Reviewerly/exHarmony huggingface dataset:
Query expansion based on term selection for Hindi – English cross lingual IR

Retrieving accurate information from collection of information available on web in a cross-lingual communication environment is a very difficult task in our world. In order to retrieve information, user specifies the needed information in the form of query. Sometimes query may not be able to express the needed information in specific way due to ambiguity or un-translated query words. This problem can be minimized by expanding the query with other suitable words that make it more specific. Purpose of query expansion is to improve the performance and quality of retrieved information in CLIR. In this paper, Q.E. has been explored for a Hindi-English CLIR in which Hindi queries are used to search English documents. We used Okapi BM25 for documents ranking and then by using Term Selection Value (TSV) translated queries have been expanded. All experiments have been performed on FIRE 2012 dataset by analysing the impact of occurrence of terms in top @3 ranked documents. Our result shows that the relevancy of retrieved results of Hindi-English CLIR using Q.E. which is performed by adding a lowest frequency term from the corpus of top @3 ranked documents is 51.33%, which is higher than before and after Q.E. (i.e. Case1, Case2).
Hybrid Intelligent Decision Support Using a Semiotic Case-Based Reasoning and Self-Organizing Maps

Human decision-making involves cognitive processes of selection, evaluation, and interpretation among candidate solutions in order to solve decision problems. Nonintelligent decision support systems (DSS) lack automatic interpretations, at least in a low level scale, which can lead to undesired solutions. To tackle this limitation, hence producing enhanced decision making, a hybrid intelligent decision support approach is presented, which combines case-based reasoning cycle, semiotic concepts, and self-organizing maps. In addition, a novel sign deconstruction mechanism is introduced as foundation of the new approach and affords better interpretability and contextualization of candidate solutions without compromising efficiency and precision. The obtained results confirm that our proposed approach has the potential to be readily applicable to decision problems, particularly the ones that are of subjective nature. Moreover, the put forward approach may integrate some unlikely elements of linguistics and cognitive science which could fundamentally help the enhancement of DSS.
privy: Privacy Preserving Collaboration Across Multiple Service Providers to Combat Telecom Spams

Nuisance or unsolicited calls and instant messages come at any time in a variety of different ways.These calls would not only exasperate recipients with the unwanted ringing, impacting their productivity, but also lead to a direct financial loss to users and service providers.Telecommunication Service Providers (TSPs) often employ standalone detection systems to classify call originators as spammers or non-spammers using their behavioral patterns.These approaches perform well when spammers target a large number of recipients of one service provider.However, professional spammers try to evade the standalone systems by intelligently reducing the number of spam calls sent to one service provider, and instead distribute calls to the recipients of many service providers.Naturally, collaboration among service providers could provide an effective defense, but it brings the challenge of privacy protection and system resources required for the collaboration process.In this paper, we propose a novel decentralized collaborative system named privy for the effective blocking of spammers who target multiple TSPs.More specifically, we develop a system that aggregates the feedback scores reported by the collaborating TSPs without employing any trusted third party system, while preserving the privacy of users and collaborators.We evaluate the system performance of privy using both the synthetic and real call detail records.We find that privy can correctly block spammers in a quicker time, as compared to standalone systems.Further, we also analyze the security and privacy properties of the privy system under different adversarial models.
Derivative-orthogonal Riesz wavelets in Sobolev spaces with applications to differential equations

Riesz wavelets in the Sobolev space Hm(R) with m∈N∪{0}, whose mth-order derivatives are orthogonal among different levels, are of particular interest and importance in computational mathematics, due to their many desirable properties such as small condition numbers and sparse stiffness matrices. We call such Riesz wavelets in the Sobolev space Hm(R) as mth-order derivative-orthogonal Riesz wavelets. In this paper we shall comprehensively study and completely characterize all compactly supported mth-order derivative-orthogonal Riesz wavelets in the Sobolev space Hm(R). More precisely, from any given compactly supported refinable vector function ϕ=(ϕ1,…,ϕr)T in Hm(R) satisfying the refinement equation ϕˆ(2ξ)=aˆ(ξ)ϕˆ(ξ) for some r×r matrix aˆ of 2π-periodic trigonometric polynomials, we prove that there exists a compactly supported mth-order derivative-orthogonal Riesz wavelet in Hm(R), which is derived from ϕ through the refinable structure, if and only if the refinable vector function ϕ has stable integer shifts and the filter a has at least order 2m sum rules. This double order of sum rules over the smoothness order m is surprising but is necessary for constructing mth-order derivative-orthogonal Riesz wavelets in Hm(R). Then we shall present several examples of such derivative-orthogonal spline Riesz wavelets with short support derived from B-splines and Hermite splines. To illustrate the developed theory and its potential usefulness, we shall apply our constructed such mth-order derivative-orthogonal Riesz wavelets for the numerical solutions of differential equations such as Sturm–Liouville equations and biharmonic equations. Our constructed derivative-orthogonal spline Riesz wavelets on the interval [0,1] have a simple structure with only one boundary wavelet at each endpoint and can easily handle different types of boundary conditions. The resulting coefficient matrices are sparse and have very small condition numbers with some examples even having the optimal condition number one.
Reduction of Speckle Noise and Image Enhancement in Ultrasound Image Using Filtering Technique and Edge Detection

