Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 1 weeks; P <. 001). Additionally, the T1- and T1- and T2-weighted models appeared to systematically underpredict GCAs. A post hoc analysis of the 10 largest error external test set cases by a pediatric neuroradiologist (Y. L. ) revealed that they had one or more of the following factors contributing to suboptimal image quality: coil inhomogeneity, motion artifact, or excessive skull stripping. Four MRI scans showed larger-than-usual terminal zones of myelination, a normal structural finding that may have been confused for abnormal myelination if underrepresented in the sample. None of the MRI scans analyzed were found to have delayed myelination. We suspected technical acquisition parameter differences between data sets (Table 2) as the source of reduced performance on the external test set, so we fine-tuned the T1- and T2-weighted model pretrained on internal data on half the NIH data set and tested the model on the other half. After this fine-tuning step, the MAE was reduced further to 5. 9 weeks (4. 4 weeks ± 3. 2 at <42 weeks to 10. 5 weeks ± 10. 6 at >118 weeks), which was better than the performance of the original model (9. 1 weeks) on the external test set (P <. 001, Mann-test) (Fig 6D). We also trained the multichannel T1- and T2-weighted model on NIH data and then tested it on our institutional data, which resulted in an MAE of 14. 7 weeks, with a positive correlation between true and predicted GCAs (r = 0. 70). assessment of brain myelination is a crucial diagnostic step in the evaluation for structural correlates of developmental delay and can prompt further work-up for underlying genetic and metabolic causes (10,11). We created a three-dimensional convolutional neural network capable of predicting gestationally corrected age (GCA) without using brain size as a factor for prediction. Training on both T1- and T2-weighted images resulted in the most accurate network (mean absolute error [MAE], 7. 7 weeks). Attention maps revealed spatial patterns of attention corresponding to known progression in myelination during the first 2 years of life, suggesting that the network focuses on MRI signal intensity corresponding to myelination as the basis for its assessment of GCA. [SEP] Justify:

Output: The text does not address the claim regarding the prediction of emotions based on brain scans. Instead, it discusses technical aspects of MRI scans and the performance of models for predicting gestationally corrected age (GCA) in relation to brain myelination. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그렇다면 실제 임상 현장에서 증례를 ChatGPT는 얼마나 정확하게 진단 내릴 수 있는지 알아보았다. 2023년도 PubMed에서 저자 소속에 Korea, 제목에 case report로 검색하여 124편의 논문을 찾았다. 이 중에 무작위로 10편을 선별하였다(Suppl. 2). ChatGPT에 10편을 우선 증상과 임상 소견만으로 감별진단명을 나열하도록 하고, 이후 진단 검사 결과를 추가하여 감별진단을 내리도록 하였다. [SEP] Justify:

Output: The text corroborates the claim by discussing a study where ChatGPT was used to generate differential diagnoses based on symptoms and clinical findings. It indicates that the AI chatbot was involved in the diagnostic process, which aligns with the claim that AI chatbots can assist in preliminary diagnosis.


Input: Task: pass the claim using the given text. Claim: Cryptocurrencies like Bitcoin use decentralized networks for secure transactions. [SEP] Text: Their augmentation is simply that’s one, you cannot tax these people because you don’t know which transactions have taken place. And two, coins could be used for terrorism, criminal activities, and so on. The other hand, people defending these privacy coins simply state that they want to keep their privacy. And if you look at Fiat currencies, these are used way more often for terrorist or criminal activities. Then you also have stablecoins. We’ll explain this later on. Important to note as well is these definitions, these types, the number of cryptocurrencies, and so on, keep on evolving over time. So in a year, two years, three years, and so on, we will have an entire different range of cryptocurrencies added to the list probably. Also very probably new types as well. And how they might be used. Important to know is that there are also so-called forks, which can take place for cache means. Basically that in a blockchain, you go for a complete new implementation. So a hearts change can be a hard fork, and that means all the people that have certain cryptocurrency receive a new token so they can actually act on the new blockchain. But sometimes developers and certain investors do not wish to continue on this new chain and remain on the old one. That’s basically what happened. So that we got Ethereum and Ethereum classic. Also again, already shortly touch upon it. It’s a regulation, which is a very important one in some countries, states, and so on, you need to have a cryptocurrency whitelisted before you can use it, and others are forbidden. Another discussion that sometimes takes place is the environmental impact of these cryptocurrencies. It’s often said that, for example, Bitcoin network alone already uses more energy than a small country, which is quite impressive. And then, of course, there’s the growing interest from states all over the world. They have their own state controlled cryptocurrencies. Next to their own Fiat currencies. And finally, we should, or at least I should warn you about the many risks involved to cryptocurrencies. I think a very recently still with the famous series Squid Game, there are now the Squid cryptocurrency as well, where all of the sudden developers, which were anonymous disappeared with 3. 3 million, which again, shows that it can be very easy to create a cryptocurrency and steal money from investors. [SEP] Justify:

Output: The text does not directly address or support the claim about cryptocurrencies like Bitcoin using decentralized networks for secure transactions. Instead, it discusses various aspects surrounding cryptocurrencies, such as their potential use in illicit activities, government regulation, and the environmental impact of their networks. While these points may relate to cryptocurrencies in general, they do not specifically corroborate or contrast the claim regarding the use of decentralized networks.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: We validated our model with an external test set of normal brain MRI scans (MAE, 9. 1 weeks), and after fine-tuning the internally trained T1- and T2-weighted model on the external data set, the MAE for the external data set was reduced to 5. 9 weeks (P <. 001). Although prior studies have used deep learning to predict brain age in adults, neonates, and infants (19,22,23,28–31), few studies have focused on the infant population (22,23), and none of these studies have accounted for brain size or skull thickness as a factor in their model, which may confound the contribution from myelination to brain-age estimates. Hong et al (22) developed a 3D CNN for predicting the brain age of children aged 0–5 years but found that predictions for children under 2 years of age were more accurate based on more discernable changes during a period of rapid brain maturation. Kawaguchi et al (23) developed a 2D CNN based on single 2D axial T1- and T2-weighted sections through the level of the callosal splenium in children aged 0–2 years and demonstrated clinical prediction of brain age similar to clinical neuroradiologists. Our study builds on these prior works by focusing on white matter myelination while controlling for other confounding factors, and our network attention maps correspond well to established myelination milestones. We compiled a diverse clinically acquired imaging data set with age labels corrected for length of gestation and evaluated model generalizability using an external test data set. In our study, a variety of races and ethnicities were represented, and the proportion of female and male neonates and infants was balanced, minimizing bias. Nearly half the MRI images included in this study were of preterm neonates or infants. We corrected for preterm status by calculating GCA, thereby incorporating preterm neonates’ or infants’ shorter length of gestation that results in a less mature brain for their chronologic age. Given that preterm neonates or infants have been reported to have increased rates of delayed myelination due to prematurity and associated systemic illnesses (32), the inclusion of preterm neonates or infants with normal myelination in our model makes it more generalizable for clinical practice. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the validation of a model for predicting brain age using MRI scans, focusing on factors like brain size and myelination, rather than advanced algorithms predicting emotions.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 과거에 나온 어떤 자연어처리 플랫폼 챗봇에 비하여 매우 정확하게 유용한 답을 제공하고, 문장의 맥락을 갖추고 있어 많은 사람들에게 각광을 받고 업무에 활용하고 있다. 의료 현장에서 특히 전문가인 의사는 해당하는 답이 어느 정도 수준으로 정확한지 감별할 수 있으나 비전문가인 경우, 이 답을 그대로 받아들이면 매우 위험할 수 있다. 그러므로 챗봇은 늘 전문가에게 자문을 구하라고 답 마지막 부분에 추가한다. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI chatbots provide accurate and useful answers, which can help in engaging with patients in a healthcare context. Although it mentions potential risks for non-experts, it highlights the chatbot's utility in delivering information that experts can evaluate, aligning with the assertion that chatbots assist in preliminary diagnosis.


