Reviewerly/exHarmony huggingface dataset:
PhishRepo: A Seamless Collection of Phishing Data to Fill a Research Gap in the Phishing Domain

Machine learning-based anti-phishing solutions face various challenges in collecting diverse multi-modal phishing data. As a result, most previous works have trained with little or no multi-modal data, which opens several drawbacks. Therefore, this study aims to develop a phishing data repository to meet the diverse data needs of the anti-phishing domain. As a result, a gap-filling solution named PhishRepo was proposed as an online data repository that collects, verifies, disseminates, and archives phishing data. It includes innovative design aspects such as automated submission, deduplication filtering, automated verification, crowdsourcing-based human interaction, an objection reporting window, and target attack prevention techniques. Moreover, the deduplication filter, used for the first time in phishing data collection, significantly impacted the collection process. It eliminated the duplicate data, which causes one of the most common machine learning errors known as data leakage. In addition, PhishRepo enables researchers to apply modern machine learning techniques effectively and supports them by eliminating phishing data hassle. Therefore, more thoughtful use of PhishRepo will lead to effective anti-phishing solutions in the future, minimising the social engineering crime called phishing.
A Survey of Traffic Classification Technology for Smart Home Based on Machine Learning

With the wide use of smart home devices and the privacy of their activities, more and more research have focused on the traffic classification for smart home, and traffic classification technology can infer the activity of devices from the encrypted traffic, which is important for the research on smart home privacy leakage, device transparency management, and so on. This paper first introduced the research content and evaluation indicator of smart home traffic classification, and then compared the advantages and disadvantages of different smart home traffic classification approaches in terms of accuracy and real-time performance. Finally, the future research direction is prospected from two aspects: classification algorithm and classification model.
Edge Resource Pricing and Scheduling for Blockchain: A Stackelberg Game Approach

Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks assumed that all ESPs have a uniform propagation delay, which is unrealistic. In this article, we consider a more practical scene where ESPs locate in diverse positions of the blockchain network, which causes different propagation delays when supporting the computation of the PoW puzzle. Additionally, these ESPs connect to a remote CSP for resource scheduling when the computing tasks exceed their maximum capacity. The blockchain mining process generally involves complicated competition and games among CSP, ESPs, and miners. Each service provider focuses on how to determine his resource price so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize individual personal utility, which depends on ESPs’ resource price and propagation delays. We first model such a resource pricing and scheduling problem as a three-stage multi-leader multi-follower Stackelberg game and aim at finding the Stackelberg equilibrium. Then, we analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.
A Real-Time Face Mask Detection-Based Attendance System Using MobileNetV2

COVID’19 has pushed the global trade and commerce in phase of recession. A dramatic loss has been seen in the gross domestic products (GDP) of many nations worldwide. To set the economies back on track, nations all over the globe are pulling back the full lockdowns and taking a step forward to help businesses and economy. In order to secure a triumph over it, wearing a protective mask should be the new normal. Hence, because of need and compulsion of a mask, the task of detecting it on the face has become vital for all of us. A very simple image classification model is trained by us with the help of fine machine learning libraries like Tensor Flow and Keras, accompanied by MobileNetV2 neural network architecture. Here, live video can be taken as an input by the webcam, which later on predicts whether face present in the ROI (Region of Interest) has mask or not. The system first detects the face of a person and then identifies whether mask is worn or not. Anyone’s face with mask, which is also in motion can also be detected with the help of this system. Instead of detecting only single face in a frame, our system is also capable of detecting multiple faces and masks on them, whose presence will be depicted in a tabular manner.
TWEERIFY: A Web-Based Sentiment Analysis System Using Rule and Deep Learning Techniques

Social media platforms have ceased to be a platform for interaction and entertainment alone, and they have become a platform where citizens express their opinions about issues that affect them. In recent years, it has become a powerful platform where elections are won and lost. Therefore, organizations and governments are increasingly interested in citizen's views expressed on social media platforms. This research presents a novel approach to carry out aspect-level sentiment analysis of users' tweets using rule and convolutional neural network (CNN)-based deep learning technique. The rule-based technique was used to detect and extract sentiments from preprocessed tweets, while the CNN-based deep learning technique was employed for the sentiment polarity classification. A total of 26,378 tweets collected using "security" and "Nigeria" keywords were used to test the proposed model. The proposed model outperformed existing state-of-the-arts GloVe and word2vec models with an accuracy of 82.31%, recall value of 82.21%, precision value of 82.75% and F1 score of 81.89%. The better performance of the proposed techniques could be as a result of the rule-based techniques that was introduced to capture sentiments expressed in slangs or informal languages which GloVe and word2vec have not been designed to capture.
Detection of Objects Using a Fast R-CNN-Based Approach

