Reviewerly/exHarmony huggingface dataset:
Forgetting in multi-agent modal logics

In the past decades, forgetting has been investigated for many logics and has found many applications in knowledge representation and reasoning. In this paper, we study forgetting in multi-agent modal logics. We adopt the semantic definition of existential bisimulation quantifiers as that of forgetting. We resort to canonical formulas of modal logics introduced by Moss. An arbitrary modal formula is equivalent to the disjunction of a unique set of satisfiable canonical formulas. We show that, for the logics of Kn, Dn, Tn, K45n, KD45n and S5n, the result of forgetting an atom from a satisfiable canonical formula can be computed by simply substituting the literals of the atom with ⊤. Thus we show that these logics are closed under forgetting, and hence have uniform interpolation. Finally, we generalize the above results to include common knowledge of propositional formulas.
Spin-qubit noise spectroscopy from randomized benchmarking by supervised learning

We demonstrate a method to obtain the spectra of $1/f$ noises in spin-qubit devices from randomized benchmarking, assisted by supervised learning. The noise exponent, which indicates the correlation within the noise, is determined by training a double-layer neural network with the ratio between the randomized benchmarking results of pulse sequences that correct noise and not. After the training is completed, the neural network is able to predict the exponent within an absolute error of about 0.05, comparable with existing methods. The noise amplitude is then evaluated by training another neural network with the decaying fidelity of randomized benchmarking results from the uncorrected sequences. The relative error for the prediction of the noise amplitude is as low as 5\% provided that the noise exponent is known. Our results suggest that the neural network is capable of predicting noise spectra from randomized benchmarking, which can be an alternative method to measure noise spectra in spin-qubit devices.
EGO: Optimized Sensor Selection for Multi-Context Aware Applications with an Ontology for Recognition Models

In recent years, there has been a significant growth of context-aware applications, which extract the user's context from multiple embedded sensors in smartphones and wearable sensors. However, running multiple context-aware applications simultaneously causes extensive battery drainage for mobile devices. To alleviate the energy limitation in multi-context setting, we propose EGO: an ontology-based framework for group sensor selection to achieve synergy across applications while trading off energy consumption, accuracy, and delay in context recognition. A new context recognition ontology is designed to capture context recognition models. It captures the alternative groups of sensors for each context and the parameters for context recognition models. EGO includes an adaptive sensor selection mechanism that selects the appropriate sensors based on the current user state, the available resources, and the requested accuracies by running applications. The framework provides an open architecture that allows integration with other sensor optimization modules for sensor scheduling. EGO is validated using a real test-bed implemented on an Android platform that provides accessibility to on-board mobile sensors and external sensors. Results show that EGO provides better trade-off than previous state-of-the-art methods. Furthermore, EGO provides 63 percent energy saving with comparable accuracy when compared to the most accurate group of sensors.
Development of Telugu-Tamil Transfer-Based Machine Translation System: An Improvization Using Divergence Index

Abstract Building an automatic, high-quality, robust machine translation (MT) system is a fascinating yet an arduous task, as one of the major difficulties lies in cross-linguistic differences or divergences between languages at various levels. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems. This paper discusses the development of Telugu-Tamil transfer-based MT and how a divergence index (DI) is built to quantify the number of parametric variations between languages in order to improve the success rate of MT. The DI facilitates MT in proposing where to put efforts for the given language pair to attain better and faster results. In addition, handling strategies of different types of divergences in a transfer-based approach to MT are discussed. The paper also includes the evaluation method and how an improvization takes place with the application of DI in MT.
Polymorphic symmetric multiple dispatch with variance

Many object-oriented languages provide method overloading, which allows multiple method declarations with the same name. For a given method invocation, in order to choose what method declaration to invoke, multiple dispatch considers the run-time types of the arguments. While multiple dispatch can support binary methods (such as mathematical operators) intuitively and consistently, it is difficult to guarantee that calls will be neither ambiguous nor undefined at run time, especially in the presence of expressive language features such as multiple inheritance and parametric polymorphism. Previous efforts have formalized languages that include such features by using overloading rules that guarantee a unique and type-sound resolution of each overloaded method call; in many cases, such rules resolve ambiguity by treating the arguments asymmetrically. Here we present the first formal specification of a strongly typed object-oriented language with symmetric multiple dispatch, multiple inheritance, and parametric polymorphism with variance. We define both a static (type- checking) semantics and a dynamic (dispatching) semantics and prove the type soundness of the language, thus demonstrating that our novel dynamic dispatch algorithm is consistent with the static semantics. Details of our dynamic dispatch algorithm address certain technical challenges that arise from structural asymmetries inherent in object-oriented languages (e.g., classes typically declare ancestors explicitly but not descendants).
Partial Awareness