Input: Task: corroborate the claim using the given text. Claim: Cryptocurrencies like Bitcoin use decentralized networks for secure transactions. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 79386 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intech by From the Blockchain and by. purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. is a digital currency built on a peer-to-peer network and on the blockchain, a public ledger where all transactions are recorded and made available to all nodes. Opposite to traditional banking transactions, based on trust for counterparty, Bitcoin relies on cryptography and on a consensus protocol for the network. The entire system is founded on an open source software created in 2009 by a computer scientist known under the pseudonym Satoshi Nakamoto, whose identity is still unknown (see [1]). Hence, Bitcoin is an independent digital currency, not subject to the control of central authorities and without inflation; furthermore, transactions in the network are pseudonymous and irreversible. Bitcoin and the underlying blockchain technology have gained much attention in the last few years. Research on Bitcoin often deals with cybersecurity and legitimacy issues such as the analysis of double spending possibilities and other cyber-threats; recently, high returns and volatility have attracted research toward the analysis of Bitcoin price efficiency as well as its dynamics (see, among others, [2, 3, 4]). [SEP] Justify:

Output: The text corroborates the claim by stating that Bitcoin is built on a peer-to-peer network and uses blockchain technology, where all transactions are recorded and made available to all nodes. This description aligns with the idea that cryptocurrencies like Bitcoin utilize decentralized networks to facilitate secure transactions, contrasting with traditional banking systems that rely on trust.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The top performance of the multimodality T1- and T2-weighted model compared with the T1- and T2-weighted models alone reflects clinical practice, where a combination of T1- and T2-weighted imaging is used to assess myelination due to complementary information. Myelination of particular substructures of the brain is achieved at different age ranges on T1- and T2-weighted images, reflecting different physiologic processes. For example, the anterior limb of the internal capsule is myelinated at 2–3 months on T1-weighted images and at 7–11 months on T2-weighted images (1). Therefore, it is not surprising that the multimodality approach incorporating two sets of clinical information performed best. Neonate and infant brain volume increases with age (33), and this change in volume on MRI scans is a potential confounder in the evaluation of GCA based only on myelination. Therefore, we controlled for brain volume by scaling all scans to the same size by registration to an atlas image. Furthermore, our attention map analysis demonstrated a characteristic shift in attention over age that mirrors changes in myelination, indicating that the algorithm used myelination as its main basis for GCA prediction. This correspondence between machine-learned spatial patterns representing myelination and visually derived patterns from prior research (1,4–7) gives credence to the model’s predictions, making it less likely that confounding features contributed to GCA predictions. The model trained using data from our institution was further validated on data from the independent external NIH data set. While GCAs were generally accurate, we found that they were slightly underpredicted. This downward bias is likely due to the differences in GCA distributions, vendor platforms, and acquisition parameters between data sets, with the internal data being collected over a longer time period (from 1995 to 2019) and encompassing various evolving MRI protocols, resulting in a more heterogeneous and generalizable data set. In contrast, the external data set was collected with a standardized MRI protocol across various institutions. Indeed, a model trained on the external data set poorly generalized to the internal data set, whereas a model trained on the more heterogeneous internal data set generalized more accurately to the external data set. [SEP] Justify:

Output: The text does not address the claim about advanced algorithms predicting emotions based on brain scans. Instead, it focuses on a multimodal model for assessing myelination in the brain using T1- and T2-weighted MRI scans and discusses validation and performance metrics of the model in a clinical context. As such, it is irrelevant to the claim regarding emotion prediction.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그러나, 비대면 진료 현장에서는 충분히 활용 가능할 것이고, 또는 진료 이후 환자에 추적 진료를 위하여 점검하는 데 사용할 수 있을 것이다. 또한 단순히 진료 현장에서 사용하는 데서 나아가, 발표 원고 작성, 교재 제작, 논문 작성, 번역, 영문 교열, 말 바꾸어쓰기(paraphrasing), 요약 등 진료 업무 이외 연구와 교육 현장에서 손쉽게 사용하여 시간을 아낄 수 있다[6]. 용어를 정의 내리고 과거 지식을 요약하여 제공하는 작업은 탁월하다. [SEP] Justify:

Output: The text corroborates the claim by suggesting that AI chatbots can be effectively used in the field of telemedicine for monitoring patients after consultations and engaging in tasks like summarizing knowledge and defining terms, which supports their role in aiding diagnostics and patient interactions.


Input: Task: contrast the claim using the given text. Claim: Many industries are exploring blockchain for applications beyond finance. [SEP] Text: Deputy members of EU Parliament (MEPs) and (for Application), representing DLT industries, have been suggesting according considerations within the created to feedback MEPs with relevant criteria from DLT industry and PDLs. Spain’s DLT (cofounded by Prof. J. IBÁÑEZ) is a founding member of thus participating since 2021 in finance WGs operating in (cf. of Authority (PoA) blockchain protocols, within this context, may play a key role to determine the consent of public supervisors in order to authorize the legal trading of NFTs in the scope and within the boundaries predetermined by the securities markets applicable rules. The reputation of validators is held by market authorities, which makes PoA impractical for public blockchains like Bitcoin and Ethereum, wherein hundreds or even thousands of validating nodes operative, and the identity requirement becomes a clear advantage to comply with regulations in the context of public-permissioned blockchains subject to legal specific requirements. In a PoA-network PPDL, a relatively small number of validating nodes re-centralize or make the market less decentralized, facilitating supervision, with enough degree of decentralization but gaining in investor and market confidence (like in the cases of networks, an implementation of offering two PoA alternatives; or in the Telsius and Besu networks), with much higher throughput capacity than in permissionless blockchains, minimal computational effort and no specialized equipment (like in PoS). The acceptation of established financial institutions (as investment banks, financial intermediaries, or crypto-asset service new providers) as validators for crypto-transactions should be a legal previous condition to the settlement of DeFi market platforms for cryptos. Previous authorization of intermediaries in traditional financial markets is essential to the market confidence and investor protection. Similarly, previous authorization of validating nodes in crypto-transactions will probably be a legal and also economic-efficient precondition for the development of token transactions in general, and in particular for high-risk token investments like NFT trading. Although PoA-based algorithms are unlikely to power big market platforms with thousands or millions of users, they are already optimal to build networks tailored to the needs of a limited number of known stakeholders (agreeing, [4]), as in the case of certain limited-client oriented NFT platforms and markets. Advertisement 4. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and specific use cases of Proof of Authority (PoA) based blockchains primarily in finance and NFT trading. It emphasizes that while some blockchain implementations, like PoA, might cater to specific applications such as limited-client NFT platforms, the overarching focus remains on regulatory compliance and the needs of established financial institutions, suggesting a more cautious approach to blockchain applications primarily within the financial sector rather than exploring broader industrial uses.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Notably, when tested on the external data set, the T2-weighted model had the best performance, while the T1-weighted model had the worst performance, suggesting that technical differences in image acquisition can impact generalizability. Internal T1-weighted sequences were predominantly 3D gradient-echo acquisitions, with over half at 3. 0 T, while the external data set was exclusively 2D spin-echo acquisitions at 1. 5 T. Conversely, internal data for T2-weighted images was predominantly 2D acquisitions, more closely approximating the external data set. Our study had limitations. First, there was an imbalance in the proportion of MRI scans at different GCAs, with fewer MRI scans in the older age categories. The network may have overtrained on data in the older age categories, potentially resulting in suboptimal performance and generalizability of the algorithm. Second, our model was trained on a single institutional data set with predominantly scanner-acquired studies, institution-specific acquisition parameters, and unique neonatal and infant sample, with notably less than 7% Black patients. Although these factors may reduce the generalizability of our model, we validated its performance on an external NIH data set. Third, the process of determining normal myelination in clinical practice is inherently subjective (8,9); thus, the use of clinical radiology reports interpreted by one neuroradiologist as our reference standard for normal myelination likely introduced some variability in the training data. Fourth, the inclusion of preterm infants in our model increases model variability yet simultaneously increases our model’s generalizability and more closely approximates clinical practice. Fifth, our model was trained using neonates or infants with reported normal myelination and without large structural abnormalities or brain abnormalities, potentially reducing generalizability to neonates or infants with structural abnormalities. Sixth, we did not directly compare algorithm performance with clinical neuroradiologist performance, but our best-performing model MAE (5. 9 weeks) is similar to the MAE reported for radiologists in a prior study (4. 4 weeks) (23). In conclusion, a deep learning approach can predict the gestationally corrected age (GCA) of a neonate or infant on the basis of clinically acquired T1- and T2-weighted brain MRI scans by relying on known spatiotemporal patterns of normal myelination across the brain. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the performance of different MRI models in predicting gestationally corrected age (GCA) rather than addressing the prediction of emotions based on brain scans. The focus is on technical specifications and model performance rather than emotional prediction.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그러나, 이런 도구를 효율적으로 적절히 사용할 수 있는 전문가로서 충분한 지식과 술기를 갖추어야 답이 어느 수준에서 타당한지 판단할 수 있을 것이다. 앞으로 이 인공지능 챗봇이 점점 더 정교하고 신뢰할 수 있는 수준으로 발전하고 더 많은 우리말 정보로 훈련도 받을 것이다. 인공지능 챗봇이 어디까지 발전할지 주시하고 또한 우리 의사도 이런 작업에 전문가로서 적극 참여하여 더욱 유용한 챗봇 개발에 일익을 담당하기 바란다. [SEP] Justify:

Output: The text corroborates the claim by suggesting that AI chatbots will become more sophisticated and reliable, and emphasizes the necessity of healthcare professionals engaging in the development of these tools. It indicates a future where AI chatbots could support medical practices through increased effectiveness and reliability.


