Reviewerly/exHarmony huggingface dataset:
Scenario-based analysis for discovering relations among interestingness measures

Many interestingness measures have been proposed for mining meaningful association rules among two events in the form of A→B, but their characteristics and semantic similarity relations have not been comprehensively investigated. This paper presents a scenario-based approach for characterizing sixty-one commonly used measures and revealing their relationships in three steps. The first step generates a set of 969 three-probability scenarios, S={s|s=(p(A),p(B),p(A,B))∧p(A),p(B),p(A|B)∈[0,1]∧p(A,B)⩽min(p(A),p(B))}, in consideration of all possible situations in the range of 0.0 to 1.0 with a step of 0.05, excluding infinity and not-a-number cases. In the second step, 937,992 pairs of scenarios are enumerated, and for each scenario pair s1 and s2, the values of a measure (M) of s1 and s2, i.e., M(s1) and M(s2), are compared with the result of greater-than (M(s1)>M(s2)), smaller-than (M(s1)<M(s2)), or equal-to (M(s1)=M(s2)) for characterizing the measure. The final step is based on three types of relations: (1) behavior-based, (2) correlation-based, and (3) association-based similarity relations. The behavior of measures is depicted using nine common algebraic/statistical properties and four special condition properties, i.e., zero, min–max, infinity, and not-a-number of the measures. Similarities among the measures can be examined by grouping measures based on their properties. With three correlation functions, i.e., correlation coefficient, joint entropy, and mutual information, a correlation analysis was performed to discover relations among interestingness measures in the form of dendrograms and clusters with thresholding. Finally, the details of the relations among these interestingness measures are explored with association rule mining. Besides support, confidence, and lift, we propose five types of rules, i.e. same-directionrule (S-rule), opposite-direction rule (O-rule), equal-both rule (E-rule), equal-left rule (EL-rule),and equal-right rule (ER-rule) for a five-gradient comparison of any two measures to outline their similarities and dissimilarities in five directions.
Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Purpose The purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA. Design/methodology/approach This study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed. Findings The results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City. Originality/value The results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.
Technical debt resulting from architectural degradation and code smells

Poor design choices, bad coding practices, or the need to produce software quickly can stand behind technical debt. Unfortunately, manually identifying and managing technical debt gets more difficult as the software matures. Recent research offers various techniques to automate the process of detecting and managing technical debt to address these challenges. This manuscript presents a mapping study of the many aspects of technical debt that have been discovered in this field of study. This includes looking at the various forms of technical debt, as well as detection methods, the financial implications, and mitigation strategies. The findings and outcomes of this study are applicable to a wide range of software development life-cycle decisions.
A Distributed Identity Authentication Scheme for Differential Fault Attack

Aiming at the problem of limited computing resources in the current Internet of Things terminals, this paper proposes a method to split ID for distributed identity authentication. This paper takes SM4 as 128-bit encryption algorithm and GIFT as 64-bit encryption algorithm, and analyzes the security of them in the case of differential fault attack. At the same time, we calculated that the 128-bit length ID can be split into two sub-IDs of 64-bit length for authentication, and each sub-ID can be sent 19 times, which can offset the security risks caused by the reduction of grouping length caused by the encryption algorithm. Finally, we simulate the time delay of 128-bit authentication and 64-bit distributed authentication, which further proves the superiority of distributed authentication in the environment with limited computing resources.
Tumor Detection In Breast Histopathological Images Using Faster R-CNN

Breast cancer is the most common type of cancer in women, and it is crucial to detect it at an early stage for a better prognosis. There are various ways of detecting and confirming breast cancer that are highly dependent on the imaging modalities, such as mammograms, ultrasound, magnetic resonance imaging (MRI), and histopathological image analysis by pathologists. With the help of recent progress in machine vision and artificial intelligence, computational methods for image analysis such as deep learning have been widely applied for automated decision-making in breast cancer diagnosis using feature extraction and localization. This study utilizes Faster Region-based Convolutional Neural Network (Faster R-CNN), one of the deep learning algorithms for tumor detection in annotated breast histopathological images, and analyzes the effects of two pre-processing procedures (color normalization and patching) on the images for optimization of the Faster R-CNN model. It was observed that the model’s sensitivity drastically increased from 1 % to 60 % by patching the images. The effect of image color normalization was conditional and improved results for only a few cases.
A gradient-type noise-tolerant finite-time neural network for convex optimization