Object detection stands as the current interest in locating the objects. Reducing the run time of the algorithms holds a challenge in this field. Deep convolutional neural network (CNN) like SPPnet, Fast R-CNN, etc. had been effectively proposed to reduce the execution time. Encouraged by these networks, a Region Program Network (RPN) has been proposed here to portray features of images with less computational cost and time. This network is capable of predicting the bounds of an object and generates scores at each position. In the proposed network, Fast R-CNN is also integrated with RPN so that exceptional region programs can be generated and entire network can easily understand which area should be focused. The proposed network has been tested on MS COCO, PASCAL VOC 2007, 2012 datasets. Results infer that the proposed RPN along with Fast R-CNN outperforms some popular models like deep VGG-16 (Du in Journal of Physics 1004(012029):1–9 2018), ImageNet, YOLO, etc. in terms of all the accuracy parameters.
Empirical Evaluation of Motion Cue for Passive-Blind Video Tamper Detection Using Optical Flow Technique

The advances in multimedia processing technologies have led to an ever-augmenting challenge to sustain the integrity and authenticity of digitized videos. The domain of digital video forensics has been crucial in devising new methodologies to counterfeit these attacks in a passive-blind manner. The paper presents an empirical evaluation of motion cues computed using the Optical Flow (OF) algorithm that enables automatic detection of inter-frame forgeries in digital videos. The optical flow deals with the estimation of the true motion field. Though various sparse OF methods are analyzed in literature for detecting forgeries in digital videos but study of Farneback OF for forensic applications has been very limited. Farneback method calculates dense optical flow and thus the motion cue is exploited as forensic footprint to detect copy-paste region in videos. The visual inspection of the statistical detail plotted from optical flow images displays superfluous spikes in forged videos that highlights the tampered region. Supervised machine learning was therefore applied to automate the process of discriminating original and forged videos. Region of interest is selected around the forged region to reduce overall computational overhead and maintains uniformity in the feature vector length from each sample. The proposed approach can detect and localize the frames where the forgery attack is carried out. A Linear SVC model is used that gives a classification accuracy of 97%. The analysis is carried out on the benchmark REWIND dataset. The study is significant in designing advanced video forensic algorithms based on motion cues.
Real-time processing pipeline for automatic streak detection in astronomical images implemented in a multi-GPU system

Abstract Detecting and tracking objects in low Earth orbit is an increasingly important task. Telescope observations contribute to its accomplishment, and telescope imagers produce a large amount of data for this task. Thus, it is convenient to use fast computer-aided processes to analyze it. Telescopes tracking at the sidereal rate usually detect these objects in their imagers as streaks, their lengths depending on the exposure time and the slant range to the object. We have developed a processing pipeline to automatically detect streaks in astronomical images in real time (i.e., faster than the images are produced) by a graphics processing unit parallel processing system. After the detection stage, streak photometric information is obtained, and object candidate identification is provided through matches with a two-line element set database. The system has been tested on a large set of images, consisting of two hours of observation time, from the Tomo-e Gozen camera of the 105 cm Schmidt telescope at Kiso Observatory in Japan. Streaks were automatically detected in approximately 0.5% of the images. The process detected streaks down to a minimum apparent magnitude of +11.3 and matched the streaks with objects from the space-track catalog in 78% of the cases. We believe that this processing pipeline can be instrumental in detecting new objects and tracking existing ones when processing speed is important, for instance, when a short handover time is required between follow-up observation stations, or when there is a large number of images to process. This study will contribute to consolidating optical observations as an effective way to control and alleviate the space debris problem.
Research on the development and path exploration of autonomous underwater robots

