Reviewerly/exHarmony huggingface dataset:
A Text Classification Model via Multi-Level Semantic Features

Text classification is a major task of NLP (Natural Language Processing) and has been the focus of attention for years. News classification as a branch of text classification is characterized by complex structure, large amounts of information and long text length, which in turn leads to a decrease in the accuracy of classification. To improve the classification accuracy of Chinese news texts, we present a text classification model based on multi-level semantic features. First, we add the category correlation coefficient to TF-IDF (Term Frequency-Inverse Document Frequency) and the frequency concentration coefficient to CHI (Chi-Square), and extract the keyword semantic features with the improved algorithm. Then, we extract local semantic features with TextCNN with symmetric-channel and global semantic information from a BiLSTM with attention. Finally, we fuse the three semantic features for the prediction of text categories. The results of experiments on THUCNews, LTNews and MCNews show that our presented method is highly accurate, with 98.01%, 90.95% and 94.24% accuracy, respectively. With model parameters two magnitudes smaller than Bert, the improvements relative to the baseline Bert+FC are 1.27%, 1.2%, and 2.81%, respectively.
MLPro — An integrative middleware framework for standardized machine learning tasks in Python

<h2>Abstract</h2> In recent years, many powerful software packages have been released on various aspects of machine learning (ML). However, there is still a lack of holistic development environments for the standardized creation of ML applications. The current practice is that researchers, developers, engineers and students have to piece together functionalities from several packages in their own applications. This prompted us to develop the integrative middleware framework MLPro that embeds flexible and recombinable ML models into standardized processes for training and real operations. In addition, it integrates numerous common open source frameworks and thus standardizes their use. A meticulously designed architecture combined with a powerful foundation of overarching basic functionalities ensures maximum recombinability and extensibility. In the first version of MLPro, we provide sub-frameworks for reinforcement learning (RL) and game theory (GT).
Lightweight Group Authentication for Decentralized Edge Collaboration

Due to tedious interactions and intensive computations, existing group authentication methods suffer from long latency and high overhead for simultaneously identifying multiple devices associated with one common task. Considering the growing importance of decentralized edge collaboration, this article proposes a lightweight group authentication scheme by utilizing the collaboration process information. Specifically, the edge devices in the same task group generate tokens for authentication based on knowledge from previous rounds of learning-based collaboration. We develop both the random token generation and privacy-preserving token generation strategies for achieving quick authentication and security enhancement, respectively. The generated tokens are easy for the legitimate group members to verify but extremely difficult for attackers to predict. A group authentication protocol is proposed for edge devices to verify the others' identities based on their tokens simultaneously. The proposed scheme achieves lightweight authentication without extra security information in addition to generating and distributing any key (secret), resulting in both enhanced security and decreased cost. More importantly, the proposed scheme enhances security and provides continuous protections for the decentralized edge collaboration by utilizing its natural update of authentication knowledge before the next round of collaboration begins. Our simulation study considers an example of decentralized edge collaboration and verifies the proposed scheme.
Intelligent Vector Field Histogram based collision avoidance method for AUV

Intelligent Vector Field Histogram (IVFH) is proposed to accomplish the collision avoidance of Autonomous Underwater Vehicle (AUV) in underwater three-dimensional dynamic space. The directed cuboid static obstacle environment model is introduced to improve the efficiency of collision avoidance decision-making. The candidate direction set is composed of relative heading and relative pitch. According to the obstacle distribution of local environment, the traveling fitness of each candidate direction is calculated from the perspectives of safety and rapidity, so as to realize the collision avoidance of AUV in three-dimensional dynamic environment. IVFH method can adaptively adjust the parameters that affect the collision avoidance action amplitude according to the obstacle distance and size. The collision avoidance velocity is adjusted by fuzzy logic according to the environment conditions, so as to ensure that AUV can fully perceive the environment and then complete the collision avoidance decision-making. The simulation results show that IVFH method can overcome the threshold sensitivity of traditional VFH method, escape from the trap area, and realize collision avoidance in three-dimensional dynamic complex obstacle environment. The proposed IVFH collision avoidance method has been verified by simulations, as well as lake and sea experiments, demonstrating its effectiveness and practicability.
An approach to multi-class imbalanced problem in ecology using machine learning