As one of powerful methods for solving optimization problems, the gradient-type neural network has attracted the attention of many scholars. Up to now, there are few studies on finite-time convergence and the ability of noise tolerance of gradient-type networks. Motivated by the superior performance of various activation functions and advantages of gradient-type networks for solving constrained optimization, this paper aims to introduce a suitable activation function in a gradient-type neural network to solve convex optimization. It is shown that the state solution of the network converges to an optimal solution of the original convex optimization and the convergence time is finite under mild conditions. The capability of the network to suppress bounded noises is also analyzed theoretically. Superior to existing models for constrained optimization, the resulting network model not only has fast convergence but also can tolerate additive noises. Some numerical results are reported, including the results for smooth and nonsmooth problems, to show that the presented network is effective for convex constrained problems in the absence and presence of additive noises.
Targeted High-Utility Itemset Querying

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a targeted HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.
Exam Cheating Detection System with Multiple-Human Pose Estimation

Cheating in exams is a persistent problem that contributes to academic dishonesty. In this paper we explore a variety of related work proposed as a solution for exam cheating, then we propose an exam cheating detection system that works for both on-site and online examinations. The proposed system applies Human Pose Estimation that includes both single-user and multiple-user tracking algorithms. Based on video footage, the system can detect whether or not a student is cheating by continuously validating their head posture and hand movement conditions during the exam. The system doesn't fully imply a student is cheating, instead, we use the term ‘warning’ for the output to indicate that the student has met an abnormal condition that is similar to cheating behavior. At last, we validate the system usage in real-life examination environments through two different experiments that resulted in accuracy numbers of 92%-97% in cheating detection.
E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users' time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and GOA produce an artificial neural network (ANN) model, referred to (GOAMLP). Two corpora are applied Spam Base and UK-2011 Web spam for this approach. Finally, the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.
A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.
Detecting Hate Speech in Cross-Lingual and Multi-lingual Settings Using Language Agnostic Representations

The automatic detection of hate speech is a blooming field in the natural language processing community. In recent years there have been efforts in detecting hate speech in multiple languages, using models trained on multiple languages at the same time. Furthermore, there is special interest in the capabilities of language agnostic features to represent text in hate speech detection. This is because models can be trained in multiple languages, and then the capabilities of the model and representation can be tested on a unseen language.In this work we focused on detecting hate speech in mono-lingual, multi-lingual and cross-lingual settings. For this we used a pre-trained language model called Language Agnostic BERT Sentence Embeddings (LabSE), both for feature extraction and as an end to end classification model. We tested different models such as Support Vector Machines and Tree-based models, and representations in particular bag of words, bag of characters, and sentence embeddings extracted from Multi-lingual BERT. The dataset used was the SemEval 2019 task 5 data set, which covers hate speech against immigrants and women in English and Spanish. The results show that the usage of LabSE as feature extraction improves the performance on both languages in a mono-lingual setting, and in a cross-lingual setting. Moreover, LabSE as an end to end classification model performs better than the reported by the authors of SemEval 2019 task 5 data set for the Spanish language.
A Virtual Reality Application of the Rubber Hand Illusion Induced by Ultrasonic Mid-air Haptic Stimulation

Ultrasonic mid-air haptic technologies, which provide haptic feedback through airwaves produced using ultrasound, could be employed to investigate the sense of body ownership and immersion in virtual reality (VR) by inducing the virtual hand illusion (VHI). Ultrasonic mid-air haptic perception has solely been investigated for glabrous (hairless) skin, which has higher tactile sensitivity than hairy skin. In contrast, the VHI paradigm typically targets hairy skin without comparisons to glabrous skin. The aim of this article was to investigate illusory body ownership, the applicability of ultrasonic mid-air haptics, and perceived immersion in VR using the VHI. Fifty participants viewed a virtual hand being stroked by a feather synchronously and asynchronously with the ultrasonic stimulation applied to the glabrous skin on the palmar surface and the hairy skin on the dorsal surface of their hands. Questionnaire responses revealed that synchronous stimulation induced a stronger VHI than asynchronous stimulation. In synchronous conditions, the VHI was stronger for palmar stimulation than dorsal stimulation. The ultrasonic stimulation was also perceived as more intense on the palmar surface compared to the dorsal surface. Perceived immersion was not related to illusory body ownership per se but was enhanced by the provision of synchronous stimulation.
Multicriteria recommender system using different approaches

Multicriteria recommender systems are widely used in all sectors of industries. Examples of recommender systems include Netflix's recommendation machine, Amazon's recommender machines, etc. They help people to take informed decisions. There are many research activities taking place in the recommender system field. In this research activity we discuss the performance of a multicriteria recommender system using different kinds of machine learning and deep learning approaches. We explain in depth about the need for a recommender system, and then discuss the history of recommender systems. We also discuss the different types of filtering techniques and phases of a multicriteria recommender system, and discuss many research activities conducted by researchers around the globe. Then, we describe in depth two machine learning classification approaches and a deep learning approach, which we have implemented in a recommender system. We also show how we have obtained our data set. A comparison table which contains the accuracy and other scores of three different approaches is illustrated, from this table we can easily determine which approach works better in the terms of accuracy, giving the correct recommendations. By following these steps, we were finally able to fulfill our goal.
Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