Ultrasound (US) imaging application in medicine and other fields is enormous. It has several advantages over other medical imaging modalities. The use of US in diagnosis is well established because of its noninvasive nature, portable, accurate, low-cost imaging modality, capable of forming real-time imaging and continuing improvement in image quality. Medical images and satellite images are usually degraded by noise during image acquisition and transmission process. It is estimated that 1 out of every form medical diagnostic image studies in the world involves ultrasonic techniques. The objectives of this article are to give an overview of the types of speckle reduction techniques in US imaging, present a new technique of speckle reduction, and carry out a comparative evaluation of despeckle filtering based on image quality metrics. A new speckle suppression method and coherence enhancement of medical US image are proposed: 9 despeckled filtering techniques and 5 edge detection operators, with the result evaluated by image quality metrics. The best edge detection hybrid with best filter, quality evaluation metrics has been found that the proposed method performance better than all other methods, while the structural details and result preserving of small and important image feature that contain diagnostic information in a better way than other despeckling filter.
Rapid detection of spammers through collaborative information sharing across multiple service providers

Spammers and telemarketers target a very large number of recipients usually dispersed across many Service Providers (SPs). Collaboration and Information sharing between SPs would increase the detection accuracy but detection effectiveness depends on the amount of information shared between SPs. Having service provider's exchange call detail records would arguably attain the best detection accuracy but would require significant network resources. Moreover, SPs are likely to feel uncomfortable in sharing their call records because call records contain user's private information as well as operational details of their networks. The challenge towards the design of collaborative Spam over Internet Telephony (SPIT) detection system is two-fold: it should attain high detection accuracy with a small false positive, and should fully protect the privacy of users and their service providers. In this paper, we propose a COllaborative Spit Detection System (COSDS)—a collaborative SPIT detection system for the Voice over IP (VoIP) network where service providers collaborate for the effective and early detection of SPIT callers without raising privacy concerns. To this extent, COSDS relies on a trusted Centralized Repository (CR) and exchange of non-sensitive reputation scores. The CR computes global reputation of users by aggregating the reputation scores provided by the respective collaborating SPs. The data exchanged to the CR is not sensitive regarding users privacy, and cannot be used to infer the relationship network of users. We evaluate the performance of our system using synthetic data that we have generated by simulating the realistic social behavior of spammers and non-spammers in a network. The results show that the COSDS approach has better detection accuracy as compared to the traditional stand-alone detection systems. For instances, in a setup where spammers are making calls to recipients of many SPs, COSDS successfully identifies spammers with the True Positive (TP) rate of around 80% and false positive (FP) rate of around 2% on a first day, which further increases to 100% TP rate and zero FP rate in three days. COSDS approach is fast, requires a small communication overhead, ensures privacy of users and collaborating SP, and requires only few iterations for the reputation convergence within the SP.
On Theoretical Principle and Practical Applicability of Ranked Nodes Method for Constructing Conditional Probability Tables of Bayesian Networks

This paper provides new insight into the theoretical principle and the practical applicability of the ranked nodes method (RNM) that is used to construct conditional probability tables (CPTs) for Bayesian networks (BNs) by expert elicitation. RNM is designed for specific types of discrete random variables called ranked nodes that are common in real-world applications of BNs. Despite its active use in recent years, there remains ambiguity about the exact theoretical basis of RNM which can hamper its effective employment. In addition, there are a lack of studies about the general ability of CPTs generated with RNM to represent probabilistic relationships in real-world applications. In this paper, it is shown how the generation of probabilities with RNM is underpinned by a regression model of continuous random variables. Then, it is experimentally determined that in typical applications of RNM, one can generate in a matter of seconds CPTs whose elements reflect well probabilities given by the underlying regression model. Another experiment discovers that CPTs generated with RNM provide a good average fit to a large portion of various real-world CPTs investigated. This confirms the usefulness of RNM in practical applications. The results of the experiment also indicate that choices made by the user of RNM can considerably impact the ability of a generated CPT to represent a given probabilistic relationship. This paper then provides practical advice on the efficient use of RNM with regard to the user-controlled features explored in the experiment.
A Low-Cost Portable Polycamera for Stereoscopic 360° Imaging

