Reviewerly/exHarmony huggingface dataset:
Learning Novelty-Aware Ranking of Answers to Complex Questions

Result ranking diversification has become an important issue for web search, summarization, and question answering. For more complex questions with multiple aspects, such as those in community-based question answering (CQA) sites, a retrieval system should provide a diversified set of relevant results, addressing the different aspects of the query, while minimizing redundancy or repetition. We present a new method, DRN , which learns novelty-related features from unlabeled data with minimal social signals, to emphasize diversity in ranking. Specifically, DRN parameterizes question-answer interactions via an LSTM representation, coupled with an extension of neural tensor network, which in turn is combined with a novelty-driven sampling approach to automatically generate training data. DRN provides a novel and general approach to complex question answering diversification and suggests promising directions for search improvements.
Approximate spectral clustering with eigenvector selection and self-tuned k

The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to propose the approximate spectral clustering (ASC). However, most of ASC methods assumed that the number of clusters $k$ was known. In practice, manual setting of $k$ could be subjective or time consuming. The proposed algorithm has two relevance metrics for estimating $k$ in two vital steps of ASC. One for selecting the eigenvectors spanning the embedding space, and the other to discover the number of clusters in that space. The algorithm used a growing neural gas (GNG) approximation, GNG is superior in preserving input data topology. The experimental setup demonstrates the efficiency of the proposed algorithm and its ability to compete with similar methods where $k$ was set manually.
The effects of video-annotated listening review mechanism on promoting EFL listening comprehension

In recent years, English instruction has emphasized the importance of listening comprehension; however, playing CDs in class is still the most widely used approach to practicing English listening skills in Taiwan. It is obvious that this training approach cannot satisfy individual needs, thus reducing the effect of promoting English listening comprehension performance. Thus, the study proposes a novel video-annotated listening review mechanism (VALRM) with two review modes – the delayed and immediate review modes to enhance learners’ listening comprehension performance. This study examines the effects of learners in the experimental group using the VALRM and those in the control group using Youtube with self-determined listening review mechanism (SDLRM) on English listening comprehension performance. Analytical results show that the learners with the VALRM support achieved significantly better listening comprehension performance than those of using the SDLRM. More importantly, the experimental results confirm that the learners in the experimental group had significantly better listening comprehension abilities even though removing the VALRM support than those of the control group removing the SDLRM support. Additionally, the interview results show most of the interviewees agreed that using the VALRM is more effective in promoting English listening learning performance than using the SDLRM because they could find out the unclear parts and replay them easily and efficiently. The main contribution of this study is to present an effective computer-assisted English listening learning tool based on the two review modes provided by the proposed VALRM in an autonomous learning environment.
Categorical Features Transformation with Compact One-Hot Encoder for Fraud Detection in Distributed Environment

Fraud detection for online banking is an important research area, but one of the challenges is the heterogeneous nature of transactions data i.e. a combination of numeric as well as mixed attributes. Usually, numeric format data gives better performance for classification, regression and clustering algorithms. However, many machine learning problems have categorical, or nominal features, rather than numeric features only. In addition, some machine learning platforms such as Apache Spark accept numeric data only. One-hot Encoding (OHE) is a widely used approach for transforming categorical features to numerical features in traditional data mining tasks. The one-hot approach has some challenges as well: the sparseness of the transformed data and that the distinct values of an attribute are not always known in advance. Other than the model accuracy, compactness of machine learning models is equally important due to growing memory and storage needs. This paper presents an innovative technique to transform categorical features to numeric features by compacting sparse data even if all the distinct values are not known. The transformed data can be used for the development of fraud detection systems. The accuracy of the results has been validated on synthetic and real bank fraud data and a publicly available anomaly detection (KDD-99) dataset on a multi-node data cluster.
Towards virtual machine introspection based security framework for cloud