We develop a modal logic to capture partial awareness. The logic has three building blocks: objects, properties, and concepts. Properties are unary predicates on objects; concepts are Boolean combinations of properties. We take an agent to be partially aware of a concept if she is aware of the concept without being aware of the properties that define it. The logic allows for quantification over objects and properties, so that the agent can reason about her own unawareness. We then apply the logic to contracts, which we view as syntactic objects that dictate outcomes based on the truth of formulas. We show that when agents are unaware of some relevant properties, referencing concepts that agents are only partially aware of can improve welfare.
Active contour model based on fuzzy c‐means for image segmentation

In this Letter, the authors propose an active contour model based on the fuzzy c-means method. Using fuzzy c-means to calculate the values of two types of cluster centres for all points in the image domain, by which local fitting functions in traditional models are substituted. Since the values are calculated before curve evolution, the proposed model can segment images with intensity inhomogeneity efficiently. Furthermore, it has the good robustness to initial contour. The effectiveness of the proposed model is tested by experiments on some synthetic and real images.
On Tightly Secure Primitives in the Multi-instance Setting

We initiate the study of general tight reductions in cryptography. There already exist a variety of works that offer tight reductions for a number of cryptographic tasks, ranging from encryption and signature schemes to proof systems. However, our work is the first to provide a universal definition of a tight reduction (for arbitrary primitives), along with several observations and results concerning primitives for which tight reductions have not been known. Technically, we start from the general notion of reductions due to Reingold, Trevisan, and Vadhan (TCC 2004), and equip it with a quantification of the respective reduction loss, and a canonical multi-instance extension to primitives. We then revisit several standard reductions whose tight security has not yet been considered. For instance, we revisit a generic construction of signature schemes from one-way functions, and show how to tighten the corresponding reduction by assuming collision-resistance from the used one-way function. We also obtain tightly secure pseudorandom generators (by using suitable rerandomisable hard-core predicates), and tightly secure lossy trapdoor functions.
Keyless Semi-Quantum Point-to-point Communication Protocol with Low Resource Requirements

Abstract Full quantum capability devices can provide secure communications, but they are challenging to make portable given the current technology. Besides, classical portable devices are unable to construct communication channels resistant to quantum computers. Hence, communication security on portable devices cannot be guaranteed. Semi-Quantum Communication (SQC) attempts to break the quandary by lowering the receiver’s required quantum capability so that secure communications can be implemented on a portable device. However, all SQC protocols have low qubit efficiency and complex hardware implementations. The protocols involving quantum entanglement require linear Entanglement Preservation Time (EPT) and linear quregister size. In this paper, we propose two new keyless SQC protocols that address the aforementioned weaknesses. They are named Economic Keyless Semi-Quantum Point-to-point Communication (EKSQPC) and Rate Estimation EKSQPC (REKSQPC). They achieve theoretically constant minimal EPT and quregister size, regardless of message length. We show that the new protocols, with low overhead, can detect Measure and Replay Attacks (MRA). REKSQDC is tolerant to transmission impairments and environmental perturbations. The protocols are based on a new quantum message transmission operation termed Tele-Fetch. Like QKD, their strength depends on physical principles rather than mathematical complexity.
Distributed Algorithms Made Secure: A Graph Theoretic Approach

