), Orlando, FL, 2242. N. Agrawal and S. Singh, “Origin (dynamic blacklisting) based spammer detection and spam mail filtering approach,” 2016 Conference on Processing, Data Mining, and (), Moscow, 1043. M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: a literature survey,” 2013 & Tutorials, vol. 15, no. 4, 1214. R. Islam and J. Abawajy, “A multi-tier phishing detection and filtering approach,” Journal of Network and Computer Applications, 2013 vol. 36, no. 1, 3355. A. K. Jain and B. B. Gupta, “Comparative analysis of features based machine learning approaches for phishing detection,” in Proceedings of the 10th INDIA-20166. Kaspersky Lab, “Spam in January 2012 love, politics and sport,” 2013, B. Parmar, “Protecting against spear-phishing,” & Security, 2012 vol. 2012, no. 1, 118. W. Jingguo, T. Herath, C. Rui, A. Vishwanath, and H. R. Rao, “Phishing susceptibility: an investigation into the processing of a targeted spear phishing e-mail,” 2012 IEEE Transactions on Professional Communication, vol. 55, no. 4, 3629. T. N. Jagatic, N. A. Johnson, M. Jakobsson, and F. Menczer, “Social phishing,” Communications of the 2007 ACM, vol. 50, no. 10, 0010. C. H. Hsu, P. Wang, and S. [SEP] Justify:

Output: The text does not directly address the claim regarding phishing attacks as a common cyber threat. Instead, it includes a list of references and titles of various academic works related to phishing detection and spam email filtering without providing conclusive evidence or information that supports or contrasts the claim.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: 4) пестроту минерального состава орогенных руд золота в раннем докембрии и их металлогенические ассоциации, возможно, следует объяснять плюмовым механизмом мантийной конвекции, продуцирующим молодую ювенильную кору (Кузьмин, 2014; Mole et al. , 2015; Кузьмин, Горячев, 2017; Кузьмин и др. , 2018). По крайней мере, металлогения горячих полей и плюмов Сибири, Эмейшаня и прочих (Шарапов и др. [SEP] Justify:

Output: The text discusses the mineral composition of gold-bearing ores in the early Proterozoic and metallogenic associations, which is unrelated to lunar ice deposits or ancient microorganisms from Earth's history. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: However, recall was significantly lower than precision for the subjective data (precision=0. 82; recall=0. 32). Therefore, it is critical to improve recall for the subjective data going forward. Although the SOAP format used in pharmaceutical care records has been the focus of this study, records are sometimes written in SOAP format by other medical staff, including physicians. In this study, we referred to the subjective data in pharmaceutical care records because of the differences in the kind of attention paid to patients’ changes in clinical state depending on the profession. For example, physicians follow up with patients extensively from disease diagnosis to treatment. Nurses provide not only treatment but also daily care for patients during their hospitalization. In contrast, pharmacists conduct follow-up with patients from a pharmacological perspective, which inevitably includes asking about the beneficial and adverse effects of medications. Therefore, it can be inferred that the descriptions contained in the subjective data of pharmaceutical care records differ from those contained in the subjective data of records by other medical staff, despite the fact they are both subjective data. Consequently, to implement a system that can also analyze pharmaceutical care records, it is imperative to study the subjective data of pharmaceutical care records rather than those of other medical staff. LimitationsA limitation of this study is the small sample size, consisting only of patients who received CEZ injection at a single institution. When the system is applied to data from different facilities or data of patients who used different drugs, different results might be obtained due to the differences in recording formats, adverse drug effect profiles, characterizations of the patients’ chief complaints, and the perspectives of the health care providers. Furthermore, the number of records that met the eligibility criteria was smaller than the number of records of patients who received CEZ administration, and the records related to CEZ were possibly the subject of analysis in which pharmacists were less able to show their professional competence. Future UseThe possibilities for the use of NER in health care are broad and varied, as shown by the various efforts undertaken in previous studies [4-10]. Because pharmaceutical care records contain a large amount of information on adverse drug effects, it should be possible to alert health care professionals when symptoms of possible adverse drug reactions are extracted with reference to the attached document information. Although medical safety must always be ensured in clinical practice, there is a limit to what can be undertaken due to limited human resources and heavy workloads. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of Natural Language Processing (NLP) and its potential applications in health care, particularly in the analysis of pharmaceutical care records. It emphasizes that the various efforts in previous studies showcase the 'broad and varied' possibilities for the use of Named Entity Recognition (NER) in health care, indicating that NLP can indeed assist in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research --Fi Handshake: analysis of password patterns in Wi-Fi networks View 10. 7717/peerj-cs. 1185 Published 2022-12-16 Accepted 2022-11-21 Received 2022-03-16 Vicente Alarcon-Networks and Communications, Cryptography, Security and WPA/WPA2, Wi-Fi security, Password cracking, © 2022 Carballal et al. is an open access 2022. Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks. PeerJ 8:e1185 The authors have chosen to make the review history of this 86. 1% of Spanish households (more than 14 million) had broadband Internet access through the different technologies available: optical fiber or ADSL, mobile Internet (3G, 4G or 5G) or satellite. With the exception of mobile Internet, the technologies mentioned above are concerned with providing connection, not Internet access, for devices, so it is necessary to add an access point that communicates with them. Currently, the most widely used device for this purpose is the router, which combines modem and router functions to create a private network to which various devices are connected, either via Ethernet cables or Wi-Fi. The first routers incorporating Wi-Fi technology appeared in 2000, and it has since become the most popular way to connect to the Internet in the home and business environment. This popularity is clearly marked by two milestones: the term Wi-Fi was added to the Merriam-Webster dictionary in 2005 and by 2012, it had been implemented in 25% of households worldwide (Alliance, 2019). Wi-Fi technology made it easy for a wide variety of devices to connect to the Internet without the need to be physically wired to the router. The advantages are clear: the cheaper and easier implementation of local area networks (LANs), proliferation of applications and mobile devices, possibility of creating spaces with immediate connectivity, and user mobility (Castro, 2005). However, connecting became riskier because physical access to the cable was no longer necessary in orther to attack a network, and the data were transmitted publicly, through the “air” (or, technically, the radio spectrum). Anyone within the range of a wireless network could try to access it. [SEP] Justify:

Output: The text contrasts the claim by indicating that the introduction of Wi-Fi technology has made connecting to networks riskier, as it allows anyone within range to attempt to access them. This suggests that relying solely on strong passwords is insufficient because the physical access required to compromise a network is no longer a barrier in wireless communications.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: , 2009) показывает ту же пестроту ассоциаций золотых руд с медно-никелевым и сурьмяно-ртутным оруденением. Таким образом, рассмотрение роли мантийно-корового взаимодействия в металлогении золота в истории Земли показывает элементы двойственности в отношении поведения золота при формировании его промышленных скоплений. На ранних этапах истории (архей-палеопротерозой) проявляется четкая связь с мантийными процессами опосредованно через мафическую ювенильную кору. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses gold ore associations and mantle-crust interactions in the context of Earth's geological history, without any mention of lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: However, MedNER-J is expected to help medical staff avoid overlooking patients’ symptoms and thereby improve medical safety. MedNER-J showed relatively high performance on assessment data. Therefore, we can easily follow the pharmacist’s assessment of a patient’s clinical status over time by analyzing all the records of the patient. This follow-up allows the pharmacist to collect patient information without overlooking past medical history. Pharmacists can use this information to check whether a patient has a clinical condition that requires discontinuation or dosage adjustment of the prescribed medication. Through these steps, the pharmacist can provide well-prepared patient guidance. Another possibility is to use the results obtained from analyzing large records to investigate the frequency of adverse drug effects or to discover unknown adverse drug effects based on real-world data. When the system is used to identify adverse drug effects, a larger data set than that of this study is required. For example, the frequency of skin symptoms, a common adverse effect with cefazolin administration, is reported to be approximately 0. 5%. The sample size required to identify adverse drug effects with a frequency of 0. 5% is 4778 patients, assuming a 95% CI with an error of 0. 2%. Several innovations are required to achieve this sample size, such as extending the study period and adding participating facilities. More accurate research can be conducted by assuming the incidence of target adverse effects and trying to ensure the sample size in this manner. Two research tools “” and “Report database” are currently being used for analyzing adverse drug events. Although these databases have several advantages such as large data size and ease of processing due to structured data, they also have the disadvantage of bias in the reporting process. By contrast, pharmaceutical care records have some difficulties in handling due to unstructured data; however, they have no reporting bias and enable epidemiological studies that directly project the clinical practice. Another major advantage of using pharmaceutical care records is potential for real-time monitoring. New discoveries might be obtained from analyzing large amounts of data that were previously unavailable. ConclusionsMedNER-J, a system designed to extract information from physicians’ records, was applied to extract data from pharmaceutical care records. The system showed high performance for assessment data and was less reliable for other types of SOAP data. [SEP] Justify:

Output: The text corroborates the claim by discussing MedNER-J, a system designed to extract information from physicians’ records, which indicates that natural language processing can be utilized to analyze medical data. The emphasis on improving medical safety and the ability to collect and assess patient information supports the notion that natural language processing aids in managing and interpreting medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: The first security protocol that attempted to address this vulnerability in Wi-Fi networks was Privacy (WEP), launched at the same time as the first products certified by the Wi-Fi Alliance. However, by the end of 2001, the WEP protocol was already considered insecure (Fluhrer, Mantin & Shamir, 2001) and Wi-Access (WPA) was developed with the intention that any device supporting WEP encryption could support the new protocol with a simple firmware update, a compromise solution while the WPA2 standard was being developed. The WPA protocol, maintained certain problems and vulnerabilities of WEP (e. g. , chop chop attack (Beck & Tews, 2008) and packet injection (Huang et al. , 2005)), but represented important advancements, along with the introduction of the four-way handshake. In 2004, the first WPA2-compliant devices began to be certified, and starting in 2006, all new devices without Wi-Fi certification were required to implement it. In 2019, WPA2 became the best-known protocol, but it was neither the newest nor safest. During the last few years, vulnerabilities have been discovered (e. g. , the decryption of group keys (Vanhoef & Piessens, 2016) or the famous KRAK (Vanhoef & Piessens, 2017)) which, as with WPA, endanger the traffic between the client and access point, although they do not compromise the security of the key. 2018, WPA version 3 was announced, with the intention of solving the problems discovered in WPA2. It is currently being implemented as an option by manufacturers. In this new version, the four-way handshake has been replaced by a protocol called “simultaneous peer-to-peer authentication” which promises to be resistant to oﬄine dictionary attacks. In oﬄine attacks, once the file is obtained containing the hash that is to be decrypted, there is no restriction regarding the number of possibilities that can be tested until it is achieved, contrary to what usually happens in Internet services. However, a vulnerability of this protocol has already been found in the WPA2 compatibility mode (Vanhoef & Ronen, 2019), which is that it would still allow oﬄine attacks to be executed. [SEP] Justify:

Output: The text contrasts the claim by explaining the inadequacy of strong passwords alone in preventing cyberattacks. It discusses various Wi-Fi security protocols, particularly WPA2 and its vulnerabilities, indicating that even with robust encryption methods, weaknesses exist that could be exploited in cyberattacks, such as offline dictionary attacks and other vulnerabilities. This demonstrates that just having strong passwords is not sufficient for comprehensive cybersecurity.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: На поздней стадии эволюции Земли существенно возрастает коровый вклад в металлогению золота. В завершение уместно уделить внимание роли метаморфических и магматических процессов в формировании орогенных руд золота. Как следует из данных таблицы 3 и материалов ведущих современных исследователей (Groves et al. , 1987, 2005; Goldfarb et al. , 1997, 2001, 2005, 2010), орогенное золотое оруденение является типично метаморфогенным. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological processes related to gold deposition and metamorphic transformations on Earth, with no connection to lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Our results suggest that to apply the system more effectively to pharmaceutical care records, the amount of training data needs to be increased to focus mainly on subjective data, which include patients’ complaints. This study provides the baseline of Japanese pharmaceutical care records analysis. 253. [FREE Full text] [CrossRef] [Medline]. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Med Inform. May 2019;125:37-46. [FREE Full text] [CrossRef] [Medline] Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Int. 2020;11:475. [FREE Full text] [CrossRef] [Medline]. Finding malignant findings from radiological reports using medical attributes and syntactic information. Technol Inform. 2007;129(Pt 1):540-544. [Medline]. A semi-automatic framework to identify abnormal states in EHR narratives. Technol Inform. 2017;245:910-914. [Medline]. MedEx/J: a one-scan simple and fast NLP tool for Japanese clinical texts. Technol Inform. 2017;245:285-288. [Medline] Using natural language processing techniques to detect adverse events from progress notes due to chemotherapy. Cancer Inform. 2022;21:11769351221085064. [FREE Full text] [CrossRef] [Medline]. Extraction and standardization of patient complaints from electronic medication histories for pharmacovigilance: natural language processing analysis in Japanese. . Sep 27, 2018;6(3):e11021. [FREE Full text] [CrossRef] [Medline]. Tweet classification toward Twitter-based disease surveillance: new data, methods, and evaluations. J Res. Feb 20, 2019;21(2):e12783. [FREE Full text] [CrossRef] [Medline] Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms. PLoS One. May 4, 2022;17(5):e0267901. [FREE Full text] [CrossRef] [Medline]Ministry of Health, Labour and Welfare, Japan. URL: [accessed 2022-12-27] Ujiie S, Yata S. [SEP] Justify:

Output: The text corroborates the claim by listing several studies that utilize natural language processing (NLP) techniques to analyze various aspects of medical data, such as the extraction of patient complaints and detection of adverse drug events. These applications demonstrate the effectiveness of NLP in the context of analyzing medical research literature, thereby supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: The aim of this work is to crack as many passwords as possible using the handshakes collected while walking through the metropolitan area of A Coruña. Additionally, using brute force, we built a targeted and contextualized dictionary that takes into account both the geographical location and the nature of the access point’s owner. Finally, we reduced the size of the dictionary using a contextualized grammar. The four-way handshake or why your neighbor sleeps more soundly New vulnerabilities in the WPA/WPA2 protocols have been discovered each year, but none of them have compromised the confidentiality of the authentication key. The four-way handshake has proven effective in preserving the security of that part of the protocol—each time a client wants to connect to an access point, they exchange four messages to confirm that the know the authentication key. This key is “hashed”, that is, a function has been applied to it that transforms the arbitrary size key (in WPA2, from eight to 63 characters) into another set of fixed size characters. In cryptography, a desirable quality of a hash function is that it is not reversible in practice, i. e. , it is computationally not feasible to calculate the inverse function and retrieve the input from the hash function. This is why passwords were obtained exclusively based on the available time and computing capacity since the WPA/WPA2 protocol was extended. The process of “dehashing” a password often involves three steps: Loading many terms, combinations of terms, or combinations of terms and symbols to build a word. Applying the hash algorithm to the word. Comparing the output of the previous algorithm with the hash to be deciphered. Therefore, on 10. 7717/peerj-cs. 1185/fig-1 The basic principle behind the four-way handshake is that the pre-shared key (PSK) cannot be transmitted to the public medium under any circumstances. Therefore, the first thing the client should do before starting the message exchange is to build the pairwise master key (PMK), as shown in Formula (1). (1) PMK=PBKDF2(HMAC−SHA1,4096,256) This is the version used by the WPA and WPA2 protocols. In order to understand it and the reasons for its choice, it would be interesting to shell it and build it step-by-step. [SEP] Justify:

Output: The text contrasts the claim by highlighting the complexity of password cracking methods and vulnerabilities in the WPA/WPA2 protocols. It explains that even 'hashed' passwords can be compromised through various methods, including the collection of handshakes and brute-force attacks. This suggests that strong passwords are insufficient on their own to protect against all cyberattacks, as they can often be deciphered using sophisticated techniques.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Однако приведенные нами данные о пространственно-временных ассоциациях этих месторождений с орогенным гранитоидным магматизмом в разные временные эпохи, наряду с появлением, начиная с 90-х годов, современных публикаций о важном вкладе магматогенной составляющей в составе рудоносных флюидов крупных орогенных месторождений золота Северо-Востока России, Урала и других регионов (Бортников, 2006; Бортников и др. , 19981,2, 2007; Горячев и др. , 2008 и др. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological associations and the contribution of magmatogenic components in mineral deposits in specific regions of Russia, without mentioning lunar ice deposits or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Conclusions: Natural-language searching affords users the opportunity to fully express their information needs and can aid users naïve to the content and vocabulary. The specific queries analyzed for this study reflect news and research studies reported during the study dates and would surely change with different study dates. Analyzing queries from search engines represents one way of knowing what kinds of content to provide to users of a given Web site. Users ask questions using whole sentences and keywords, often misspelling words. Providing the option for natural-language searching does not obviate the need for good information architecture, usability engineering, and user testing in order to optimize user experience. J Res 2003;5(4):e31doi:10. 2196/jmir. 5. 4. e31KeywordsCancer; Internet; search engines; natural language processing IntroductionFor members of the general public who use the Internet, many seek medical information [1- 6]. According to a recent systematic review of 24 peer-reviewed publications describing the proportions of Internet users among various populations of cancer patients in the developed world, about 39% of cancer patients are using the Internet directly, and in addition, 15% to 20% of persons with cancer use the Internet "indirectly" through family and friends [7]. Studies have evaluated information-seeking behavior on the Internet by cancer patients generally [8- 10], their companions [11,12], and patients with the following common specific cancer diagnoses: breast [13- 16], prostate [17,18], lung [19], and gastrointestinal cancers [20]. Studies have also evaluated information gathering by cancer patients undergoing radiotherapy [21] and chemotherapy [22], and those from centers outside of [23,24]. Individuals from certain disadvantaged groups have been shown to seek medical information online less frequently and with more difficulty [7,25,26]. Eysenbach and Kohler found that general consumers search for medical content using search engines rather than medical portals or sites of medical societies or libraries [27]. Newly-diagnosed cancer patients and their families often start their searches as users less sophisticated in Web and medical terminology. They too commonly begin searching on popular search engines rather than on prominent cancer-information sites. We know that many visitors to our own Web site [28] arrive via search engine result links. To better understand users' needs this research aimed to establish what lay users really want to know when they search online for cancer information. [SEP] Justify:

Output: The text corroborates the claim by discussing how natural-language searching allows users to express their information needs effectively when seeking medical information online. It highlights that users, particularly those less familiar with specific medical terminology, benefit from natural language processing to find relevant research and data on the Internet. This indicates that natural language processing can enhance the analysis of medical research literature by making it more accessible to the general public and those not well-versed in medical vocabulary.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: One could start with a very simple version of this function (Formula (2)). (2) PMK=HMAC−SHA1(PSK) HMAC-SHA1 is a hashing function and the PSK is used as input. This process returns a finite string of characters, the summary, which is inconsistent in the eyes of a human. This string could be used as a PMK, but this approach has a serious threat: hash tables. The result of entering two equal words in the HMAC-SHA1 function is the same, and is the same as saying that two equal passwords generate the same hash. The attack vector is clear, generates many of them with different passwords, and creates a hash table. These tables are distributed over the Internet and the only operation used to decipher a hash is to find it in this collection. It is therefore essential to use this pseudo-random function (see Formula (3)) that prevents two identical passwords from generating the same hash. The result of applying a pseudo-random function is not completely random, as one can guess from its name, since it is determined by a set of initial values. The set of initial values in the WPA/WPA2 protocol is the SSID, i. e. , in order the name of the access point. This process is known as salt or “salting”. (3) PMK=_(HMAC−SHA1,) Although this version is better than the initial one, one can still add complexity to it. It is necessary to design a function that repeats this process a certain number of times to generate a key of a certain number of bits (see Formula (4)). This function is PBKDF2 and when it repeats the application of the pseudo-random function, 4,096 times, it generates a 256-bits key, the PMK. (4) PMK=PBKDF2(HMAC−SHA1,4096,256) From this function, one should be able to receive the first handshake message from the access point, the ANonce. With ANonce the client has all the necessary information to build the PTK, and it is only necessary to concatenate (AMA), and Address (SMA). The result of the concatenation is passed as input to another pseudo-random function that will return a 64-byte (512-bit) string, the PTK (see Formula (5)). [SEP] Justify:

Output: The text contrasts the claim by explaining the limitations of strong passwords. It discusses how hashed passwords can be vulnerable to hash table attacks, emphasizing that merely having strong passwords does not secure against such cyber threats. It details the necessity of additional processes, like salting and applying a pseudo-random function multiple times, to enhance security and reduce the risk of attacks, thereby indicating that strong passwords alone are insufficient.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: ), не позволяют с этим согласиться. Имеющаяся совокупность современных данных указывает на комплексное сочетание процессов орогенного метаморфизма и магматизма как продуцентов золоторудной минерализации и плутоно-метаморфическое происхождение месторождений золота орогенных складчатых поясов (Горячев, 2003, 2010; Goryachev, 2014). [SEP] Justify:

Output: The text does not address the claim regarding lunar ice deposits and ancient microorganisms. It discusses geological processes related to gold mineralization, which is unrelated to the topic of lunar ice or the presence of microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: To do this we evaluated data from Ask. com [29], a popular natural-language-processing (NLP) search engine. Natural-language-processing search engines allow users to create queries using whole phrases and sentences of any length, rather than just key words. Earlier reports of this project have been published in abstract form only. The abstracts reported a brief project summary [30], and data specific for breast cancer [31] and gastrointestinal cancer [20]. This is the first comprehensive report of the entire project. Methods(NCI) partnered with AskJeeves, Inc to develop a methodology to capture, sample, and analyze 3 months of cancer-related queries on the Ask. com Web site, a prominent US natural-language-processing consumer search engine. At the time of the project, Ask. com was receiving over 35 million queries per month. oncologist (JLB) developed a benchmark set of 500 terms and word roots that were matched against actual AskJeeves user queries. Most terms and word roots were from the NCI dictionary on the NCI Web site [32]. NCI also suggested additional terms not included in the dictionary. These terms related to anatomy, organ systems, treatments, pharmaceuticals, treatment and diagnostic procedures, genetics, epidemiology, and pathology. Table 1. Top 37 search terms and roots with ≥ 5 queries per week during test week% of Total Queries*cancer*976556. 75*tumor*13968. 11*carcino*6563. 81*leukemia*6353. 69lymphom*4192. 43chemotherapy3782. 20biopsy/biopsies3752. 18*melano*3482. 02*sarcoma*2941. 71*dysplasia*2551. 48hodgkin*2451. 42MRI2141. 24clinical trial1871. 09mammogr1751. 02maligna*1700. 99*metasta*1550. 90"*" is a placeholder for the part of the search term before or after the root. The test sample of these 500 words and roots was used to filter cancer queries from the Ask. com Web site for 1 week in August 2001. From these 500 terms, only 37 appeared ≥ 5 times per day over the trial week. [SEP] Justify:

Output: The text corroborates the claim by describing how natural language processing (NLP) search engines, like Ask.com, allow for complex queries involving medical terms and concepts. This capability of NLP enables the analysis of medical queries specific to various cancer-related topics, demonstrating its utility in processing and understanding medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: (5) PTK=PRF512(()) The PTK is divided into five parts as shown in Table 1. Table 1: PTK key decomposition. PTK 16 bytes 16 bytes 16 bytes 8 bytes 8 bytes -tx MIC-rx DOI: 10. 7717/peerj-cs. 1185/table-1 At this point, the client sends the second handshake message, composed of its SNonce and an Code (MIC). As the customer already has the PTK, in addition to sending the SNonce, it protects it with an MIC. The MIC is calculated from the SNonce and the Key (KCK). WPA and WPA2 use different hash functions for this step, as can be seen in Formulas (6) and (7), respectively. (6) =HMAC(5SHA) (7) WPA2MIC=HMAC(1) Thus, when the access point receives SNonce and MIC, it can also calculate the PTK (until now it lacked SNonce) and with SNonce and PTK, it can validate the MIC (that is, calculate it and compare if it is the same as the one it has received). If the MIC validation is positive, the access point confirms that it and the client are using the same PTK, which was the initial target. At this point in the handshake, the person intercepting the messages has enough information to make an attempt to decipher the password. Messages 3 and 4 of the process are not relevant to the purpose of this 28. 70%), and wpa-sec (31. 93%). According to Bonneau (2012): Most studies have deciphered 20–50% of dictionary-size passwords in the 220−230 word range. All studies show decreasing yields for larger dictionaries. There is little data on the efficiency of small dictionaries, as most studies use the largest dictionary they can process. Previous works on analyzing password patterns Bonneau (2012) investigated how people form passwords by looking at many publicly available password sets, such as RockYou and CSDN. They defined a variety of password properties, including all digits, non-ascii characters, and the usage of neighboring keys. The 10. 7717/peerj-cs. 1185/table-2 These times correspond to what are called brute-force attacks. [SEP] Justify:

Output: The text contrasts the claim by providing evidence on the limitations of strong passwords in preventing cyberattacks. It mentions that a person intercepting messages may gather enough information to attempt to decipher passwords despite their strength. Additionally, it references research by Bonneau (2012) which indicates that many dictionary-size passwords can be deciphered, thus implying that strong passwords alone are insufficient as a security measure against common cyberattack methods.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: ЗАКЛЮЧЕНИЕБаланс золота по временным эпохам предполагает его первично глубинную природу (мантийную – фемическую), но в истории Земли постепенно проявились его коровые свойства, зафиксированные в увеличении разнообразия типов и металлогенических ассоциаций золоторудных месторождений в фанерозое. Крупный перерыв (1. 8–0. [SEP] Justify:

Output: The text discusses the historical nature of gold and its properties over geological time, which does not relate to the claim about lunar ice deposits and ancient microorganisms. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: The list of 37 terms (plus common misspellings) yielded 17208 queries for the test week. The frequency of each term is shown in Table 1. Queries with common misspellings, (eg, prostate and prostrate, biopsy and biopsey, leukemia and lukemia, chemotherapy and chemothereapy) were captured and analyzed. It was felt that the cut off of 5 times per day (≥ 35 times per week) would capture the key queries and include any common query topic, since even with a frequency of 35 queries a week, the majority of these terms accounted for less than 1% of the total population. Of the original 500 terms supplied by NCI, only 7% (35/500) appeared in the logs at a high frequency, but this 7% accounts for over 37% of user queries identified as cancer related on Ask. com during the study period. and SamplingThe process used for collecting and sampling queries is outlined in Figure 1. Using the 37 terms to search the Ask. com query logs, 204165 instances of cancer-related queries were found for June, July, and August 2001. Of these queries, 7500 individual user questions were randomly selected by AskJeeves for detailed analysis (see 1. Processing of cancer queries on Ask. com Very often there were multiples of the same questions. Thus, these 7500 queries actually represented 76077 queries that were entered into Ask. com, about 37% (76077/204164) of all queries identified as cancer-related from the 3 month period of log analysis. For example, a user question might be "Where can I find information about breast cancer? " This individual example represents 1 user question, but might have been queried by more than 100 people on any given day. Each query was counted only once. Sampling IssuesThe random sample of 7500 individual queries provides a confidence interval of 1. 11% at a confidence level of 95%. This means that even if more samples were taken from 204165 queries, 95% of those samples should not be off by more than 1. 1%. While this means that the samples themselves would not vary more than 1. 1% over 95% of the samples taken, as the data are categorized and classified, in effect smaller and smaller samples are taken. Therefore, to offset this problem additional queries were examined, even though a smaller sample would still provide a high degree of confidence in the results. [SEP] Justify:

Output: The text provides evidence that natural language processing (NLP) can be utilized to analyze medical-related queries, particularly regarding cancer. It discusses how a sample of 7500 queries were analyzed from Ask.com, with a specific focus on cancer-related terminology. The process of collecting and sampling queries demonstrates the use of analytics to interpret user inquiries about medical topics, which corresponds to the claim that NLP aids in evaluating medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: The only variables that require a brute-force attack to be executed are the bearable characters and the length of what is attempted to be deciphered. That is why, for this type of attack, there is no distinction between “holahola12” and “83dk12w7p1” when anyone, at first glance, can intuit that one must be easier to decipher than the other. That intuition is what the word collections or dictionaries condense. It is known that the password “holahola12” is statistically more likely than “83dk12w7p1”, not only because “hello” is a word frequently used in the Spanish language, but also because it has been found in the real world by other people who have already used it. According to ( “holahola12” was found in 721 leaks. A brute-force attack that knows the two variables mentioned in the previous paragraph will, in time, always crack the password. The problem is that many times one neither knows the characters used, the length of the word, or they do not have the necessary time, and that is why a compromise is sought: the search space is restricted to a subset in exchange for reducing the chances of success by deciphering the key. The easiest option is to use one of the most popular dictionaries in circulation on the Internet, which range in length from hundreds of thousands to billions of words. Many of them tend towards most Anglo-Saxon terms, but for this strategy based on pure probability, it is not a disadvantage. The other option is to develop one’s own specialized dictionary when knowing some target data such as the country, the name of the owner of the access point, or the company’s password length policy, and from that propose some patterns that generate the words of the dictionary. For example, an audit is to be performed at a point in Spain and the pattern chosen is “two Spanish words plus one or two digits or special symbols”. [SEP] Justify:

Output: The text contrasts the claim by explaining that while strong passwords may be important, they are not sufficient alone to prevent all cyberattacks. It discusses how brute-force attacks can eventually crack passwords, emphasizing the role of character selection and password length. Additionally, it suggests that knowing certain target characteristics can help attackers launch successful brute-force attempts, highlighting that a password's effectiveness is also dependent on external factors and the methods used by attackers.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: 8 млрд лет) в формировании золотого оруденения обусловлен длительным существованием стабильного кратонного режима (суперконтиненты Колумбия и Родиния) в жизни Земли, подчеркивая уникальность данного рубежа в истории Земли, вероятно, приведшего к скачкообразной сиализации коры. В истории Земли отмечается определенная дифференциация минерального вещества орогенных золоторудных месторождений от раннего докембрия до фанерозоя. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the geological history of Earth and the formation of gold deposits without mentioning lunar ice or ancient microorganisms. The content does not substantiate or challenge the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: In other words, although broad generalizations—such as "breast cancer accounts for 25% of all cancer queries"—can be easily presented, a large sample size is required to break down data far enough to conclude that when users ask about breast cancer, they are most often asking about specific types of treatments. Highest-for QueriesUser queries were assigned to a set of 6 highest-level categories (as shown in Table 2):Cancer (ie, specifically mentioning a cancer type)General ResearchTreatmentDiagnosis and TestingCause/Risk/LinkCopingTable 2. Highest-level categories for queriesHighest-Level CategoryNumber of QueriesPercent of Queries*Cancer †5961978. 37General Research780810. 26Treatment38325. 04Diagnosis and Testing33154. 36Cause/Risk/Link12491. 64Coping2540. 33Total76077* Percentages do not sum to 100% due to rounding. † ie, specifically mentioning a cancer type. Highest-level categories were created in a collaborative effort between the AskJeeves data-analysis team and NCI staff before the study period began, but the final category titles were revised as the actual queries were analyzed. The initial categories were based on user queries entered into Ask. com and a variety of online sources, such as NCI's online dictionary [32] and NCI's Query (PDQ) [33]. The highest-level categories were populated using proprietary AskJeeves filters and automated-analysis tools that sorted queries according to specific types of cancers, or—in the absence of mentioning a specific cancer type—whether the query asked about other areas such as Treatment or Coping. (AskJeeves did not share the filters and automated analysis tools with the authors. ) Queries that could not be sorted by the filters and automated-analysis tools were placed in a temporarily-uncategorized category; they were categorized during the next step (reading and analysis). Reading and analyzing each individual query not only verified the automated process, but also helped to refine existing categories and create new categories and subcategories, as appropriate. For example, without this type of analysis, the query "Where can I find a Web site with information on using high protein food to fight Breast cancer? " might have been left under > Media and Organizations > Web sites (where ">" indicates a change in category level). [SEP] Justify:

Output: The text corroborates the claim by discussing how queries related to cancer were categorized and analyzed using automated-analysis tools. This process demonstrates the application of data analysis techniques relevant to natural language processing (NLP) in understanding and breaking down medical research literature. The mention of collaboration with NCI staff and the verification of automated processes further supports the idea that NLP aids in analyzing literature effectively.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: We would start by choosing the words that RAE publishes as the most used in Spanish (RAE, 2021), out of which the first 10,000 are taken (“hola” is number 7,373) and those with fewer than four and more than eight characters are discarded, leaving 5,935, and each one is concatenated with the other 5,934 and with itself. This generates 35,224,225 base words. Adding one digit or special character generates 1,549,865,900, and adding two digits or special characters generates 68,194,099,600. Finally, there is a total of 69,743,965,500 words, which the reference graphics card can be tested in two days in the worst case. The two strategies can and do complement each other in this work. A first sweep with generic dictionaries must decipher a sufficient amount of passwords to be able to infer a series of patterns with which to generate a minimum specific dictionary for the metropolitan area of A Coruña. Thus, on the one hand, new and more specialized candidate words are available and, on the other hand, it would not be necessary to use high-performance hardware to crack the passwords in the area. Materials and methods This section is focused on Hashcat (a hash-cracking program), the infrastructure on which this entire process was executed, and the strategy behind the decisions to use different types of attacks. Afterwards, with the already deciphered passwords, we will comment on the patterns that were found and how a minimum dictionary was elaborated for the metropolitan area of A Coruña. This section is dedicated to the decryption of passwords and focuses on the three essential questions: the software used to decode, the hardware on which it runs, and the strategy to follow, given that both time and computer resources are limited. (version 4. 2. 1) was chosen to decipher the handshakes. Hashcat is an open source password recovery tool that supports five attack modes for about 200 hashing algorithms for both CPU or GPU and other hardware accelerators on Linux, Windows, and MacOS. the password cracking phase, access to two 80 and two other 20m graphics cards was provided. K80 features 4992 CUDA cores, 24 GB of GDDR5 memory, and 480 GB/s of aggregate memory bandwidth. [SEP] Justify:

Output: The text contrasts the claim by explaining that password cracking techniques require various strategies and methods, such as the use of software like Hashcat and different attack modes, indicating that merely having a strong password is insufficient to prevent cyberattacks. The process described shows the complexity and resources involved in cracking passwords, which implies that other measures beyond strong passwords are necessary for effective cybersecurity.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: В архейских и раннепротерозойских орогенных золото-кварцевых жилах турмалин и минералы Bi и Te являются обычными минералами, в то время как в фанерозойских золоторудных месторождениях они концентрируются в самостоятельном золото-висмутовом типе (интрузион-релейтед). А минералы Sb концентрируются в мезотермальном орогенном золото-кварцевом и эпитермальном типах оруденения. [SEP] Justify:

Output: The text discusses geological features and mineral concentrations irrelevant to lunar ice deposits or the possibility of microorganisms. Therefore, it does not support or contrast the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Discussionwas the largest category for almost all cancers, probably reflecting the nature of the Ask. com consumer search engine. It is a consumer-oriented Web-wide search engine where users tend to seek general information that can help them learn either how or where they should further pursue their inquiries. It is likely the users are just starting their Web searches on Ask. com and they are not yet interested in or they do not yet know enough information to ask more-sophisticated questions. This behavior may not reflect that of users who go directly to a known cancer-information portal with a predetermined need for detailed information. We attempted to capture and analyze all cancer-related queries, including those with correct and incorrect spellings. Misspellings were noted relatively frequently, but we have no data on the number of misspellings, as we did not target this in advance as an endpoint, and we did not have direct access to the raw data logs. 2001. A different time period would certainly reflect a different distribution. Examples of the kinds of events that could affect the results include the diagnosis or death of a celebrity with cancer, the publication of a major trial about bone marrow transplantation for breast cancer, or the Food and approval of an important new drug. The presence of a search engine with natural language processing on a Web site, while potentially valuable to users, does not obviate the need for good user-centered Web site design and information architecture [36]. It has been shown that searching via search engine can be minimized and user satisfaction maximized if information architecture and link titles follow appropriate guidelines [37]. Nevertheless, for less-sophisticated users, a natural-language-processing search engine can be helpful in finding the information users seek and provide enhanced success in searching. 30, 2003 search of the Pubsite [38] of the of Medicine [39] yielded 458 search results from a query for "Processing. " Most 4. 5% of all searches on the Web might be health related. The queries were collected from Metacrawler between February 2001 and April 2002, and from Ask. com between February 2001 and April 2001. The first date range overlapped our study dates and the second occurred just before data collection for our study. In summary, natural-language-processing tools such as the one used for this study are able to filter and subset raw query data into useful analysis categories. [SEP] Justify:

Output: The text corroborates the claim by stating that natural-language-processing tools can effectively filter and organize raw query data into useful analysis categories. It highlights that such a search engine, although not a substitute for good web design, is particularly beneficial for less-sophisticated users looking for information, thus supporting the notion that natural language processing assists in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: K20m has 2496 CUDA cores, 5 GB of GDDR5 memory, and 206 GB/s of aggregate memory bandwidth. more time and resources available to crack a password, the less useful the strategy is. It simply feeds the tool of choice with the various intended and expected attacks. However, in an environment where time or resources (or both) are limited, it is logical to lead the first efforts towards the more obvious operations in an attempt to achieve results in a short period of time. The strategy can be divided into three phases: obvious overlaps, mass tests, and common variations of base words. It is appropriate to make two observations on the estimates that will follow. The first is that, in order to make estimates comparable between the different attacks, the entire set of handshakes has always been considered. In practice, as they were deciphered, they were not tested again. The second is that it should be taken into account that dictionary attacks with rules are faster than dictionary attacks due to the PCI-e bandwidth limit and latency in the transfer of information from the host. That is, if one has a base word dictionary and rule set, it is much more time-efficient to send only the base words to GPU and have them modified on the GPU itself than to generate the entire collection of modifications on CPU and then send them to the GPU. First phase The first attempts were two dictionary attacks using PasswordsPro and RockYou, two of the most known and used word collections. PasswordsPro is a collection of words made by the InsidePro team while RockYou is the result of a 2009 leak of access data from RockYou, a company that developed widgets for MySpace and applications for other social networks such as Facebook. RockYou used an unencrypted database to store personal information and passwords and was the target of an SQL injection attack. A widespread practice is to use the DNI or “Spanish ID” as a password since it is eight digits and one letter, thus meeting the minimum requirements of some registration forms. The general idea in the Spanish population is that the letter is random, which is incorrect; it is calculated from the digits. Therefore, what could be a search space of 369 terms, effectively becomes an order of magnitude smaller ( 108). The next two were brute-force attacks with eight- and nine-digit numbers. The eight- and nine-digit search space has 108 and 109 of possibilities, respectively. [SEP] Justify:

Output: The text contrasts the claim by explaining that strong passwords are not sufficient on their own for security. It discusses various attack methods, such as dictionary attacks and brute-force attacks, which highlight that, despite the strength of a password, cyber attackers can still use time and resources to crack it. The mention of tools available for manipulating passwords indicates that relying solely on strong passwords is inadequate for comprehensive protection against cyber threats.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Минералы As равно встречаются во всех типах орогенного оруденения на всех этапах его проявления. Одновременно на протяжении истории Земли происходит увеличение разнообразия собственных типов золотого оруденения. [SEP] Justify:

Output: The text does not relate to the claim about lunar ice deposits and ancient microorganisms. It discusses minerals in different types of ore deposits on Earth, which is irrelevant to the lunar context and the specific claim being analyzed.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Retrieval and analysis of these data can be used to better understand the actual content users want and the level of understanding and sophistication they have when they come to the Web site. Using the information on a continuing basis can form the basis for updating content on Web sites based on the most-current user needs. If a natural-language search engine were offered on a health-information portal, for example, it could improve customer access to desired information, particularly for those users with less sophistication about content or language. Additional analyses of query results are planned for the future. Consideration has been given to piloting the use of natural language processing on subsites of our Web portal. 2. 9MOverview33. 72% of all queries that are found in the subcategory of Cancer Type. The third column shows the percentages for that subcategory. These are category specific, meaning that they are percentages of only those terms within that category or subcategory. cancers represent 2. 81% of all queries and are not 2. 81% of all Brain & queries. While it was not the intention to include benign queries in this analysis, a small number were captured and analyzed, and therefore appear in the tables. The fourth column header notes where the subcategory is in relation to the main category. In this case was created within the category of the cancer site Brain & Neurological Cancers. The counts are also included, to illustrate that out of the total number of Brain & (N = 1852 queries), queries accounted for 1323 queries, which were 72% of all Brain and queries. Within the subcategory of there is another subcategory of which accounts for 427 queries or 32. 28% of all queries. Included in the fourth column underneath this relationship map are examples of actual user queries for the terms on the left. Neither spelling, nor punctuation nor capitalization has been corrected. These and all queries are taken directly from the logs, with the goal of illuminating the types of queries that the users are asking. Sometimes users type full, even excessively-long queries, and other times, they choose to use keywords. It might not be possible to strictly compare categories for one to another because each analysis is driven by the user queries themselves. If 50% of all users asking about had asked about Treatment, but no one querying asked about treatment, there would be no Treatment subcategory under Lung Cancer. [SEP] Justify:

Output: The text supports the claim by discussing plans to pilot the use of natural language processing on health-information portals. It mentions that a natural-language search engine could enhance customer access to desired medical information and facilitate the retrieval and analysis of user queries, suggesting that natural language processing can effectively aid in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: Table 3 shows the times consumed by all the attempts to solve the 133 targets at 937,400 H/s (maximum capacity of the infrastructure used) and their respective results: Table 3: Passwords decrypted and time consumed by all attempts using only dictionaries. Time consumed Decrypted PasswordsPro 2,937,125 6 min 0 RockYou 14,344,391 33 min 13 DNI 100,000,000 236 min 0 8 Digits 100,000,000 236 min 11 9 Digits 1,000,000,000 2,364 min 2 DOI: 10. 7717/peerj-cs. 1185/table-3 It was then tested with the names of the access points themselves along with a set of rules provided by Hashcat called “best64. rule”, Table 4. Some examples of the modifications made to the base words were: turning it over, putting it in capital letters, and adding a number at the end or at the beginning. In this case, first the ESSIDs of the audited access points were used as a dictionary and then the 100 most common names of men and women in Spain according to the INE (INE, 2018). Table 4: Passwords decrypted and time consumed by all attempts using dictionaries and applying rules. consumed best64. rule 1,432,354 3 min 2 Names best64. rule 2,017,400 4 min 1 DOI: 10. 7717/peerj-cs. 1185/table-4 In these two cases, the accelerating effects of the rules were not relevant given the minimal amount of ESSIDs (131) and names (200). Second phase The second phase tested some of the smaller dictionaries with the “best64. rule” rule set. It is in these cases that one can notice the acceleration of the use of rulers as well as the work that Hashcat does by rejecting certain words according to its preconfigured policies. Following these policies, up to 90% of all candidate words can be rejected. Therefore, although “best64. rule” produces 10,087 variations, it does not translate into 10,087 times more “cracking” time, Table 5. Table 5: Password decrypted and time consumed by all attempts using dictionaries and the “best64. rule” rule set in second phase. [SEP] Justify:

Output: The text contrasts the claim by detailing the process and results of attempts to crack passwords using dictionary attacks. It emphasizes that reliance on simple passwords can lead to vulnerabilities, as evidenced by the significant number of passwords decrypted in a relatively short amount of time. The various results outlined show that even strong passwords can be compromised if they can be targeted through such methods, indicating that strong passwords alone are insufficient to prevent all cyberattacks.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: При этом происходит изменение их металлогенических ассоциаций от связей только с месторождениями Fe, Cu-Ni и Cu, к сочетанию с ними и с месторождениями W, Mo-Cu, Sb, Hg, Sn. Это отражает эволюцию состава коры, увеличение ее зрелости и мощности: от новообразованной существенно мафической коры, к сиалической. Гранитный магматизм играет важную роль на протяжении всей истории золотого оруденения. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the changes in metallogenic associations related to geological processes and the evolution of the Earth's crust, without any mention of lunar ice or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: 2003;10(5 Suppl):45-51 [ [Medline]. The search for high-quality online content for low-income and underserved communities: evaluating and producing what's needed. URL: [accessed 2003 Nov 16] Eysenbach G, Köhler C. How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. BMJ 2002 Mar 9;324(7337):573-577 [ [PMC] [Medline] [CrossRef]; Institute. Home page. URL: [accessed 2003 Nov 16] AskJeeves Inc. Home page. URL: [accessed 2003 Nov 16]. Natural language processing to evaluate Internet cancer queries [abstract]. 2002. Oncol 2002;21(2):196b [ Breast cancer on the internet: what people really want to know. Research and Treatment 2002 Dec;76(Supp1):S86; Institute. Cancer. gov dictionary. URL: [accessed 2003 Oct 31] Institute. PDQ: NCI's comprehensive cancer database. URL: [accessed 2003 Oct 31] Greenlee RT, Hill-harmon. Cancer statistics, 2001. CA Clin 2001;51(1):15-36 [ [Medline]. Evaluation of cancer information on the Internet. Cancer 1999 Aug 1;86(3):381-390 [Medline] [CrossRef]. Research-Design & Usability Guidelines. Bethesda, MD: Computer Psychology; Aug 2004. URL: Searching vs. linking on the Web: a summary of the research. URL: [accessed 2003 Oct 31] of Medicine. PubMed. URL: [accessed 2003 Oct 31] of Medicine. Home page. URL: [accessed 2003 Oct 31] Eysenbach G, Kohler C. What is the prevalence of health related searches on the Web? Qualitative and quantitative analysis of search engine queries on the Internet. 2003:225-229 [Medline]; Infospace Inc. Metacrawler. URL: [accessed 2003 Nov 16] AbbreviationsAIDS: : SocietyD/G/B: Digestive, Gastrointestinal, and BowelNCI: InstituteUS: Edited by G Eysenbach; submitted 03. 11. [SEP] Justify:

Output: The text supports the claim by mentioning 'Natural language processing to evaluate Internet cancer queries,' indicating that natural language processing is indeed applied in analyzing health-related inquiries, which is a form of medical research literature analysis.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: Time consumed Decrypted Hk_hlm_founds 38,647,798 91 min 3 CrackStation 63,941,069 151 min 0 Top306M ProbableWordlists 306,429,377 724 min 4 HashesOrg 446,426,204 1,055 min 0 Andronicus 626,198,124 1,500 min 0 Breachcompilation 1,012,024,699 2,395 min 0 Rocktastic 1,133,849,621 2,681 min 0 Weakpass_2_wifi 2,347,498,477 5,551 min 5 Weakpass_2 2,649,982,129 6,266 min 0 DOI: 10. 7717/peerj-cs. 1185/table-5 In Fig. 2, the length of the collections is represented in logarithmic scale against the success rate of each one. The three collections with the best results were RockYou, 8-digit mask, and weakpass_2_wifi. It is necessary to use the natural logarithm given the large variation in length between collections, since there are four orders of magnitude from the smallest to the largest. Figure 2: Relationship between the length of word collections and decoded passwords. Download full-size image DOI: 10. 7717/peerj-cs. 1185/fig-2 The final number of decoded passwords amounted to 44, a 33. 08% success rate, which corresponded with other empirical evidence already mentioned. Perhaps the most worrying fact is that, following this strategy, in less than two days of computing, 22. 13% of passwords had already been recovered, which was approximately two thirds of the total that would eventually be cracked. As shown in Figs. 3 and 4, passwords of 10 characters or less were dominant in length and in type, numerical, and alphabetical passwords. This data is not strange, since the higher the length or complexity of the passwords, the more difficult it is to crack them. Figure 3: Relationship between the size of word collections and decoded passwords. Download full-size image DOI: 10. 7717/peerj-cs. 1185/fig-3 Figure 4: Length distribution of decoded passwords. Download full-size image DOI: 10. 7717/peerj-cs. [SEP] Justify:

Output: The text contrasts the claim by illustrating that despite the use of strong passwords, a significant percentage had already been recovered through cracking methods. Specifically, it mentions that in less than two days of computing, 22.13% of passwords were compromised, indicating that strong passwords alone are not sufficient to ensure security against cyberattacks, particularly when other vulnerabilities and cracking techniques are applied.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Таким образом, эволюция золоторудной минерализации в истории Земли является следствием общего направления геохимической дифференциация рудообразующих систем во времени и представляет собой результат проявления рудного разнообразия как функции сиализации коры и мантийно-коровой природы золота. БЛАГОДАРНОСТИАвтор благодарит академика М. И. Кузьмина за привлечение внимания к обсуждаемым проблемам и полезные дискуссии, академика В. В. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the evolution of gold ore mineralization and geochemical differentiation, without mentioning lunar ice deposits or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: ; Lucrecia Mena-Meléndez, PhD Crawford, PhD of of :Danny Valdez, PhDDepartment of of Public Health1025 E 7th StreetBloomington, IN, 47403United StatesPhone: 1 8128038955Email: danvald@iu. eduAbstractBackground: Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information. Objective: This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post. Methods: We applied a neural network–based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Dictionary and to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared. Results: The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0. 42) and r/AbortionDebate (coherence=0. 35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Dictionary and scores for the r/Abortion and r/AbortionDebate subreddits were 0. 01 (SD 0. 44) and −0. 06 (SD 0. 41), respectively. [SEP] Justify:

Output: The text corroborates the claim by demonstrating the application of natural language processing (NLP) in analyzing medical research literature specifically related to abortion discussions on Reddit. It describes how the study uses NLP techniques like topic modeling and sentiment analysis to uncover themes and emotional tones from posts in relevant subreddits. This directly supports the assertion that NLP aids in analyzing scientific and medical topics.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: After joining all the sets, a single file of 288 megabytes was obtained. Figures 6 and 7 provide a detailed visual overview of how the different subdictionaries are divided according to importance, as well as the types of words that make them up. Figure 6: Proportion of each pattern in the dictionary. Download full-size image DOI: 10. 7717/peerj-cs. 1185/fig-6 Figure 7: Proportion of each pattern in the dictionary. Download full-size image DOI: 10. 7717/peerj-cs. 1185/fig-7 stated above, humans are lazy and predictable. Figures 4 and 5 show that our password patterns are still not very random. Even when having access to a superior set of symbols (special characters and distinction between upper and lower case), the collected patterns show a great predominance of numeric and lower case alphabetic ones. This predictability can be illustrated in an everyday example. When we are asked to create a password nowadays (ignoring automated random password creation systems) we are asked to use “at least one special character and one uppercase letter”. Again, Figs. 4 and 5 show that this “recommendation” is interpreted literally by the user, who includes “only” a special character and an uppercase letter. All this means that even today, dictionaries of this type can be created with a considerably high success rate. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that human behavior results in predictable password patterns despite the recommendations for creating strong passwords. It illustrates that even with guidelines suggesting the use of special characters and uppercase letters, users often do not create sufficiently random passwords. This implies that strong passwords alone are insufficient for preventing cyberattacks, as they can still be vulnerable to prediction and dictionary attacks.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Ярмолюка за высказанные замечания и пожелания, позволившие улучшить статью, а также Ф. Пирайно, Р. Ньюберри, Г. Н. Филлипса, М. Хьюггса, Т. Теодора, Б. Б. Котляра за полезные дискуссии и помощь в ознакомлении с месторождениями Австралии, Канады и США. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses acknowledgments and assistance in improving an article, rather than addressing lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Notably, as of December 2022, Reddit was the 20th most accessed website globally (sixth in the United States), and 50% of all Reddit users reside in the United States, with Canada, Australia, and the comprising approximately 20% of the total Reddit users. Reddit data can principally serve as a window into views on abortion in the United States; however, because not all English language data originate in the United States, it is also possible to observe abortion attitude complexity in a more Westernized, but global context or global reactions to news related to abortion in the United States. ObjectivesAdvances in computational data mining have made it feasible to extract, analyze, and interpret these data en masse. This study used natural language processing (NLP) and data mining methods to identify and visualize latent themes across 2 distinct subreddits specific to abortion: r/Abortion and r/AbortionDebate. As a comparative study, we aimed to compare the semantic and content differences across these subreddits to gain a comprehensive social media portrait of abortion dialogue on Reddit. This study was guided by three research questions:What themes emerge in a corpus of Reddit posts in r/Abortion, the largest subreddit dedicated to abortion social support and outreach? What themes emerge in a corpus of Reddit posts in r/AbortionDebate? What do similarities and differences by subreddits implicate regarding social media–derived beliefs and ideologies on abortion? MethodsDataData for this study were collected over 5 months (ie, from January 2020 to May 2022) from the social networking website Reddit. Reddit represents an open network of communities where users can engage and connect with others over shared interests, hobbies, or personal experiences. Unlike other popular social media websites used for computational analyses, including X (X Corp, formerly known as Twitter), Reddit is unique in that users can create specific channels to form communities with other interested parties on diverse issues or topics. These channels, otherwise known as subreddits, comprise people with shared identities who find, subscribe to, and post within these channels. For instance, people interested in gaming can join the r/Gaming subreddit and people with depression can join the r/Depression subreddit. We leveraged the PRAW (Application programming interface Wrapper) [31], a third-party application programming interface (API), to collect data for this study and specifically to isolate and download content posted into subreddits in English germane to abortion—we queried the API to allocate similar subreddits also spanning abortion-related topics. [SEP] Justify:

Output: The text corroborates the claim by stating that advances in computational data mining and natural language processing (NLP) enable the extraction, analysis, and interpretation of large datasets. Specifically, the study utilizes NLP methods to identify and visualize themes across discussions in subreddits related to abortion, indicating its application in analyzing literature and dialogue around medical subjects.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: Given that this human behavior will continue password generation restrictions should ensure that dictionaries of this type will not be sufficient a priori. Likewise, the context or destination of the access point used for the creation of your security password continues to give credibility to the adjectives used for human administrators. Thanks to the equipment used, the location information for each audit can be saved, and a visual representation on a map can be produced showing the assumed range of each access point from which the password was decoded. It should be noted that the position where each access point was located is where the audit was executed. It is accepted that in each audit, we waited for the GPS to determine a position with a margin of error of 7 m or less because only targets with a good signal quality were set. In Wi-Fi networks the range depends largely on the environment in which the router is deployed. We made a very basic estimate and did not consider any key elements such as signal obstacles, height (the access point may be on a high floor of a building), or router model. Heights can vary from 15 m inside a house to 100 m outdoors. For the sake of simplicity, the 15, 30, and 45 m radios were chosen to represent the quality of the signal. It can be observed that with one handshake per 795m2, and with a 30% average success rate in deciphering passwords, the audited geographical area can be completely covered (Fig. 8). It would be possible to go through the entire area by connecting to different routers without ever losing the Internet connection. Figure 8: Heatmap of the routers form which the password has bees decoded. Map data ©2022 Inst. Geogr. Nacional, Google. Download full-size image DOI: 10. 7717/peerj-cs. 1185/fig-8 Fernandez-Lozano is an for PeerJ. conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the 10. 6084/m9. figshare. 19362971. v1. work is supported by the of Culture, Education and of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the for Research (Ref. ED431D 2017/23), Groups (Ref. [SEP] Justify:

Output: The text contrasts the claim by indicating that simply using strong passwords may not suffice to prevent cyberattacks, as it discusses various factors affecting password security such as the context of access points, environmental influences, and the potential for passwords to be deciphered through methods like GPS audits. It emphasizes that human behavior and technical environmental factors contribute to vulnerabilities that strong passwords alone cannot mitigate.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: ФИНАНСИРОВАНИЕРабота выполнена при финансовой поддержке госзадания (тема № АААА-А17-117022850032-9) и, частично, при поддержке грантов РФФИ 16-05-283, проектов ДВО РАН 15-I-2-073 и 18-2-001 и междисциплинарного проекта СО РАН МП64. × About the authors Nikolay A. Goryachev North-Institute of Branch of the of Sciences; of Geochemistry, of the of for correspondence. Email: goryachev@neisri. ru Russian Federation, Magadan; Irkutsk 1955. 60 с. Бортников Н. С. Геохимия и происхождение рудообразующих флюидов в гидротермально-магматических системах в тектонически активных зонах/ Геология руд. месторождений. 2006. Т. 48. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses financial support and author information without addressing the topic of lunar ice deposits or ancient microorganisms. There is no mention of the claim or related scientific concepts within the text.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: This query returned 1 additional subreddit: r/AbortionDebate. Given observable differences in framing (ie, people’s abortion experiences vs debates about abortion), we included this subreddit in our study as an additional but mutually distinct unit of analysis; that is, we collected and stored data for r/Abortion and r/AbortionDebate as separate corpora intended for separate analyses. All data collected for this study were in English, which we selected for 2 reasons: first, >70% of all Reddit users originated from English-speaking countries, and second, at the time of data collection, Reddit posts originating in languages other than English were insufficient for analysis. In Spanish, for example, r/Aborto contained only 5 members, with no activity since 2019; similarly, we observed <50 Spanish-language posts in either r/Abortion or r/AbortionDebate. Once we identified our subreddits of interest, we queried the API to collect new posts and top posts from the r/Abortion and r/AbortionDebate subreddits. After filtering our data for duplicates and accounting for API data scraping limits, our final sample size comprised 4966 posts, divided into 2 corpora: 56. 68% (2815/4966) of r/AbortionDebate posts and 43. 31% (2151/4966) of r/Abortion posts. AnalysesWe aimed to use NLP to identify salient categories in the r/Abortion and r/AbortionDebate subreddits. In numerous studies, latent Dirichlet allocation (LDA) topic models have been predominantly used for this purpose. LDA is a well-regarded unsupervised probabilistic model that evaluates word co-occurrence patterns using an iterative Gibbs sampling method [32]. Although LDA is often considered the gold standard within many academic and professional communities, advancements in NLP, artificial intelligence, and neural networks have introduced innovative topic modeling methods that can more closely approximate the potential meaning in these categories [33]. For this study, we applied one such advancement, the Representations from Transformers (BERT) topic modeling tool, BERTopic. BERTopic is an NLP topic modeling approach used to identify latent themes or topics within a collection of interrelated documents [34]. [SEP] Justify:

Output: The text corroborates the claim as it discusses the application of natural language processing (NLP) in analyzing subreddit discussions related to abortion, specifically noting the use of NLP methods like the Representations from Transformers (BERT) topic modeling tool, BERTopic. This indicates that NLP techniques are actively being utilized to identify themes and analyze content within a corpus of medical research literature.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: ED431C 2018/49) and the of Economy and Competitiveness via funding of the unique installation (UNLC08-1E-002, UNLC13-13-3503) and the (FEDER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 1999. Users are not the enemy. Communications of the ACM 42(12):40-46 Alliance W-F. 2019. Infographic 20 years of Wi-Fi. (accessed 29 December 2021) Beck M, Tews E. 2008. Practical attacks against WEP and WPA. IACR Cryptology e2008:472 Bonneau J. 2012. The science of guessing: analyzing an anonymized corpus of 70 million passwords. Bryant K, Campbell J. 2006. User behaviours associated with password security and management. of 14(1):81-100. 2006. Sp 800-63-1. Electronic authentication guideline. Castelluccia C, Chaabane A, Dürmuth M, Perito D. 2013. When privacy meets security: leveraging personal information for password cracking. ArXiv preprint. Castro R. 2005. Avanzando en la seguridad de las redes Wi-Fi. 73:23-33. 2014. The tangled web of password reuse. Dell’. 2010. Password strength: an empirical analysis. . 2012. It’s not stealing if you need it: a panel on the ethics of performing research using public data of illicit origin. In: eds. . Berlin, Heidelberg: Springer. 124-132. 2001. Weaknesses in the key scheduling algorithm of RC4. Forget A, Chiasson SC, van Oorschot P, Biddle R. 2008. Improving text passwords through persuasion. Fremantle P, Scott P. 2017. A survey of secure middleware for the internet of things. PeerJ 5(15):e1241 Gehringer EF. 2002. Choosing passwords: security and human factors. Google. 2019. Online security survey. (accessed 29 December 2021) Gosney J. 2016. 8x Nvidia GTX 1080 Hashcat benchmarks. (accessed 29 December 2021). 2005. Security analysis of Michael: The IEEE 802. 11i message integrity code. INE. 2018. [SEP] Justify:

Output: The text discusses various aspects of password security and management, highlighting that while users can adopt strong passwords, there are multiple attack vectors and challenges mentioned, such as password reuse and the effectiveness of password strength. The inclusion of various studies and analyses suggests that strong passwords alone may not suffice to prevent all cyberattacks, thereby contrasting the claim.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: № 1. С. 3-28. Бортников Н. С. , Гамянин Г. Н. , Алпатов В. В. , Наумов В. Б. , Носик Л. П. , Миронова О. Ф. Минералого-геохимические особенности и условия образования Нежданинского месторождения золота (Саха-Якутия, Россия)/ Геология руд. месторождений. 19981. № 2. С. 137-156. Бортников Н. С. , Сазонов В. Н. , Викентьева О. В. и др. Роль магматогенного флюида в формировании Березовского мезотермального месторождения/ Докл. РАН. 19982. Т. 363. № 1. С. 82-85. Бортников Н. С. , Гамянин Г. Н. , Викентьева О. В. [SEP] Justify:

Output: The text is irrelevant to the claim as it presents a list of authors and titles related to geological studies in Russia, with no mention of lunar ice deposits or ancient microorganisms. It does not provide any information that supports, refutes, or relates to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Unlike LDA, which uses probabilistic modeling to identify latent topics, BERTopic leverages pretrained embeddings from one of many transformer models, a type of neural network architecture in which an input sequence is compared against large-scale language models to calculate embeddings [35]. Embeddings are used to convert unstructured data, including words and sentences, into fixed-length continuous vectors. These vectors enable mathematical operations to capture semantic meanings, relationships, and other properties related to natural human language. The vectors calculated using this approach tend to be highly dimensional and difficult to interpret. To reduce dimensionality while maintaining the integrity of our data, we applied a principal component analysis, which is commonly applied in NLP approaches for general dimensionality reduction purposes [36]. This analysis allowed us to extract and more easily interpret a range of possible clusters or topics in both the r/Abortion and r/AbortionDebate subreddit data. Once we reduced the dimensionality of our vectors, we applied a Hierarchical Density-Clustering of Applications with Noise to identify latent clusters or topics [37], CountVectorizer to tokenize each topic, and class term frequency–inverse document frequency to extract topic words for each cluster [38]. Furthermore, to gauge the emotional tone or mood represented in each post from the studied corpora, we applied a Dictionary and (VADER), a rule-based sentiment analysis tool [39], and Text2Emotion, a rule-based emotion analysis tool [40]. VADER sentiment analysis is an algorithm and analysis that examines the polarity of words within each social media post. Posts are fed through a lexicon or web-based dictionary, which is precoded with values for all positive and negative words in the English language. When posts are run through the VADER lexicon, they receive a composite score. values denote lower affect (ie, −0. 99 to −0. 01), and positive values denote higher affect (ie, 0. 01 to 0. 99). Although an older tool, VADER is commonly used to assess content affect and emotional affect. In contrast, the Text2Emotion tool for emotion analysis scans each entry for key phrases and terms denoting one of four base emotions: (1) happy, (2) surprise, (3) fear, and (4) sadness. [SEP] Justify:

Output: The text corroborates the claim as it discusses various natural language processing (NLP) techniques, such as the use of embeddings from transformer models for converting unstructured data into interpretable fixed-length vectors and applying principal component analysis for dimensionality reduction. These methodologies illustrate how NLP approaches can effectively analyze large datasets, including text, to extract meaningful insights. Additionally, the mention of sentiment analysis tools further emphasizes NLP's role in understanding and evaluating the emotional tone within medical literature or related discussions.


Input: Task: contrast the claim using the given text. Claim: Strong passwords alone are enough to prevent all cyberattacks. [SEP] Text: Encuesta sobre equipamiento y uso de tecnologías de información y comunicación en los hogares. (accessed 29 December 2021) Klein DV. 1990. Foiling the cracker: a survey of, and improvements to, password security. . 2011. Of passwords and people: measuring the effect of password-composition policies. . 2016. A study of personal information in human-chosen passwords and its security implications. . 2013. Measuring password guessability for an entire university. Morris R, Thompson K. 2002. Password security: a case history. Communications of the ACM 22(11):594-597 Narayanan A, Shmatikov V. 2005. Fast dictionary attacks on passwords using time-space tradeoff. Notoatmodjo G, Thomborson C. 2009. Passwords and perceptions. Conferences in Research and Practice in Series 98:71-78 RAE. 2021. Current spanish reference corpus. (accessed 29 December 2021) Riley S. 2006. Password security: what users know and what they actually do. 8(1):2833-2836 Schneier B. 2005. Write down your password. (accessed 29 December 2021). 2017. Ethical issues in research using datasets of illicit origin. Vanhoef M, Piessens F. 2016. Predicting, decrypting, and abusing WPA2/802. 11 group keys. Vanhoef M, Piessens F. 2017. Key reinstallation attacks: forcing nonce reuse in WPA2. Vanhoef M, Ronen E. 2019. Dragonblood: a security analysis of WPA3’s SAE handshake. IACR Cryptology e2019:383. 2014. On semantic patterns of passwords and their security impact. . 2021. Usable comprehensive-factor authentication for a secure time attendance system. PeerJ 7(3):e678. 2016. Targeted online password guessing: an underestimated threat. . 2016. Understanding password choices: how frequently entered passwords are re-used across websites. . 2010. Testing metrics for password creation policies by attacking large sets of revealed passwords. . 2009. Password cracking using probabilistic context-free grammars. WPEngine. 2021. Unmasked: what 10 million passwords reveal about the people who choose them. [SEP] Justify:

Output: The text provides a variety of references that pertain to password security. It implies that mere reliance on strong passwords may not be sufficient, as various studies highlighted the vulnerabilities and threats associated with password usage. For instance, mentions such as 'Fast dictionary attacks on passwords using time-space tradeoff' and 'Targeted online password guessing: an underestimated threat' indicate that attacks against passwords can be sophisticated and that additional security measures may be required beyond just having a strong password.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: , Прокофьев В. Ю. , Алпатов В. А. , Бахарев А. Г. Состав и происхождение флюидов в гидротермальной системе Нежданинского золоторудного месторождения (Саха-Якутия, Россия)/ Геология руд. месторождений. 2007. Т. 49. № 2. С. 99-145. Будяк А. Е. , Горячев Н. А. Условия формирования протерозойских рудоносных горизонтов Байкало-Патомского нагорья/ Месторождения стратегических металлов: закономерности размещения, источники вещества, условия и механизмы образования: Матер. Всеросс. конф. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological studies related to hydrothermal systems and ore deposits in Russia without mentioning lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Collectively, these 2 tools can identify potential tonal differences in each post, again implicating the different uses of each subreddit included in the analysis. Both tools have been applied extensively in computational public health studies owing to their ease of access, replicability, and numerous validation studies [16,21,41,42]. ProcedureOur workflow is depicted in Figure 1. First, we queried the Reddit API to archive top and new posts from the r/Abortion and r/AbortionDebate subreddits. Data collected from the r/Abortion and r/AbortionDebate subreddits were saved as separate data files. After removing duplicate and non-English posts in either data file, we applied standard preprocessing steps to remove parts of speech that would detract from the clarity of our models, including 0. 42 and 0. 35, which indicates a marginal fit. After we extracted key terms per topic, we applied a sorting function to examine key terms in each entry. Each entry was then classified into one of 10 possible topics in either corpus. We lastly performed a VADER sentiment analysis and Text2Emotion emotion analysis for each entry in both corpora. Figure 1. Workflow depicting the study’s methodological pipeline. API: application programming interface; cTF-IDF: class term frequency–inverse document frequency; : Hierarchical Density-Clustering of Applications with Noise; PCA: principal component analysis; VADER: Dictionary and Sentiment Reasoner. Ethical ConsiderationsThis study involved a secondary analysis of deidentified and anonymized Reddit posts collected between January and May 2022. As this was an observational study with no contact between human subjects and no possible way to trace posts to any individual author, this study was exempt from Board review. ResultsOur study applied computational tools to collect and analyze subreddits specific to abortion. We aimed to examine how abortion was discussed on the social media platform Reddit, both as an information-sharing tool and as a platform for debating worldviews. Emerged in a Corpus of About r/Abortion, the Dedicated to Support and Outreach? Our coherence score analysis indicated a 10-topic solution for the r/Abortion subreddit. Table 1 outlines each topic by keywords, the number of sentences belonging to each topic, and the percentage of each topic relative to the larger corpus. [SEP] Justify:

Output: The text corroborates the claim by detailing how computational tools, specifically natural language processing methods, were used to analyze posts from Reddit regarding abortion. It mentions applying techniques such as sentiment analysis and emotion analysis to understand discussions in the r/Abortion subreddit, thus supporting the claim that natural language processing can aid in analyzing medical research literature.


Input: Task: pass the claim using the given text. Claim: Only large companies are at risk of cyberattacks. [SEP] Text: Daniela Conte4 1097 Accesses AbstractBased on the reinforced relations between and taxpayers as suggested by the OECD, the tax law has introduced the cooperative compliance regime, marking the passage from the ex post control of income tax returns to the ex ante cooperative dialogue on the tax base. Under this regime, the relations between tax authorities and large companies are based on a preliminary dialogue to settle interpretative disputes over income tax returns before the taxpayer submits his tax return. The purpose is to guarantee large companies a considerable reduction in the risk of ex post assessment, tax disputes and penalizing consequences and to favor the spontaneous emergence of the taxable base. However, this work examines the numerous criticisms of the cooperative compliance regime that may hamper the success of this scheme. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 139. 00 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e179. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Similar content being viewed by others Businesses: in Action 1. The discipline of the cooperative compliance regime is contained in the 1573. On the CC regime, see G. Salanitro, Profili giuridici dell’adempimento collaborativo tra la tutela dell’affidamento ed il risarcimento del danno, in Riv. dir. trib. , n. 5, 2016, 623 ss. 2. Cf. G. Marino (Ed. ) Governance, Milan, 2018, 20 ss. On this subject reference may be made to the author’s monograph, Dal controllo fiscale sul dichiarato al confronto preventivo sull’imponibile. Dall’accertamento tributario alla compliance, Padova, 2017, 163 s. 3. On the point M. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the cooperative compliance regime in tax law and its implementation for large companies. It discusses the dialogue between tax authorities and large corporations and criticisms of this regime without addressing anything related to cyberattacks or risks associated with them, hence not supporting or contrasting the claim.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: , посвящ. 85-летию ИГЕМ РАН. Москва, 25-27 ноября 2015 г. М. : ИГЕМ РАН, 2015. С. 275-276. Будяк А. Е. , Горячев Н. А. , Скузоватов С. И. Геодинамические предпосылки формирования масштабного оруденения в южном обрамлении Сибирского кратона в протерозое/ ДАН. 2016. 470. № 5. С. 562-565. Гамянин Г. Н. , Горячев Н. А. , Бахарев А. Г. , Колесниченко П. П. , Зайцев А. П. , Диман Е. Н. , Бердников Н. В. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss lunar ice deposits or ancient microorganisms. It appears to be an academic reference that does not relate to the topic of astrobiology or lunar studies.