Reviewerly/exHarmony huggingface dataset:
Clustering by Orthogonal Non-negative Matrix Factorization: A Sequential Non-convex Penalty Approach

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found to provide improved clustering performance over the K-means. The ONMF model is a challenging optimization problem due to the orthogonality constraint, and most of the existing methods directly deal with the constraint in its original form via various optimization techniques. In this paper, we propose an equivalent problem reformulation that transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a penalty approach to handle these non-convex constraints. The penalized formulation is smooth and has convex constraints, which is amenable to efficient computation. We analytically show that the penalized formulation will provide a feasible stationary point of the reformulated ONMF problem when the penalty is large. Numerical results show that the proposed method greatly outperforms the existing methods.
Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation

Magnetic resonance images of neonates, compared with toddlers, exhibit lower signal-to-noise ratio and spatial resolution. In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual-structured sparse representation with convex regularization. Specifically, we introduce a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites. The base layer consists of the smoothed version of the image obtained via Gaussian filtering. The detail layer is the difference between the original image and the base layer. High-frequency details in the detail layer are borrowed across subjects for super-resolution reconstruction. Experimental results on T1 and T2 images demonstrate that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.
Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources

In this paper, we propose a clustering based physical-layer authentication scheme (CPAS) to overcome the drawback of traditional cipher-based authentication schemes that suffer from heavy costs and are limited by energy-constrained intelligent devices. CPAS is a novel cross-layer secure authentication approach for edge computing system with asymmetric resources. The CPAS scheme combines clustering and lightweight symmetric cipher with physical-layer channel state information to provide two-way authentication between terminals and edge devices. By taking advantage of temporal and spatial uniqueness in physical layer channel responses, the non-cryptographic physical layer authentication techniques can achieve fast authentication. The lightweight symmetric cipher initiates user authentication at the start of a session to establish the trust connection. Based on theoretical analysis, the CPAS scheme is secure and simple, but there is no trusted party, while it can also resist small integer attacks, replay attacks, and spoofing attacks. Besides, experimental results show that the proposed scheme can boost the total success rate of access authentication and decrease the data frame loss rate, without notable increase in authentication latencies.
Optimal Merging in Quantum $$k$$-xor and k-sum Algorithms

The k-xor or Generalized Birthday Problem aims at finding, given k lists of bit-strings, a k-tuple among them XORing to 0. If the lists are unbounded, the best classical (exponential) time complexity has withstood since Wagner’s CRYPTO 2002 paper. If the lists are bounded (of the same size) and such that there is a single solution, the dissection algorithms of Dinur et al. (CRYPTO 2012) improve the memory usage over a simple meet-in-the-middle. In this paper, we study quantum algorithms for the k-xor problem. With unbounded lists and quantum access, we improve previous work by Grassi et al. (ASIACRYPT 2018) for almost all k. Next, we extend our study to lists of any size and with classical access only. We define a set of “merging trees” which represent the best known strategies for quantum and classical merging in k-xor algorithms, and prove that our method is optimal among these. Our complexities are confirmed by a Mixed Integer Linear Program that computes the best strategy for a given k-xor problem. All our algorithms apply also when considering modular additions instead of bitwise xors. This framework enables us to give new improved quantum k-xor algorithms for all k and list sizes. Applications include the subset-sum problem, LPN with limited memory and the multiple-encryption problem.
Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018–2019 Field Testing in Southwest China

Earthquake Early Warning (EEW) was proved to be a potential means of disaster reduction. Unfortunately, the performance of the EEW system is largely determined by the density of EEW network. How to reduce the cost of sensors has become an urgent problem for building a dense EEW. A low-cost seismic sensor integrated with a Class C MEMS accelerometer was proposed in this paper. Based on minimal structure design, the sensor’s reliability was enhanced, while the costs were cut down as well. To fully reveal the performance, ten of the seismic sensors were installed and tested in Sichuan Province, southwest of China from May 2018 to February 2019. The seismic records obtained by the MNSMSs were compared with those by the traditional strong motion seismographs. The records obtained by the MNSMSs have good consistency with the data obtained by the Etnas. The MNSMSs can obtain clear seismic phases that are enough to trigger earthquake detections for EEW. By noise analysis, different channels of the same sensor and different sensors have good consistency. The tested dynamic range (over 87 dB) and useful resolution (over 14.5 bits) are completely in conformity with the designed parameters. Through real field testing, small earthquakes (M 3.1–3.6) can be detected by all three components E-W, N-S, and U-D within 50 km. In all, the low-cost seismic sensor proposed as a high-performance Class C MEMS sensor can meet the needs of dense EEW in terms of noise, dynamic range, useful resolution, reliability, and detecting capabilities.
Efficient Multi-person Hierarchical 3D Pose Estimation for Autonomous Driving

