Reviewerly/exHarmony huggingface dataset:
NOCAQF: An Efficient Nonlinear Online Data Compression Algorithm

Industrial real-time data stream provides great potential in producing big data to support industrial system optimization and intelligent management decision-making. However, storingall massive industrial data without compression requires a huge storage space. Meanwhile,the online transmission consumes a lot of network bandwidth, which brings high pressure to the data transmission line. In practice, the real-time data collected by industry are not always variable and valuable, and contain many redundant data, which obviously affects the efficiency of big data analysis. How to efficiently compress the industrial real-time data, relieve the pressure of industrial real-time data transmission, reduce the storage overhead, and improve the analysis efficiency, is the key technical problem to be solved in industrial manufacturing. To address this problem, the industry has proposed a variety of data compression algorithms, such as revolving door compression algorithm, sliding window compression algorithm and Bayesian compression algorithm. However, there are still some issues such as low compression efficiency, large amount of computation, and poor compression effect. Aiming at the requirement of industrial real-time data compression, this work proposes a nonlinear on-line process data compression algorithm termed NOCAQF based on quadratic function. Simulation and comparative experiments show that the proposed algorithm has the advantages of simple parameters, high compression rate and small amount of computation. When the data collection system processes massive real-time concurrent data streams, NOCAQF can significantly reduce the system load and improve the comprehensive compression rate on the premise of small amount of computation.
Hybrid Machine Learning Methods with Malay Lexicon for Public Polarity Opinion on Water Related Issue

Opinion classifications from Twitter are still in demand among research works on related opinions or feelings expressed on various issues. One of the concerns expressed in Twitter is on water-related issues such as the lack of clean water supply. It has been found that the issue highlighted in Twitter is the frequent disruption of clean water supply in Malaysia. The discussions concerning this issue contain positive and negative emotions like anger, joy, worry, and frustration. The focal point of this article is to evaluate hybrid sentiment analysis using a machine learning classifier to analyze the polarity of opinions employing real data from Twitter. A series of experiments were performed on a hybrid of deep learning, support vector machine, Na&#x00EF;ve Bayes and random forest with a lexicon-based model. In addition, the Malay sentiment lexicon score is proposed. The Malay sentiment lexicon scores have improved the accuracy and F1-score of all hybrid methods. The analysis uncovers that negative and positive polarity opinions can be beneficial to the relevant authorities to overcome the water supply disruption issue.
A Secure Electronic Voting System Using Multifactor Authentication and Blockchain Technologies

This chapter presents a distributed e-voting system that solves the problems of vote-rigging, voter impersonation, and vote falsification, all of which are prevalent in traditional paper ballot systems. In general, the digitization of democratic decision-making is convenient, fast, and cost-saving but can become a gateway for electoral fraud if not properly secured. Authentication and the simultaneous achievement of confidentiality, integrity, and availability represent major challenges toward establishing e-voting as a reliable means of democratic decision-making. In this chapter, a combination of multifactor authentication (MFA) and blockchain techniques is used to secure electronic voting. MFA hampers the compromising of voters’ identities and allows for easy verification, while blockchain technology protects the integrity of the votes and ensures the verifiability of the cast votes. Combining a facial recognition algorithm and RFID authenticates and authorizes voters to participate in the election process. A smart contract implemented on an Ethereum network provides the required measures of integrity and verifiability for secure e-voting. Performance evaluations of the proposed approach show that the MFA yielded a 0.1% false acceptance rate and a 0.8% false rejection rate for 100 voters, respectively. This illustrates that the proposed technique can solve issues of authentication and integrity, thereby paving the way for free, fair, and credible e-democratic decision-making in digitally enabled voting scenarios.
Website Fingerprinting Attack Through Persistent Attack of Student

Illegal users usually use Tor to hide their malicious behavior for browsing website. Website fingerprinting (WF) attack can help local network administrator to prevent illegal behavior of anonymous users. Although a lot of researches have improved website fingerprinting attacks, they still cannot address the concept drift problem effectively. In this paper, we propose a novel WF attack framework, Persistent Attack of Student (PAS), by integrating self-training mechanism with advanced deep learning (DL) related WF attack. PAS can train new DL model by using concept drift dataset with pseudo label for alleviating concept drift issue. In addition, we present a new deep convolutional neural network (DCNN) attack with stable accuracy by using automatic and local feature extraction. Then, we evaluate PAS application with different advanced deep learning WF attacks for alleviating concept drift issue. The experimental results show that DCNN attack achieves 96.50%-98.88% accuracy with 0.7-0.8x time cost of DF attack in closed world of 95-900 monitored websites, and reaches 96.32% precision and 96.31% recall in open world of 400,000 unmonitored websites. The PAS attack framework with different deep learning methods achieves 87.56%-91.46% in concept drift dataset of 56 days for 200 monitored websites, which is 2.27% 2.36% better than each original deep learning attack. The experimental results demonstrate that PAS framework can help alleviate concept drift issue effectively and DCNN can perform WF attack with less time cost efficiently.
Low-Resource NMT: A Case Study on the Written and Spoken Languages in Hong Kong