To meet the increasing demand for group activities, single-user recommender systems need to be scaled up to provide recommendations to groups of users. This issue is solved by aggregating item preference profiles of individual group members into a single item preference profile, thereby allowing recommendations to be created for this item preference profile. In this paper, we introduce the concept of deep profiles of users, and we propose group recommendation methods based on the aggregation of group members’ deep profiles, instead of item preference profiles as in previous studies. The term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep profile</i> refers to the users’ profiles that lie deep within the recommendation algorithms. Experiments have shown that group recommendations based on deep profiles give higher efficiency in terms of F1-score and nDCG than those based on item preference profiles.
Analyzing Advanced Persistent Threats Using Game Theory: A Critical Literature Review

Advanced persistent threats present significant security challenges due to their customized, stealthy and adaptive nature. Since no generic solution exists to combat advanced persistent threats, the recommended option is to employ information security best practices. While practitioner-oriented security guidelines have been published by the International Organization for Standardization and the U.S. National Institute of Standards and Technology, they cannot be employed in rigorous quantitative analyses required for objective decision making such as choosing countermeasures that balance security, cost and usability. In contrast, game-theoretic approaches, which express the behavior of rational agents that maximize their utility, provide appropriate models for objective decision making. This chapter conducts a critical analysis of several game-theoretic approaches for analyzing advanced persistent threats. Eleven highly-cited, peer-reviewed articles from the research literature are examined in terms of their objectives, features, game models and solutions. The models provide valuable insights into advanced persistent threat behavior, support resource-optimal decision making and can be mapped to the various risk management stages. However, they have some delicate modeling and analysis limitations. The critical analysis exposes the omissions in the literature and points to future research focused on integrating practitioner perspectives in game-theoretic approaches to advance information security risk management.
Recreating Reality: Classification of Computer-Assisted Environments

In everyday life, we understand actual physical reality through our sense perception. In the recent years, technology has added many new elements to the perception of reality around us. In this regard, many terms are in use—virtual reality (VR), augmented reality (AR), mixed reality (MR), and more recently, extended reality (XR). In general, these are computer-assisted environments that attempt to recreate reality for the users using cameras, sensors, display and projection devices. However, there are some subtle different among these terms depending upon their relationship with actual physical reality. This chapter makes sense of these terms, with suitable examples in order to present a clear picture for academics and practitioners.
A Hybrid Framework for Text Recognition Used in Commodity Futures Document Verification

The document verification is significantly important for regulatory agencies to supervise the commodity futures trading market. The intelligent recognition of unstructured text data can remarkably improve the efficiency of document verification. Traditional text recognition methods have some limitations for documents with fixed templates, such as lacking the ability to make full use of known information, which results in low detection efficiency. Meanwhile, the background of documents without stable templates is relatively complicated, and the text position is not fixed, which makes it difficult for existing methods to capture all text information through location information. In response to the above problems, this paper proposes a new hybrid framework for text recognition used in commodity futures document verification. In the text detection stage, manually labeled anchor points are used to determine the position of the valid text refer to the known template. The position of the text without known template is detected based on the end-to-end scene text recognition model. In the text recognition stage, Connectionist Temporal Classification (CTC) loss in speech recognition is introduced to realize the recognition of random length text. The hybrid framework proposed in this paper is applied to the text recognition of quality inspection certificates and business contracts, achieving reliable text recognition results and saving labor costs significantly.
Tile-Based Panoramic Video Quality Assessment

As part of video transmission, video quality assessment plays an important role in improving the user’s visual quality and reducing transmission bitrate. However, the existing assessment schemes for the tile-based transmission mechanism cannot accurately predict the video quality. In this paper, we propose a tile-based panoramic video quality assessment method to optimize the transmission strategy for tile-based streaming. Considering the distortion of panoramic video projection, the inverse of gnomonic projection and bilinear interpolation are utilized to convert the planar tiles to sphere. Video Multimethod Assessment Fusion (VMAF) is applied to obtain the tile quality score. Besides, the user’s head movement and eye movement are combined to generate a tile weight map, and generate the final score of the panoramic video through weighted averaging. In addition, considering the impact of the quality fluctuation of the tiles within field of view, the tile quality loss model is established from multiple dimensions including the encoding parameter, viewport position and video content. Experimental results show that there is a high correlation between the predicted results and actual subjective scores, under two video quality assessment datasets. Furthermore, we integrate the method into the panoramic video transmission system. Compared with the tile-based adaptive rate algorithm, the proposed method degrades the video quality by only 3%, while reducing the bandwidth consumption by 16.99%.