This paper proposes a low-cost and portable polycamera system and accompanying methods for capturing and synthesizing stereoscopic 360° panoramas. The polycamera consists of only four cameras with fisheye lenses. Synthesizing panoramas from only four views is challenging because the cameras view very differently and the captured images have significant distortions and color degradation including vignetting, contrast loss, and blurriness. For coping with these challenges, this paper proposes methods for rectifying the polyview images, estimating depth of the scene, and synthesizing stereoscopic panoramas. The proposed camera is compact in size, light in weight, and inexpensive. The proposed methods allow the synthesis of visually pleasing stereoscopic 360° panoramas using the images captured with the proposed polycamera. We have built a prototype of the polycamera and tested it on a set of scenes with different characteristics of depth ranges and depth variations. The experiments show that the proposed camera and methods are effective in generating stereoscopic 360° panoramas that can be viewed on popular virtual reality displays.
On efficient computation of inter-simplex Chebyshev distance for voxelization of 2-manifold surface

Determining an appropriate voxel size is the foremost step for topology-aware voxelization of a 2-manifold surface. In this paper, we show how this is related to computation of inter-simplex Chebyshev distance (ICD) and how this computation is efficiently doable using a hierarchical decomposition of ICD function for higher-order simplices to that of lower-order ones. A novel idea of partitioning the manifold surface based on functional components is used alongside to reduce the number of distance computations. Geometric data structures and efficient query algorithms are also used to speed up the computation further. Detailed experimental results have been furnished to demonstrate its merit and aptness.
Modern art challenges face detection

There is a widely held belief that computer vision, in general, and face authentication, in particular, are to a large extent solved problems. This paper challenges this belief regarding face authentication using examples from modern art that significantly confound face detection. The challenges are made concrete using a new MAFD-150 dataset (Modern Art Face Detection) composed mostly of modern art examples that cover much diversity in style and artists. MAFD-150 challenges the belief that singleton and crowd face detection is an almost solved problem, and provides baselines and preliminary results that highlight the inadequacy of current expertise and methods to address face detection. In particular, we show that well-known face detection algorithms are only able to achieve an F1 score of less than 35% overall across the new dataset. Additionally, we discuss the performance of the selected face detectors on varying art categories (such as Impressionism, Pop Art, et al.) to show how style and face representation may impact these algorithms. The paper concludes with suggestions on how to advance face processing by leveraging the complementarity between Show-and-Tell-like methods and a context and cooperative driven visual question answering framework using relevance-based triage. The very challenges detailed throughout are then shown to be helpful with developing novel, robust, and secure access protocols that combine text and modern art images using the visual question answering framework.
Instance Migration Validity for Dynamic Evolution of Data-Aware Processes

Likely more than many other software artifacts, business processes constantly evolve to adapt to ever changing application requirements. To enable dynamic process evolution, where changes are applied to in-flight processes, running process instances have to be migrated. On the one hand, as many instances as possible should be migrated to the changed process. On the other hand, the validity to migrate an instance should be guaranteed to avoid introducing dynamic change bugs after migration. As our theoretical results show, when the state of variables is taken into account, migration validity of data-aware process instances is undecidable. Based on the trace of an instance, existing approaches leverage trace replaying to check migration validity. However, they err on the side of caution, not identifying many instances as potentially safe to migrate. We present a more relaxed migration validity checking approach based on the dependence graph of a trace. We evaluate effectiveness and efficiency of our approach experimentally showing that it allows for more instances to safely migrate than for existing approaches and that it scales in the number of instances checked.
Sparse representation based image super-resolution on the KNN based dictionaries