Virtualization enables provision of resources to users according to their requirement through Infrastructure as a Service (IaaS) delivery model in cloud computing environment. Malicious users could lease cloud resources and use them as platforms to launch attacks. In this paper, we propose a Virtual Machine Introspection (VMI)-based security framework to monitor cloud users’ in-VM activities and detect malicious one if any. We justify our selection of VMI method based on hardware knowledge for proposed framework by discussing its key advantages over other VMI methods. We propose design of multi-threaded analysis component that can introspect number of virtual machines hosted on cloud servers in real time. Experimental results demonstrate that our framework performs well with a set of metrics appropriate for cloud IaaS environment.
Unauthorized access crime in Jordanian law (comparative study)

This research cared about clarifying the legal provisions of the unauthorized access crime contained in article 3 of the Jordanian Cybercrime act of 2015 and comparing it to other Arabic legislations and French law as well as clarifying the position of international conventions on this crime. The analysis of the crime included clarifying its elements, its sanction and the aggravating circumstances of its penalty. At the end of the research we reached some recommendations which we hope from the Jordanian legislator to adopt.
WAEF: Weighted Aggregation with Enhancement Filter for Visual Object Tracking

In the recent years, convolutional neural networks (CNN) have been extensively employed in various complex computer vision tasks including visual object tracking. In this paper, we study the efficacy of temporal regression with Tikhonov regularization in generic object tracking. Among other major aspects, we propose a different approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion. We provide a statistics based ensembler approach for integrating the conventionally driven spatial regression results (such as from ECO), and the proposed temporal regression results to accomplish better tracking. Further, we exploit the obligatory dependency of deep architectures on provided visual information, and present an image enhancement filter that helps to boost the performance on popular benchmarks. Our extensive experimentation shows that the proposed weighted aggregation with enhancement filter (WAEF) tracker outperforms the baseline (ECO) in almost all the challenging categories on OTB50 dataset with a cumulative gain of 14.8%. As per the VOT2016 evaluation, the proposed framework offers substantial improvement of 19.04% in occlusion, 27.66% in illumination change, 33.33% in empty, 10% in size change, and 5.28% in average expected overlap.
Recommendations with context aware framework using particle swarm optimization and unsupervised learning

Context aware recommender system has become an area of rigorous research attributing to incorporate context features, thereby increases accuracy while making recommendations. Most of the researches have proved neighborhood based collaborative filtering to be one of the most efficient mechanisms in recommender systems because of its simplicity, intuitiveness and wide usage in commercial domains. However, the basic challenges observed in this area include sparsity of data, scalability and utilization of contexts effectively. In this study, a novel framework is proposed to generate recommendations independently of the count and type of context dimensions, hence pertinent for real life recommender systems. In the framework, we have used k-prototype clustering technique to group contextually similar users to get a reduced and effective set. Additionally, particle swarm optimization technique is applied on the closest cluster to find the contribution of different context features to control data sparsity problem. Also, the proposed framework employs an improved similarity measure which considers contextual condition of the user. The results came from the series of experiments using two context enriched datasets showcasing that the proposed framework increases the accuracy of recommendations over other techniques from the same domain without consuming extra cost in terms of time.
Efficient Symmetry Breaking for SAT-Based Minimum DFA Inference

Inference of deterministic finite automata (DFA) finds a wide range of important practical applications. In recent years, the use of SAT and SMT solvers for the minimum size DFA inference problem (MinDFA) enabled significant performance improvements. Nevertheless, there are many problems that are simply too difficult to solve to optimality with existing technologies. One fundamental difficulty of the MinDFA problem is the size of the search space. Moreover, another fundamental drawback of these approaches is the encoding size. This paper develops novel compact encodings for Symmetry Breaking of SAT-based approaches to MinDFA. The proposed encodings are shown to perform comparably in practice with the most efficient, but also significantly larger, symmetry breaking encodings.
Clustering based one-to-one hypergraph matching with a large number of feature points