With the increasing development of marine science and technology, autonomous underwater robots have become a hot spot in the field of underwater research and applications, and have been widely used in deep-sea scientific research, marine resources research, and deep-sea security. This paper focuses on analyzing the current research and development status and application prospects of autonomous underwater robots and predicts their future development trend. Aiming at the difficulties in engineering practice of path search in underwater unknown space, the traditional space path exploration method is analyzed, and a hybrid search algorithm is proposed to plan a reasonable and efficient dynamic path for target search in unknown underwater space.
Human Computer Interface Using Electrooculogram as a Substitute

Electric signals are generated by the movement of eyes that can be utilized for communication with humans or environment with the use of generated signals. Such electric signals are measured by Electrooculogram (EOG) for human computer interface (HCI). Electrooculograms have been found to have vast applications in the field of medical in ophthalmological diagnosis, eye movements, robotic devices using vision etc. and other advanced HCI applications like virtual systems. Basic features of EOGs are utilized in research areas of signal processing, recognitions like pattern recognition, iris recognition, object recognition etc. EOGs are considered as an important source of information as well as communication between real and the virtual world and is considered as a reliable tool for human computer interface. The systems using electrooculography as a communication tool between real and virtual worlds is a simple yet robust system in terms of performance. The paper discusses about the use of Electrooculograms with Human computer interface and its applications.
Diagnostics of the System of Value Orientations of Students and Cadets of Educational Organizations of the Federal Penitentiary Service of Russia

The paper reveals the results of a comparative analysis of the pronouncedness of value orientations of students and cadets of departmental universities of the Federal Penitentiary Service of Russia. It was hypothesized that there are differences in the value sphere of the respondents due to the specifics of their educational organizations. The study involved 280 students aged 19–21 years (M=20.05; σ=0.3); 69% of them were males and 31% were females. The study was carried out using the following methods: “diagnostics of the real structure of the value orientations of an individual” (by Bubnova S.S.), “questionnaire of terminal values” (Senin I.G.), “diagnostics of the professional and value orientations of an individual” (Samoylik N.A.). It is pointed out that in a departmental university they organize a targeted process of developing a system of professionally significant orientations in cadets. Such orientations are the leading factor in the pattern of cadets’ future professional activities and reflect the success in the accomplishment of their designated tasks. Students of civil universities also have a strong value orientation towards their future profession, but there are minor differences with cadets in understanding of the substance of their personal and professional development.
Artificial Intelligence Impact in Marketing

The effort to develop computer based systems that behave like humans includes natural languages, robotics, perceptual, expert systems, and “intelligent” machines! AI builds, through analogies, a system that seeks knowledge to solve any problem it faces.
Remface: Study on Mini-sized Mobilenetv2 and Retinaface

Nowadays, with the rapid development of mobile communication, big data and artificial intelligence technology, the optimization of face detection technology has become an important research direction in all fields. However, the existing face detection technology is not good enough to meet the requirements of all computing scenarios, and there are series of shortcomings, such as too many network parameters, longtime training and low detection success rate. In this paper, Widerface dataset published by the Chinese University of Hong Kong is used to study RetinaFace model and its effect. And the inverted residual structure is introduced by updating the main neural network, by loading which we work out RemFace at last. While adjusting the network structure, the neural degeneration phenomenon existing in previous studies is optimized, and the prediction effect is improved. Compared with MobileNetV1, RemFace has higher prediction accuracy and fewer model parameters, thus reducing computational overhead and enhancing real-time prediction. Finally, the paper summarizes the experimental results, and makes a simple prospect for the future research direction.
The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique

Abstract Verification and validation (V&amp;V) is a crucial step for the certification and deployment of deep neural networks (DNNs). Neuron coverage, inspired by code coverage in software testing, has been proposed as one such V&amp;V method. We provide a summary of different neuron coverage variants and their inspiration from traditional software engineering V&amp;V methods. Our first experiment shows that novelty and granularity are important considerations when assessing a coverage metric. Building on these observations, we provide an illustrative example for studying the advantages of pairwise coverage over simple neuron coverage. Finally, we show that there is an upper bound of realizable neuron coverage when test data are sampled from inside the operational design domain (in-ODD) instead of the entire input space.
Me, Myself, and the (Virtual) World: A Review of Learning Research in 4E Cognition and Immersive Virtual Reality