Input: Task: contrast the claim using the given text. Claim: Many industries are exploring blockchain for applications beyond finance. [SEP] Text: However, this does not mean that blockchain is not vulnerable to cyberattacks and security fraud. People with malicious intent can exploit blockchain security vulnerabilities for their own benefit. There are already many cybersecurity incidents related to blockchain usage. For example, a cyberattack on Bithumb, a major cryptocurrency exchanges for bitcoin, and Ethereum caused an economic loss of 870,000 USD and the exposure of more than 30,000 users’ information. The potential security threats could discourage firms willingness to use blockchain. 5. 2 Organizational considerations5. 2. 1 Top management supportTop management support and commitment play a pivotal role in the successful implementation of any technological innovations. The higher the support and commitment, the more likely the firm will develop new organizational policies to clarify the usage of blockchain and reduce the resistance from employees to embrace the new system. Lack of technology awareness and involvement from upper management would impede the allocation of adequate human, financial, and technological resources for technology adoption projects. 5. 2. 2 CostThe adoption of blockchain incurs diverse costs, such as recruiting blockchain expert, investment in additional equipment (e. g. sensors and radio-frequency identification tags). In addition, adopters need to spend a lot of time and resources to master the complexity of blockchain. The cost may be justifiable for the leading firms that initiate blockchain implementation projects, because they are usually large firms with bountiful resources. It is financially viable for them to invest in costly project that promises long-term returns. However, this is not the case for small- and medium-sized food supply chain actors with low margins. Therefore, high implementation cost may hinder the adoption intention of these actors. 5. 2. 3 Return on investment (ROI)According to [21] global blockchain survey, more than 33% of the respondents claims that their current ROI in blockchain technology remains uncertain. The lack of clear ROI is recognized as the top barrier for blockchain adoption [29]. With the increasingly usage of blockchain in agri-food industry, it is likely that the financial benefits of blockchain implementation will be clarified in the foreseeable future. 5. 2. 4 Lack of knowledge and expertiseBlockchain is still largely an emerging technology, and the skills required to develop and use it are in short supply. The willingness to invest in blockchain requires a certain degree of technological awareness and knowledge. The technical complexity of blockchain makes it a challenge for individual users to understand, accept, and confidently participate in it. [SEP] Justify:

Output: The text contrasts the claim by outlining significant barriers to blockchain adoption, including cybersecurity vulnerabilities and high implementation costs, which may discourage firms from exploring its applications. The mention of incidents that undermine confidence in blockchain technology implies that despite its potential, the willingness of industries to adopt it may be hindered.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Although we demonstrate the capability of a deep learning model to predict GCA based on brain myelination, further evaluation in the clinical context, likely with further improvements in model accuracy, will be necessary for the model to be used on a routine clinical basis. Additional research is necessary to evaluate whether model performance can be improved with different technical approaches or additional information, such as inclusion of apparent diffusion coefficient maps. Further research using brain MRI scans with structural and myelination abnormalities will be needed to evaluate whether this approach can be extended to help radiologists identify neonates or infants with abnormal or delayed myelination. Disclosures of conflicts of interest: J. V. C. No relevant relationships. G. C. No relevant relationships. C. P. H. Consultant to GE Healthcare; expert witness for various medicolegal firms; support for research travel from Siemens Healthineers; DataBoard member for Foundation and uniQure; on the for in Medicine board of trustees. O. A. G. No relevant relationships. L. P. S. No relevant relationships. A. M. R. Radiology AI trainee editorial board from 2019 to 2021. Y. L. 2019–2021; honoraria from University of Francisco and of Otolaryngology–Head and Neck Surgery. Author contributions: Guarantors of integrity of entire study, J. V. C. , G. C. , A. M. R. , Y. L. ; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, J. V. C. , G. C. , O. A. G. , A. M. R. , Y. L. ; clinical studies, C. P. H. , L. P. S. , Y. L. ; experimental studies, J. V. C. , G. C. , C. P. H. , L. P. S. , A. M. R. , Y. L. ; statistical analysis, J. V. C. , G. C. , A. M. R. , Y. L. ; and manuscript editing, all authors* J. V. C. and G. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the capability of a deep learning model to predict GCA based on brain myelination rather than addressing any suggestions or claims related to predicting emotions based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3257323/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractPlant leaf diseases pose significant threats to agricultural productivity and food security. Accurate and timely detection of these diseases is crucial for effective disease management. This research paper proposes a novel approach for plant leaf disease detection using ensemble learning. Ensemble learning combines multiple base models to improve classification accuracy by leveraging their collective intelligence. The proposed ensemble model integrates Networks (CNN), ResNeXt, and InceptionV3 architectures, exploiting their diverse strengths in image classification tasks. Extensive experiments on a diverse dataset demonstrate that the ensemble model outperforms individual base models, achieving higher classification accuracy. Its ability to capture complementary features enhances generalization and robustness against variations in disease patterns and image quality. The ensemble model also offers benefits such as improved interpretability, model stability, and reduced overfitting risks. The proposed approach contributes to the advancement of automated plant disease diagnosis systems, enabling early detection and timely intervention to mitigate crop losses and ensure food security. By combining multiple models, the ensemble learning approach improves the accuracy and reliability of plant leaf disease detection, addressing a critical need in agricultural practices. Plant leaf disease detectionEnsemble learningNetworks (CNN)ResNeXtInceptionV3Agricultural productivityFiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7I. diseases have always been a major concern for agriculture, causing significant economic losses and posing threats to global food security. Timely and accurate detection of plant leaf diseases plays a vital role in preventing severe yield losses and implementing effective disease management strategies. Traditional methods of disease diagnosis, which rely on manual inspection and visual analysis, are labor-intensive, time-consuming, and prone to human error. Therefore, there is a pressing need to develop automated and reliable techniques for plant leaf disease detection. In recent years, machine learning algorithms, particularly deep learning models, have demonstrated remarkable success in various computer vision tasks, including image classification. Networks (CNNs) have emerged as a popular choice for detecting and classifying objects within images. However, due to the complex and diverse nature of plant diseases, achieving high classification accuracy remains a challenge. [SEP] Justify:

Output: The text corroborates the claim by discussing the success of deep learning models, particularly CNNs, in improving the accuracy of image classification tasks, which includes disease detection. It mentions that deep learning has demonstrated remarkable success in tasks such as these, which supports the notion that such technologies enhance detection accuracy in medical contexts, paralleling advancements in plant disease detection.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Figures AbstractMost of the existing multi-recipient signcryption schemes do not take the anonymity of recipients into consideration because the list of the identities of all recipients must be included in the ciphertext as a necessary element for decryption. Although the signer’s anonymity has been taken into account in several alternative schemes, these schemes often suffer from the cross-comparison attack and joint conspiracy attack. That is to say, there are few schemes that can achieve complete anonymity for both the signer and the recipient. However, in many practical applications, such as network conference, both the signer’s and the recipient’s anonymity should be considered carefully. Motivated by these concerns, we propose a novel multi-recipient signcryption scheme with complete anonymity. The new scheme can achieve both the signer’s and the recipient’s anonymity at the same time. Each recipient can easily judge whether the received ciphertext is from an authorized source, but cannot determine the real identity of the sender, and at the same time, each participant can easily check decryption permission, but cannot determine the identity of any other recipient. The scheme also provides a public verification method which enables anyone to publicly verify the validity of the ciphertext. Analyses show that the proposed scheme is more efficient in terms of computation complexity and ciphertext length and possesses more advantages than existing schemes, which makes it suitable for practical applications. The proposed scheme could be used for network conferences, paid-TV or DVD broadcasting applications to solve the secure communication problem without violating the privacy of each participant. Key words: Multi-recipient signcryption; Signcryption; Complete Anonymity; Public verification. Citation: (2013) Multi-Scheme with Public Verification. 8(5): e63562. Francesco Pappalardo, University of Catania, ItalyReceived: October 18, 2012; Accepted: April 3, 2013; Published: May 10, 2013Copyright: © 2013 Pang et al. This is an open-access 0006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. IntroductionWith development of network technology and its applications, a lot of group-oriented network services such as network multicasting or broadcasting have been proposed. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of anonymity in multi-recipient signcryption schemes. It states that while the anonymity of the signer has been considered in some schemes, there are very few that provide complete anonymity for both the signer and the recipient. The passage emphasizes the necessity of revealing recipient identities in existing schemes, implying that not all transactions on a blockchain can be fully anonymous.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: C. contributed equally to this work. ** A. M. R. and Y. L. are co-senior authors. 1. Dean DC 3rd, O’Muircheartaigh J, Dirks H, et al. Characterizing longitudinal white matter development during early childhood. Funct 2015;220(4):1921–1933. Medline2. Fields RD. White matter in learning, cognition and psychiatric disorders. 2008;31(7):361–370. Medline3. . Plasticity in oligodendrocyte lineage progression: An OPC puzzle on our nerves. Neurosci 2021;54(5):5747–5761. Medline4. . Pediatric Neuroimaging. J Tomogr 1991;15(1):179. 5. . Sequence of in Human Infancy. II. Patterns of Myelination in Autopsied Infants. J Neurol 1988;47(3):217–234. Medline6. Welker KM, Patton A. Assessment of normal myelination with magnetic resonance imaging. 2012;32(1):15–28. Medline7. 4th, et al. MR evaluation of early myelination patterns in normal and developmentally delayed infants. 1988;150(4):889–896. Medline8. . Comparison of spin-echo and gradient-echo T1-weighted and spin-echo T2-weighted images at 3T in evaluating term-neonatal myelination. 2015;36(2):411–416. Medline9. . Comparison of spin-echo T1- and T2-weighted and gradient-echo T1-weighted images at 3T in evaluating very preterm neonates at term-equivalent age. 2013;34(5):1098–1103. Medline10. . in Infants and Young Children: Radiographic and Clinical Correlates. J 1995;10(2):100–104. Medline11. Barkovich AJ. Concepts of myelin and myelination in neuroradiology. 2000;21(6):1099–1109. Medline12. . for Medical Imaging. RadioGraphics 2017;37(2):505–515. 13. . Explainable deep learning models in medical image analysis. arXiv 2005. [SEP] Justify:

Output: The text is largely a list of authors and references without any mention or discussion of emotional prediction using advanced algorithms or brain scans. As such, it does not provide any information that supports, rebuts, or relates to the claim.