Hypergraph matching is a useful technique for multiple feature point matching. In the last decade, hypergraph matching has shown great potential for solving many challenging problems of computer vision. The matching of a large number of feature points in hypergraph constraints is an NP-hard problem. It requires high computational complexity in many algorithms such as spectral graph matching, tensor graph matching and reweighted random walk matching. In this paper, we propose a computationally efficient clustering based algorithm for one-to-one hypergraph matching, which clusters a large hypergraph into many sub-hypergraphs. These sub-hypergraphs can be matched based on a tensor model, which guarantees the maximum matching score. The results from the sub-hypergraphs are then used to match all feature points in the entire hypergraph. Simulation results on real and synthetic data sets validates the efficiency of the proposed method.
The dark mirror of capital: on post-neoliberal formations and the future of education

This paper examines how transformations in political economy and digital technology point toward potential post-neoliberal futures. Outlining the contours of a crisis of neoliberal hegemony, the authors explore how technological developments have contributed to political economic dynamics of market failure and oligarchic capture, resulting in new forms of digital precarity. As evidence, the paper examines a specific post-neoliberal discourse called neoreactionism (NRx), which has developed out of the distinct culture of Silicon Valley. NRx represents a specific vision for resolving conflicts inherent to neoliberalism by intensifying oligarchic capture and the acceleration of technology. While NRx is a techno-authoritarian discourse that exists largely outside of mainstream academic discussions, it is indicative of how transformations in late capitalism are fueling the rise of anti-democratic consciousness and identification. In response, the authors conclude with suggestions for developing post-neoliberal approaches to educational studies that are able to challenge these troubling reactionary trends.
PFP: Improving the Reliability of Deduplication-based Storage Systems with Per-File Parity

Data deduplication weakens the reliability of storage systems since by design it removes duplicate data chunks common to different files and forces these files to share a single physical date chunk, or critical chunk, after deduplication. Thus, the loss of a single such critical data chunk can potentially render all referencing (sharing) files unavailable. However, the reliability issue in deduplication-based storage systems has not received adequate attention. Existing approaches introduce data redundancy after files have been deduplicated, either by replication on critical data chunks, i.e., chunks with high reference count, or RAID schemes on unique data chunks, which means that these schemes are based on individual unique data chunks rather than individual files. This can leave individual files vulnerable to losses, particularly in the presence of transient and unrecoverable data chunk errors such as latent sector errors. To address this file reliability issue, this paper proposes a Per-File Parity (short for PFP) scheme to improve the reliability of deduplication-based storage systems. PFP computes the XOR parity within parity groups of data chunks of each file after the chunking process but before the data chunks are deduplicated. Therefore, PFP can provide parity redundancy protection for all files by intra-file recovery and a higher-level protection for data chunks with high reference counts by inter-file recovery. Our reliability analysis and extensive data-driven, failure-injection based experiments conducted on a prototype implementation of PFP show that PFP significantly outperforms the existing redundancy solutions, DTR and RCR, in system reliability, tolerating multiple data chunk failures and guaranteeing file availability upon multiple data chunk failures. Moreover, a performance evaluation shows that PFP only incurs an average of 5.7 percent performance degradation to the deduplication-based storage system.
An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems.This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity.
Grid-based Evaluation Metrics for Web Image Search

Compared to general web search engines, web image search engines display results in a different way. In web image search, results are typically placed in a grid-based manner rather than a sequential result list. In this scenario, users can view results not only in a vertical direction but also in a horizontal direction. Moreover, pagination is usually not (explicitly) supported on image search search engine result pages (SERPs), and users can view results by scrolling down without having to click a "next page" button. These differences lead to different interaction mechanisms and user behavior patterns, which, in turn, create challenges to evaluation metrics that have originally been developed for general web search. While considerable effort has been invested in developing evaluation metrics for general web search, there has been relatively little effort to construct grid-based evaluation metrics.
Unscented Recursive Filtering for Inequality Constrained Systems

