Reviewerly/exHarmony huggingface dataset:
Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.
Implementasi dan Evaluasi Pembelajaran Daring di SDN Perumnas 9 Tangerang

This study aims to determine the implementation and evaluation of online learning at SDN Perumnas 9 Tangerang. This type of research is a qualitative descriptive research. The research method used in collecting information is by using observation. The results showed that online learning at SDN Perumnas 9 Tangerang mostly used the WA group application as a medium in providing learning materials and the Quizizz application as a learning evaluation medium. There are several shortcomings in this online learning, such as not all students have Android phones, the background and characteristics of children are different, so it is necessary to evaluate learning as a benchmark for the success of learning. Based on the results of research conducted, online learning is not the best learning method because it is still less effective and difficult to do. Teachers cannot directly assess the development of students. The teacher also cannot see whether the students have understood or not the material that has been delivered.
Semantic layout aware generative adversarial network for text-to-image generation

Text-to-image(T2I) generation methods aim to synthesize a high-quality image which is semantically consistent with the given text descriptions. Previous (T2I) generative adversarial networks generally first create a low-resolution image with rough shapes and colors, and then refine the initial image into a high-resolution image. Most stacked architecture still remains two main problems. (1) The final images generated by these methods depend heavily on the quality of the initial image. If the initial one is not initialized correctly, the resulted image seems like a simple combination of visual features from several images scales. (2) The cross-modal fusion methods about text and image that previous works widely adopted is limited in the text-image fusion process. In the paper, we propose a novel generation model, which introduce a one-stage backbone directly generate high-quality images without multi generators and a novel semantic layout deep fusion network to sufficiently fuse text features and image features. Experiments on the challenging CUB and COCO-Stuff datasets demonstrates the ability of our model in generating images, regarding both semantic consistency with input text description and visual fidelity.
Toward Deep Transfer Learning for Realistic Activity Recognition in Videos

Today, videos have become popular on the internet and specified in social network and media platforms such as Youbue, Ticktok, and Vimeo. Video understanding has attracted much attention in the research community in recent years. Automatically recognizing human activity in wild videos is a trending research topic with a wide range of applications in advertising, smarthome, and surveillance camera systems. Deep convolutional neural networks have become a new de facto visual recognition problem. It achieved much success in image recognition problems that are leveraged from the ImageNet dataset. Many researchers have applied CNNs to the video domain, but the results in realistic video still have many challenges, and the recognition rate is not as expected. Because the realistic activity in video is extremely small, we cannot train a large deep convolutional network to achieve good performance. In this work, we answer the question “how could we transfer the visual features from the ImageNet dataset into video for activity recognition tasks?”. We propose an approach based on the pretrained models from ImageNet for activity recognition in video that is based on transforming video into an image map for temporal information and a frame-based description for spatial information. We test our proposed approach on three datasets, UCF11, UCF50 and UCF101, and it achieves an accuracy of over 95% for the backbone of ResNet and over 88% for the backbone of MobileNet. The experimental results show that our proposed method is robust and efficient in wild video datasets.
Before Ethereum. The Origin and Evolution of Blockchain Oracles

Before the advent of alternative blockchains such as Ethereum, the future of decentralization was all in the hands of Bitcoin. Together with Nakamoto itself, early developers were trying to leverage Bitcoin’s potential to decentralize traditionally centralized applications. However, because Bitcoin was a decentralized machine, the available non-trustless oracles were considered unsuitable. Therefore, strategies had to be elaborated to solve the so-called “oracle problem” in the newborn scenario. By interviewing early developers and crawling early forums and repositories, this paper aims to retrace and reconstruct the chain of events and contributions that gave birth to oracles on Bitcoin. The evolution of early protocols, along with the difficulties encountered in their development, are also outlined. Analyzing technical and social barriers to building oracles on Bitcoin, the transition to Ethereum will also be discussed.
Image inpainting with SVM-based scratch identification and fast Fourier convolution

Old photographs often suffer from a variety of imperfections, such as scratches, stains, and fading. Various techniques have been proposed for image restoration, including image inpainting, denoising, deblurring, and super-resolution. In this paper, we propose a novel approach to restoring such images by leveraging deep learning techniques. Specifically, to accurately identify scratches in images, we train a classification network on a large dataset of paired old and repaired photos. Additionally, we employ a Fourier convolution-based neural network to repair the damaged areas of the images. Our results show that our approach outperforms existing methods in terms of both objective metrics and visual quality. We believe that our work has the potential to preserve valuable memories and historical artifacts for future generations.
Fusion scale adaptive multi-person pose estimation