Input: Task: corroborate the claim using the given text. Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: 9 June 2023 [5] Sunflower dataset description. 2335 Cauliflower dataset description. 4156 EfficientNetV2B2 and EfficientNetV2B3 89. 2%. 6 Yang B, et at. [6] 2023 31,718 disease image Deep multitask learning framework 99% 7 et. al[7],2023 Plantvillage dataset -54,306 images An ultra‑lightweight efficient network 99% 8 Lee et al. [8], 2021 PlantVillage: 54,305 VGG16, GoogleNetBN (34 layers) and Inceptionv3 (48 layers) PlantVillage: 99. 00% IPM: 44. 54% Bing: 26. 56% 9 Darwish et al. [9], 2020 Maize: 15,408 Ensemble model, VGG16, VGG19, orthogonal, 3 and Xception model. 98. 2% 10 Karlekar and Seal [10], 2020 PDDB: 14,240 images background removal, CNN 98. 14% This review emphasizes the vital role of plants for energy and nutrition, but plant diseases can devastate crops. Researchers have explored machine learning (ML) and deep learning (DL) methods for disease detection, showing DL's superiority. Comparing ML () and DL (Inception-v3, VGG-16, VGG-19) models, DL achieves higher accuracy: VGG-16 (89. 5%) > Inception-v3 (89%) > VGG-19 (87. 4%), outperforming ML methods. [11] The paper highlights the importance of disease early detection in agriculture for optimal crop yield. Tomato crops are vulnerable to bacterial spot, late blight, septoria leaf spot, and yellow curved leaf diseases. This study introduces a Network (CNN) and Quantization (LVQ) based approach for automatic tomato leaf disease detection and classification. The dataset comprises 500 images of tomato leaves, and the CNN extracts features while LVQ aids in classification. By leveraging color information, the proposed method successfully identifies and distinguishes the four diseases. [12] The significance of plants as an energy source is acknowledged. Plant diseases impacting crop production and economic value necessitate timely leaf disease detection. Machine learning offers a solution by analyzing plant leaf data and classifying diseases based on morphological features, color, intensity, and dimensions. [SEP] Justify:

Output: The text corroborates the claim by explaining that researchers have explored machine learning (ML) and deep learning (DL) methods for disease detection, highlighting DL's superiority in achieving higher accuracy compared to ML methods. It specifically mentions that the DL models, such as VGG-16 and Inception-v3, outperform ML models in terms of accuracy in detecting diseases, which supports the assertion that deep learning has enhanced accuracy in disease detection.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Usually, in these services, a message sender is required to securely send the same messages to a group of recipients, such that only a certain number of recipients can read the messages while unauthorized recipients can extract nothing useful from these messages [1]. Therefore, the concept of multi-recipient encryption was put forward [2]–[6], and it has been considered as one of most promising solutions to solve the security problem of securing multicasting or broadcasting. Later, combining the concept of multi-recipient encryption with the idea of Zheng’s signcryption [7], Duan et al. [8] proposed the first multi-recipient signcryption scheme. In their scheme, to achieve the goal of sending the same message to all authorized recipients confidentially, the sender only needs to execute one signcryption operation, and at the same time, each recipient can verify the validity of messages. Since then, many excellent multi-recipient signcryption schemes [9]–[11] were proposed, which take more security properties into consideration than Duan et al. ’s scheme. In general, multi-recipient signcryption can be used in many important applications, such as paid-TV or DVD broadcasting systems [10], where only authorized or paying users should be able to access such services. Nevertheless, today, more and more people are concerned regarding personal privacy, thus participant anonymity should be taken into account when designing multi-recipient signcryption [12]. For example, in paid-TV and DVD broadcasting application systems, service providers do not want others to obtain the real identities from the ciphertext messages. Therefore, multi-recipient signcryption with the sender (or called the signer) anonymity had been introduced. In literature, there have been several multi-recipient signcryption schemes [13]–[17] which try to assure anonymity of the sender. The concept of anonymous signature was firstly proposed by Rivest et al. [18]. In 2005, Huang et al. [19] proposed the first anonymous signcryption scheme, which used an ID-based ring signature to assure anonymity of the signer. However, their scheme is only a single-recipient scheme. Later, based on similar thoughts, Lal et al. [13] extended this method for multi-recipient environments. Furthermore, a multi-recipient scheme with anonymity of the sender [14]–[17] was proposed. [SEP] Justify:

Output: The text contrasts the claim by discussing mechanisms such as multi-recipient signcryption that aim to assure anonymity of the sender but does not imply that all transactions, specifically in the context of blockchains, are completely anonymous. It highlights that participant anonymity is an important design consideration, indicating that there are complexities surrounding anonymity that the claim oversimplifies.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 13799 [preprint] 28, 2020. 10, 2020. 14. Truelove- in Children and Adolescents. J Neurosci 2020;40(6):1265–1275. Medline15. Age of Based on. 2020;10:1346. Medline16. Brain age prediction using deep learning uncovers associated sequence variants. 2019;10(1):5409. Medline17. . From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Mapp 2020;41(12):3235–3252. Medline18. . Accurate brain age prediction with lightweight deep neural networks. bioRxiv preprint bioRxiv:2019. 12. 17. 879346. 6, 2020. 19. . Classification of in Using 3D-CNN based on Brain MRI. In: 2019 on Bioinformatics and Biomedicine (BIBM), San Diego, CA, November 18–21, 2019. Piscataway, NJ:IEEE,2019; 2373–2378. 20. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016;136:1–9. Medline21. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Anal 2021;72:102091. Medline22. Prediction of via Deep Learning. 2020;11:584682. Medline23. Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning. Imaging 2021;79:38–44. Medline24. . The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 2013;8(7):e67863. Medline25. . Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv1502. 01852 [preprint] 6, 2015. 26. iNNvestigate neural networks! arXiv 1808. 04260 [preprint]. 13, 2018. 10, 2020. 27. Evans AC; Cooperative Group. study of normal brain development. Neuroimage 2006;30(1):184–202. Medline28. [SEP] Justify:

Output: The text does not provide any information directly related to the claim regarding advanced algorithms predicting emotions based on brain scans. Instead, it discusses various models and studies related to brain age prediction and machine learning applications in neuroimaging, which are not explicitly connected to the prediction of emotions.


Input: Task: corroborate the claim using the given text. Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: [17] Automated plant disease diagnosis using deep learning often suffers from overfitting. This 7. 4%, outperforming vanilla CycleGAN's 0. 7% improvement. The approach addresses the challenge of reliable and diverse training data for practical plant diagnosis systems. [18] Tomato leaf diseases significantly impact tomato yield. Detecting agricultural diseases is crucial. Traditional data augmentation methods lack effectiveness. To enhance tomato leaf disease recognition, we propose a generative adversarial network (GAN)-based data augmentation technique. Using deep convolutional GANs (DCGAN) for image augmentation alongside original images, our model combined with GoogLeNet achieves a 94. 33% top-1 average accuracy. We iteratively optimize hyperparameters, modify convolutional neural network architecture, and select GAN variants to improve performance across five tomato leaf disease classes. DCGAN-generated images not only augment the dataset size but also offer diverse characteristics, enhancing model generalization. t-(t-SNE) and Test validate DCGAN image quality. Our approach benefits by enlarging the dataset for neural network training, reducing data collection costs, and boosting model generalization and diversity. [19] This project focuses on detecting tomato leaf diseases using Networks (CNNs), a subset of deep neural networks. The process involves dataset segmentation, transfer learning with ResNet-50, and data augmentation for improved results. Utilizing PyTorch, a tomato leaf disease detection model is developed using deep CNNs. The testing dataset is validated with learned ResNet-50 parameters, achieving 97% accuracy. Six common tomato crop diseases are classified, with data augmentation expanding the dataset fourfold to enhance accuracy. [20] Plant diseases' impact on growth underscores the urgency of early identification. Learning (ML) models have been pivotal, (DL) advancements offer increased accuracy potential. Modified DL architectures combined with visualization techniques enhance symptom detection and classification. Performance metrics evaluate these techniques. This review comprehensively discusses DL models for visualizing plant diseases, pinpointing research gaps for enhanced disease detection before symptoms manifest. [21] Efficient plant disease identification is pivotal for preventing agricultural losses. Disease studies involve observing plant patterns, but manual monitoring is laborious. Image processing offers a solution, automating disease detection through steps like acquisition, pre-processing, segmentation, feature extraction, and classification. This paper examines leaf image-based plant disease detection methods, highlighting segmentation and feature extraction algorithms. [SEP] Justify:

Output: The text corroborates the claim by providing multiple instances where deep learning techniques, specifically deep convolutional networks (DCGANs) and modified architectures, are employed to improve the accuracy of disease detection in agriculture, particularly with tomato leaf diseases. It states that these methods not only enhance model accuracy, achieving results such as 97% accuracy, but also improve model generalization and reduce data collection costs. These improvements in disease detection align with the claim that deep learning enhances accuracy across various applications.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Although these schemes [13]–[17] provide solutions for assuring signer anonymity, there are still some unsolved issues. For example, they suffer from two new attacks known as the cross-comparison attack [20] and the joint conspiracy attack [21]. Based on the ring signature, schemes [13]–[17] construct a list which includes the real signer and several valid participants which are chosen randomly by the signer hiding the real signer in this list. Although this perfectly works to some extent, an attacker can obtain a number of different ciphertexts from the same message source by closely monitoring network traffic, thus by comparing the signers’ identities from different lists an attacker can narrow down the scope of the target signer. Using this scheme, an attacker can directly obtain the identity of the real signer. Even if the attacker does not directly obtain the real signer’s identity, he/she has narrowed down the scope of the attacker’s guess. In addition, it is still possible for such an attacker to retrieve a list which includes the real signer. Then, he/she can cooperate with some participants in the list to narrow down the scope and guess the real signer with a larger probability. In addition, the list of chosen participants can increase the length of the ciphertext quite significantly, potentially reducing the transmission efficiency. More important, the identities of all the authorized recipients are usually included in the ciphertext of these anonymous schemes in plaintext [13]–[19], which is not always wanted. Generally speaking, anonymity of participants includes both the sender’s and the recipient’s anonymity. Besides the anonymity of the sender, the anonymity of the recipient is often equally important so that designers of multi-recipient signcryption schemes should pay attention. For example, in paid-TV and DVD broadcasting application systems, no user should accept that his/her subscription of these services is publicly viewable to others especially when the service is quite sensitive. However, unfortunately, almost none of the existing schemes take the anonymity of recipients into consideration because the identity of each recipient must be included in the ciphertext as a necessary element for decryption. The list of the authorized recipients’ identities in the ciphertext is used to show who are the authorized recipients and how each authorized recipient gets his/her person-specific data for encryption from the ciphertext during the decryption process. [SEP] Justify:

Output: The text contrasts the claim by outlining various vulnerabilities in current anonymity schemes used in blockchain technology. It explains that certain attacks can potentially unmask the real identity of a signer, suggesting that transactions are not completely anonymous. For example, it mentions an attacker can narrow down the identity of the signer through cross-comparison and joint conspiracy attacks, ultimately leading to the identification of the real signer. Moreover, it states that the identities of authorized recipients are often included in the ciphertext in plaintext, which compromises anonymity further.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Academy of Enter your Email address: Subscribe to eTOC Home > on CT Sc. . . on CT the Neurology/October 3, 2019 Explained0. 777) between patients with MRI-positive findings and patients with MRI-negative findings in patients with CT-negative traumatic brain injury. GFAP at 9-16 hours post injury (B) shows improved discrimination to good—area under the curve (AUC) 0. 852—suggesting temporal effects of GFAP release or accumulation in the systemic circulation after traumatic brain injury and time for optimal or repeat measurement. Dr. Manley said that he does not believe that GFAP or any other biomarker will be the singular tool for diagnosing TBI. He expects that like with heart attack, where troponin testing is one component of evaluation, TBI biomarkers will be among several diagnostic tools used to arrive at a firm diagnosis. “I see this as being a triage tool,” he said, but cautioned that the study included only adult patients who got to the emergency room because of a car accident or a fall. He said more research is needed before he could say whether the test “is ready for use in kids with concussions. ”A Subset AnalysisThe latest report on the GFAP biomarker test stems from a study known as TRACK-TBI ( in Injury), a large prospective study being conducted at 18 Level 1 trauma centers in the US. A sub-analysis was done of 450 of the participants, most of whom were classified as having mild TBI due to a traffic accident or fall. They all had normal findings on CT scan (and a Scale score 13-15) and consented to the blood test within 24 hours of their injury and an MRI scan seven to 18 days post-injury. The study also included orthopedic trauma patients and healthy controls for comparison. Of the 450 suspected TBI patients with normal CT scans, 120 (27 percent) had MRIs indicative of TBI. When the researchers compared MRI findings to GFAP levels, they found that GFAP concentrations were higher in patients with positive MRI scans (median plasma GFAP concentration of 414. 4 pg/mL) compared with those with negative MRI scans (median plasma GFAP concentration of 74. 0 pg/mL). [SEP] Justify:

Output: The text does not address the claim about advanced algorithms predicting emotions based on brain scans. Instead, it discusses the GFAP biomarker in relation to traumatic brain injury and the diagnostic tools like MRI and CT scans, which are unrelated to emotional predictions through advanced algorithms.


Input: Task: corroborate the claim using the given text. Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: The base CNN provides a solid foundation for learning basic features, and then the ResNeXt and Inception-v3 models can further refine and augment these features to capture intricate patterns and subtle variations in the images. The ensemble approach allows us to combine the predictions of multiple models, effectively reducing bias and variance, leading to improved generalization and overall performance. Moreover, the auxiliary classifiers used in Inception-v3 assist in better gradient propagation during training, addressing the challenges of vanishing gradients and facilitating faster convergence. As a result, our ensemble learning method achieves a higher level of accuracy in detecting plant leaf diseases compared to using a single architecture alone. The synergy between these models ensures robustness and adaptability, enabling the system to handle a wide range of plant diseases effectively, making it a reliable and valuable tool for plant disease diagnosis in agricultural settings. The complete model architecture of our ensemble learning model is as follows: C. to feeding the images into the ensemble neural network, a preprocessing stage will be applied to reduce the image size to the specific input size required by the individual base models (and InceptionV3). This preprocessing step is crucial to ensure consistency in the input dimensions across the models. preprocessing stage involves resizing the images to the target input size while preserving their aspect ratio. This is achieved by either padding the image or cropping it from the center, depending on whether the original aspect ratio is greater or smaller than the target ratio. By using this technique, we can resize the images without distorting or losing critical visual information. resizing, the pixel values of the images will be normalized to bring them to a common scale. Normalization ensures that the pixel values lie within a specific range, typically [0, 1] or [-1, 1], which aids in faster convergence during model training and prevents the dominance of certain features due to large pixel value differences. To increase the variability of the dataset and improve the model's ability to generalize, data augmentation techniques will be applied during preprocessing. These techniques include random rotation, horizontal flipping, and zooming. Data augmentation introduces variations in the training set without adding new data points, thus helping the model become more robust to different orientations and viewpoints of the plant leaves. Loss in the resizing process, the aspect ratio of the original image is preserved, ensuring no loss of critical visual information. [SEP] Justify:

Output: The text corroborates the claim by describing how advanced deep learning models, such as ResNeXt and Inception-v3, refine features for better pattern recognition in images. It emphasizes the improved accuracy of detecting plant diseases using ensemble methods, which suggests that similar techniques could enhance disease detection in other domains, including cancer.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Thus, schemes [9]–[11] directly expose the recipient’s identity and therefore violate their privacy. Also, the fact that different recipients have different person-specific data for decryption can lead to decryption unfairness. This means that if some recipient’s person-specific data are damaged due to communication errors, he/she cannot decrypt the ciphertext but the others can still decrypt the ciphertext correctly [12]. Therefore, it is urgent and challenging for researchers to solve the recipient anonymity issue of multi-recipient signcryption. Following the arguments above, it is known that almost none of the existing multi-recipient signcryption schemes take the full anonymity of recipients and senders into account. Although there are several schemes that provide a solution for anonymity of the signer, they are not perfect, that is, they suffer from the cross-comparison attack and the joint conspiracy attack. Therefore, existing schemes cannot deal with the anonymity of the sender or the recipient properly. Furthermore, these schemes are not suitable for applications that need complete anonymity for the sender and the recipient. For example, in a network conference application, every conference participant often wants to be kept anonymous when he/she is taking part in the conference discussion. Furthermore, if a participant (i. e. sender) wants to publish criticism or objections, he/she hopes that others (i. e. recipients) do not know his/her identity. At the same time, the recipient cannot want the other recipients to reveal that he/she is an authorized recipient. In fact, today, anonymity is one of the most important prerequisites for people to talk freely and make objective decisions. Motivated by the above, this paper proposes a completely anonymous multi-recipient signcryption scheme which meets: (1) The identity of the sender is kept secret; (2) The identities of all the recipients are kept secret; (3) Each recipient can easily judge whether the received message is from an authorized source, but he/she cannot determine the real identity of the sender; (4) Each recipient can easily judge whether he/she is an authorized recipient, but he/she cannot determine the identity of any other authorized recipient; (5) The validity of ciphertext can be verified publicly. [SEP] Justify:

Output: The text contrasts the claim by explaining that existing multi-recipient signcryption schemes do not adequately ensure the anonymity of senders and recipients. It highlights that while some schemes may attempt to provide anonymity, they are not perfect and can be vulnerable to attacks that compromise this anonymity. For instance, it states that none of the existing schemes take full anonymity into account, indicating a significant gap in the notion of complete anonymity within blockchain transactions.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: “I see this as being a triage tool, but the study included only adult patients who got to the emergency room because of a car accident or a fall. More research is needed before I could say whether the test is ready for use in kids with concussions. ”—DR. “The new study on GFAP was well designed and informative, but I think biomarkers are quite a ways off from being a gold standard test. ”—DR. the 90 patients with GFAP concentrations in the lowest quintile, only seven (8 percent) had abnormal MRI findings, while 58 (64 percent) of those with GFAP concentrations in the highest quartile had abnormal MRI findings, the study said. GFAP levels also correlated with MRI lesion types, the researchers said. “These results indicate that the diagnostic utility of GFAP might extend beyond CT-visible pathology and might help to identify patients with more subtle injury,” the researchers said. The study was funded by the of and Stroke and the US Department of Defense. Dr. Manley said the portable biomarker test might also one day prove useful for detecting TBI on the battlefield. One of the limitations of the study was the lack of longer-term follow-up of patients to determine, for instance, whether those with high GFAP levels had more neurocognitive symptoms. There may have also been selection bias in the sub-analysis since it included only those TRACK-TBI participants who had MRI. Are NeededAn editorial that accompanied the study in Neurology underscored the need for better diagnostic tools for TBI. “The ability to accurately diagnose the severity of traumatic brain injury (TBI) to establish the consequent need for treatment still eludes modern emergency care systems,” wrote of the University of Sheffield. She said that while a CT scan is good at detecting patients in need of neurosurgery or neurocritical care for life-threatening injury, “a CT brain scan is negative on more than 90 percent of patients with TBI symptoms, leading to uncertainty as to the extent of TBI and likely need for further treatment. ”Dr. Lecky said the findings that GFAP concentration at nine to 16 hours post-injury discriminated well between patients with MRI-positive findings and MRI-negative findings were positive. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the diagnostic utility of GFAP in diagnosing traumatic brain injury (TBI) and the limitations of current diagnostic tools, rather than any advanced algorithms predicting emotions based on brain scans. There is no mention or exploration of algorithms, emotions, or their prediction via brain scans in the text.


Input: Task: corroborate the claim using the given text. Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: When cropping the images, the central region is selected, which usually contains the most relevant and discriminative features for disease detection. Therefore, the preprocessing techniques used in this research guarantee that no crucial information is lost during the transformation. For data augmentation, this research will utilize the Random Rotation, Horizontal Flip, and techniques. applies random angle rotations to the images, simulating various orientations. flips the images horizontally, reflecting their contents. randomly zooms the images in or out, further increasing the dataset's diversity. These techniques enhance the model's ability to handle variations in plant leaf diseases' appearance and improve its robustness in detecting different disease patterns. By applying preprocessing techniques such as resizing, cropping, normalization, and data augmentation, we can ensure that the input images adhere to the required dimensions of the individual base models while preserving essential visual details. The consistent input size and increased variability of the dataset contribute to more effective learning and generalization of the ensemble model. Moreover, the preprocessing stage enhances the model's ability to detect plant leaf diseases accurately across diverse agricultural scenarios, ultimately improving the overall performance and reliability of the automated plant disease diagnosis system. Analysis (EDA) is a crucial step in understanding the dataset and gaining insights before constructing our plant leaf disease detection system. During the EDA process, we first carefully examine the data distribution and perform various statistical analyses to comprehend its characteristics. Additionally, data visualization techniques, such as histograms, bar charts, and scatter plots, aid in identifying potential patterns, class imbalances, and outliers within the dataset. EDA also involves exploring the relationships between classes and their corresponding images to gain an understanding of the variations and complexities present in the images. Moreover, we evaluate the quality and diversity of the images to ensure the dataset's representativeness and suitability for training a robust model. Moving on to the dataset-specific facts, our plant leaf disease detection dataset comprises 38 distinct classes, each representing a different plant leaf disease. On average, each class contains around 450 images, making the dataset reasonably balanced. The dataset is extensive, consisting of a total of 70,295 images, which will serve as training data for the model. For validating the model's performance and generalization ability, we have set aside 17,572 images as a validation set. The dataset encompasses 14 different plant species, and within these species, there are 26 different diseases being studied. [SEP] Justify:

Output: The text corroborates the claim by discussing various preprocessing and data augmentation techniques designed to enhance the model's ability to detect plant leaf diseases accurately, which reflects the broader application of deep learning in improving disease detection systems. The emphasis on ensuring that crucial information is preserved during preprocessing, as well as the mention of the model's improved robustness and performance in disease diagnosis, aligns with the assertion that deep learning has led to improvements in accuracy within this field.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: If yes, it means that IDi is one of the recipients designated by the signer and the ciphertext is valid. Otherwise, the recipient quits the decryption process. De-signcryption. The authorized user can recover the plaintext by the following steps: Compute xi = H4(IDi) and then compute. Compute and then get the plaintext as. The one who receives the broadcasting ciphertext can verify the validity of the message and judge whether he/she is authorized by the public verification or judgement algorithm. If necessary, he/she can use the de-signcrypt algorithm to decrypt the ciphertext. In a network conference application, due to the nature of the broadcast communication, anyone, authorized recipients or unauthorized ones, can easily receive the ciphertext and check the validity of the message and the authorization of the decryption. But, only the authorized recipients can decrypt it correctly. Results and 1. The public verification algorithm in the De-signcrypt algorithm is correct. Proof. (3)In our scheme, although the identity of the real signer is not included in the ciphertext, his/her private key is definitely necessary in the signcryption process, which ensures that only legal participants who have registered himself/herself with PKG can generate a valid ciphertext. That is to say, through this algorithm, anyone can check whether a ciphertext is generated by an authorized participant, but he/she cannot determine the real identity of the signer. Theorem 2. The judgement algorithm in the De-signcrypt algorithm is correct. Proof. For each authorized IDi where we have (4)Similarly, because the private key of the real signer is necessary in the signcryption process, this algorithm can also be used to check the validity of ciphertext. At the same time, this algorithm can help a participant, who has registered himself/herself with PKG, to judge whether himself/herself is an authorized recipient, because the private key of the recipient is also necessary in the judgement. Theorem 3. The decryption algorithm in the De-signcrypt algorithm is correct. Proof. The authorized IDi, can compute as follows:(5) Thus, we can get because(6) Then, we can get the plaintext through the computations and. We shall give security proof of the proposed scheme on confidentiality, unforgeability and anonymity under the random oracle model. Theorem 4. [SEP] Justify:

Output: The text contrasts the claim by explaining that while the identity of the real signer is not included in the ciphertext, the private key of the signer is necessary for generating valid ciphertext, indicating that participants' identities can be verified. This implies that transactions are not completely anonymous, as authorized recipients can be authenticated through the use of private keys, and the verification process allows for the determination of authorization without disclosing the real identity.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: “These results are promising,” she wrote, “but, as the investigators acknowledge, are some distance from suggesting that GFAP assays should become part of current emergency department management of patients with TBI and negative CT scans. The clinical significance of the positive MRI findings is unknown; data on patient symptom severity at 2 weeks or later follow-up were not reported in the study. If the frequencies of disabling concussion symptoms do not differ in the MRI-positive and MRI-negative cohorts during follow-up, it is hard to argue that either GFAP or the MRI scans are providing key information to guide further management. ”She noted that nearly half of the study participants with “negative CT scans did not have MRI as part of their follow-up, and because their characteristics are not reported, it is difficult to assess the potential for selection bias. ”In addition, she said it would be time consuming and expensive to do an MRI on every suspected TBI patient who had a normal CT. Dr. Lecky said a biomarker test could be useful for sorting out which suspected TBI patients need head imaging and/or medical follow up, but she cautioned that the biomarker test needs to be further investigated before it is ready for clinical use. Ramon Diaz-of Neurology at the of Medicine at the University of Pennsylvania and director of the said it would be beneficial to have a biomarker test that could help eliminate unnecessary CT scans (and radiation exposure). But he said adopting a new routine screening test into emergency room workflow would take planning and a change in procedures. “To be honest, I am not sure that US emergency room physicians would stop using CT scans,” in part because of medical-legal concerns, said Dr. Diaz-Arrastia, who was a coauthor of the current study. He said the biomarker test, if it becomes available, might be more quickly adopted in settings such as concussion clinics and urgent-care centers. Dr. Diaz-Arrastia said he often sees patients who come in with neurocognitive issues long after they've had a minor head injury, some having been told that since their CT was normal there was no need for concern. He said the gaps in diagnosing mild TBI mean that important care instructions and follow-up assessments are not getting done. [SEP] Justify:

Output: The text does not address the claim regarding advanced algorithms predicting emotions through brain scans. Instead, it discusses the clinical significance of MRI and GFAP assays in the context of TBI management and emphasizes the need for further investigation into biomarker tests. Therefore, the content is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Some of the earliest studies of this kind emerged in the early 2000s. In 2003, for example, Dennis Lo, a chemical pathologist at the of Hong Kong, and his colleagues reported that in a study of 22 pregnant women, a specific RNA found in the placenta was much more abundant during the third trimester in those who had preeclampsia than in those who did not. Over the years, Lo’s group and others have looked at broader changes in RNA during pregnancy in larger groups of people. In a 2018 study, Moufarrej, who was then a doctoral student, her advisor Stephen Quake, a biophysicist at Stanford University, and colleagues reported that cell-free RNA could help determine when labor would occur. The researchers recruited 38 pregnant women in the known to be at risk of preterm birth, and then drew a blood sample from each in either the second or third trimester. By comparing cell-free RNA in those who eventually delivered prematurely to those who gave birth at full term, they were able to identify a set of RNAs that appeared up to two months prior to labor that could pinpoint around 80 percent of premature births. That proof-of-concept investigation spurred the researchers to look further and examine whether cell-free RNA could also predict preeclampsia. Other groups had previously reported RNA-based signatures of preeclampsia — in 2020, for instance, scientists working with the California-based biotech company Illumina reported dozens of RNA transcripts that were unique to pregnant women with the condition. But Moufarrej, Quake and their colleagues wanted to track RNA changes throughout pregnancy to see whether it might be possible identify people at risk of preeclampsia during early pregnancy, before symptoms began. RNA provides information about which genes are turned on — a process known as gene expression — and can be correlated with different pregnancy outcomes. In this study, researchers analyzed RNA in maternal blood and found 38 genes that were expressed at different levels in eight women who delivered their babies preterm versus seven who gave birth at full term. Each column represents a woman; each row shows the activity of a different gene (blue = less active, red = more active). In a study published in 2022, the researchers recruited 73 mothers at heightened risk of preeclampsia and drew blood from them four times: at or before 12 weeks, between 13 and 20 weeks, at or after 23 weeks, and after birth. [SEP] Justify:

Output: The text corroborates the claim by discussing various studies that examine gene expression through RNA analysis in pregnant women at risk of conditions like preeclampsia. Particularly, the research conducted by Moufarrej and colleagues indicates the potential for RNA to serve as a biomarker in identifying genetic risk factors during pregnancy. This application of genomics, using RNA data, aligns with the claim that AI is utilized to analyze genetic information to gauge disease risks.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: B sets Ppub = bP, and gives <G1, G2, q, e, P, Ppub, H1, H2, H3, H4> to the attacker F as the public parameters. Upon receiving the system parameters, F outputs n target identities. Attack. F adaptively performs polynomially bounded number of queries to the various oracles in this phase, which are similar to those in Theorem 4. Forgery. F generates a target ciphertext. If the forgery is successful, the equation holds. Define QS* = lS*P = aP, and then we have. Now, it will be very easy to extract the CDH problem’s solution. We consider the advantage of F’s success here. As in the anony-signcryption query, the probability for B to answer a failure signcryption query is not greater than qs/2k, and then the advantage is. Theorem 6. In the ANON-sID-CCA security model, if an adversary A has advantage against the game defined in Definition 6 within running time t (where A makes at most qe private key extraction queries, qs anony-signcryption queries, qd de-signcryption queries and queries to the Hash functions H1,H2,H3 and H4, respectively), then there is an algorithm B in solving the DBDH-M problem with an advantage. Proof. The challenger B is challenged with an instance of the DBDH-M problem. Assume that there is an adversary A who is capable of breaking the ANON-sID-CCA security with a non-negligible advantage. B makes use of A to solve the DBDH-M instance. B simulates the system with hash functions H1, H2, H3 and H4, and allows A to make polynomially bounded number of queries. The game between B and A is demonstrated below: Phase 1. Suppose that A outputs a target identity pair (ID1*, ID2*). Setup. B sets the public key and lets where and aP and bP are given from the instance of the DBDH-M problem. Here, B does not know a and b. [SEP] Justify:

Output: The text contrasts the claim by discussing security models and adversarial attacks that focus on the public parameters and identities in a blockchain setting. It implies that despite efforts to create security, the existence of attackers and methods to exploit system parameters suggests that transactions may not be completely anonymous. The presence of an attacker who can interact with oracles and perform key extraction queries indicates vulnerabilities in maintaining anonymity.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3968021/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractclassification of brain tumors play an essential role in improving prognosis, diagnosis, and therapy strategy in glioblastoma patients. In this paper, we provide a novel (MGMT-PMP) approach that extracts latent features fused with radiomic data to predict the genetic subtype of glioblastoma. The novice rejection method has been shown to be highly effective at picking and isolating negative training cases from the original dataset. In this study, the fused feature vectors are used to train and test CNN classifiers. We examined classification performance for the first time in published form, using metrics such as accuracy, F1-score, and Matthews’s correlation coefficient. Jackknife tenfold cross-validation was used to train and evaluate the BraTS-2021 dataset validation. The maximum classification accuracy for detecting MGMT methylation status in glioblastoma patients is 99. 13 percent. Deep learning feature extraction with Radiogenomic characteristics, merging imaging phenotypes and molecular structure, and using a rejection method were found to outperform in diagnosing the MGMT methylation status of glioblastoma patients. The approach connects genetic variance to radiomic characteristics, bridging two areas of research that may be valuable for clinical treatment planning and leading to better outcomes. MRI scansglioblastomarejection algorithmdeep learningstatusFiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 121 IntroductionBrain tumors occur when billions of brain cells divide uncontrollably and develop aberrant areas, resulting in the highest global mortality rates for both adults and children [1]. Tumors in the brain have variable growth rates and are classified as primary or secondary. The former, which originates inside the brain, is the deadliest and most commonly cancerous. Gliomas handle around 30% of primary brain tumors, 80% of malignant ones, and most primary brain tumor-related deaths [2]. Grades II and III gliomas grow swiftly and require immediate treatment. Grade-IV gliomas, or glioblastoma (GB), are the most aggressive and toughest to treat. [SEP] Justify:

Output: The text does not relate to the claim as it discusses the classification of brain tumors and the use of algorithms in that context, specifically focusing on predicting genetic subtypes of glioblastoma rather than emotions based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Afterward, the researchers compared cell-free RNA for the 24 women who indeed developed preeclampsia against the remaining 49 who did not. The team identified RNAs corresponding to 544 genes whose activity differed in those who developed preeclampsia and those who did not. (The study did not differentiate between maternal and fetal RNA, but since the majority of cell-free RNA in a pregnant person’s blood is their own, Moufarrej says most of these RNAs are likely maternal in origin. )Then, using a computer algorithm, the researchers developed a test based on 18 genes that could be used prior to 16 weeks of pregnancy to predict a woman’s risk of developing preeclampsia months later. The test correctly identified all the women who would later develop preeclampsia — and, equally important, all of the women who the test predicted wouldn’t develop preeclampsia did in fact escape the disease. (About a quarter of the women predicted to develop preeclampsia did not, in fact, get the disease. ) The same 18-gene panel also correctly predicted most cases of preeclampsia in another group of 118 women. The team also took a closer look at which tissues the RNA of interest originated from. This included the usual suspects, such as the lining of the blood vessels (also known as the endothelium), which scientists already know contributes to preeclampsia, as well as other, more unexpected sources, such as the nervous and muscular systems. The authors note that, in the future, this information could be used both to understand how preeclampsia affects different parts of the body and to assess which organs are at highest risk of damage in a particular patient. According to Quake, studies like these from both his team and others are starting to reveal the diversity of changes throughout the body that contribute to pregnancy complications — and providing evidence for something that clinicians and researchers have long suspected: that both preeclampsia and preterm birth are conditions with a range of underlying causes and outcomes. “There are now strong indications that you should be defining multiple subtypes of preeclampsia and preterm birth with molecular signatures,” says Quake. “That could really transform the way physicians approach the disease. [SEP] Justify:

Output: The text corroborates the claim by describing how researchers used a computer algorithm to develop a test based on genetic activity (specifically 18 genes) that predicts the risk of developing preeclampsia. This process demonstrates the application of AI in genomics for identifying genetic risk factors associated with a disease.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: The specific comparison results are shown in Table 3, from which one can see that our scheme performs much better than most of the existing schemes in terms of number of parameters, computation complexity and the ciphertext length. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 3. Comparison. the de-signcryption in our scheme, some calculations of the de-signcryption algorithm are used to judge the validity of ciphertext and the authorization of the recipient, which is important for broadcast-based communications to avoid receiving unwanted information (e. g. SPAM). Note that although the schemes [9], [10], [11], [13], [15] directly provide the recipients’ true identities in the ciphertext, in fact the recipient cannot absolutely ensure whether he/she is authorized before checking the validity of the ciphertext. The number of pair operations (Tp, the most time-consuming operation in the existing schemes and our scheme) in our decryption algorithm is smaller than those of the existing schemes, which makes our scheme more attractive in terms of computation performance. Table 4 shows a comparison between the proposed scheme and the existing ones [9], [10], [11], [12], [13], [15], [17], [19]. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 4. De-signcryption Efficiency Comparison. Discussion of Merit and to existing schemes, our scheme has some advantages. To achieve the signer’s anonymity, the identity of the signer is no longer included in the ciphertext, although the private key of the signer is necessary for signcryption. The recipients can therefore only judge if the ciphertext received is from a trusted signer, but they cannot determine the real identity of the signer. To achieve the recipient’s anonymity, the ID information of all authorized recipients is mixed by the Lagrange interpolation polynomial during the signcryption process, which prevents the recipient’s ID from being exposed. This method also ensures that only the recipient, who has got the entire ciphertext, can decrypt the ciphertext, thus achieving the decryption fairness. [SEP] Justify:

Output: The text contrasts the claim by clarifying that while the signer’s identity is not included in the ciphertext, which contributes to anonymity, it also states that the recipient cannot ensure who the signer is before checking the validity of the ciphertext. Furthermore, it mentions that although recipients can judge if the ciphertext is from a trusted signer, they cannot determine the real identity of the signer, which indicates that there is a lack of complete anonymity.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Grade-IV gliomas, often known as glioblastoma (GB), have the most challenging prognosis and clinical results [3]. Adult GB and diffuse astrocytic glioma are the deadliest central nervous system tumors because of their high variability in shape and microscopic structure [4]. Despite multiple therapeutic choices, the prognosis for GB patients has not much improved over the previous 20 years. (WHO) has supplied information on the classification of central nervous system malignancies, emphasizing the importance of integrated diagnostics [5]. Clinical tumor diagnosis involves combining molecular and cytogenetic markers [6]. Deep learning is a branch of machine learning that makes use of artificial neural networks. Deep learning methods enable computer programs to learn and uncover patterns in enormous datasets. 1. 1 O6-methylguanine-DNA methyltransferase (MGMT): O6-methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme that plays a crucial role in chemoresistance to alkylating drugs [7]. Methylation of the promoter is a predictive factor for chemotherapy response in early-stage GB diagnosis. The most common treatments for GB are surgery, radiation, and adjuvant chemotherapy. Chemotherapy effectiveness is often linked to the methylation status of MGMT, a biomarker that repairs guanine nucleotides and inhibits cell death from alkylating drugs [8]. In most cases, having the MGMT enzyme avoids DNA damage, making it beneficial. In GB patients, the presence of MGMT reduces the efficacy of chemotherapy by making alkylating drugs ineffective [9]. This study focuses on predicting the methylation status of the MGMT promoter ("Preprocessing"), which results in MGMT gene silencing in GB patients treated with alkylating agent chemotherapy. The presence of preprocessing prevents cell division by cross-linking DNA strands. Predicting the methylation status of MGMT promoters in GB can aid decision-making and treatment planning, which is the primary goal of this research. 1. 2 Convolutional neural networks (CNN) : Deep convolutional neural networks are used for various tasks, such as medical image processing and picture classification [10]. The deep learning architecture uses many layers of encoding blocks. The feature maps in lower levels are transmitted to higher-complexity layers [11]. [SEP] Justify:

Output: The text does not address or provide any information relevant to the claim about advanced algorithms predicting emotions based on brain scans. Instead, the text focuses on glioblastoma, treatments, and the use of deep learning methods in medical image processing, which does not relate to the emotional prediction aspect mentioned in the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Home E-and catalog us 243. Published online: December 31, 2012 DOI: of Kwak 1, Hak C. Jang 2, 3, Park 1, 2, 4 1Department of 110-744, Korea. 2Department of of Medicine, Seoul 110-799, Korea. 3Department of 463-707, Korea. 4WCU Department of of and Technology and College of Medicine, University, Seoul 110-744, Korea. Corresponding author: Tel: +82-2-2072-2946, Fax: +82-2-3676-8309, kspark@snu. ac. kr 02, 2012 15, 2012 16, 2012 Copyright © 2012 by (open-access, It is identical to the Attribution Non-( diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care. Keywords: genetics, gestational diabetes, type 2 diabetes diabetes mellitus (GDM) is defined as abnormal glucose tolerance that is first identified or diagnosed during pregnancy [1]. It is estimated that approximately 2% to 5% of all pregnancies in Korean women are complicated by GDM [2]. The clinical significance of GDM is that it increases the risk of adverse pregnancy outcomes [3]. [SEP] Justify:

Output: The text corroborates the claim by discussing the progress made in identifying genetic risk factors for gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM). It mentions that current investigations using next-generation sequencing are improving insights into the genetic aspects of GDM, indicating the role of advanced technologies, which may include AI, in genomics research. Furthermore, the aspirations for personalized genomic medicine and improved patient care highlight the relevance of genomics in understanding genetic risks, supporting the claim.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: The ID-based cryptography enables one user to confidentially send messages to other users, despite of whether the latter is a registered user, and the public verification property of our scheme enables unregistered users to judge the validity of the received ciphertext before having to register himself/herself with PKG. The merit/demerit comparison between the existing schemes and our scheme is summarized in Table 5. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 5. Comparison of merits and demerits. Table 5, we can see: (1) The schemes [12], [13], [15], [17], [19] have taken anonymity of the sender into account. However, they are all prone to the cross-comparison attack and joint conspiracy attack. In these schemes, in order to protect the privacy of the sender, the sender randomly chooses some legitimate participants to hide the true identity. But in practice, these schemes are vulnerable to the cross-comparison attack and joint conspiracy attack mentioned above. (2) The schemes [9], [10], [11] cannot assure the anonymity of the sender because the identity of the sender is directly given in the ciphertext. (3) The schemes [9], [10], [11], [13], [15], [17], [19] cannot assure the anonymity of the recipient. In these schemes, the ciphertext includes two parts: a recipient identity list and each recipient’s specific data. A recipient identity list is required so that an authorized recipient is able to find his/her specific data required for decryption of the ciphertext. Because the recipient identity list is given in plaintext, the ID information of each recipient is exposed, and thus the anonymity of recipients is not assured. This has the advantage that, as long as an authorized recipient receives his/her specific data correctly, he/she can decrypt the ciphertext to retrieve the corresponding message even if other recipients’ specific data are invalidated during transmission. While this seems to represent an advantage on the first sight, it also represents a problem regarding decryption fairness. Decryption unfairness can cause the sender to cheat some recipients actively by just sending incorrect recipient-specific data. [SEP] Justify:

Output: The text contrasts the claim by detailing that various schemes, including those relevant to blockchain transactions, do not guarantee the anonymity of the sender or the recipient. It mentions that in certain schemes, the sender's identity is directly provided in the ciphertext, and the identity of the recipient is exposed through a recipient identity list given in plaintext. This directly contradicts the assertion that all transactions on a blockchain are completely anonymous.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Convolutional neural networks (CNN) have significantly fewer neurons than shallow neural networks because of sparse interactions. Deep convolutional neural networks are used for various tasks, such as medical image processing and image classification [12]. The deep learning architecture uses numerous layers of encoding blocks. The feature maps in lower levels are transmitted to higher-complexity layers [13]. Convolutional neural networks (CNN) use sparse interaction to significantly reduce the number of neurons compared to shallow neural networks [14]. Transfer learning using CNN based on for the ImageNet dataset is well known deep learning approach [15]. The CNNs are extensively used in vision-related applications including object detection, spanning classification, and segmentation [16]. The combination of data pre-processing and augmentation with transfer learning can be helpful for improved classification results. In our case, since the dataset is enormous, only pre-processing seems of excellent value along with a fine-tuned CNN architecture [17]. The 3D CNN is an array of three-dimensional CNNs that use a multifiber unit in conjunction with dilated weighted convolutions to extract feature attributes at different scales for volumetric segmentation [18]. Pre-processing: Before the data is delivered to the network for training, it is improved through a variety of methods (mirroring, rotation, and cropping). Training: The model was created using a fixed size of 128 × 128 and a redesigned loss function that included both the focused and generalized loss [19]. Eq. (1) provides a mathematical expression for the output value γ at position (x, y, z) of the jth feature map in the ith 3D convolutional layer [20]. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the technical aspects of convolutional neural networks (CNN) and their applications in image processing and classification, without any mention of emotions or brain scans.