Reviewerly/exHarmony huggingface dataset:
HYBRID THE ARITHMETIC OPTIMIZATION ALGORITHM FOR CONSTRAINED OPTIMIZATION PROBLEMS

Since many real-world problems can be designed as optimization problems, heuristic algorithms are increasingly preferred by researchers. The Arithmetic Optimization Algorithm (AOA) is a newly developed heuristic algorithm. It uses four arithmetic operations in its structure. The addition and subtraction operators enhanced the AOA's local search capability, while the multiplication and division operators enhanced the AOA's global search capability. It has been hybridized with the Tree Seed Algorithm (TSA) to increase the success of AOA. Thus, hybrid AOA-TSA (HAOA) has been proposed. The seed production mechanism of TSA is placed in the random walking stage of AOA. New candidate solutions (seeds) have been produced with the arithmetic operators involved in AOA and the candidate solutions have been compared with the existing solutions. Thus, the performance of AOA has increased. In this study, the success of AOA and HAOA was tested in thirteen constrained optimization problems. The success of AOA and HAOA has been tested for their performance in six different population sizes. The Wilcoxon Signed-Rank test was applied to the obtained results and its success has been proved statistically. The results proved the superiority of HAOA. HAOA has been compared with other heuristic methods in the literature and the success of HAOA has been shown. Additionally, AOA and HAOA have also been tested on three different engineering design problems. The results are discussed and evaluated.
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis

Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension.Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia.In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information.Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared encoder.Extensive experiments on two public datasets demonstrate the effectiveness of our model.
OBJECT DETECTION SYSTEM FOR THE BLIND WITH VOICEGUIDANCE

—As object recognition technology has developed recently, various technologies have been applied to autonomousvehicles, robots, and industrial facilities. However, the benefits ofthese technologies are not reaching the visually impaired, who need it the most. This paper proposed an object detection system for the blind using deep learning technologies. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes the You Only Look Once(YOLO) algorithm and a voice announcement is synthesized using text-tospeech (TTS) to make it easier for the blind to get information about objects. Asa result, it implements an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.
Comprehensive Evaluation for Teaching Quality of College Faculty

The fair, objective, scientific, and comprehensive evaluation of teaching quality helps college teachers improve the quality of teaching, and enables colleges to realize their goals of talent training. Therefore, it is important to find a way to comprehensively evaluate the teaching quality of college teachers. Drawing on the theories of analytic hierarchy process (AHP), this paper sets up an evaluation index system (EIS) for teaching quality of college teachers, and determines the weight of each index. Taking the teachers of a college for example, the multi-level fuzzy comprehensive evaluation (FCE) was carried out to quantify the teaching quality of these teachers. The results show that AHP and FCE can quantify and qualify the teaching quality at the same time. The combined use of the two techniques is applicable to comprehensive evaluation of teaching quality, and the assessment of a single factor, offering a complete understanding of the teaching quality of college teachers. The research provides a scientific way to evaluate the teaching quality of college teachers, and a realistic tool to enhance the teaching quality in colleges.
SafeSearch: Obfuscated VPN Server using Raspberry Pi for Secure Network

Virtual Private Network (VPN) is a private network that uses public network to tunnel the connection from the users’ end to the VPN server. VPN allows users to create a secure connection to another network over the public Internet. VPNs can be used to shield users’ browsing activity and encrypts data transmitted over the network to prevent sniffing attack. Nowadays, users can either pay a premium price for a good VPN service or risk their privacy using free browser-based VPN. Thus, SafeSearch is developed to address these issues in mind. With SafeSearch, users will not need to fork out a lot of money for premium VPN subscription services or expose themselves to targeted advertising when utilising free browser-based VPN. In this study, open VPN protocol was used to create the VPN server on a microcomputer called Raspberry Pi. The software used was mostly open-source except for the VPN client. Obfuscation technique was used to hide VPN traffic by disguising it as just another normal Internet traffic against Deep Packet Inspection when passing through firewall. After the VPN server was established, tests were carried out to evaluate the functionality and reliability of the VPN server in “real-world” environment. The tests conducted were network restriction penetration assessment, network performance and user acceptance test. Penetration assessment result showed that SafeSearch is capable of bypassing web filtering and deep packet inspection. Network performance during SafeSearch connection has slight latency and bandwidth decline, although it is not overly affected. The outcome of the user acceptance test was positive as the majority of participants of the study were confident that SafeSearch can secure their connection and protect their privacy when browsing the web. To conclude, both objectives of this project were fully achieved and the scope of study was followed thoroughly.
On the Core Elements of Volleyball Players' Special Physical Training

volleyball pays attention to mutual cooperation and confrontation. The whole game not only consumes a lot of physical energy, but also depends on the professional competitive level. The competitive level is affected by our own experience and professional skills. We can see the phenomenon of chess meeting our opponents in many stadiums. A series of physical training has also been carried out for volleyball in all parts of China. Physical training can exercise athletes' physical speed to a great extent and significantly improve the training effect. This paper discusses the training of volleyball players, puts forward solutions to the problems faced in the process of physical training, better promotes the development of volleyball, and puts forward solutions to the problems faced in the process of physical training.
Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.
Towards Authorship Attribution in Arabic Short-Microblog Text