The majority of inhabitants in Hong Kong are able to read and write in standard Chinese but use Cantonese as the primary spoken language in daily life. Spoken Cantonese can be transcribed into Chinese characters, which constitute the so-called written Cantonese. Written Cantonese exhibits significant lexical and grammatical differences from standard written Chinese. The rise of written Cantonese is increasingly evident in the cyber world. The growing interaction between Mandarin speakers and Cantonese speakers is leading to a clear demand for automatic translation between Chinese and Cantonese. This paper describes a transformer-based neural machine translation (NMT) system for written-Chinese-to-written-Cantonese translation. Given that parallel text data of Chinese and Cantonese are extremely scarce, a major focus of this study is on the effort of preparing good amount of training data for NMT. In addition to collecting 28K parallel sentences from previous linguistic studies and scattered internet resources, we devise an effective approach to obtaining 72K parallel sentences by automatically extracting pairs of semantically similar sentences from parallel articles on Chinese Wikipedia and Cantonese Wikipedia. We show that leveraging highly similar sentence pairs mined from Wikipedia improves translation performance in all test sets. Our system outperforms Baidu Fanyi's Chinese-to-Cantonese translation on 6 out of 8 test sets in BLEU scores. Translation examples reveal that our system is able to capture important linguistic transformations between standard Chinese and spoken Cantonese.
Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter

The significant challenge that occurs due to medical image processing is to acquire the image without the loss of any crucial data. The image data can be degraded by noise or other factors while acquiring or processing the image. This noise affects the image quality since the contrast of medical images is already very low, and it is hard for the experts to identify the infections from the images. Henceforth, image denoising is an essential process in the medical imaging systems. In this paper, a hybrid tunicate rat swarm optimization with median filter (TRSWOAMF) has been proposed to remove or reduce the noise from the medical blood cell image, thus to restore a high-quality image. The proposed TRSWOAMF method uses median filter to remove the noise from the blood cell images, which then optimizes the parameters by using the tunicate rat swarm optimization algorithm. This TRSWOAMF method detects the noise in the blood cell image, wherein the median filter computes the median value for every pixel and best value is replaced. Then the parameters in the image are optimized by the tunicate rat swarm optimization algorithm to retain the original quality of the image. The result shows that the proposed TRSWOAMF method produces high-quality denoised image with a significantly reduced error rate.
Improved Bi-GRU Model for Imbalanced English Toxic Comments Dataset

Deep learning is widely used in the study of English toxic comment classification. However, most existing studies failed to consider data imbalance. Aiming at an imbalanced English Toxic Comments Dataset, we propose an improved Bi-gated recurrent unit (GRU) model that combines an oversampling and cost-sensitive method. We use random oversampling in the improved model to reduce the data imbalance, introduce a cost-sensitive method, and propose a new loss function for the Bi-GRU model. Experimental results show that the improved Bi-GRU model demonstrates a significantly improved classification performance in the imbalanced English Toxic Comments Dataset.
Towards a Better Understanding of Side-Channel Analysis Measurements Setups

The evaluation of side-channel measurement setups and the impact they can have on physical security evaluations is a surprisingly under-discussed topic. In this paper, we initiate a comprehensive study of such setups for embedded software and hardware (FPGA) implementations. We systematically investigate a design space including the choice of the probing method, the clock frequency of the device under test, its supply voltage and the sampling rate of the adversary's oscilloscope. Our results quantify the impact (i.e., the risk of security over-estimations) that suboptimal setups can cause and lead to easy-to-use guidelines for security evaluators. Despite some of our conclusions are device-dependent, we argue that the proposed methodology and some of the proposed guidelines are of general interest and could be applied to other setups.
VR Karaoke Using Expressive 3D Avatars

