10131509
Using Approximation Hardness to Achieve Dependable Computation
Redundancy has been utilized to achieve fault tolerant computation and to achieve reliable communication in networks of processors. These techniques can only be extended to computations solely based on functions in one input in which redundant hardware or software (servers) are used to compute intermediate and end results. However, almost all practical computation systems consist of components which are based on computations with multiple inputs. Wang, Desmedt, and Burmester have used AND/OR graphs to model this scenario. Roughly speaking, an AND/OR graph is a directed graph with two types of vertices, labeled ∧-vertices and ∨-vertices. In this case, processors which need all their inputs in order to operate could be represented by ∧-vertices, whereas processors which can choose one of their "redundant" inputs could be represented by ∨-vertices. In this paper, using the results for hardness of approximation and optimization problems, we will design dependable computation systems which could defeat as many malicious faults as possible. Specifically, assuming certain approximation hardness result, we will construct k-connected AND/OR graphs which could defeat a ck-active adversary (therefore a ck-passive adversary also) where c > 1 is any given constant. This result improves a great deal on the results for the equivalent communication problems.
1602403
Using Mobile Agent Results to Create Hard-to-Detect Computer Viruses
The theory of computer viruses has been studied by several authors, though there is no systematic theoretical study up to now. The long time open question in this area is as follows: Is it possible to design a signature-free (including dynamic signatures which we will define late) virus? In this paper, we give an affirmative answer to this question from a theoretical viewpoint. We will introduce a new stronger concept: dynamic signatures of viruses, and present a method to design viruses which are static signature-free and whose dynamic signatures are hard to determine unless some cryptographic assumption fails. We should remark that our results are only for theoretical interest and may be resource intensive in practice.
8247434
NP-Hard Sets are Superterse Unless NP is Small
Abstract We show that the class P- mc ( n ) of polynomial time n -membership comparable sets (defined by Ogihara, 1995) has p -measure 0. Using this result, we will show that the class of sets which are ⩽ tt P -reducible to some P-selective set has p -measure 0. Furthermore, we will show that if NP does not have p -measure 0 then no ⩽ tt p -hard set for NP is P-approximable.
8968053
Resource-Bounded Balanced Genericity, Stochasticity and Weak Randomness
We introduce balanced t(n)-genericity which is a refinement of the genericity concept of Ambos-Spies, Fleischhack and Huwig [2] and which in addition controls the frequency with which a condition is met. We show that this concept coincides with the resource-bounded version of Church's stochasticity [6]. By uniformly describing these concepts and weaker notions of stochasticity introduced by Wilber [19] and Ko [11] in terms of prediction functions, we clarify the relations among these resource-bounded stochasticity concepts. Moreover, we give descriptions of these concepts in the framework of Lutz's resource-bounded measure theory [13] based on martingales: We show that t(n)-stochasticity coincides with a weak notion of t(n)-randomness based on so-called simple martingales but that it is strictly weaker than t(n)-randomness in the sense of Lutz.
16463784
Privacy Preserving Data Generation for Database Application Performance Testing
Synthetic data plays an important role in software testing. In this paper, we initiate the study of synthetic data generation models for the purpose of application software performance testing. In particular, we will discuss models for protecting privacy in synthetic data generations. Within this model, we investigate the feasibility and techniques for privacy preserving synthetic database generation that can be used for database application performance testing. The methodologies that we will present will be useful for general privacy preserving software performance testing.
41292415
Randomness, Stochasticity and Approximations
Polynomial time unsafe approximations for intractable sets were introduced by Meyer and Paterson [9] and Yesha [19], respectively. The question of which sets have optimal unsafe approximations has been investigated extensively; see, e.g., [1], [5], [15], and [17]. Recently, Wang [15], [17] showed that polynomial time random sets are neither optimally unsafe approximable nor Δ -levelable. In this paper we show that: (1) There exists a polynomial time stochastic set in the exponential time complexity class which has an optimal unsafe approximation. (2) There exists a polynomial time stochastic set in the exponential time complexity class which is Δ -levelable. The above two results answer a question asked by Ambos-Spies and Lutz [2]: What kind of natural complexity property can be characterized by p -randomness but not by p -stochasticity? Our above results also extend Ville's [13] historical result. The proof of our first result shows that, for Ville's stochastic sequence, we can find an optimal prediction function f such that we will never lose our own money betting according to f (except the money we have earned), that is to say, if at the beginning we have only $1 and we always bet $1 that the next selected bit is 1 , then we always have enough money to bet on the next bit. Our second result shows that there is a stochastic sequence for which there is a betting strategy f such that we will never lose our own money betting according to f (except the money we have earned), but there is no such optimal betting strategy. That is to say, for any such betting strategy, we can find another betting strategy which could be used to make money more quickly.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
13343820
The error concealment feature in the H.26L test model
This paper presents the error concealment (EC) feature implemented by the authors in the test model of the draft ITU-T video coding standard H.26L. The selected EC algorithms are based on weighted pixel value averaging for INTRA. pictures and boundary-matching-based motion vector recovery for INTER pictures. The specific concealment strategy and some special methods, including handling of B-pictures, multiple reference frames and entire frame losses, are described. Both subjective and objective results are given based on simulations under Internet conditions. The feature was adopted and is now included in the latest H.26L reference software TML-9.0.
10282937
Decoding Algorithm in Statistical Machine Translation
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.
14888347
Error Resilient Mode Decision in Scalable Video Coding
Error resilient macroblock mode decision has been extensively investigated in the literature for single-layer video coding, for which error resilient mode decision is also called as intra refresh. In this paper, we present a loss-aware rate-distortion optimized macroblock mode decision algorithm for scalable video coding, wherein more macroblock coding modes than intra and inter are involved. Thanks to the good performance, the proposed method has been adopted into the joint scalable video model by the joint video team.
16068554
The SIMBA user alert service architecture for dependable alert delivery
Alerts refer to the delivery of user-subscribed information to the user. As the number of alert services and the types of information delivery devices increase, a new model that allows users to manage alert delivery and avoid alert overflow is needed. The unique dependability challenge in the management of alerts is in the proper use of redundancy to achieve timeliness and reliability without being unduly intrusive or cumbersome. We describe the design, implementation, and user experience of an alert service architecture, called SIMBA. SIMBA utilizes Instant Messaging with acknowledgements as the universal, reliable alert delivery channel, with emails being the fallback channel. All alerts that a user subscribes to are first directed to the user's MyAlertBuddy, which allows centralized delivery preference customization and acts as a personal alert router to protect the privacy of user addresses. Delivery modes, each of which involves multiple user addresses to accommodate communication failures, are supported as an abstraction for specifying personalized dependability levels. A working implementation of the SIMBA system, which integrates five different types of alert services, is described. Challenges and techniques in maintaining a highly available MyAlertBuddy to avoid single-point of failure are discussed. The concept of exception handling automation is introduced for enhancing the robustness of applications that drive third-party communication client software through automation interfaces.