Authorship attribution is the study to identify individuals by their writing styles without knowing their actual identities.This is a challenging task in natural language processing.Most work on authorship attribution focused on English, whereas, the problem is understudied in Arabic language.However, due to the complex and distinct morphological nature of the Arabic language, techniques developed for English are not directly applicable to Arabic.This paper explored the possibility of using state-of-the-art classifiers, Support Vector Machines (SVM), K-Nearest Neighbours(KNN) and Random Forest, to predict authorship in Arabic short-microblog text.We employed three commonly used linguistic features, character-, lexical-and syntactic-based, in an incremental manner to predict the accuracy of the selected classifiers.The results elucidate that a systematic combination of linguistic features improves authorship classification.However, an inverse correlation was observed in authorship classification accuracy and the number of authors.Overall, SVM and Random Forest classifier are comparable and attained 65% accuracy, whereas KNN hardly attained 35% accuracy.In addition, lexical features offer more discriminatory power as compared to the character and syntactic features.
Modification of working conditions of teaching staff of higher education institutions: legal aspects

Introduction. Working conditions of the teaching staff of higher education institutions are not static, as they reflect the changes taking place in the outside world. The increase in the role of informatization, the acceleration of the information flow, the changing of the information field, the introduction of scientometric indicators, the recognition of the impact of stress in the workplace on the health of an employee – all this accompanies the work of the teaching staff. The analysis of these phenomena makes it possible to single out three large groups of working conditions for the teaching staff of higher education institution, which today are undergoing changes, to identify the prerequisites for their occurrence and to formulate their consequences for the entire educational process. The purpose of the study was to conduct a comprehensive analysis of the working conditions of higher education teachers, which are currently undergoing changes. Methods and methodology. The work used the methodology of a comprehensive study, including methods of document analysis, comparative analysis, secondary use of sociological, economic and medical data. The results of the study show that the digitalization of vocational education, including higher education, is carried out in different conditions than the digitalization of schools, which leads to the emergence of a new paradigm of interaction between higher education teachers and students. As a result of research, the absence of a centralized acquisition of skills in the use of digital technologies by teachers was confirmed: 45% of respondents carry out advanced training on their own, 41% – using online platforms, 31% – ask for help from colleagues in their educational institution, 30% – exchange experience with colleagues from others vocational institutions. In the absence of legislative regulation of the issue of mastering new digital competencies by the teaching staff of higher education institutions, the ways of solving this issue are proposed. The results of the research revealed that meeting the scientific productivity requirements for the teaching staff is one of the main sources of stress in the workplace (40% of respondents). It is concluded that in order to achieve quality indicators in the field of scientific activity, in order to prevent opportunistic behavior of the teaching staff, higher education institutions need to develop tools of internal motivation. The authors believe that an integrated approach to regulating the emotional burnout of teachers could also be applied in relation to minimizing the risks of professional burnout, that is, to adopt acts of legal regulation at the federal and local levels in order to prevent professional burnout.
SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions.
Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.
Finding a Balanced Degree of Automation for Summary Evaluation

Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs.Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment.In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method.Semi-automatic Lite 2 Pyramid retains the reusable human-labeled Summary Content Units (SCUs) for reference(s) but replaces the manual work of judging SCUs' presence in system summaries with a natural language inference (NLI) model.Fully automatic Lite 3 Pyramid further substitutes SCUs with automatically extracted Semantic Triplet Units (STUs) via a semantic role labeling (SRL) model.Finally, we propose in-between metrics, Lite 2.x Pyramid, where we use a simple regressor to predict how well the STUs can simulate SCUs and retain SCUs that are more difficult to simulate, which provides a smooth transition and balance between automation and manual evaluation.Comparing to 15 existing metrics, we evaluate human-metric correlations on 3 existing meta-evaluation datasets and our newlycollected PyrXSum (with 100/10 XSum examples/systems).It shows that Lite 2 Pyramid consistently has the best summary-level correlations; Lite 3 Pyramid works better than or comparable to other automatic metrics; Lite 2.x Pyramid trades off small correlation drops for larger manual effort reduction, which can reduce costs for future data collection. 1
Characterizing entanglement using quantum discord over state extensions

