Reviewerly/exHarmony huggingface dataset:
Online distributed distance-based outlier clearance approaches for wireless sensor networks

One key challenge for sensor networks is to provide the real-time high reliable sensor measurements with the minimum resource consumption. Outlier clearance in sensor networks can ensure the quality of sensor measurements and dependable monitoring. In this paper, we propose two online distributed outlier clearance approaches with low computational complexity and memory usage that can identify and remove the spurious sensor measurements. The proposed approaches are operated locally and thus save communication overhead as well as possess good scalability. The evaluation performance of proposed approaches and existing widely used methods on synthetic and real-life dataset illustrates that our Adaptive Top-n WAD approach achieves remarkable outlier clearance performance as compared to these methods.
Virtual reconstruction of random moving image capturing points based on chaos embedded particle swarm optimization algorithm

Abstract During the exercise process, such as jumping, and running, the angles of different joints of the athlete's body will produce different changes, resulting in obvious mismatches in different joint feature points. The chaotic matching error makes the calculation results of the basic shape features of the athletes in different parts have large deviations. In this paper, an improved algorithm of CEPS (Chaos embedded particle swarm) optimization algorithm interpolation is introduced, which can improve the interpolation precision and reduce the calculation time of interpolation. Through interpolation, the fast Fourier transform can be used to realize the fast reconstruction of the target 3d image based on biomechanical characteristics. The experimental and simulation results show that the three-dimensional image reconstruction algorithm based on the interpolation method can reconstruct the three-dimensional image of the target, achieve the detection of the target and have good resolution, and improve the authenticity of the human motion image sequence three-dimensional dynamic simulation.
Incorporating expert prior in Bayesian optimisation via space warping

Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.
Secure Hash Authentication in IoT based Applications

Secure authentication while communicating data between sender and receiver nodes is a key concept when providing Internet of Things as a service. Hash algorithms can provide such an authentication mechanism for IoT based applications. The most recent secure hash algorithm technique standardized by the NIST is SHA-3. SHA-3 is fully apt in ensuring authentication for a sender and receiver transaction. A novel signature generating technique based on SHA-3 is presented in the paper. Two of the popular IoT communication models of publish subscribe and request response are examined under this authentication scheme. The sender generates a unique hash code for the data that it is about to send. This hash code serves as the authenticating token for the transaction. Mechanisms for the sender receiver interaction are built into the program codes in both the simulated communication models. The system architecture also manages interaction of the receiver with the cloud. This interaction ensures proper authentication through the mechanisms and api(s) provided by the cloud service providers, and these mechanisms are also integrated into the simulation. Both the cloud services used in the system architecture, i.e. cloud messaging as a service, and cloud database as a service ensure that the sender's identity is verified. In this way a blanket authentication system is set up for the aforementioned IoT architectures.
Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier

Although recent studies have shown that a Bayesian network classifier (BNC) that maximizes the classification accuracy (i.e., minimizes the 0/1 loss function) is a powerful tool in both knowledge representation and classification, this classifier: (1) focuses on the majority class and, therefore, misclassifies minority classes; (2) is usually uninformative about the distribution of misclassifications; and (3) is insensitive to error severity (making no distinction between misclassification types). In this study, we propose to learn the structure of a BNC using an information measure (IM) that jointly maximizes the classification accuracy and information, motivate this measure theoretically, and evaluate it compared with six common measures using various datasets. Using synthesized confusion matrices, twenty-three artificial datasets, seventeen UCI datasets, and different performance measures, we show that an IM-based BNC is superior to BNCs learned using the other measures—especially for ordinal classification (for which accounting for the error severity is important) and/or imbalanced problems (which are most real-life classification problems)—and that it does not fall behind state-of-the-art classifiers with respect to accuracy and amount of information provided. To further demonstrate its ability, we tested the IM-based BNC in predicting the severity of motorcycle accidents of young drivers and the disease state of ALS patients—two class-imbalance ordinal classification problems—and show that the IM-based BNC is accurate also for the minority classes (fatal accidents and severe patients) and not only for the majority class (mild accidents and mild patients) as are other classifiers, providing more informative and practical classification results. Based on the many experiments we report on here, we expect these advantages to exist for other problems in which both accuracy and information should be maximized, the data is imbalanced, and/or the problem is ordinal, whether the classifier is a BNC or not. Our code, datasets, and results are publicly available http://www.ee.bgu.ac.il/~boaz/software .
A quantum Hopfield neural network model and image recognition

