Reviewerly/exHarmony huggingface dataset:
Attendance Management System Using Face Recognition

Face recognition has been a hot topic of research for a long time. Face recognition is a difficult problem to solve and that is what attracts researchers toward it. Automated attendance system is a practical application of face recognition technology. As we know, we can identify any person by just watching his face, so we are trying to develop automatic attendance system based on face recognition. In this paper, we have used high-definition camera, and video captured by camera is being used by our model to detect and recognize human faces. With this approach, we are able to recognize students in the class, and their attendance is being marked in a database. In this paper, we are providing a user interface which will help the faculty to mark the student’s attendance from their computer screen.
The Impact of M-Learning on Sustainable Information Society

The rapid development of information and communication technologies (ICT) comprises the development of the information society on many different levels. One of them is connected with sustainability. The concept of a sustainable information society has been hotly debated by researchers for the last fifteen years. In this paper we focus on the education’s point of view and specifically on one of the ways to increase student motivation – m-learning. The purpose of the publication is to propose a conceptual model of a m-learning system that integrates approach to information society and the views of the stakeholders (i.e. teachers, students, university management, etc.). Such model is crucial for the best use of m-learning and its potential to reduce the digital division which subsequently leads to a more sustainable information society. To achieve this goal, we surveyed the opinion of academics at Polish and Bulgarian universities. For the processing of opinions, we have applied the tools of statistical, sentiment and emotional analysis.
Research on the Influence of Big Data Information Processing Technology on English Listening Anxiety

Listening anxiety not only affects students' listening performance, but also has a negative impact on their English learning, which is not conducive to the development of their comprehensive language ability. This article mainly analyzes the big data technology used in computer network environment for English teaching mode. It is pointed out that such a teaching mode can provide rich knowledge base and information base in real time. Teachers can make full use of these favorable conditions to adjust the difficulty of listening materials and select materials suitable for students' actual listening level to carry out teaching activities, which can alleviate students' listening anxiety, realize personalized listening teaching environment and improve students' English listening level.
Improving End-to-end Models for Set Prediction in Spoken Language Understanding

The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts. Advances in end-to-end (E2E) speech modeling have made it possible to train solely on semantic entities, which are far cheaper to collect than verbatim transcripts. We focus on this set prediction problem, where entity order is unspecified. Using two classes of E2E models, RNN transducers and attention based encoder-decoders, we show that these models work best when the training entity sequence is arranged in spoken order. To improve E2E SLU models when entity spoken order is unknown, we propose a novel data augmentation technique along with an implicit attention based alignment method to infer the spoken order. F1 scores significantly increased by more than 11% for RNN-T and about 2% for attention based encoder-decoder SLU models, outperforming previously reported results.
Research on the Application of Health Detection Technology in Intelligent Elderly Care

The intensification of population aging and change in family structure make it difficult for the traditional elderly care model to meet people's needs for senior care services. However, with the development of Internet of Things, Big Data, Cloud Computing, Health Detection and other technologies, the emergence of intelligent elderly care model brings an opportunity to improve the quality of senior care services. Through the combination of advanced science and technology with the traditional elderly care model, the intelligent elderly care model provides more healthy, comfortable, convenient and safe elderly care services. This paper mainly analyzes the application of health detection technology in the intelligent elderly care, and proposes the existing problems of home health detection products and their countermeasures, hoping to help the improvement of the intelligent elderly care service.
Application of Big Data Analysis Technology in Teaching Management

With the popularization of artificial intelligence and big data (BD) analysis technology in various fields, these fields have greatly improved work efficiency. Nowadays, the enrollment scale of schools is constantly expanding. How to obtain the largest development results with the least cost has become a new topic that needs to be solved urgently, and it also brings new development and challenges to teaching management. This topic is mainly to understand the application of big data (ABD) analysis technology in teaching management, and to make better use of the advantages of BD in teaching management. This article mainly adopts experimental research method and statistical analysis method to analyze the experimental performance test of the teaching system and the attitude of teachers and students (TS) to the performance of the teaching system. According to the survey results, the performance of the system can meet the needs of basic users, and most TS like the performance of the teaching system. Therefore, it is necessary to study the ABD analysis technology in teaching management.
Swarm of Honey Bees for Association Rule Mining Using CUDA

