Reviewerly/exHarmony huggingface dataset:
Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.
Evaluating the Use of QR Codes on Food Products

Today, consumers consider food packaging to be as equally important as a product brand. In addition, the increase in smartphone usage by consumers has led marketers to design new forms of packaging. Among the latest marketing trends, smart packaging with the use of QR Codes is emerging as one of the most promising technologies to enhance the information provided to consumers and influence their buying behavior. This study evaluates the use of a QR Code on bottled milk and more specifically on milk produced by one of the most well-known “boutique” Greek dairy producers. It consists of two phases. In the first one, data was gathered from 537 consumers of the product to capture and analyze their (i) buying behavior, (ii) perception of the product’s package, and (iii) knowledge about the product. In the second phase, a Quick Response (QR) Code was placed on the bottle’s label. Consumers who scanned it were linked to a web page containing information on the product. A total of 308 from the 537 initial respondents scanned the code, accessed the site, and answered the second questionnaire. Similar to the first stage, (i) the consumers’ buying behavior, (ii) their perception of the product’s package, and (iii) their knowledge about the product were examined, following their visit to the above-mentioned website through the QR Code. The objective was to evaluate the use of web applications using enriched text information. The results show that a QR Code on the packaging of food products, which directs consumers to entertaining and enriched content, results in an increased level of usage intention. Moreover, they proved that comprehension and self-confidence are higher with the adoption of the QR Code. In addition, the use of QR Codes enables businesses to provide timely and accurate information and positively influence consumers′ buying behavior.
A compression strategy to accelerate LSTM meta-learning on FPGA

Driven by edge computing, how to efficiently deploy the meta-learner LSTM in the resource constrained FPGA terminal equipment has become a big problem. This paper proposes a compression strategy based on LSTM meta-learning model, which combined the structured pruning of the weight matrix and the mixed precision quantization. The weight matrix was pruned into a sparse matrix, then the weight was quantified to reduce resource consumption. Finally, a LSTM meta-learning accelerator was designed based on the idea of hardware–software cooperation. Experiments show that compared with mainstream hardware platforms, the proposed accelerator achieves at least 50.14 times increase in energy efficiency.
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of research conducted in this area in recent decades, typically on social media platforms such as Twitter, due to the proliferation of data generated today during discussions on climate change. However, there is not much research on the performances of different sentiment analysis approaches using lexicon, machine learning and hybrid methods, particularly within this domain-specific sentiment. This study aims to find the most effective sentiment analysis approach for climate change tweets and related domains by performing a comparative evaluation of various sentiment analysis approaches. In this context, seven lexicon-based approaches were used, namely SentiWordNet, TextBlob, VADER, SentiStrength, Hu and Liu, MPQA, and WKWSCI. Meanwhile, three machine learning classifiers were used, namely Support Vector Machine, Naïve Bayes, and Logistic Regression, by using two feature extraction techniques, which were Bag-of-Words and TF–IDF. Next, the hybridization between lexicon-based and machine learning-based approaches was performed. The results indicate that the hybrid method outperformed the other two approaches, with hybrid TextBlob and Logistic Regression achieving an F1-score of 75.3%; thus, this has been chosen as the most effective approach. This study also found that lemmatization improved the accuracy of machine learning and hybrid approaches by 1.6%. Meanwhile, the TF–IDF feature extraction technique was slightly better than BoW by increasing the accuracy of the Logistic Regression classifier by 0.6%. However, TF–IDF and BoW had an identical effect on SVM and NB. Future works will include investigating the suitability of deep learning approaches toward this domain-specific sentiment on social media platforms.
Extensions of the Mandelstam–Tamm quantum speed limit to systems in mixed states

Abstract The Mandelstam–Tamm quantum speed limit (QSL) puts a bound on how fast a closed system in a pure state can evolve. In this paper, we derive several extensions of this QSL to closed systems in mixed states. We also compare the strengths of these extensions and examine their tightness. The most widely used extension of the Mandelstam–Tamm QSL originates in Uhlmann’s energy dispersion estimate. We carefully analyze the underlying geometry of this estimate, an analysis that makes apparent that the Bures metric, or equivalently the quantum Fisher information, will rarely give rise to tight extensions. This observation leads us to address whether there is a tightest general extension of the Mandelstam–Tamm QSL. Using a geometric construction similar to that developed by Uhlmann, we prove that this is indeed the case. In addition, we show that tight evolutions of mixed states are typically generated by time-varying Hamiltonians, which contrasts with the case for systems in pure states.
Aircraft Image Recognition Network Based on Hybrid Attention Mechanism