Ecologists collect their data manually by visiting multiple sampling sites. Since there can be multiple species in the multiple sampling sites, manually classifying them can be a daunting task. Much work in literature has focused mostly on statistical methods for classification of single species and very few studies on classification of multiple species. In addition to looking at multiple species, we noted that classification of multiple species result in multi-class imbalanced problem. This study proposes to use machine learning approach to classify multiple species in population ecology. In particular, bagging (random forests (RF) and bagging classification trees (bagCART)) and boosting (boosting classification trees (bootCART), gradient boosting machines (GBM) and adaptive boosting classification trees (AdaBoost)) classifiers were evaluated for their performances on imbalanced multiple fish species dataset. The recall and F1-score performance metrics were used to select the best classifier for the dataset. The bagging classifiers (RF and bagCART) achieved high performances on the imbalanced dataset while the boosting classifiers (bootCART, GBM and AdaBoost) achieved lower performances on the imbalanced dataset. We found that some machine learning classifiers were sensitive to imbalanced dataset hence they require data resampling to improve their performances. After resampling, the bagging classifiers (RF and bagCART) had high performances compared to boosting classifiers (bootCART, GBM and AdaBoost). The strong performances shown by bagging classifiers (RF and bagCART) suggest that they can be used for classifying multiple species in ecological studies.
Does Perceived Organizational Support Have a Mediating Role in Directing the Relationship Between E-Banking and Corporate Digital Responsibility?

The aim of this research paper is to investigate E-Banking impact on Corporate Digital Responsibility in the Jordanian banking service sector. Meanwhile, then this work also focuses to propose a mediating role of Perceived Organizational Perspective (POS) to measure the association influences between exogenous and endogenous variables. Modeling tests based on hypotheses was performed through e-surveys, totaling 88 respondents from the financial department gathered from 16 Jordanian banks. Results based on the respondents’ opinions reveal determination that E-Banking is a direct element in the dimensional model that assists in indicating a positive relationship Social Corporate Digital Responsibility (SCDR), Economic Corporate Digital Responsibility (ECDR), and Technological Corporate Digital Responsibility (TechCDR). POS has a partial positive mediating effect in the relationship between E-Banking and ECDR, and between E-Banking and TechCDR, with no mediating effect in the relationship between E-Banking and SCDR.
Metaverse Applications in Business

This study aims to identify the main applications of metaverse in business based on scientific publications. More specifically, the chapter aims to identify conceptual frameworks used in the literature. The authors conducted a systematic review based on the Web of Science and Scopus databases to achieve the objectives. This study followed a systematic literature review methodology consisting of protocol development, identified inclusion, and exclusion criteria for relevant publications; data extraction; and synthesis. The research resulted in 26 articles, which were identified and classified according to applications to the business environment, concluding that the main implications and applications of the metaverse in the business environment focus on an integrative framework that includes ethical issues, innovation management, marketing management, and knowledge management. This research has put down the foundation for establishing metaverse as a research topic in business research. With this study, the contribution lies both at the theoretical and practical levels.
Preserving Privacy of Social Media Data Using Artificial Intelligence Techniques

Social media has a huge volume and variety of big data, which has enabled machine learning (ML) procedures and artificial intelligence (AI) frameworks. A few types of research have featured the dangers of revealing large amounts of information at various stages. The main aim is to connect the exploration and normalisation edge to build the consistency and proficiency of AI framework advancements ensuring user loyalty and moving towards a serious level of reliability. User security insight and the standards of its administrative assurance decide how the tech field works. It also introduces ways to deal with AI while demonstrating distinctions that are a result of comprehension of security, increasing user data privacy concerns and guidelines identified with information protection. The effect of AI systems on the relationship between clients and organisations has been stressed and examined concerning guidelines and client impression of security. With the rise of big data and AI, this issue of privacy of data has become significantly important.
An analysis of the acquisition of a monetary function by cryptocurrency using a multi-agent simulation model

Abstract Many types of cryptocurrencies, which predominantly utilize blockchain technology, have emerged worldwide. Several issuers plan to circulate their original cryptocurrencies for monetary use. This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function. We use a multi-agent model, referred to as the Yasutomi model, which simulates the emergence of money. We analyze two scenarios that may result from the actions taken by the issuer. These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop. We find that a cryptocurrency can attain a monetary function in two cases. One such case occurs when 20% of all agents accept the cryptocurrency for payment and 50% of the agents are aware of this fact. The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.
Living with Drones, Robots, and Young Children: Informing Research through Design with Autoethnography

Supporting the study of child-drone interaction in domestic spaces is a difficult endeavour, but of value to the development of this robotic platform. This paper presents an autoethnographic study, serving as an exploratory first-person method to surface issues and opportunities in this design space. Autoethnography is increasingly popular in HCI, but to further support its application, I combine it with a Sociotechnical Systems (StS) perspective, informing the analysis and development of descriptive narratives with systems theory. This paper is based on a year-long documentation of the interaction between my family and a set of three land robots and one flying robot. I present work in the form of critical incidents and lessons learned, and a set of design opportunities for child-drone interaction to inform a research through design probe development. The combination between StS and autoethnography proved fruitful in understanding how drones may currently be brought or gifted into the home without fully considering the effects and implications of their use. Furthermore, I offer reflections on the use of autoethnography for other researchers when living and involving their family with their research material.
Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.
Does privacy breach affect firm performance? An analysis incorporating event-induced changes and event clustering