Association Rule mining (ARM) is well studied and famous optimization problem which finds useful rules from given transactional databases. Many algorithms already proposed in literature which shows their efficiency when dealing with different sizes of datasets. Unfortunately, their efficiency is not enough for handling large scale datasets. In this case, Bees swarm optimization algorithm for association rule mining is more efficient. These kinds of problems need more powerful processors and are time expensive. For such issues solution can be provided by graphics processing units (GPUs) and are massively multithreaded processors. In this case GPUs can be used to increase speed of the computation. Bees swarm optimization algorithm for association rule mining can be designed using GPUs in multithreaded environment which will efficient for given datasets.
On the Construction of $$4 \times 4$$ Lightweight Involutory MDS Matrices Over $$\mathbb {F}_{2^{8}}$$

In this paper, we consider the problem of building lightweight maximum distance separable (MDS) linear layers for substitution permutation networks (SPNs) ciphers. We focus on the construction of $$4 \times 4$$ involutory MDS matrices which can be implemented by the lightest (to the best of our knowledge) exclusive OR (XOR) count over $$\mathbb {F}_{2^{8}}$$ . We handle local optimization techniques as well as global optimization techniques, then we give $$4 \times 4$$ involutory MDS matrices which require the best implementation cost of 155 XOR gates, in terms of local optimization over $$\mathbb {F}_{2^{8}}$$ . Moreover, we generate a new $$4 \times 4$$ involutory MDS matrix over $$\mathbb {F}_{2^{8}}$$ that can be implemented by only 90 XOR gates by using a global optimization technique.
A Big Impact of Social Network Analysis and Machine Learning Algorithms for Predicting Facts of Covid-19 Pandemic

As we can see, covid-19 is becoming a global pandemic. At first, it was seen in India on 30 January, 2020. In such situation, there has been two important challenges before the government of India. The first is to fight the pandemic and second is to make awareness about it. Since analyses of social networks reveal technological advancement and show how World Health Organization’s post is beneficial for awareness and prevention from covid-19 as well as showing impact of algorithm and identifying ‘networkx’ technique. We are trying to show through this article, about the interaction of all public health organizations on Facebook portal and how it could be used by employing random forest machine learning algorithm on the datasets generated by these interactions. We collected data from World Health Organization’s dataset for predicting future forecast to know new cases in India by using Arima and Sarima model.
A Modal View on Resource-Bounded Propositional Logics

Abstract Classical propositional logic plays a prominent role in industrial applications, and yet the complexity of this logic is presumed to be non-feasible. Tractable systems such as depth-bounded boolean logics approximate classical logic and can be seen as a model for resource-bounded agents whose reasoning style is nonetheless classical. In this paper we first study a hierarchy of tractable logics that is not defined by depth. Then we extend it into a modal logic where modalities make explicit the assumptions discharged in propositional proofs, thereby expressing blueprints for proofs. A natural deduction system is provided that permits to reason about and manage such proof blueprints.
Development Processes of Vulnerability Detection Systems: A Systematic Review, Approaches, Challenges, and Future Directions

In cybersecurity, a vulnerability is any software or hardware failure that compromises the information’s integrity, availability, or confidentiality. Nowadays, the number of vulnerabilities is increasing exponentially. The early detection, analysis, and efficient treatment of vulnerabilities constitute significant challenges for organizations, as they are arduous and expensive processes. This study aims to thoroughly and systematically research the approaches, techniques, and tools used in implementing vulnerability detection and scanning systems. We conduct a systematic literature review based on the methodological guide of Barbara Kitchenham to carry out a synthesis of the evidence available in primary studies in the last five years. The results show that studies evaluate the efficiency and complexity of the development process for vulnerability detection through a combination of methods, techniques, tools, and metrics. Moreover, this study serves as a baseline for establishing a new development process proposal to benefit organizations planning to create custom vulnerability detection systems. Finally, current challenges are highlighted, and future research directions for addressing them are explored.
Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view, this paper presents a smart deep-based human behavior classification (SDL-HBC) model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering (AMF) based pre-processing to reduce the noise content.Also, the capsule network (CapsNet) model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally, the differential evolution (DE) with stacked autoencoder (SAE) model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.
Small-Object Detection with Super Resolution Embedding