With the deepening of deep learning research, progress has been made in artificial intelligence. In the process of aircraft classification, the precision rate of aircraft picture recognition based on traditional methods is low due to various types of aircraft, large similarities between different models, and serious texture interference. In this article, the hybrid attention network model (BA-CNN) to implement an aircraft recognition algorithm is proposed to solve the above problems. Using two-channel ResNet-34 as a characteristic extraction function, the depth of network is increased to improve fine-grained characteristic extraction capability without increasing the output characteristic dimension. In the network to introduce a hybrid attention mechanism, respectively, between the residual units of two ResNet-34 channels, channel attention and spatial attention modules are added, more abundant mixed characteristics of attention are obtained, space and characteristics of the local characteristics of the channel response are focused, the characteristics of redundancy are reduced, and the fine-grained characteristics of learning ability are further enhanced. Trained and tested on FGVC-aircraft, a public fine-grained pictures dataset, the recognition precision rate of the BA-CNN networks model reached 89.2%. It can be seen from the experimental results, the recognition precision rate of the original model is improved effectively by using this method, and the recognition precision rate is higher than most of the existing mainstream aircraft recognition ways.
Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset

Cancer is the second leading cause of death globally, and breast cancer (BC) is the second most reported cancer. Although the incidence rate is reducing in developed countries, the reverse is the case in low- and middle-income countries. Early detection has been found to contain cancer growth, prevent metastasis, ease treatment, and reduce mortality by 25%. The digital mammogram is one of the most common, cheapest, and most effective BC screening techniques capable of early detection of up to 90% BC incidence. However, the mammogram is one of the most difficult medical images to analyze. In this paper, we present a method of training a deep learning model for BC diagnosis. We developed a discriminative fine-tuning method which dynamically assigns different learning rates to each layer of the deep CNN. In addition, the model was trained using mixed-precision training to ease the computational demand of training deep learning models. Lastly, we present data augmentation methods for mammograms. The discriminative fine-tuning algorithm enables rapid convergence of the model loss; hence, the models were trained to attain their best performance within 50 epochs. Comparing the results, DenseNet achieved the highest accuracy of 0.998, while AlexNet obtained 0.988.
Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.
Step by step: A hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

Recently, knowledge graph reasoning has sparked great interest in research community, which aims at inferring missing information in triples and provides critical support to various tasks (e.g., question answering and recommendation). To date, multi-hop reasoning is a dominant approach which infers the target answer by walking along the path connecting entities and relations, ensuring both accuracy and interpretability. However, in most knowledge graphs, there are multiple relations related to an identical entity, and multiple tail entities for an identical pair of head entity and relation. Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning. In order to deal with such an issue, this work presents a novel paradigm for knowledge graph reasoning by decomposing it to a two-level hierarchical decision process. We apply the hierarchical reinforcement learning framework which dismantles the task into a high-level process for relation detector and a low-level process for entity reasoning, respectively. In this way, the action space is effectively controlled where the policies can be optimized. The interactions between entity and relation decision enhance the rationality of reasoning. Moreover, we introduce a dynamic prospect mechanism for low-level policy where the information can guide us to a refined and improved action space, assisted by embedding based method. Our proposed model is evaluated on four benchmark datasets and the results validate its superiority over state-of-the-art baselines, showing the interpretability of reasoning process simultaneously.
Classifications of Sustainable Factors in Blockchain Adoption: A Literature Review and Bibliometric Analysis

Blockchain is a cutting-edge technology that is transforming and reshaping many industries. Hence, the adoption of Blockchain is becoming an increasingly significant topic. The number of publications discussing the potential of Blockchain adoption has been expanding significantly. In addition, not enough attention has been given to Blockchain adoption in the software development industry. As a result, a systematic overview to investigate the research trends in this area is needed. This study uses a Scientometric analysis and critical review to examine the evolution of Blockchain adoption research on the Web of Science Principal Collection. In addition, a systematic literature review (SLR) was conducted to identify gaps in Blockchain adoption research and the top reasons for adopting Blockchain with the intention of proposing a sustainable adoption framework. This study extends the body of knowledge by discussing the most influential countries, authors, organizations, publication themes, and most cited publications on Blockchain adoption research. Additionally, this study identifies the 30 relevant studies from the Web of Science and Scopus, including their industries, countries, methods, and respondent sample size, and the top 18 adoption factors among them. Consequently, this study proposes a suitable Blockchain adoption framework based on these top 18 factors. Finally, this study’s aim and unique contribution is to serve as an initial launching point for upcoming Blockchain adoption in software development industry research.
Log-based sparse nonnegative matrix factorization for data representation

Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness. Moreover, we propose a novel column-wisely sparse norm, named ℓ2,log-(pseudo) norm to enhance the robustness of the proposed method. The ℓ2,log-(pseudo) norm is invariant, continuous, and differentiable. For the ℓ2,log regularized shrinkage problem, we derive a closed-form solution, which can be used for other general problems. Efficient multiplicative updating rules are developed for the optimization, which theoretically guarantees the convergence of the objective value sequence. Extensive experimental results confirm the effectiveness of the proposed method, as well as the enhanced sparseness and robustness.
Cybersecurity Issues and Challenges for E-Government During COVID-19