Quantum computing is a new mode that follows the laws of quantum mechanics. It performs computational tasks based on the control of quantum units. From the view of computable problems, quantum computers can only solve the problems that traditional computers can solve, but considering the efficiency of computation, due to the existence of quantum superposition, some known quantum algorithms are exponentially faster than traditional general-purpose computers. In this article, we combine quantum computing with a classical neural network to design a quantum Hopfield network. Each neuron is initialized to a specified state and evolved into a steady state. The output result is one of patterns in a training set. We also present an application of this protocol in image recognition. The simulation results show that this network works in the quantum environment and the output images are correct, therefore the feasibility of this protocol is verified.
Deep Learning Versus Conventional Learning in Data Streams with Concept Drifts

In many real-world applications, the characteristics of data collected by activity logs, sensors and mobile devices change over time. This behavior is known as concept drift. In complex environments, which produce high dimensional data streams, machine learning tasks become cumbersome, as models become outdated very quickly. In our study, we assess hundreds of combinations of data characteristics and methods on network traffic data. Specifically, we focus on seven conventional machine learning and deep learning methods and compare their generalization power in the presence of concept drift. Our results show that Convolutional Neural Networks (CNNs) outperform conventional methods, even when compared to an idealized upper bound on their performance created in a piecewise manner by selecting the best method and its best configuration at each point in time, thus mimicking the output of a perfect meta-learning architecture. In the context of sequential data subject to concept drift, our results appear to defy the usually accepted ”No Free Lunch Theorem (NFL)”, which stipulates that no method dominates all the others in every situation. While this is by no means a rejection of the NFL Theorem, which captures a much more complex phenomenon, it is nonetheless a surprising result worth further investigations. As a matter of fact, our results show that, when data availability is limited, a meta-learning approach is preferable to CNNs, as it requires less data for training.
Deep Neural Network Compression for Image Classification and Object Detection

Neural networks have been notorious for being computational expensive. This is mainly because neural networks are often over-parametrized and most likely have redundant nodes or layers as they are getting deeper and wider. Their demand for hardware resources prohibits their extensive use in embedded devices and puts restrictions on tasks like real time image classification or object detection. In this work, we propose a network agnostic model compression method infused with a novel dynamical clustering approach to reduce the computational cost and memory footprint of deep neural networks. We evaluated our new compression method on five different state-of-the-art image classification and object detection networks. In classification networks, we pruned about 95% of network parameters. In advanced detection networks such as YOLOv3, our proposed compression method managed to reduce the model parameters up to 59.70% which yielded 110× less memory without sacrificing much in accuracy.
OHDA: An Opposition based High Dimensional optimization Algorithm

One of the challenging problems to-date is to deal with high dimensional data. This problem is getting more severe as the data gathering tools are progressing. This paper proposes an opposition-based optimization algorithm suitable for high dimensions. Its novelty is the angular movement according to a few selected dimensions that makes it effective to search in high dimensions. Accordingly, the Opposition-based High Dimensional optimization Algorithm (OHDA) is proposed. Its performance is studied using functions from CEC2005 for high dimensional data including 1000D and 2000D. In addition, the performance of the proposed algorithm is tested with CEC2014, which is more complicated than CEC2005 and CEC2013. The performance of OHDA is also examined using CEC2017 constraint optimization test suit. The comparing algorithms are CA, ICA, AAA, ABC, KH, MVO, WOA, RW-GWO, B-BBO, LX-BBO and LSHADE44-IEpsilon. The results verify that the proposed algorithm outperforms some conventional optimization algorithms in terms of their accuracies. The efficiency of employing opposite points in optimization is also validated in this paper.
Performance analysis of Leap motion controller for finger rehabilitation using serious games in two lighting environments