In the area of distributed graph algorithms a number of network's entities with local views solve some computational task by exchanging messages with their neighbors. Quite unfortunately, an inherent property of most existing distributed algorithms is that throughout the course of their execution, the nodes get to learn not only their own output but rather learn quite a lot on the inputs or outputs of many other entities. This leakage of information might be a major obstacle in settings where the output (or input) of network's individual is a private information (e.g., distributed networks of selfish agents, decentralized digital currency such as Bitcoin).While being quite an unfamiliar notion in the classical distributed setting, the notion of secure multi-party computation (MPC) is one of the main themes in the Cryptographic community. The existing secure MPC protocols do not quite fit the framework of classical distributed models in which only messages of bounded size are sent on graph edges in each round. In this paper, we introduce a new framework for secure distributed graph algorithms and provide the first general compiler that takes any “natural” non-secure distributed algorithm that runs in r rounds, and turns it into a secure algorithm that runs in Õ(r · D · poly(Δ)) rounds where Δ is the maximum degree in the graph and D is its diameter. A “natural” distributed algorithm is one where the local computation at each node can be performed in polynomial time. An interesting advantage of our approach is that it allows one to decouple between the price of locality and the price of security of a given graph function f. The security of the compiled algorithm is information-theoretic but holds only against a semi-honest adversary that controls a single node in the network.This compiler is made possible due to a new combinatorial structure called private neighborhood trees: a collection of n trees T(u1), …, T(un), one for each vertex ui ∊ V(G), such that each tree T(ui) spans the neighbors of ui without going through ui. Intuitively, each tree T(ui) allows all neighbors of ui to exchange a secret that is hidden from ui, which is the basic graph infrastructure of the compiler. In a (d, c)-private neighborhood trees each tree T(ui) has depth at most d and each edge e ∊ G appears in at most c different trees. We show a construction of private neighborhood trees with d = Õ(Δ · D) and c = Õ(D), both these bounds are existentially optimal.
Recommendation systems with the quantum k–NN and Grover algorithms for data processing

In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows a main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps during recommendation system was also indicated. The verification correctness of a proposed recommendation system was also analysed, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.
Relational cost analysis for functional-imperative programs

Relational cost analysis aims at formally establishing bounds on the difference in the evaluation costs of two programs. As a particular case, one can also use relational cost analysis to establish bounds on the difference in the evaluation cost of the same program on two different inputs. One way to perform relational cost analysis is to use a relational type-and-effect system that supports reasoning about relations between two executions of two programs. Building on this basic idea, we present a type-and-effect system, called ARel, for reasoning about the relative cost of array-manipulating, higher-order functional-imperative programs. The key ingredient of our approach is a new lightweight type refinement discipline that we use to track relations (differences) between two arrays. This discipline combined with Hoare-style triples built into the types allows us to express and establish precise relative costs of several interesting programs which imperatively update their data.
X-DMM: Fast and Scalable Model Based Text Clustering

Text clustering is a widely studied problem in the text mining domain. The Dirichlet Multinomial Mixture (DMM) model based clustering algorithms have shown good performance to cope with high dimensional sparse text data, obtaining reasonable results in both clustering accuracy and computational efficiency. However, the time complexity of DMM model training is proportional to the average document length and the number of clusters, making it inefficient for scaling up to long text and large corpora, which is common in realworld applications such as documents organization, retrieval and recommendation. In this paper, we leverage a symmetric prior setting for Dirichlet distribution, and build indices to decrease the time complexity of the sampling-based training for DMM from O(K∗L) to O(K∗U), where K is the number of clusters, L the average length of document, and U the average number of unique words in each document. We introduce a Metropolis-Hastings sampling algorithm, which further reduces the sampling time complexity from O(K∗U) to O(U) in the nearly-to-convergence training stages. Moreover, we also parallelize the DMM model training to obtain a further acceleration by using an uncollapsed Gibbs sampler. We combine all these optimizations into a highly efficient implementation, called X-DMM, which enables the DMM model to scale up for long and large-scale text clustering. We evaluate the performance of X-DMM on several real world datasets, and the experimental results show that XDMM achieves substantial speed up compared with existing state-of-the-art algorithms without clustering accuracy degradation.
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-Level Semantic Match

Relevance feedback techniques assume that users provide relevance judgments for the top k (usually 10) documents and then re-rank using a new query model based on those judgments. Even though this is effective, there has been little research recently on this topic because requiring users to provide substantial feedback on a result list is impractical in a typical web search scenario. In new environments such as voice-based search with smart home devices, however, feedback about result quality can potentially be obtained during users' interactions with the system. Since there are severe limitations on the length and number of results that can be presented in a single interaction in this environment, the focus should move from browsing result lists to iterative retrieval and from retrieving documents to retrieving answers. In this paper, we study iterative relevance feedback techniques with a focus on retrieving answer passages. We first show that iterative feedback is more effective than the top-k approach for answer retrieval. Then we propose an iterative feedback model based on passage-level semantic match and show that it can produce significant improvements compared to both word-based iterative feedback models and those based on term-level semantic similarity.
Classification enhancement of breast cancer histopathological image using penalized logistic regression

