Reviewerly/exHarmony huggingface dataset:
LongArms: Fraud Prediction in Online Lending Services Using Sparse Knowledge Graph

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extreme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deficiency of information associations</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakness of data labels</i> . In this work, we mainly address the challenges by enhancing the utility of associations (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recovering missing associations</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mining underlying associations</i> ) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Connected Component Embedding Simplification Scheme</i> (ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recovering</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mining</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clustering</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predicting</i> , for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LongArms</i> over the RMCP framework.
Critical Review of Extended Reality Applications in Aviation

In recent years, the growing availability of extended reality (XR) technology through mobile, tablet, headset, and wearable devices has led to increased research and development on XR applications in aviation. XR has the potential to enrich the task and training environment of pilots, air traffic controllers, maintenance technicians, and flight crews by augmenting elements of the physical world, enabling remote task support, and providing immersive, controllable environments for safety-critical training. This technology can also improve human factors experiments as it allows for the emulation of environments and systems in various areas of aviation. As this technology develops and new trends emerge, understanding the current state of research can illuminate best practices, limitations, and key considerations to support the application of XR. The purpose of this paper is to provide a critical review of XR applications within the field of aviation through two means: a scientometric analysis and qualitative review of recent scientific studies. Common trends, themes, and applications are identified in the existing literature providing a snapshot of the research at this time. Implications of these findings for future research directions and applications within aviation are discussed.
Quantitative Analysis of Chinese and English Verb Valencies Based on Probabilistic Valency Pattern Theory

This study examines the similarities and differences between Chinese and English verb valencies based on the Probabilistic Valency Pattern Theory (PVPT). We adopted the Parallel Universal Dependencies treebanks of Chinese and English to ensure that the comparison is under the same semantic meanings conveyed. The results show that (1) The verb valencies of both languages share similar distributions. One important difference is that Chinese has significantly more monovalent verbs (valency equals one) than English does; (2) For conveying similar meanings, Chinese adopts more verbs than English does, while the average combinatorial ability of Chinese verbs is relatively smaller; (3) The overall probabilistic valency pattern (PVP) of verbs in Chinese and English are similar; however, those of specific high-frequent verbs in Chinese and English demonstrate their own features. The findings may shed light on depicting the characteristics of Chinese and English verbs, thus facilitating studies in both linguistics and natural language processing.
Using LUPI to Improve Complex Activity Recognition

Sensor-based activity recognition can recognize simple activities such as walking and running with high accuracy, but it is difficult to recognize complex activities such as nursing care activities and cooking activities. One solution is to use multiple sensors, which is unrealistic in real life. Recently, learning using privileged information (LUPI) has been proposed, which enables training using additional information only in the training phase. In this paper, we used LUPI for improving the accuracy of complex activity recognition. In short, training is performed with multiple sensors during the training phase, and a single sensor is used during testing. We used four published datasets for evaluating our proposed method. As a result, our proposed method improves by up to 16% in F1-Score to 67% compared to the baseline method when we used random-split cross-validation of each subject.
Arabic Sign Language Detection Using Deep Learning Based Pose Estimation

It is necessary to determine if the person is signing or not and if the type of sign is static or dynamic when processing a series of video frames captured by the camera. The benefits of sign detection are: First, whether there is a sign to be recognized. Second, in the case of a static sign, only one frame should be used for sign recognition, while in the case of a dynamic sign, a series of frames should be used for sign recognition. The presented research aims to develop a model for a detect signer in a video stream for Arabic sign language and classify the signs among static, dynamic, and non-sign. A large dataset is needed to identify signs and get better results. Seven thousand five hundred videos were captured and collected for this purpose. The proposed system extracts keypoints of human poses in video frames using the MediaPipe library. Then it uses these keypoints to compute important features (distance and angles), training an Bidirectional Gated Recurrent Unit (BiGRU) model with those features to detect Sign Language of 99% test accuracy in real-time.
Text Encryption Approach Using DNA Computation and Hyperchaotic System

“Security” has formed into a critical concern in the digital data storage and transfer via communication that is not secure channels, which can be addressed by employing trusted encryption methods. Because of its remarkable superior qualities, such as simplicity, high security, and speed faster, chaos-based encryption algorithms are becoming more prominent. Furthermore, employing DNA in cryptography opens a new area due to three factors: storage capacity, computation power, and parallelism. “This paper for text encryption, a hyperchaotic sequence and a DNA sequence are used together. The intensity levels of an input text are translated to a serial binary digit stream, and the hyperchaotic sequence scrambles this bitstream globally. To achieve a robust encryption performance, DNA operations are done between the hyperchaotic sequence and the DNA sequence. The findings of the experiment show that the encryption algorithm outperforms state-of-the-art approaches in terms of quality, security, and robustness. resistant to brute force attacks. and has a bigger key space.
Web Server Load Balancing Based on Number of Client Connections on Docker Swarm

the code that runs on the server that receives requests from clients is called the back-end. Most of the web servers still use a single back-end, therefore, weaken the ability to manage the large and growing data generated by a large number of requests. One solution to enhance the stability of the webserver is to aggregate some web servers in one cluster. These clusters must be adopted through the use of virtualization technologies such as virtual containers. A container is a standard unit of software that aggregates code and all of its dependencies so that an application runs quickly and reliably from one computing environment to another. Docker is a container-based virtual machine used for creating clusters, it provides a tool called “docker swarm” for developing distributed systems. To increase the stability of servers, this paper suggested a new load balancer strategy used in the docker swarm that aims to distribute requests to web servers by balancing the load efficiently and fairly, but not necessarily equally.
Radar Target Tracking Algorithm Based On New Particle Swarm Optimization Particle Filter