For nonlinear systems with state inequality constraints, the existing unscented recursive filtering methods utilize the constraint information in the sampling and update steps, rather than the prediction step. In practice, the constraints are known a priori and they are always satisfied by any true trajectory. To sufficiently incorporate the valuable information, this paper proposes a constrained unscented recursive filter, which applies the constraints to the whole filtering procedures. First, the constrained dynamic model is constructed by using the system projection technique, which optimally fuses the constraint information and the unconstrained dynamics. Next, the state evolutions of general inequality constraints and their special forms are derived, including linear inequality constraints (LIEC), quadratic inequality constraints (QIEC), and coexistence of LIEC and QIEC. Especially, it is proved that the dynamic model with constraints has a smaller uncertainty than the one without constraints, implying that introducing inequality constraint information definitely improves modeling accuracy. Finally, the numerical simulations in the context of target tracking verify the superiority of the proposed unscented recursive filter over the typical constrained ones for inequality constrained systems.
Elephants Herding Optimization for Solving the Travelling Salesman Problem

This paper proposes a novel metaheuristic called Elephant Herding Optimization (EHO) to solve the Travelling Salesman Problem (TSP), which is a combinatorial optimization problem classified as NP-Hard. The EHO algorithm is bio-inspired from the natural herding behavior of elephants groups, which proved its efficiency to solve continued optimization problems. To extend the application of this algorithm, we had proposed a novel adaptation of the EHO by respecting the natural herding behavior of elephants. To test the efficiency of our proposal adaptation, we applied the adapted EHO algorithm on some benchmark instances of TSPLIB. The obtained results shows the excellent performance of the proposed method.
Neural networks for personalized item rankings

Most users typically interact with products only through implicit feedback such as clicks or purchases rather than explicit user-provided information like product ratings. Learning to rank products according to individual preferences using only implicit feedback can be helpful to make useful recommendations. In this paper, a neural network architecture to solve collaborative filtering problems for personalized rankings on implicit feedback datasets is presented. It is shown how a layer of constant weights forces the network to learn pairwise item rankings. Additionally, similarities between the proposed neural network and a matrix factorization model trained with the Bayesian Personalized Ranking optimization criterion are proven. The experiments indicate state-of-the-art performance for the task of personalized ranking.
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system.While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature such as combined transportability and selection bias, or multiple sources of selection bias.To tackle these new settings, we present a search algorithm directly over the rules of do-calculus.Due to the generality of do-calculus, the search is capable of taking more advanced datagenerating mechanisms into account along with an arbitrary type of both observational and experimental source distributions.The search is enhanced via a heuristic and search space reduction techniques.The approach, called do-search, is provably sound, and it is complete with respect to identifiability problems that have been shown to be completely characterized by do-calculus.When extended with additional rules, the search is capable of handling missing data problems as well.With the versatile search, we are able to approach new problems for which no other algorithmic solutions exist.We perform a systematic analysis of bivariate missing data problems and study causal inference under case-control design.We also present the R package dosearch that provides an interface for a C++ implementation of the search.
Performance analysis of efficient data distribution in P2P environment using hybrid clustering techniques

In this paper, K-means algorithm has been applied for distributed large data using hybrid clustering techniques. K-means is a simple and scalable algorithm which can be applied on large datasets. It is one of the well-known unsupervised clustering algorithms that fail in providing structured to unstructured data to enable extraction of valuable information. Peer-to-peer (P2P) technologies divide the data or resources between the peers for managing the network bandwidth, network participants and processing powers. During the data distribution process in the P2P environments, accuracy, computation complexity and distributed clustering accuracy are the important issues as they reduce the entire system performance. So, the author in this paper considered the system for the distribution of data in P2P environment using mining techniques. The data have been distributed using the hybrid map reducing method which analyzes the large volume of data by performing filtering and sorting. The cluster approach analyzes and manages the neighboring relationship about the peer nodes that helps in the management of the cluster distribution in the dynamic environment. Determination of the efficiency of the cluster formed is done with the help of the hybrid clustering algorithm, and the related system architecture is proposed. The clustering efficiency has been enhanced in the P2P environment using the distributed data network. The efficiency of the formed cluster was evaluated in terms of Jaccard index, F-measures, mutual information and rand measure. The performance of the system was analyzed using the experimental results and discussions, namely, error rate, accuracy and time. The multi-objective system helps in easing the difficulties in the implementation of P2P environment sensitive to initial solutions.