&lt;p&gt;Classification of breast cancer histopathological images plays a significant role in computer-aided diagnosis system. Features matrix was extracted in order to classify those images and they may contain outlier values adversely that affect the classification performance. Smoothing of features matrix has been proved to be an effective way to improve the classification result via eliminating of outlier values. In this paper, an adaptive penalized logistic regression is proposed, with the aim of smoothing features and provides high classification accuracy of histopathological images, by combining the penalized logistic regression with the smoothed features matrix. Experimental results based on a publicly recent breast cancer histopathological image datasets show that the proposed method significantly outperforms penalized logistic regression in terms of classification accuracy and area under the curve. Thus, the proposed method can be useful for histopathological images classification and other classification of diseases types using DNA gene expression data in the real clinical practice.&lt;/p&gt;
Leakage Resilient CCA Security in Stronger Model: Branch Hidden ABO-LTFs and Their Applications

Lossy trapdoor functions (LTFs) have already found various applications in cryptography with many priorities. But constructing leakage resilient (LR) CCA secure PKE schemes through this way received less attention. Existing works could only be proven secure in a weakened model due to the power of leakage attack. To address this problem, we introduce a new variant of ABO-LTF which is called branch hidden ABO-LTF (BHABO-LTF) in this paper. This primitive provides protection for the information of evaluated branches rather than lossy branches, which means even an adversary knows the information of the set of lossy branches, it can still not determine whether the output is evaluated on an injective or a lossy branch as long as the inversion key is kept secret. We observe that if this primitive has Chameleon property, we could present a generic construction of CCA secure PKE schemes. Due to the transparency of lossy branches of the primitive, we find that our construction can naturally be extended to accommodate leakage resilience. We give a generic construction with a realization of LR-BHABO-LTFs under the decisional composite residuosity assumption. This construction can be proved secure in a well-accepted security model rather than existing LTF-based ones in weak key-leakage model. Besides, without the need to hide the information of lossy branches, a Chameleon BHABO-LTF can be constructed by additively homomorphic CPA secure PKE alone. So our work can also be viewed as an interesting progress to give a construction of CCA secure PKE from additively homomorphic CPA secure PKE with certain properties as well as their LR counterparts, which has been a longstanding problem.
A Survey on Face Recognition in Video Surveillance

In today’s world, enormous amount of threats arises due to terrorists, criminals, thieves and also illegal access of the data from the unwanted person, etc. This leads to a lot of challenges in our daily life. With the increase in threat globally the need to deploy reliable surveillance is to increase. Video surveillance is considered to be the major breakthrough in monitoring and security. In video surveillance, the facial recognition furthermore enhances the security and defense progressively. By face recognition the probe person can be recognized more accurately, efficiently and with short time. Various methods, approaches, algorithms were available for face recognition from surveillance video. The main objective of the paper is to discuss and analyze about the various facial recognition techniques.
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks

Deep neural networks (DNNs) have been widely used in the fields such as natural language processing, computer vision and image recognition. But several studies have been shown that deep neural networks can be easily fooled by artificial examples with some perturbations, which are widely known as adversarial examples. Adversarial examples can be used to attack deep neural networks or to improve the robustness of deep neural networks. A common way of generating adversarial examples is to first generate some noises and then add them into original examples. In practice, different examples have different noise-sensitive. To generate an effective adversarial example, it may be necessary to add a lot of noise to low noise-sensitive example, which may make the adversarial example meaningless. In this paper, we propose a noise-sensitivity-analysis-based test prioritization technique to pick out examples by their noise sensitivity. We construct an experiment to validate our approach on four image sets and two DNN models, which shows that examples are sensitive to noise and our method can effectively pick out examples by their noise sensitivity.
Multilayer neural network synchronized secured session key based encryption in wireless communication

In this paper, multilayer neural network synchronized session key based encryption has been proposed for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.