Because of COVID-19's social isolation, it's difficult to go to karaoke. Singing songs with other people in a karaoke room is prohibited. As a result of these circumstances, we created a virtual reality (VR) platform that allows users to enjoy karaoke at home or in a private setting. The platform is designed to analyze and score users' singing performances in real time, as well as incorporate emotional 3D avatar reactions to give users the impression of being in a karaoke room with other people. Our platform assesses the user's singing voice based on the beat, notes, and lyrics. We were successful in achieving the desired outcome.
Fast Cluster Optimization Method for Concentrated Solar Power Plants Toward Power Systems Under High Renewable Penetration

With the development of power systems under high renewable penetration, the concentrating solar power (CSP) plant, which can provide both renewable energy and operational flexibility, have attracted more and more attention. However, most existing CSP modeling and optimization methods have the following limitations: applying the mixed integer linear programming model to the annual operation cost analysis of the long-term planning problem for large-scale systems is complicated. In this paper, we introduce a fast cluster optimization method for CSP plants toward power systems under high renewable energy penetration. Through this method, the CSP plants with similar operation characteristics in adjacent geographical areas are clustered and grouped, so as to optimize the group behavior, rather than the behavior of individual plant. The number of decision variables is greatly reduced and the mixed integer linear programming model is transformed into a linear optimization model by introducing additional linear (continuous) variables instead of integer variables, thus significantly reducing the computational complication and realizing the rapid calculation of long-term planning for large-scale power systems. We also apply the proposed model to collaborative optimization of power system planning and operation for a province in China to prove the effectiveness of the proposed model and the solution method.
Algorithmic decision-making employing profiling: will trade secrecy protection render the right to explanation toothless?

Abstract Algorithmic decision-making based on profiling may significantly affect people’s destinies. As a rule, however, explanations for such decisions are lacking. What are the chances for a “right to explanation” to be realized soon? After an exploration of the regulatory efforts that are currently pushing for such a right it is concluded that, at the moment, the GDPR stands out as the main force to be reckoned with. In cases of profiling, data subjects are granted the right to receive meaningful information about the functionality of the system in use; for fully automated profiling decisions even an explanation has to be given. However, the trade secrets and intellectual property rights (IPRs) involved must be respected as well. These conflicting rights must be balanced against each other; what will be the outcome? Looking back to 1995, when a similar kind of balancing had been decreed in Europe concerning the right of access (DPD), Wachter et al. (2017) find that according to judicial opinion only generalities of the algorithm had to be disclosed, not specific details. This hardly augurs well for a future right of access let alone to explanation. Thereupon the landscape of IPRs for machine learning (ML) is analysed. Spurred by new USPTO guidelines that clarify when inventions are eligible to be patented, the number of patent applications in the US related to ML in general, and to “predictive analytics” in particular, has soared since 2010—and Europe has followed. I conjecture that in such a climate of intensified protection of intellectual property, companies may legitimately claim that the more their application combines several ML assets that, in addition, are useful in multiple sectors, the more value is at stake when confronted with a call for explanation by data subjects. Consequently, the right to explanation may be severely crippled.
Ensemble Feature Selection With Block-Regularized <i>m</i> × 2 Cross-Validation

Ensemble feature selection (EFS) has attracted significant interest in the literature due to its great potential in reducing the discovery rate of noise features and stabilizing the feature selection results. In view of the superior performance of block-regularized m × 2 cross-validation on generalization performance and algorithm comparison, a novel EFS technology based on block-regularized m × 2 cross-validation is proposed in this study. Contrary to the traditional ensemble learning with a binomial distribution, the distribution of feature selection frequency in the proposed technique is approximated by a beta distribution more accurately. Furthermore, theoretical analysis of the proposed technique shows that it yields a higher selection probability for important features, lower selected risk for noise features, more true positives, and fewer false positives. Finally, the above conclusions are verified by the simulated and real data experiments.
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

Reinforcement Learning (RL) has achieved significant success in application domains such as robotics, games and health care. However, training RL agents is very time consuming. Current implementations exhibit poor performance due to challenges such as irregular memory accesses and thread-level synchronization overheads on CPU. In this work, we propose a framework for generating scalable reinforcement learning implementations on multi-core systems. Replay Buffer is a key component of RL algorithms which facilitates storage of samples obtained from environmental interactions and data sampling for the learning process. We define a new data structure for Prioritized Replay Buffer based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -ary sum tree that supports asynchronous parallel insertions, sampling, and priority updates. To address the challenge of irregular memory accesses, we propose a novel data layout to store the nodes of the sum tree that reduces the number of cache misses. Additionally, we propose lazy writing mechanism to reduce thread-level synchronization over-heads of the Replay Buffer operations. Our framework employs parallel actors to concurrently collect data via environmental interactions, and parallel learners to perform stochastic gradient descent using the collected data. Our framework supports a wide range of reinforcement learning algorithms including DQN, DDPG, etc. We demonstrate the effectiveness of our framework in accelerating RL algorithms by performing experiments on CPU + GPU platform using OpenAI benchmarks. Our results show that the performance of our <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -ary sum tree based Prioritized Replay Buffer improves the baseline implementations by around 4x~100x. Our proposed synchronization optimizations improve the performance by around 2x~4.4x compared with using a global lock. By plugging our Replay Buffer implementation into existing open source reinforcement learning frameworks, we achieve 1.19x~ 1.75x speedup for various algorithms.
A News Recommendation Algorithm Based on SVD and Improved K-means