We propose a framework to characterize entanglement with quantum discord, both asymmetric and symmetric, over state extensions. In particular, we show that the minimal Bures distance of discord over state extensions is equivalent to the Bures distance of entanglement. This equivalence places quantum discord in a more primitive position than entanglement conceptually in the sense that entanglement can be interpreted as an irreducible part of discord over all state extensions. Based on this equivalence, we also offer an operational meaning of the Bures distance of entanglement by connecting it to quantum state discrimination. Moreover, for the relative entropy part, we prove that the entanglement measure introduced by Devi and Rajagopal [A. R. U. Devi and A. K. Rajagopal, Phys. Rev. Lett. 100, 140502 (2008)] is actually equivalent to the relative entropy of entanglement. We also provide several quantifications of entanglement based on discord measures.
Examining the Correlation between Excessive Recreational Smartphone Use and Academic Performance Outcomes

The purpose of this study is to ascertain whether excessive recreational smartphone use significantly affects students’ academic performance outcomes, study patterns, learning abilities and interactions with fellow students and faculty. Data was collected from 257 students attending a small community college in Bermuda to identify if there is a direct correlation between the grade point averages of participants and excessive recreational time spent on their smartphones. This study will also highlight creative technology-based learning platforms and productive strategies that can assist faculty with converting excessive recreational smartphone use into productive and engaging learning opportunities.
Pendeteksian Fraud: Peran Big Data dan Audit Forensik

The increasing number of fraud cases in Indonesia resulted in significant losses incurred by this country. Therefore, finding an effective method to detect fraud is the focus of many parties, especially the government. This research investigates the influence of big data on forensic audit. A part from that, the influence of big data and forensic audits as mediating variable on fraud detection is also examined. This research is quantitative research with survey method by distributing questionnaires. The respondents in this research were 221 auditors who worked at Badan Pemeriksa Keuangan (BPK) and Badan Pengawasan Keuangan dan Pembangunan (BPKP) Republic of Indonesia. The statistical test in this research was the structural equation modelling (SEM) of SmartPLS. The result showed that big data had a significant and positive effect on forensic audit. This research also proved that big data and forensic audits have a significant and positive influence on fraud detection. In addition, forensic audits have been proven to partially mediate the relationship between big data and fraud detection.
Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection

Outlier detection refers to the problem of the identification and, where appropriate, the elimination of anomalous observations from data. Such anomalous observations can emerge due to a variety of reasons, including human or mechanical errors, fraudulent behaviour as well as environmental or systematic changes, occurring either naturally or purposefully. The accurate and timely detection of deviant observations allows for the early identification of potentially extensive problems, such as fraud or system failures, before they escalate. Several unsupervised outlier detection methods have been developed; however, there is no single best algorithm or family of algorithms, as typically each relies on a measure of ‘outlierness’ such as density or distance, ignoring other measures. To add to that, in an unsupervised setting, the absence of ground-truth labels makes finding a single best algorithm an impossible feat even for a single given dataset. In this study, a new meta-learning algorithm for unsupervised outlier detection is introduced in order to mitigate this problem. The proposed algorithm, in a fully unsupervised manner, attempts not only to combine the best of many worlds from the existing techniques through ensemble voting but also mitigate any undesired shortcomings by employing an unsupervised feature selection strategy in order to identify the most informative algorithms for a given dataset. The proposed methodology was evaluated extensively through experimentation, where it was benchmarked and compared against a wide range of commonly-used techniques for outlier detection. Results obtained using a variety of widely accepted datasets demonstrated its usefulness and its state-of-the-art results as it topped the Friedman ranking test for both the area under receiver operating characteristic (ROC) curve and precision metrics when averaged over five independent trials.
BDFPN: Bi-Direction Feature Pyramid Network for Scene Text Detection

Scene text detection in the natural environment is widely used in real-world applications, ranging from autonomous driving, image search and assistance for the blind. However, a vast of the existing methods have limited ability to detect text instances in challenging scenes such as texts with low contrast or blur. To address the problem, we propose a novel Bi-Direction Feature Pyramid Network (BDFPN), which draws inspiration from the two-way visual information processing mechanism of human beings. Specifically, the bottom-up path is data-driven for fine details and the top-down path is task-driven for obtaining semantic information. In the top-down path, the Feature Alignment Module (FAM) is proposed to narrow the semantic differences that exist in features of adjacent levels. To combine features from two paths, we propose a novel fusion strategy named Attention Fusion Module (AFM). We conduct extensive experiments on ICDAR2015, Totaltext and MSRA-TD500 to demonstrate the effectiveness and robustness of BDFPN.