Reviewerly/exHarmony huggingface dataset:
Keyword guessing attacks on a public key encryption with keyword search scheme without random oracle and its improvement

Public key encryption with keyword search (PEKS) is a useful paradigm that enables a user to delegate searching capabilities on publicly encrypted data to an untrusted third party without revealing the data contents. In 2013, Fang et al. proposed a secure channel free PEKS (SCF-PEKS) scheme without random oracle and asserted that their scheme can withstand the keyword guessing (KG) attacks by outsider attackers. Later, Shao and Yang presented an improvement of Fang et al.’s SCF-PEKS scheme so as to obtain the security against KG attacks by malicious insider servers. In this paper, we demonstrate that both Fang et al.’s SCF-PEKS scheme and the improved scheme proposed by Shao and Yang fail in achieving their security goal. Our cryptanalysis shows that the former is vulnerable to the KG attacks by outsider attackers while the latter is vulnerable to the KG attacks by malicious insider servers. To overcome the security weaknesses in these two schemes, we present a new improvement of Fang et al.’s SCF-PEKS scheme. The analysis indicates that the improved scheme provides resistance against KG attacks by either outsider attackers or malicious insider servers.
Parallax

The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in deep learning (DL). DL frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist DL researchers to train their models in a distributed manner. Although current DL frameworks scale well for image classification models, there remain opportunities for scalable distributed training on natural language processing (NLP) models. We found that current frameworks show relatively low scalability on training NLP models due to the lack of consideration to the difference in sparsity of model parameters. In this paper, we propose Parallax, a framework that optimizes data parallel training by utilizing the sparsity of model parameters. Parallax introduces a hybrid approach that combines Parameter Server and AllReduce architectures to optimize the amount of data transfer according to the sparsity. Experiments show that Parallax built atop Tensor-Flow achieves scalable training throughput on both dense and sparse models while requiring little effort from its users. Parallax achieves up to 2.8x, 6.02x speedup for NLP models than TensorFlow and Horovod with 48 GPUs, respectively. The training speed for the image classification models is equal to Horovod and 1.53x faster than TensorFlow.
An Energy-Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width

Due to less memory requirement, low computation overhead and negligible accuracy degradation, deep neural networks with binary/ternary weights (BTNNs) have been widely employed on low-power mobile and Internet of Things (IoT) devices with limited storage capacity. Some hardware implementations have been proposed to accelerate the inference of BTNNs by utilizing the multiplication-free feature. However, some implicit characteristics in BTNN convolution, such as high arithmetic complexity and numerous redundant operations, are never considered. In this paper, we propose four optimization techniques to fully exploit these features. First, a feature-integral-based convolution (FIBC) method is proposed to reduce the arithmetic complexity of convolutional layers. Second, a kernel-transformation-feature-reconstruction (KTFR) convolution method is presented to remove redundant operations in BTNN convolution. Third, a hierarchical load-balancing mechanism (HLBM) is designed to eliminate zero value computation and improve resource utilization. Finally, a joint optimization approach for convolutional layers is proposed to search optimal calculation pattern for each layer. Based on the proposed four techniques, we design a reconfigurable processor in a 28-nm CMOS technology to accelerate the inferences of BTNNs. The four proposed techniques improve energy efficiency by 2.07×, 1.65×, 1.25×, and 2.24× for BTNNs respectively, compared with the baseline implementation which disables the proposed techniques. Benchmarked with binary-weight AlexNet, the processor achieves an energy efficiency of 19.9 TOPS/W at 200 MHz and 0.9 V.
ExFaKT

Fact-checking is a crucial task for accurately populating, updating and curating knowledge graphs. Manually validating candidate facts is time-consuming. Prior work on automating this task focuses on estimating truthfulness using numerical scores which are not human-interpretable. Others extract explicit mentions of the candidate fact in the text as an evidence for the candidate fact, which can be hard to directly spot. In our work, we introduce ExFaKT, a framework focused on generating human-comprehensible explanations for candidate facts. ExFaKT uses background knowledge encoded in the form of Horn clauses to rewrite the fact in question into a set of other easier-to-spot facts. The final output of our framework is a set of semantic traces for the candidate fact from both text and knowledge graphs. The experiments demonstrate that our rewritings significantly increase the recall of fact-spotting while preserving high precision. Moreover, we show that the explanations effectively help humans to perform fact-checking and can also be exploited for automating this task.
VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture

Vehicle re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search vehicles in a multi-camera network usually having non-overlapping field-of-views. Majority of the approaches dealing with the re-ID problem tackle it in a supervised manner which have certain limitations that pose challenges of generalization e.g., large amount of annotated data is required for training and is often limited to the dynamic growth of the data. Unsupervised learning techniques can potentially cope with such issues by drawing inference directly from the unlabeled input data and have been effectively employed in the context of person re-ID. To this end, this paper presents an approach that essentially formulates the whole vehicle re-ID problem into an unsupervised learning paradigm using a progressive two step cascaded framework. It combines a CNN architecture for feature extraction and an unsupervised technique to enable self-paced progressive learning. It also incorporates the contextual information into the proposed progressive framework that significantly improves the convergence of the learned algorithm. Moreover, the approach is generic and has been the first attempt to tackle the vehicle re-ID problem in an unsupervised manner. The performance of the proposed algorithm has been thoroughly analyzed over two large publically available benchmark datasets VeRi and VehicleID for vehicle re-ID using image-to-image and cross-camera search strategies and achieved better performance in comparison to current state-of-the-art approaches using standard evaluation metrics.
Efficient approach for incremental high utility pattern mining with indexed list structure

Since traditional frequent pattern mining approaches assume that all the items in binary databases have the same importance regardless of their own features, they have difficulty in satisfying requirements of real world applications such as finding patterns with high profits. High utility pattern mining was proposed to deal with such an issue, and various relevant works have been researched. There have been demands for efficient solutions to find interesting knowledge from specific environments in which data accumulates continuously with the passage of time such as social network service, wireless network sensor data, etc. Although several algorithms have been devised to mine high utility patterns from incremental databases, they still have performance limitations in the process of generating a large number of candidate patterns and identifying actually useful results from the found candidates. In order to solve the problems, we propose a new algorithm for mining high utility patterns from incremental databases. The newly proposed data structures in a list form and mining techniques allow our approach to extract high utility patterns without generating any candidates. In addition, we suggest restructuring and pruning techniques that can process incremental data more efficiently. Experimental results on various real and synthetic datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory, and scalability.
Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique

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A novel matrix factorization model for recommendation with LOD-based semantic similarity measure

Collaborative Filtering (CF) algorithms have been widely used to provide personalized recommendations in e-commerce websites and social network applications. Among them, Matrix Factorization (MF) is one of the most popular and efficient techniques. However, most MF-based recommender models only rely on the past transaction information of users, so there is inevitably a data sparsity problem. In this article, we propose a novel recommender model based on matrix factorization and semantic similarity measure. Firstly, we propose a new semantic similarity measure based on semantic information in the Linked Open Data (LOD) knowledge base, which is a hybrid measure based on feature and distance metrics. Then, we make an improvement on the traditional MF model to deal with data sparsity. Specifically, the MF process has been extended from both the user and item sides with implicit feedback information and semantic similar items, respectively. Experiments on two real datasets show that our proposed semantic similarity measure and recommender model are superior to the state-of-the-art approaches in recommendation performance.
Mobile Banking: Evolution and Threats: Malware Threats and Security Solutions

Mobile banking refers to the use of a mobile device (e.g., a smartphone) to perform online banking activities, while away from the home computer, for transferring funds, monitoring account balance and bill payments, and so on. It provides a great convenience to bank customers, but it suffers from various types of attacks that are possible on the mobile banking infrastructure. We present the evolution of mobile banking and discuss the various threats associated with it as well as some of the most recent malware attacks. Finally, we include a review of recent security solutions for enabling secure mobile banking.
CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality

Unsupervised learning is an important problem in statistics and machine learning with a wide range of applications.In this paper, we study clustering of high-dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based on the EM algorithm and a direct estimation method for the sparse discriminant vector.Both theoretical and numerical properties of CHIME are investigated.We establish the optimal rate of convergence for the excess misclustering error and show that CHIME is minimax rate optimal.In addition, the optimality of the proposed estimator of the discriminant vector is also established.Simulation studies show that CHIME outperforms the existing methods under a variety of settings.The proposed CHIME procedure is also illustrated in an analysis of a glioblastoma gene expression data set and shown to have superior performance.Clustering of Gaussian mixtures in the conventional low-dimensional setting is also considered.The technical tools developed for the highdimensional setting are used to establish the optimality of the clustering procedure that is based on the classical EM algorithm. Introduction.Clustering analysis, which aims to partition unlabeled data into homogeneous groups, is an ubiquitous problem in statistics and machine learning with a broad range of applications, including pattern recognition, disease diagnostics and information retrieval (see [6,19] and the references therein).A number of clustering algorithms have been proposed in the literature.The well-known kmeans and k-medians algorithms [8] are centroid-based.Hierarchical clustering [37] builds a hierarchy of clusters based on the empirical measures of dissimilarity among sets of observations.Clustering algorithms have also been developed and analyzed under the probabilistic mixture model framework [14,31].Among the possible probability distributions for the mixture components, the Gaussian distribution is the most commonly used for both theoretical and computational considerations [7,15,23], and has been widely used in a range of applications for clustering and discriminant analysis [16,30].
Neural Chinese word segmentation with dictionary

Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for Chinese word segmentation. However, these methods require a large number of labeled sentences for model training, and usually cannot utilize the useful information in Chinese dictionary. In this paper, we propose two methods to exploit the dictionary information for CWS. The first one is based on pseudo labeled data generation, and the second one is based on multi-task learning. The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
Hybrid Stochastic Exploration Using Grey Wolf Optimizer and Coordinated Multi-Robot Exploration Algorithms

Multi-robot exploration is a search of uncertainty in restricted space seeking to build a finite map by a group of robots. It has the main task to distribute the search assignments among robots in real time. In this paper, we proposed a stochastic optimization for multi-robot exploration that mimics the coordinated predatory behavior of grey wolves via simulation. Here, the robot movement is computed by the combined deterministic and metaheuristic techniques. It uses the Coordinated Multi-Robot Exploration and GreyWolf Optimizer algorithms as a new method called the hybrid stochastic exploration. Initially, the deterministic cost and utility determine the precedence of adjacent cells around a robot. Then, the stochastic optimization improves the overall solution. It implies that the robots evaluate the environment by the deterministic approach and move on using the metaheuristic algorithm. The proposed hybrid method was implemented on simple and complex maps and compared with the Coordinated Multi-Robot Exploration algorithm. The simulation results show that the stochastic optimization enhances the deterministic approach to completely explore and map out the areas.
SamWalker: Social Recommendation with Informative Sampling Strategy

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Only positive feedback are observed and the unobserved feedback can be attributed to two reasons: unknow or dislike. Existing methods address this challenge by treating all the un-observed data as negative (dislike) but downweight the confidence of these data. However, this treatment causes two problems: (1) Confidence weights of the unobserved data are usually assigned manually, which lack flexible and may create empirical bias in evaluating user's preference. (2) To handle massive volume of the unobserved feedback data, most of the existing methods rely on stochastic inference and data sampling strategies. However, since users are only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes the item rather than does not know it.
Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate

Accurate predictions of the solar resource are essential tools for the design, performance analysis, and economic evaluation of various solar energy projects. This paper is proposed in an attempt to overcome the lack of solar radiation data and predicting models in Algeria. New and readjusted models have been developed to estimate the daily and monthly mean daily global solar irradiations over Algerian Big South, based on measured ground data of air temperature and sunshine hours, as well as the day/month number. After a careful evaluation of the models' performances using the standard K-fold cross-validation method, it was found that only the sunshine-based models can offer excellent estimations in the daily time scale, with validation RMSEs in the range of 1.470–2.425 MJ/m2 day. Meanwhile, all types of models have superior estimations of the monthly mean global irradiation (RRMSE less than 10% in most cases). The merits of using the K-fold cross-validation method in optimizing the error estimates, stabilizing the performances, as well as selecting the appropriate models, have been demonstrated.
A labeled random finite set online multi-object tracker for video data