With the popularity of mobile phones and other mobile devices, users can easily read news anytime and anywhere. Through the news recommendation algorithm, users can quickly find the news that matches their reading interest and improve their reading experience. How to select the type of news that users are interested in from the mass of news, this paper proposes a news recommendation algorithm based on SVD and improved K-means. This paper firstly uses TF-IDF combined with information entropy generates the news word frequency matrix, and secondly uses the result of SVD for the news-word matrix as the initial cluster center of K-means clustering, and finally uses the collaborative filtering algorithm based on users to explore the potential interests of users. Experimental results show that the algorithm has a good recommendation effect.
Exploratory Methods for Imbalanced Data Classification in Online Recruitment Fraud Detection: A Comparative Analysis

Online recruitment platforms have been increasingly used by companies and applicants. However, there have been a growing number of online recruitment frauds (ORFs) in recent years, seriously affecting the company's reputation and accounting for personal information leakage. On the other hand, identifying ORF with classification models is challenging, as the ORF datasets are typically highly imbalanced such that the accuracy in predicting the minority class in practical recruitment systems is not satisfiable. To overcome these limitations, with empirical Employment Scam Aegean Dataset (EMSCAD), we implement a comprehensive comparative evaluation on processing approaches for imbalanced data, including data sampling techniques, cost-sensitive learning, and ensemble learning. And we design a LightGBM ORF detection model based on hybrid sampling. The results indicate that our model has a higher value in F1-measure, precision and recall of 0.93, 0.93 and 0.94, respectively, and that comparative analysis shows that the model with a combination of data sampling and ensemble learning can achieve improved performance in finding frauds in ORF datasets.
Effects of Personal Factors and Organizational Reinforcing Tools in Decreasing Employee Engagement in Unhygienic Cyber Practices

Employee engagement in unhygienic cyber practices (UCP) is a concern for organizations across the world. The purpose of this paper is to explore the effects of personal and environmental factors in decreasing workers’ engagement in UCP in a developing country: A personal-environment-behavior model was adapted for the study. Data was collected from working MBA students in Ethiopia. The key results show that the personal factor of self-regulation related to acceptable cyber practices decreases workers’ engagement in UCP, while self-efficacy did not. The environmental factor of computer monitoring (CM) decrease workers’ engagement in UCP, while the availability of security education and training awareness (SETA) programs did not. Both CM and SETA have positive effects in improving self-efficacy. Only SETA programs positively impact self-regulation. This study adds to the understanding of end-user security behavior by focusing on UCP with insights from a developing country.
Heterogeneous Modeling and Testing of Software Product Lines

Software product line (SPL) engineering is a widely accepted approach to systematically realizing software reuse in an industrial environment.Feature models, a centerpiece of most SPL engineering techniques, are appropriate to model the variability and the structure of SPLs, but not their behavior.This paper uses the idea to link feature modeling to modelbased behavior modeling and to determine the test direction (top-down or bottom-up) based on the variability binding.This heterogeneous modeling enables a holistic system testing for validating both desirable (positive) and undesirable (negative) properties of the SPL and variants.The proposed approach is validated by a non-trivial example and evaluated by comparison.
Relevance of Accurately Determining the Center of Radial Distortion in 3-D Reconstruction

It is commonly accepted that there is no significant gain in computing precisely the center of radial distortion (CoD). Indeed, practitioners take the center of the image (CI) and the principal point (PP) as proxies for camera lens distortion correction. This article assesses the relevance of accurately measuring the CoD to obtain reliable 3-D reconstructions from multiple views. We found the reconstruction error arising when displacing the CoD from its correct location with a simulation. Then, we study the cases when the PP and the CoD are considered the same or different parameters. Using real images of a chessboard-like pattern, we observe that, for a high distortion lens, the reconstruction error significantly increases when using the CI as a replacement to the CoD, especially at the corners of an image. Although the error lessens for a low distortion lens, it is still significant.