The detection performance of ground vehicles on the airport surface in video-sensor based air traffic control surveillance has not been satisfactory compared to larger planes, especially in panoramic images of remote tower applications. Inspired by the success of GAN-based super resolution and oversampling augmentation methods, we apply a new super-resolution network to resize cropped small samples and augment each of those images by resize-and-pasting small objects many times. It allows us to trade off the quantity of the detector on large objects with that on small objects. We propose an architecture with two components: ESRGAN and Detection network. We use residual dense blocks for ESRGAN, and for the detector network, we use one state-of-the-art detector (YOLOv3). Extensive experiments on a public (car overhead with context) dataset and another self-assembled airport surface dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.
Automatic Recognition of Traditional Analog Gauge and Intelligent Management by IOT Method

The purpose of this research is using the technology of the Internet of Things to improve current medical or traditional industries in meter recording system. This research develops an IoT automatic meter reading system that is practical, precise, and highly feasible. The main device includes Arduino, two-axis slide rail, stepping motor and driver, Raspberry PI4, camera and server PC. Two-axis stepping motor is driven by the Arduino through the motor drive board, so that the camera on the slide rail accurately aligns with the center of the meter surface to improve the deviation cause by reader's viewing angle. After the Arduino positioning is completed, it notifies the Raspberry PI4, and then PI4 issues a screen capture command to the camera. We use OpenCV image recognition software to correctly identify and calculate the best value to achieve a goal that is more accurate than human reading. Finally, the original image and the recognized value are sent back and save on the PC server database (My SQL) through the TCP/IP Socket which is running on the Raspberry PI4. And then, Big Data analysis is applied, and its result is presented via Web-Service Page. This saves manpower trip or avoid the risk of entering a dangerous area and realize unmanned gauge recording. And when the system equipment is upgraded, there is no need to replace the original equipment components to achieve seamless system conversion.
Development of a Mobile Augmented Reality System for Radiotherapy Practitioner Training

Radiotherapy (RT) is the use of radiation to treat diseases in particular cancer. Recent advancements of imaging in RT and improved precision in treatment delivery require extensive training among the team. A good training approach need to be applied which aims to equip healthcare practitioners with a combination of essential understanding, clinical professional skills and technical competencies. This will provide accurate, precise and effective RT delivery. However, the expensive nature of high energy RT delivery systems and rapid introduction of new technology has until recently made their use in an academic training environment unfeasible. Augmented reality (AR) platforms, which can be run on mobile devices today constitute a possibility for academic training at lower cost. This paper describes the development of a proof of concept for an Android based device. We showed that the developed app can track small objects as well as a large object with a superimposed LINAC (Linear Accelerator) model. This proof of concept can thus be developed further for actual RT practitioner training.
HESIP: A Hybrid System for Explaining Sub-symbolic Predictions

Machine learning models such as neural networks have been successfully used in many application domains such as mission critical systems, digital health and autonomous vehicles. It is important to understand why particular predictions are made by a sub-symbolic machine learning (ML) model, because humans use these predictions in their decision making process. In this paper, we introduce HESIP, a hybrid system that combines symbolic and sub-symbolic representations to explain a prediction in natural language for an image prediction task. A sub-symbolic ML model makes a prediction for an image, and based on this predicted image, the system selects sample images from the dataset. Afterwards, a symbolic ML model learns probabilistic rules using the representation of positive and negative sample image instances where the decision about a positive or negative image instance comes from the sub-symbolic ML model. The prediction of an image can then be explained in natural language using the learned rules. Our evaluation shows that the probabilistic rules can be learned with high accuracy.
Speed Scaling on Parallel Servers With MapReduce Type Precedence Constraints

A multiple server setting is considered, where each server has tunable speed, and increasing the speed incurs an energy cost. Jobs arrive to a single queue, and each job has two types of sub-tasks, map and reduce, and a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precedence</b> constraint among them: any reduce task of a job can only be processed once all the map tasks of the job have been completed. In addition to the scheduling problem, i.e., which task to execute on which server, with tunable speed, an additional decision variable is the choice of speed for each server, so as to minimize a linear combination of the sum of the flow times of jobs/tasks and the total energy cost. The precedence constraints present new challenges for the speed scaling problem with multiple servers, namely that the number of tasks that can be executed at any time may be small but the total number of outstanding tasks might be quite large. We present simple speed scaling algorithms that are shown to have competitive ratios, that depend on the power cost function, and/or the ratio of the size of the largest task and the shortest reduce task, but not on the number of jobs, or the number of servers.
Improving the Robustness of GraphSAINT via Stability Training