The particle filter based on particle swarm optimization algorithm has low precision and is easy to fall into local optimization, which is difficult to meet the needs of target tracking. In this paper, a new particle swarm optimization particle filter algorithm is proposed. The algorithm designs adaptive inertia weight and adaptive learning factor to balance the global search ability and local search ability. Meanwhile, the mutation based on arithmetic crossover and the replacement of natural selection mechanism are proposed, which increases the diversity of particles and improves the convergence accuracy of the algorithm. Finally, Gaussian perturbation is added to make the particles vibrate and jump out of the local optimum more easily. Experimental results show that the algorithm has high accuracy and strong robustness, which can be effectively applied to radar maneuvering target tracking.
Masked Face Recognition by Applying SSD and ResNet Model for Attendance System

The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67&#x0025;. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays.
Realtime Vehicle Counting Method Using Haar Cascade Classifier Model

The proliferation of digital camera technology has provided many digital images for object detection and counting in public places. This paper presents an experiment on the realtime vehicle counting method to be implemented in a single board computer. The proposed design&#x0027;s objective is to detect and count vehicles entering and exiting at entrances using haar cascade classifier algorithms and other automatic open and close door systems controlled by a single board computer (SBC). The proposed system was tested using raspberry pi 4 model B with a 4GB RAM platform. The empiric results using 3-minute video as input with a total of 113 passing vehicles the proposed method achieve of 91.2 % of average accuracy.
Multifrequency and Multipolarization High-Resolution Sar Datasets for Demarcation of Structural Pattern of Base Metal Bearing Carbonate Rocks of Zawar Region

Remote Sensing is widely used in the field of geology for lithology mapping, mineral identification, and demarcation of regional structural features. In the present study, multifrequency (L and S-band) and multipolarization datasets of ASAR Level &#x2013; 2 were used for the structural mapping in the Zawar region. The VH and RV polarization datasets were selected for the texture analysis, principal component analysis, and directional filtering for the extraction of the lineaments in the Zawar and Hati Magra regions. The extracted lineaments of Zawar show the major trend in E-W and WNW-ESE directions while WNW-ESE and ENE-WSW trends are observed in the northern and southern part of Hati Magra dolomite. The swing in the lineaments of Hati Magra dolomite may be attributed to the structural heterogeneity and subsequent faulting in the region.
Identification of Dynamic Hand Gestures with Force Myography

Hand gestures are efficient ways to perform natural human-computer interaction. However, the current approaches rely on complex and expensive systems to recognize static poses. This work proposes a force myography sensor to identify dynamic gestures. It employs a single-channel optical fiber transducer to assess the forearm muscles, producing time-varying waveforms with distinct patterns, further processed by the classification algorithm. Assuming a set of 26 Latin letters handwritten in the air, the system provided the correct discrimination with 99.2% accuracy. Nevertheless, one may generalize this method for detecting any dynamic hand gesture, enabling applications in user interfaces, assistive technologies, and serious games.
Predicting Service Availability In SOA-Based Systems : Service Dependency Tree (SDT)

This paper focuses on managing the loss of service availability due to scheduled maintenance activities in SOA-based systems. The main aim of this paper is to make services more resilient so that in case of unavailability of any specific critical service other dependent services should not break the user experience. In this paper, an effort has been made to find out SOA-specific scheduled outages. Also, downtime of a particular service is predicted using Service Dependency Tree (SDT). Using this availability of the system can be improved by minimizing the service downtime.
A Brief Systematics Visualization of blockchain technology in healthcare and insurance: A bibliometrics Analysis

Blockchain is a decentralized technology that enables people to transact and manage their data. Its wide application is healthcare, insurance, and supply chain management etc. &#x201C;the aim of this study is to analyze the work carried out in healthcare and insurance using blockchain technology for security and privacy of patient&#x0027;s health records&#x201D;. The evolution and author productivity of blockchain in healthcare and insurance from 2016 to 2021 (August 2021) have been considered for survey. Where 1323 articles based on healthcare and 351 documents based on insurance were retrieved documents from Scopus. Using IEEE of database 666 documents has been found for the healthcare domain and 173 documents has been extracted for the Insurance domain, PubMed database 306 documents has been extracted for the healthcare domain. There are also over 30 documents for the insurance domain. The search included the year 2021 as we are still in the third quarter of the year. The US, China and India are the major contributors. VOS viewer was used as a bibliometric analysis tool to study the various aspects of blockchain technology in health care &#x0026; insurance. &#x201C;The main point of databases is analyzed in the terms of number of documents per year, sources of publications, documents per country, authors correlation etc. parameters are statistically analyzed. Using statistical and network analysis we can conclude the there is huge scope and future to work in the blockchain domain to achieve more integrity, security and privacy&#x201D;.
A Privacy-Aware Cross-Domain Device Authentication Scheme for IIoT Based on Blockchain