Serious games have been implemented to assist people in their rehabilitation processes to recover mobility of a body part. Leap motion controller (LMC) is a sensor, which provides 3D-coordinates of the elbow, wrist and phalanges of the fingers. This study proposes a serious game composed of three melodies to assess the performance of LMC on identifying finger flexion in two lighting environments. Ten participants played the three melodies in two lighting environments (lighting = 136.987 lux, non-lighting = 0.0513 lux) using three experimental settings. Sensitivity, specificity and accuracy were computed to compare the two lighting environments. Statistical tests revealed that only there was a significant difference between the performances of LMC in lighting and non-lighting environments in the pairwise sensitivity for the thumb in the experimental setting 1 (p-value = 0.036). Grand mean accuracy rates of 88.22% and 84.17% were achieved in the lighting and non-lighting environments, respectively.
An Ontology of Digital Video Forensics: Classification, Research Gaps &amp; Datasets

The world lives in an epoch where people have an incredible variety of digital multimedia technologies. Although historically it was established that the computerized information is authentic information, but the technology of today has begun to erode this assuredness because an opponent uses several distortions to edit the original video for trademark infringement. Many investigators have introduced video forgery detection systems, due to the advent of omnipresent digital videos on the internet and to overcome the difficulties. This paper depicts a comprehensive overview of different video forgery detection techniques. It specifically discusses the importance of the techniques by classifying them according to functionality. This study delivers an authoritative and thorough review of existing digital video forgery detection techniques and also highlights various research gaps of the current techniques. Furthermore, the existing video forgery detection techniques are assessed by analyzing accuracy, precision and recall. Finally, the paper discusses different public and private datasets available for evaluating video forgery detection techniques.
End-to-End Joint Opinion Role Labeling with BERT

Opinion mining has raised growing interest both in industry and academia in the past decade. Opinion role labeling (ORL) is a task to extract opinion holder and target from natural language to answer the question “who express what”. Recent years, neural network based methods with additional lexical and syntactic features have achieved state-of-the-art performances in similar tasks. Moreover, Bidirectional Encoder Representations from Transformers (BERT) has shown impressive performances among a variety of natural language processing (NLP) tasks. To investigate BERT based end-to-end model in ORL, we propose models using BERT, Bidirectional Long short-term Memory (BiLSTM) and Conditional Random Field (CRF) to jointly extract opinion roles (e.g., opinion holder and target). Experimental results show that our models achieve remarkable scores without using extra syntactic and/or semantic features. To our best knowledge, we are among the pioneers to successfully integrate BERT in this manner. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and providing strong baselines for future work.
Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB

Neural network inference on embedded devices has to meet accuracy and latency requirements under tight resource constraints. The design of suitable network architectures is a challenging and time-consuming task. Therefore, automatic discovery and optimization of neural networks is considered important for continuing the trend of moving classification tasks from cloud to edge computing. This paper presents an evolutionary method to optimize a convolutional neural network (CNN) architecture for classification tasks. The method runs efficiently on a single GPU-workstation and provides simple means to direct the tradeoff between complexity and accuracy of the evolved network. Using this method, we achieved a 11x reduction in the number of multiply-accumulate (MAC) operations of the winning network for the German Traffic Sign Recognition Benchmark (GTSRB) without accuracy reduction. An ensemble of four of our evolved networks competes the winning ensemble with a 0.1% lower accuracy but 70x reduction in MACs and 14x reduction in parameters.
Bactran: A Hardware Batch Normalization Implementation for CNN Training Engine