Graph Neural Networks (GNNs) field has a dramatic development nowadays due to the strong representation capabilities for data in non-Euclidean space, such as graph data. However, as the scale of the dataset continues to expand, sampling is commonly introduced to obtain scalable GNNs, which leads to the instability problem during training. For example, when Graph SAmpling based INductive learning meThod (GraphSAINT) is applied for the link prediction task, it may not converge in training with a probability range from 0.1 to 0.4. This paper proposes the improved GraphSAINTs by introducing two normalization techniques and one Graph Neural Network (GNN) trick into the traditional GraphSAINT to solve the problem of the training stability and obtain more robust training results. The improved GraphSAINTs successfully eliminate the instability during training and improve the robustness of the traditional model. Besides, we can also accelerate the training procedure convergence of the traditional GraphSAINT and obtain a generally higher performance in the prediction accuracy by applying the improved GraphSAINTs. We validate our improved methods by using the experiments on the citation dataset of Open Graph Benchmark (OGB).
Industrial transfer and regional economy coordination based on multiple regression model

Abstract In the development of urban construction, regional economic differences have always been the focus of the government, society and academia. As an eastern coastal province in China, although Jiangsu has a relatively high level of economic development from an overall point of view, there is still a serious problem of regional economic disparity within it. Therefore, based on the development of local economic construction and the understanding of the phenomenon of industrial transfer, this paper uses a multiple regression model to study the regional economic coordination and thus puts forward appropriate solutions.
Self-supervised Pretraining Isolated Forest for Outlier Detection

Unsupervised anomaly detection of structured tabular data is a very important issue as it plays a key role in decision making in production practices. The mainstream unsupervised learning methods VAE (Variational Auto Encoder), GAN (Generative Adversarial Network) and other deep neural networks (DNNs) have achieved remarkable success in image, text and audio data recognition and processing, however, they are not suitable for tabular data, with over parameterisation and lack of proper inductive bias often lead to their inability to find optimal solutions for tabular decision manifolds. In this work, we propose to fuse a deep learning network architecture TabNet model with an isolated forest model to design a self-supervised learning algorithm, the Tab-iForest, to better apply deep neural networks to anomaly detection on tabular data. Firstly, we use the tabnet pre-training architecture to select the highest weighted features at each decision step using a sequential attention mechanism, which allows the learning power to be applied to the most salient features, resulting in interpretable and more efficient learning, which provides efficient and fine-grained representational information for model training for downstream unsupervised anomaly detection tasks---the isolated forest model. In this case, random down-sampling is used to construct sub-samples of small data volumes. Experimental results on credit card fraud datasets in the financial security domain show that the Tab-iForest anomaly detection algorithm achieves an accuracy and AUC of 99.895% and 0.8186 respectively, which is a significant improvement over the isolated forest anomaly detection algorithm alone, with a 45.12% and 38.947% improvement in recall and accuracy performance respectively, and a significant advantages in anomaly detection.
A virtual ethnography study of Indonesian migrant domestic workers’ empowerment in online communities

This paper discusses how online practices relate to empowering outcomes at individual and community levels. To understand the phenomenon in a natural setting, a virtual ethnography approach to Indonesian online domestic migrant workers’ communities was adopted to explore the empowerment of migrant domestic workers. The findings show that, at an individual level, migrant domestic workers need to engage in various practices, which contribute to their well-being. The well-being of migrants can be assessed in terms of their self-efficacy, self-esteem, reduced loneliness and reduced stress, all of which enhance their capabilities to take control over migration processes. But, as a community, they can conduct online practices that contribute to their ability to improve their communal external engagement. Community’s external engagement contributes to the awareness of their power and abilities to take action towards change. Online communities enable migrant domestic workers, who live in an isolated workplace, to penetrate the boundaries and engage in various practices that empower them as individuals and community as a whole.