Cyber-attacks can steal information by applying different methods and activities of cyber criminals, thereby destroying data, computer programs, and networking on one or more computers. There is an increase in new technology among users, and it provides them with more convenience. On the other hand, cyber-attacks are increasing dramatically today. The world is completely focused on creating social distancing between people. During the WHO-recommended COVID-19 pandemic, billions of people around the world are working from home, with shops and businesses closed. In their investigation of the environment, researchers have uncovered a notable type of cybercrime that has an impact on society and businesses. The pandemic has accelerated the transition of government employees and businesses to an actual workplace ecosystem. Dramatic changes in the workplace have created new and multi-layered challenges in dealing with cybersecurity risks and threats. Cyber-attacks can create problems that are detrimental to the economy, human privacy, and national security. These attacks have different perspectives on the problem and need to be understood first. In this chapter, the authors highlight several essential concerns and challenges facing e-government development as well as different departments that provide e-services. They also focus on and peer evaluate the major concerns and challenges facing e-government growth from a holistic perspective, offering methodologies and policy recommendations to address them in a complete and inclusive manner.
Faster R-Transformer: An efficient method for insulator detection in complex aerial environments

Insulator inspection is important for high-voltage transmission line safety. However, existing object-detection algorithms cannot complete the task robustly in complex aerial environments. To improve the performance of the detection task, this paper proposes an algorithm, called Faster R-Transformer, which combines the advantage of Convolutional neural networks and the self-attention mechanism for aerial insulator detection. First, the D-ResNet performs feature extraction to obtain a larger receptive field. Then, the RPN module is used for region recommendation, and the Align Pooling module is used for region Pooling. Finally, the self-attention mechanism is adopted in the proposed region to perform local region feature weighting. The Faster R-Transformer was evaluated in complex aerial environments, and the evaluation showed that the proposed method was capable of detecting insulators of various sizes under different illuminations and viewpoints. We also explored hyperparameters of Faster R-Transformer to meet the requirements of various scenarios.
News Recommendation with Candidate-aware User Modeling

News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance.
A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization

The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with various industrial applications. This paper proposes a discrete cuckoo search algorithm based on random walk and cluster analysis to solve the traveling salesman problem (TSP). Although the original cuckoo search algorithm is not suitable to solve the TSP, this algorithm is modified to solve the TSP. Lévy flights and random preference walk in the original cuckoo search algorithm are replaced with novel tools, including the local adjustment operator and discrete random walk. The former is utilized to preserve superior solutions found by the algorithm, while the latter is employed to maintain the diversity of the population. For large-scale TSP problems, the k-means algorithm is first utilized to divide cities into k categories for optimization, while random algorithms are adopted to combine them. A simple 2-opt operator is utilized as the local optimization operator to accelerate the algorithm's convergence rate. Multiple groups of standard TSPLIB datasets are chosen to compare the proposed method with state-of-the-art optimization algorithms that also use the 2-opt/k-opt optimization operator. According to the experimental results, the stability and accuracy of the proposed algorithm are superior to the state-of-the-art optimization algorithms. Regarding the solution accuracy, the algorithm improves 0.39%, 0.29%, 0.84%, 0.4%, 0.07% and 2.22% in the optimal solution over discrete cuckoo search (DCS), discrete bat algorithm (DBA), discrete sine–cosine algorithm (DSCA), random-key cuckoo search (RKCS) and discrete spider monkey optimization (DSMO) on the standard test cases, and 0.58%, 0.19%, 0.85%, 0.43%, 0.36% and 5.83% in the average optimal solution. Regarding stability, the variance of the optimal solutions of RKCS, DSMO, and novel discrete differential evolution (NDEE) for 30 independent runs is 38.5, 52.6, and 59.4 times higher than that of the algorithm in this paper. Finally, the proposed algorithm optimizes air knife migration during glass cutting. Accordingly, the path for air knife migration is reduced by 8556 mm.
OPTE: Online Per-Title Encoding for Live Video Streaming

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find an optimized bitrate ladder for each video content in Video on Demand (VoD) applications. However, in live streaming applications, a bitrate ladder with fixed bitrate-resolution pairs is used to avoid the additional latency caused to find optimum bitrate-resolution pairs for every video content. This paper introduces an online per-title encoding scheme (OPTE) for live video streaming applications. In this scheme, each target bitrate’s optimal resolution is predicted from any pre-defined set of resolutions using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for each video segment. Experimental results show that, on average, OPTE yields bitrate savings of 20.45% and 28.45% to maintain the same PSNR and VMAF, respectively, compared to a fixed bitrate ladder scheme (as adopted in current live streaming deployments) without any noticeable additional latency in streaming.