This paper examines how the principles of 4E cognition can be used to interpret, and potentially improve, immersive virtual reality (I-VR). A selection of relevant 4E + I-VR studies are reviewed to illustrate this relationship. Towards this end, this paper provides: 1) an overview of Immersive Virtual reality (I-VR), 2) a summary of 4E cognition (embodied, enacted, embedded, extended), and 3) a discussion relevant literature in 4E cognition applied to learning in I-VR. The paper concludes with a series of recommendations that may offer possible routes of research that could expand this important area of inquiry.
Improvement in Breast Cancer Detection Using Deep Learning

It is essential to recognize breast cancer disease as right on time as could be expected. As we all know breast malignancy is the most well-known disease in ladies around the world, with almost 1.68 million new cases analysed in 2012, addressing around 24.8% of all tumours in ladies. Also, a developing global acknowledgement of Western style and practices has been related with an expansion in overall malignancy rates. In 2020, an expected 276,500 new instances of obtrusive breast disease are analysed in ladies in the USA just as 48,525 new instances of nonintrusive (in situ) breast malignant growth. This cancer credibly investigated by various tests, together with mammogram, radiology scan, biopsy, and MRI. A mammogram is a X-ray test of breast. Here, the method used is mammography. Mammography is the path towards using low-energy X radiates (generally speaking around 30 kVp) to take a gander at the human breast for investigation and screening. The deep convolutional neural organization (DCNN) is utilized for include extraction. Further patch extraction and pixel-based extraction are used to detect the breast cancer at the early stage. In patch-level abstraction, “patch-wise” network goes about as an auto-encoder that concentrates the most notable highlights of image patches”, and in “pixel-level” abstraction, the presence of cancer is detecting by using pixel values as this is a sort of more elevated-level morphological idea that can be applied to pictures which is particularly valuable in pictures that are diagrams, maps, and so forth. Diminishing with the end goal of text acknowledgement shows up less fitting.
Improving Navigational Parameters During Robot Motion Planning Using SOMA Technique

Science and technology have progressed in recent years as robots gained their popularity in industrial applications with real-time scenarios. The effective and efficient use of robots in real-time applications become a challenging task for the researchers. Use of intelligent algorithms for trajectory generation with proper motion planning while performing required task is required criterion for robotic agents. The Self-Organizing Migrating algorithm (SOMA) is used in this study to plan optimal paths for many mobile robots in both static and dynamic environments. This technique was simulated in V-REP simulator, and the outcomes have been validated in an experimental platform with real Khepera III robots under laboratory conditions. The simulation and experimental outcomes with very less navigational parameter deviation depict the effectiveness of the implemented intelligent path planning algorithm.
The effect of external factors moderated by digital literacy on the actual use of e-learning during the Covid-19 pandemic in Islamic universities in Indonesia

The future of education is increasingly worrying due to the impact of the Covid-19 pandemic since the end of 2019. Restrictions on community activities and campus closures have forced university administrators to use e-learning. On the other hand, online learning has encountered many obstacles. Barriers to the use of e-learning are thought to stem from external problems (online facilities and infrastructure) or educators and students (internal factors), such as lack of literacy, low absorption, level of understanding, and other non-technical factors. This study aims to examine further the influence of external factors and digital literacy and the moderating effect of digital literacy with external factors (System Design, User Friendly, Devices, Internet, Electricity) on the actual use of campus e-learning at Islamic universities. This study found that: External factor variables have a significant positive effect on the actual use of e-learning. The digital literacy variable has a significant positive effect on the actual use of e-learning. The digital literacy variable weakens the influence of external factors on the actual use of e-learning at Islamic universities in Indonesia.
Review on the Students’ Speaking Skill (A Case Study of the 6th Semester Students Attending Microteaching Class)

Teaching practice is one of the main activities for the 6th semester students who are attending Microteaching class. Students have the opportunity to have teaching practice with their peers. They have to learn the basic skill in teaching and also learn how to speak in front of the class by applying the language classroom expression. To be a good teacher, they have to speak well in using the language classroom expression and when they speak well it will affect their teaching activity in class. Since speaking skill is very important in teaching, it is part of our interest to find out how well the students’ speaking skill in microteaching class. This paper will share the result of our observation and analysis on the students’ speaking skill by analyzing their competence in: 1) pronunciation 2) grammar, and 3) vocabulary. Twenty (20) students, as the sample, will be video recorded and then the records were analyzed based on those three components. It shows that intensive practice is still needed concerning with those three components as mentioned before. The result of this observation and analysis will be very important for the lecturers to consider when they teach microteaching class.