This paper proposes an online multi-object tracking algorithm for image observations using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, handling of false positives, false negatives and occlusion into a single recursion. This is achieved by modeling the multi-object state as labeled random finite set and using the Bayes recursion to propagate the multi-object filtering density forward in time. The proposed filter updates tracks with detections but switches to image data when detection loss occurs, thereby exploiting the efficiency of detection data and the accuracy of image data. Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that detection loss in the middle of the scene are likely to be due to occlusions. Such prior knowledge can be exploited to improve occlusion handling, especially long occlusions that can lead to premature track termination in on-line multi-object tracking. Tracking performance is compared to state-of-the-art algorithms on synthetic data and well-known benchmark video datasets.
Distributed Reinforcement Learning for Multi-robot Decentralized Collective Construction

Inspired by recent advances in single agent reinforcement learning, this paper extends the single-agent asynchronous advantage actor-critic (A3C) algorithm to enable multiple agents to learn a homogeneous, distributed policy, where agents work together toward a common goal without explicitly interacting. Our approach relies on centralized policy and critic learning, but decentralized policy execution, in a fully-observable system. We show that the sum of experience of all agents can be leveraged to quickly train a collaborative policy that naturally scales to smaller and larger swarms. We demonstrate the applicability of our method on a multi-robot construction problem, where agents need to arrange simple block elements to build a user-specified structure. We present simulation results where swarms of various sizes successfully construct different test structures without the need for additional training.
ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm

The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived from pathogens and displays pathogens on the cell surface to facilitate T-cell recognition and perform a series of immune functions. MHC molecules are critical in transplantation, autoimmunity, infection, and tumor immunotherapy. Combining machine learning algorithms and making full use of bioinformatics analysis technology, more accurate recognition of MHC is an important task. The paper proposed a new MHC recognition method compared with traditional biological methods and used the built classifier to classify and identify MHC I and MHC II. The classifier used the SVMProt 188D, bag-of-ngrams (BonG), and information theory (IT) mixed feature representation methods and used the extreme learning machine (ELM), which selects lin-kernel as the activation function and used 10-fold cross-validation and the independent test set validation to verify the accuracy of the constructed classifier and simultaneously identify the MHC and identify the MHC I and MHC II, respectively. Through the 10-fold cross-validation, the proposed algorithm obtained 91.66% accuracy when identifying MHC and 94.442% accuracy when identifying MHC categories. Furthermore, an online identification Web site named ELM-MHC was constructed with the following URL: http://server.malab.cn/ELM-MHC/ .
Technologies for Enhancing Collocated Social Interaction: Review of Design Solutions and Approaches

Collocated interaction has received growing interest in both academic research and the design of information and communication technological applications. An emergent research topic within this area relates to technological enhancement of social interaction. Various envisioned systems aim beyond simply enabling interaction, to actively enhance—i.e., improve the quality or extent of—social interaction between collocated people. However, there is little understanding of the optimal design solutions and roles of technology considering this goal. This literature review outlines the landscape of design explorations in this emergent research topic. We contribute an in-depth study of 92 publications that present relevant solutions or prototypes, analyzing their focus areas, design objectives, and design and evaluation approaches. To contribute with a new theoretical perspective, we identify various roles of technology relevant for enhancement, representing three abstract categories: facilitating, inviting and encouraging. This review helps researchers to describe, analyze, and position relevant prior research and identify gaps in scientific knowledge.
Identification of the natural background levels in the Phlaegrean fields groundwater body (Southern Italy)

The paper deals with the identification of Natural Background Levels (NBLs) in a groundwater body located in a volcanic area. NBLs were assessed through spatial analysis adopting both the probability plot and the pre-selection methods. They were performed on 33 sampling points, focusing on F, Fe, Mn and As ions. The approach is discussed adopting the groundwater body of Phlaegrean Fields as relevant case study. The area is an active volcanic caldera, hosting a geothermal system superimposed on the regional groundwater flow and characterized by hydrogeochemical anomalies. The results, especially referred to As, suggest a local geogenic contamination or concentrated volcano-related phenomena, as fluids upwelling along faults, in the most active portion of the caldera. The application of the probability maps generated by the indicator kriging, resulted effective to distinguish within the same groundwater body areas characterized by peculiar geochemical anomalies.