With increasing need of analyzing human poses for autonomous driving, multi-person 3D pose estimation using monocular moving camera in real world scenarios is of great concern. Existing 3D human pose estimation features either large scale training data, or high computation complexity due to the high degrees of freedom in 3D human poses. We propose a novel scheme to hierarchically estimate 3D human poses in natural videos by static or moving cameras in an efficient fashion. Our method does not need 3D training data. We formulate torso estimation into a Perspective N Point (PNP) problem, formulate limb pose estimation into an optimization problem, and structure the high dimensional poses to address the challenge efficiently. Experiments show good performance and high efficiency of multi-person 3D pose estimation on real world street scenario videos, resulting in great new opportunities to understand and predict human behaviors for autonomous driving.
Innovativeness and Factors that Affect the Information Technology Adoption in the Classroom by Primary Teachers in Chile

This research examines the factors that influence the intention to use information technology in the classroom by primary school teachers based on the Unified Theory of Acceptance and Use of Technology and their technological predisposition.In particular, the relationship between teachers' innovativeness and their beliefs associated with the use of technology in the classroom was analyzed.To this end, 212 teachers from three provinces of Chile were surveyed.Data were analyzed using partial least squares statistical technique.Results indicate that performance expectancy, social influence, and facilitating conditions influence the intention to use information technology in the classroom.It was found that the intention to use construct is a determinant of the use of technology, which validates the robustness of the model in a context of primary education.In addition, it was validated that teachers' innovativeness determines their beliefs about the use of information technology in the classroom.
One-Shot Scale and Angle Estimation for Fast Visual Object Tracking

Visual object tracking is the process of tracking an arbitrary object in a video, where the bounding box of the object is given in the first frame. Siamese network-based visual object tracking approaches have recently received considerable attention for their high speed and superior performance. However, for scale and angle estimation, Siamese trackers require multiple search regions, which increases the computation time, thereby decreasing the real-time tracking performance. This paper proposes a one-shot Siamese network, named Siam-OS, for fast and efficient visual object tracking. Siam-OS uses only a single search region and estimates the scale and angle of the target bounding box. This significantly reduces the number of computations required for the deep convolutional feature extraction, and thus increases the tracking speed. The experimental results with Visual Object Tracking (VOT) benchmarks show the effectiveness of the proposed Siam-OS in terms of the accuracy, robustness, expected average overlap, and speed.
The adequacy of the international cooperation means for combating cybercrime and ways to modernize it

The era of scientific and technological development has witnessed an extensive use of Internet and electronic devices in various aspects of life.This widespread use has increased security risks, privacy and cyberattacks that threaten both individuals and States.This kind of crime is difficult to prevent as a result of the constant digital technological advances and globalization.There is a growing concern among States and government agencies that such intrusions could critically affect the security and the economics of any State.Combating this kind of crime requires international cooperation.Therefore, many States have called for the need to define cybercrime and to hold conventions to adopt effective legal framework to combat and restrict the progress of cybercrime worldwide.This study concluded that cooperative mechanisms are needed to coordinate and unify joint efforts and to modernize means of combating cybercrime using the latest techniques.In addition, it is necessary to upgrade existing mechanisms and develop other methods to achieve various aspects of cooperation.
Local-Aggregation Graph Networks

Convolutional neural networks (CNNs) provide a dramatically powerful class of models, but are subject to traditional convolution that can merely aggregate permutation-ordered and dimension-equal local inputs. It causes that CNNs are allowed to only manage signals on Euclidean or grid-like domains (e.g., images), not ones on non-Euclidean or graph domains (e.g., traffic networks). To eliminate this limitation, we develop a local-aggregation function, a sharable nonlinear operation, to aggregate permutation-unordered and dimension-unequal local inputs on non-Euclidean domains. In the context of the function approximation theory, the local-aggregation function is parameterized with a group of orthonormal polynomials in an effective and efficient manner. By replacing the traditional convolution in CNNs with the parameterized local-aggregation function, Local-Aggregation Graph Networks (LAGNs) are readily established, which enable to fit nonlinear functions without activation functions and can be expediently trained with the standard back-propagation. Extensive experiments on various datasets strongly demonstrate the effectiveness and efficiency of LAGNs, leading to superior performance on numerous pattern recognition and machine learning tasks, including text categorization, molecular activity detection, taxi flow prediction, and image classification.
Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation

Internet and network technologies have evolved dramatically in the last two decades, with rising users' demands to preserve their identities and privacy.Researchers have developed approaches to achieve users' demands, where the biggest part of the internet has formed, the Deep Web.However, as the Deep Web provides the resort for many benign users who desire to preserve their privacy, it also became the perfect floor for hosting illicit activities, which generated the Dark Web.This leads to the necessity of finding automated solutions to support law and security agencies in collecting information from the Dark Web to disclose such activities.In this paper, we illustrate the concepts needed for the development of a crawler that collects information from a dark website.We start from discussing the three layers of the Internet, the characteristics of the hidden and private networks, and the technical features of Tor network.We also addressed the challenges facing the dark web crawler.Finally, we presented our experimental system that fetches data from a dark market.This approach helps in putting a single dark website under investigation, and can be a seed for future research and development.
Latex Gloves: Protecting Browser Extensions from Probing and Revelation Attacks

Browser extensions enable rich experience for the users of today's web.Being deployed with elevated privileges, extensions are given the power to overrule web pages.As a result, web pages often seek to detect the installed extensions, sometimes for benign adoption of their behavior but sometimes as part of privacy-violating user fingerprinting.Researchers have studied a class of attacks that allow detecting extensions by probing for Web Accessible Resources (WARs) via URLs that include public extension IDs.Realizing privacy risks associated with WARs, Firefox has recently moved to randomize a browser extension's ID, prompting the Chrome team to plan for following the same path.However, rather than mitigating the issue, the randomized IDs can in fact exacerbate the extension detection problem, enabling attackers to use a randomized ID as a reliable fingerprint of a user.We study a class of extension revelation attacks, where extensions reveal themselves by injecting their code on web pages.We demonstrate how a combination of revelation and probing can uniquely identify 90% out of all extensions injecting content, in spite of a randomization scheme.We perform a series of large-scale studies to estimate possible implications of both classes of attacks.As a countermeasure, we propose a browser-based mechanism that enables control over which extensions are loaded on which web pages and present a proof of concept implementation which blocks both classes of attacks.
Reinforcement Learning With Parsimonious Computation and a Forgetting Process

Decision-making is assumed to be supported by model-free and model-based systems: the model-free system is based purely on experience, while the model-based system uses a cognitive map of the environment and is more accurate. The recently developed multistep decision-making task and its computational model can dissociate the contributions of the two systems and have been used widely. This study used this task and model to understand our value-based learning process and tested alternative algorithms for the model-free and model-based learning systems. The task used in this study had a deterministic transition structure, and the degree of use of this structure in learning is estimated as the relative contribution of the model-based system to choices. We obtained data from 29 participants and fitted them with various computational models that differ in the model-free and model-based assumptions. The results of model comparison and parameter estimation showed that the participants update the value of action sequences and not each action. Additionally, the model fit was improved substantially by assuming that the learning mechanism includes a forgetting process, where the values of unselected options change to a certain default value over time. We also examined the relationships between the estimated parameters and psychopathology and other traits measured by self-reported questionnaires, and the results suggested that the difference in model assumptions can change the conclusion. In particular, inclusion of the forgetting process in the computational models had a strong impact on estimation of the weighting parameter of the model-free and model-based systems.
Disaster Recovery Techniques in Cloud Computing

Electronic data has been created today in large quantities requiring data recovery services organization's work may experience the various type of disasters whether it was natural or man-made, which may result in huge loss of data. The purpose of recovery technology is the possibility of retrieving information from the backup server when the main data server is lost in the event of disasters. There are some difficulties such as time and cost complexity that make it difficult to implement such techniques. When you use disaster conditions as a service, these disasters can be remedied and data recovery speeds at low cost. In this paper, we compared and discuss the various techniques to create a unique backup and recovery system. In general, all these techniques focus on three different aspects: cost control, data replication, and security issues.
Antlion optimizer algorithm based on chaos search and its application