In recent years, convolutional neural networks (CNNs) have been widely used. However, their ever-increasing amount of parameters makes it challenging to train them with the GPUs, which is time and energy expensive. This has prompted researchers to turn their attention to training on more energy-efficient hardware. batch normalization (BN) layer has been widely used in various state-of-the-art CNNs for it is an indispensable layer in the acceleration of CNN training. As the amount of computation of the convolutional layer declines, its importance continues to increase. However, the traditional CNN training accelerators do not pay attention to the efficient hardware implementation of the BN layer. In this letter, we design an efficient CNN training architecture by using the systolic array. The processing element of the systolic array can support the BN functions both in the training process and the inference process. The BN function implemented is an improved, hardware-friendly BN algorithm, range batch normalization (RBN). The experimental results show that the implementation of RBN saves 10% hardware resources, reduces the power by 10.1%, and the delay by 4.6% on average. We implement the accelerator on the field programmable gate array VU440, and the power consumption of the its core computing engine is 8.9 W.
Hierarchical Attentional Factorization Machines for Expert Recommendation in Community Question Answering

The most challenging task of Community Question Answering (CQA) is to provide high-quality answers to users' questions. Currently, a variety of expert recommendation methods have been proposed and greatly improved the effective matching between questions and potential good answerers. However, the performance of existing methods can be adversely affected by many common factors such as data sparsity and noise problem, which cause less precise user modeling. Moreover, existing methods often model user-question interactions through simple ways, failing to capture the multiple scale interactions of question and answerers, which make it difficult to find answerers who are able to provide the best answers. In this paper, we propose an attention-based variant of Factorization Machines (FM) called Hierarchical Attentional Factorization Machines (HaFMRank) for answerer recommendation in CQA, which not only models the interactions between pairs of individual features but emphasizes the roles of crucial features and pairwise interactions. Specifically, we introduce the within-field attention layer to capture the inner structure of features belonging to the same field, while a feature-interaction attention layer is adopted to examine the importance of each pairwise interaction. A pre-training procedure is designed to generate latent FM feature embedding that encode question context and user history into the training process of HaFMRank. The performance of the proposed HaFMRank is evaluated by using real-world datasets of Stack Exchange and experimental results demonstrate that it outperforms several state-of-the-art methods in best answerer recommendation.
Towards Robust and Reproducible Active Learning using Neural Networks

Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental settings, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously reported results. We also found that under strong regularization, AL methods show marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL evaluations. We believe our findings and recommendations will help advance reproducible research in AL using neural networks.
Privacy-Preserving QoS Forecasting in Mobile Edge Environments

Mobile Edge Computing is an emerging technology offering low latency responses by deploying edge servers near mobile devices. We propose a novel privacy-preserving QoS forecasting approach – Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments) to address the challenges of user mobility and information leakage encountered by QoS forecasting in mobile edge environments. Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users’ accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.
A Longitudinal Analysis of Online Ad-Blocking Blacklists

Websites employ third-party ads and tracking services leveraging cookies and JavaScript code, to deliver ads and track users' behavior, causing privacy concerns. To limit online tracking and block advertisements, several ad-blocking (black) lists have been curated consisting of URLs and domains of well-known ads and tracking services. Using Internet Archive's Wayback Machine in this paper, we collect a retrospective view of the Web to analyze the evolution of ads and tracking services and evaluate the effectiveness of ad-blocking blacklists. We propose metrics to capture the efficacy of ad-blocking blacklists to investigate whether these blacklists have been reactive or proactive in tackling the online ad and tracking services. We introduce a stability metric to measure the temporal changes in ads and tracking domains blocked by ad-blocking blacklists and a diversity metric to measure the ratio of new ads and tracking domains detected. We observe that ads and tracking domains in websites change over time, and among the ad-blocking blacklists that we investigated, our analysis reveals that some blacklists were more informed with the existence of ads and tracking domains, but their rate of change was slower than other blacklists. Our analysis also shows that Alexa top 5K websites in the US, Canada, and the UK have the most number of ads and tracking domains per website, and have the highest proactive scores. This suggests that ad-blocking blacklists are updated by prioritizing ads and tracking domains reported in the popular websites from these countries.