With the increasing cross-factory cooperation in manufacturing, the communication between Industrial Internet of Things (IIoT) devices from discrete domains (e.g., indepen-dent factories) has become common. Authentication schemes are widely adopted to secure device-to-device communications. However, most existing schemes directly use the real identities of IIoT devices for communication and even publish them onto a publicly available blockchain. It allows attackers to easily identify devices, relate them with specific factories owned them, and further extract the factories&#x0027; private information. To address this issue, we propose a privacy-aware cross-domain authentication scheme for IIoT devices based on blockchain. In the proposed scheme, IIoT devices use different pseudonyms for each session instead of their real identities to maintain anonymity and un-linkability. Besides, the proposed certificateless signature scheme can eliminate the reliance on certificates that might expose the ownership of devices. We also proposed an identity management mechanism to realize identity revocation and malicious device tracking. The security analysis and performance evaluation results show that our scheme is feasible and efficient for cross-domain authentication.
Cyberbullying Detection Neural Networks using Sentiment Analysis

The advances in technical evolution have given rise to a serious problem of cyberbullying. Cyberbullying is the use of electronic communication to bully a person, typically by sending messages of an intimidating or threatening nature. Social networking sites in particular Twitter is becoming a platform for this type of bullying. Machine learning (ML) techniques have been widely used to detect cyberbullying through detecting some language patterns that are exploited by bullies to attack their victims. Sentiment Analysis (SA) of text can also contribute useful features in detecting offensive or abusive content. Deep learning specifically the Convolutional Neural Networks (CNN) has been used to improve the performance of feature extraction during the detection of cyberbullying process. In this research, a SA model is proposed for recognizing cyberbullying tweets in Twitter web-based media. Convolutional Neural Network, Support Vector Machines (SVM) and Na&#x00EF;ve Bayes (NB) are utilized in this model as supervised ML classifiers. The aftereffects of the analyses led on this model demonstrated empowering results when a higher n-grams language models are applied on such tweets in comparison with comparable past exploration. Moreover, the results showed that CNN classifiers have outperformed NB and SVM classifiers in several measures.
MalDeWe: New Malware Website Detector Model based on Natural Language Processing using Balanced Dataset

The increasing use of the Internet in everyday life and the huge number of users leads to increasing the number of malicious websites, which aim is to damage a computer system or compromise data without the owner&#x2019;s consent. In this paper we propose a Natural Language Processing (NLP) model, called Malware Website Detector (MalDeWe), for malware web page identification that is trained on a domain-specific corpus. The major goal of this model is to transfer English word knowledge from the pre-trained model RoBERTa into a dataset of JavaScript codes that included the website&#x2019;s text context as well as certain JavaScript expressions. With this model, we obtain a Roc Auc score of 0.95. Therefore, we can conclude that our model is doing admirably in terms of identifying a malicious web page. On the other hand, we may infer that one of the most essential aspects to consider while training a classification model is dataset balance. Whereas the model trained on the initial unbalanced dataset failed to detect harmful websites, the model trained on the balanced dataset, correctly identified 95% of dangerous websites. In that sequence, we may deduce that the metrics used for the model evaluation are critical. Therefore, we recommend using the Roc Auc score, Recall, Precision, and Confusion matrix as evaluation metrics. So, in this paper we propose a new NLP model, and we discuss the reasons why the choice of evaluation metrics are important and how the dataset balance makes changes on the model efficacy.
(n, n) XOR-based visual cryptography schemes with different meaningful shares<sup>1</sup>

The main difference between visual cryptography (VC) and traditional cryptography is that when decrypting, only human vision is used instead of complicated calculations. With the reduction of hardware cost, XOR-based visual cryptography is the latest development of VC, which can solve the problem of low quality of recovered images in OR-based VC. In this paper, we will give three XOR-based VC schemes, which have meaningful sharing and can import n different cover images. Experimental results and analysis show that our schemes are better than currently known schemes.
Fusion of Visual and Textual Features for Table Header Detection in Handwritten Text Images

This paper introduces a new procedure to improve table header detection in handwritten text images from the fusion of the posterior probabilities provided by two baseline classifiers. Each classifier considers a different modality, namely visual or textual features. Both baseline classifiers implements convolutional neural networks, particularly adopting the U-Net architecture. Four fusion methods are considered: the mean; linear discriminant analysis and random forest as meta-classifiers; and a recently developed method called alpha integration. The testing dataset consisted of 89 page images drawn from the Passau dataset. The improved performance provided by the fusion methods in the specific experiments is interesting considering the complexity of the challenging problem approached. In terms of area under the receiver operating characteristic curve the best results were obtained by alpha integration. This method incorporates least mean square parameter optimization. The improvement is relevant in the context of the targeted problem.