Aiming at the problems of premature convergence and easily falling into local optimums of the antlion optimization algorithm, a chaos antlion optimization algorithm based on the chaos search is proposed. Firstly, in the algorithm, the population is initialized by using the tent chaotic mapping, and the self-adaptive dynamic adjustment of chaotic search scopes is proposed in order to improve the overall fitness and the optimization efficiency of the population. Then, the tournament strategy is used to select antlions. Finally, the logistic chaos operator is used to optimize the random walk of ants, which forms a global and local parallel search mode with the antlion's foraging behavior. The performance algorithm is tested through 13 complex high-dimensional benchmark functions and three dimensional path planning problems. The experimental results of six complex high-dimensional benchmark functions show that the presented algorithm has a better convergence speed and precision than the standard antlion algorithm and other optimization algorithms, and is suitable for the optimization of complex high dimensional functions. The trajectory planning experimental results show that compared with the antlion optimizer (ALO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm, it has advantages in speed and accuracy to obtain a specific path, and it is of great value in actual problems.
ProductQnA: Answering User Questions on E-Commerce Product Pages

Product pages on e-commerce websites often overwhelm their customers with a wealth of data, making discovery of relevant information a challenge. Motivated by this, here, we present a novel framework to answer both factoid and non-factoid user questions on product pages. We propose several question-answer matching models leveraging both deep learned distributional semantics and semantics imposed by a structured resource like a domain specific ontology. The proposed framework supports the use of a combination of these models and we show, through empirical evaluation, that a cascade of these models does much better in meeting the high precision requirements of such a question-answering system. Evaluation on user asked questions shows that the proposed system achieves 66% higher precision1 as compared to IDF-weighted average of word vectors baseline [1].
The Impact of Using YouTubes and Audio Tracks Imitation YATI on Improving Speaking Skills of EFL Learners

The purpose of this study is to shed light on a developed approach to be adopted in EFL speaking classes and show the effectiveness of using YouTube videos and Listening Audio Tracks Imitation (YATI) for teaching English language in speaking classrooms as pedagogical tools to improve EFL learners&amp;rsquo; speaking skills. To find out the impact of using You Tubes and Audio Tracks Imitation (YATI) on improving speaking skills of EFL learners, the qualitative experimental approach is used to conduct this study. The participants of this study are 48 students studying major English, divided into two sections studying Listening &amp;amp; Speaking Course at College of Science &amp;amp; Arts Muhayil, King Khalid University. One section was used as a control group and the other as an experimental group. Data was collected using speaking tests results which were analyzed using SPSS Pearson correlation coefficient. The results revealed that employing YATI technique has a positive impact on the effectiveness of the speaking skills, fluency and pronunciation of EFL learners. This study concluded that YouTube videos and Listening Audio Tracks Imitation (YATI) is a very effective CALL (Computer-Assisted Language Learning) tool towards improving students&amp;rsquo; speaking skills. This study recommends the use of YATI approach in order to help students overcome speaking problems.
Privacy-preserving Quantum Sealed-bid Auction Based on Grover’s Search Algorithm

Sealed-bid auction is an important tool in modern economic especially concerned with networks. However, the bidders still lack the privacy protection in previously proposed sealed-bid auction schemes. In this paper, we focus on how to further protect the privacy of the bidders, especially the non-winning bidders. We first give a new privacy-preserving model of sealed-bid auction and then present a quantum sealed-bid auction scheme with stronger privacy protection. Our proposed scheme takes a general state in N-dimensional Hilbert space as the message carrier, in which each bidder privately marks his bid in an anonymous way, and further utilizes Grover's search algorithm to find the current highest bid. By O(lnn) iterations, it can get the highest bid finally. Compared with any classical scheme in theory, our proposed quantum scheme gets the lower communication complexity.
Convergence of Distributed Stochastic Variance Reduced Methods Without Sampling Extra Data

Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and disjointly stored on different machines, it becomes imperative to distribute the implementation of such variance reduced methods. In this paper, we consider a general framework that directly distributes popular stochastic variance reduced methods in the master/slave model, by assigning outer loops to the parameter server, and inner loops to worker machines. This framework is natural and friendly to implement, but its theoretical convergence is not well understood. We obtain a comprehensive understanding of algorithmic convergence with respect to data homogeneity by measuring the smoothness of the discrepancy between the local and global loss functions. We establish the linear convergence of distributed versions of a family of stochastic variance reduced algorithms, including those using accelerated and recursive gradient updates, for minimizing strongly convex losses. Our theory captures how the convergence of distributed algorithms behaves as the number of machines and the size of local data vary. Furthermore, we show that when the data are less balanced, regularization can be used to ensure convergence at a slower rate. We also demonstrate that our analysis can be further extended to handle nonconvex loss functions.