Previous studies have analyzed the impact of cybersecurity breaches on firm performance, but the impact of the privacy breach on firm performance is less explored. Needless to say, the privacy of the individual's personal information has been a rising concern for organizations over the years. Previous studies, primarily focused on cybersecurity breaches, have used a simple market model (MM) to observe the impact of these breaches on firm performance. Our study has used an advanced market model (AMM), which observes the event-induced changes in the variance of stock returns and changes in MM parameters, which are ignored by the MM. The ignorance of this may lead to a biased cumulative abnormal return (CAR) computation. We have also included the event clustering observation (events that are very close to each other) in our study and evaluated AMM with seemingly unrelated regression (SUR), i.e., AMM-SUR. We have used data of 193 privacy breaches related to the US firms listed on NYSE (USA), NASDAQ (USA) for the years 2012 (43 breaches), 2013 (37 breaches), 2014 (51 breaches), and 2018 (62 breaches). Abnormal returns for the firms are found negative due to these privacy breaches, but AMM consistently reports more negative abnormal returns than MM for all the years. The AMM-SUR, observing event clustering, consistently reports slightly lower negative abnormal returns than the AMM for all the years. We have also calculated the average financial loss due to a privacy breach for these years and for three different models, i.e., MM, AMM, and AMM-SUR.
Imbalance multi-label data learning with label specific features

A class of machine learning problem where each instance may either belong to one or more than one class simultaneously is known as Multi-label classification problem. A well-known paradigm for multi-label classification is binary relevance (BR) where multi-label classification is decomposed into binary (one-vs-rest) classification subproblems, one for each label. However, it has a number of drawbacks. First, each binary classifier can be affected by the class-imbalance issue. Second, it does not take label correlations into account. Third, it has an inconsistency problem i.e. training instances with multi-label characteristics are considered both as positive as well as negative instance simultaneously. We attempt to resolve these problems in our proposed formulation. The problem of class imbalance is addressed by applying different weights to positive and negative examples depending on the class distribution, the inconsistency issue is addressed by selecting label-specific features. We have also considered the associations that exist among the set of possible labels. We have used hinge loss function which ensures less sensitivity towards outliers and the accelerated proximal gradient method (APG) to efficiently solve the underlying optimization problem. Our proposed approach is competitive with other state-of-the-art approaches according to experimental findings on several benchmark data sets.
Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.
A Fast Circle Detection Algorithm Based on Information Compression

Circle detection is a fundamental problem in computer vision. However, conventional circle detection algorithms are usually time-consuming and sensitive to noise. In order to solve these shortcomings, we propose a fast circle detection algorithm based on information compression. First, we introduce the idea of information compression, which compresses the circular information on the image into a small number of points while removing some of the noise through sharpness estimation and orientation filtering. Then, the circle parameters stored in the information point are obtained by the average sampling algorithm with a time complexity of O(1) to obtain candidate circles. Finally, we set different constraints on the complete circle and the defective circle according to the sampling results and find the true circle from the candidate circles. The experimental results on the three datasets show that our method can compress the circular information in the image into 1% of the information points, and compared to RHT, RCD, Jiang, Wang and CACD, Precision, Recall, Time and F-measure are greatly improved.
Filter-GAN: Imbalanced Malicious Traffic Classification Based on Generative Adversarial Networks with Filter

In recent years, with the rapid development of Internet services in all walks of life, a large number of malicious acts such as network attacks, data leakage, and information theft have become major challenges for network security. Due to the difficulty of malicious traffic collection and labeling, the distribution of various samples in the existing dataset is seriously imbalanced, resulting in low accuracy of malicious traffic classification based on machine learning and deep learning, and poor model generalization ability. In this paper, a feature image representation method and Adversarial Generative Network with Filter (Filter-GAN) are proposed to solve these problems. First, the feature image representation method divides the original session traffic into three parts. The Markov matrix is extracted from each part to form a three-channel feature image. This method can transform the original session traffic format into a uniform-length matrix and fully characterize the network traffic. Then, Filter-GAN uses the feature images to generate few attack samples. Compared with general methods, Filter-GAN can generate more efficient samples. Experiments were conducted on public datasets. The results show that the feature image representation method can effectively characterize the original session traffic. When the number of samples is sufficient, the classification accuracy can reach 99%. Compared with unbalanced datasets, Filter-GAN has significantly improved the recognition accuracy of small-sample datasets, with a maximum improvement of 6%.