This paper addresses the problem of single image super-resolution (SR). In recent years, sparse representation based SR methods have been proposed and achieved great success. Traditional sparse representation based SR methods learn a unified high-resolution (HR) and low-resolution (LR) dictionary pair. All LR patches share the same dictionary pair to conduct reconstruction to get their corresponding HR patches. The reconstruction process introduces reconstruction error, which limits the performance of SR results. In this paper, to minimize the reconstruction error, we utilize a K-Nearest-Neighbours (KNN) based dictionary pair for each LR patch instead of the unified dictionary pair to conduct the reconstruction process. The KNN based dictionaries are selected among a huge dictionary which contains hundreds of millions of patch pairs. To speed up the KNN retrieval process, we adopt a binary encoding method which preserves local information for the LR patches, and retrieve the KNN of each LR patch in the Hamming space. Besides, since a large patch contains more structured information than a small patch, we utilize large patches instead of small ones as the atoms of dictionaries, which further improves the SR results. Experimental results demonstrate that our method outperforms the existed state-of-the-art methods, especially when the magnification factor is large or the image is blurred.
Trending topics detection of Indonesian tweets using BN-grams and Doc-p

Researches on trending topics detection, especially on Twitter, have increased and various methods for detecting trending topics have been developed. Most of these researches have been focused on tweets written in English. Previous researches on trending topics detection on Indonesian tweets are still relatively few. In this paper, we compare two methods, namely document pivot and BN-grams, for detecting trending topics on Indonesian tweets. In our experiments, we examine the effects of varying the number of topics, n-grams, stemming, and aggregation on the quality of the resulting trending topics. We measure the accuracy of trending topics detection by comparing both algorithms with trending topics found in local news and Twitter trending topics. The results of our experiments show that using ten topics produces the highest topic recall; that using trigrams in BN-grams results in the highest value topic recall; and that using aggregation reduces the quality of trending topics produced. Overall, BN-grams has a higher value of topic recall than that of document pivot.
Bayesian Optimal Design for Ordinary Differential Equation Models With Application in Biological Science

Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome the parameter dependence of frequentist design and being less reliant on asymptotic approximations. However, the necessary approximation and maximization of the, typically analytically intractable, expected utility results in a computationally challenging problem. These issues are further exacerbated if the solution to the differential equations is not available in closed-form. This article proposes a new combination of a probabilistic solution to the equations embedded within a Monte Carlo approximation to the expected utility with cyclic descent of a smooth approximation to find the optimal design. A novel precomputation algorithm reduces the computational burden, making the search for an optimal design feasible for bigger problems. The methods are demonstrated by finding new designs for a number of common models derived from differential equations, and by providing optimal designs for the placenta experiment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Fault Attacks on Nonce-Based Authenticated Encryption: Application to Keyak and Ketje

In the context of fault attacks on nonce-based authenticated encryption, an attacker faces two restrictions. The first is the uniqueness of the nonce for each new encryption that prevents the attacker from collecting pairs of correct and faulty outputs to perform, e.g., differential fault attacks. The second restriction concerns the verification/decryption, which releases only verified plaintext. While many recent works either exploit misuse scenarios (e.g. nonce-reuse, release of unverified plaintext), we turn the fact that the decryption/verification gives us information on the effect of a fault (whether a fault changed a value or not) against it. In particular, we extend the idea of statistical ineffective fault attacks (SIFA) to target the initialization performed in nonce-based authenticated encryption schemes. By targeting the initialization performed during decryption/verification, most nonce-based authenticated encryption schemes provide the attacker with an oracle whether a fault was ineffective or not. This information is all the attacker needs to mount statistical ineffective fault attacks. To demonstrate the practical threat of the attack, we target software implementations of the authenticated encryption schemes Keyak and Ketje. The presented fault attacks can be carried out without the need of sophisticated equipment. In our practical evaluation the inputs corresponding to 24 ineffective fault inductions were required to reveal large parts of the secret key in both scenarios.
Who Can Maintain This Code?: Assessing the Effectiveness of Repository-Mining Techniques for Identifying Software Maintainers

In large and complex systems, identifying developers capable of maintaining a piece of code is an essential task. Repository-mining techniques can help by providing some level of automation; however, whether such techniques effectively identify skilled software maintainers is still unclear.
Non-Contact Thermo-Visual Augmentation by IR-RGB Projection

This paper presents an approach for non-contact haptic augmentation with spatial augmented reality (SAR). We construct a thermo-visual projection system by combining a standard RGB projector and a fabricated infrared (IR) projector. The primary contribution of this paper is that we conduct thorough psychophysical experiments to investigate a design guideline for spatiotemporal projection patterns for both RGB and IR projectors to render a warm object with high presence. We develop application systems to evaluate the validity of the proposed system and design guideline. The evaluation results demonstrate that the proposed system can render warm objects with significantly higher presence than a standard SAR system.