Currently, the mainstream top-down multi-person pose estimation algorithms based on convolutional neural networks have achieved high estimation accuracy, but in the standard convolutional neural networks, the perceptual field size of artificial neurons in each layer is designed to be fixed, lacking consideration of the multi-scale problem. To solve this problem, we propose a scale-adaptive multi-person pose estimation method based on the SKNet network introduced in the feature extraction phase of the pose estimation network, which improves the model's ability to extract important features at different scales by weighted fusion of features under different scale branches. DO-Conv is used in the pose estimation network to replace the original two-dimensional convolution, so that it can better capture the important features in the input image, thus further improving the algorithm performance. In addition, a better human detector can improve the performance of the subsequent single person pose estimation network to a certain extent, so the latest YOLOv7 target detector is used in this paper as the human detector in the multi-person pose estimation network. The proposed algorithm is based on the current mainstream multi-person pose estimation algorithms for comparison experiments and shows better performance than the original network itself in the validation experiments on the COCO dataset and the Halpe dataset.
Medical image denoising based on 2D adaptive compact variational mode decomposition

This paper proposes a medical image denoising method based on two-dimensional adaptive compact variational mode decomposition (2D-ACVMD). First, a binary support function is introduced into 2D-VMD in order to enhance its spatial compactness. The optimal decomposition mode number K and the penalty factor &alpha; can be determined by the moving average power spectrum method and the genetic algorithm, respectively. Then, the decomposed high-frequency noise component is removed, and the other modes are reconstructed to obtain the denoised image. According to the experimental results, this method can effectively remove the noise interference, retain the original edge and detail features, and the PSNR and SSIM obtained are higher than the previous methods.
A dynamic gesture recognition method fused with MGN-LSTM network

In order to solve the problem of insufficient feature expression ability in gesture recognition in emergency rescue, this paper proposes a multi-feature fusion dynamic gesture recognition network (MGN-LSTM). The model first extracts the global information and local information of each granularity through a Multiple Granularity Network (MGN). Then, a layer of Long Short-Term Memory network (LSTM) is used to extract the time information of gestures. Finally, in the model prediction stage, the candy algorithm is added to detect the edge feature information of the gesture, and the recognition result is obtained by combining the global, local and time information. The experimental results show that the multi-feature fusion method proposed in this paper has good feature expression ability and has been verified in both the self-built emergency rescue gesture dataset and the public dataset IsoGD, which improves the accuracy of gesture recognition.
Research and application of knowledge graph construction technology in the field of intelligent operation inspection of power transformer equipment

The question answering method based on knowledge atlas has become a hot research field in natural language processing, and has been gradually applied in the field of electric power. In order to solve the problems such as the difficulty of using unstructured text data and the shallow application depth of equipment knowledge, which exist in the process of power operation and maintenance personnel carrying out transformer equipment inspection, this paper puts forward the technical framework of transformer equipment knowledge map, adopts the intelligent identification and extraction method of structured text, and establishes the calculation model of equipment semantic similarity. On this basis, this paper verifies the functions of automatic audit of transformer equipment status evaluation report, auxiliary diagnosis and recognition of equipment fault knowledge. The experimental results show that the recognition method based on knowledge atlas improves the accuracy of fault defect text recognition, and provides a new idea for improving the operation and maintenance efficiency of field equipment.
Quantitative refueling action recognition algorithm

An algorithm for identifying the action of quantitative refueling that can be deployed to edge equipment is proposed for the problem that refueling' action in production scenarios is not subject to real-time supervision. The algorithm firstly uses a YOLOv5s-improved object detection network for rapid human detection, then uses a tracking algorithm that combines IOU and histogram similarity to track the detected human. The traced sequence images are used to predict the skeletal key-point sequence of the human body through a quantitative pose estimation network, and finally, the skeletal key-point sequence data is input into the fully-connected network classifier on the sixth floor for action classification, to determine whether the refueling's actions are normally completed. Experimental data show that the algorithm greatly reduces the network weight and calculation amount. The human body detection speed on the BITMAIN Sophon SE5 terminal can reach 18 ms, and the action detection accuracy can reach 95.92% on the actual scene dataset.
A virtual-real occlusion based on monocular camera

Virtual-real occlusion is a key step in augmented reality systems. The traditional virtual-real occlusion method has some problems, such as low accuracy and poor adaptation to the scene. On this basis, this paper proposes a virtual real occlusion method based on monocular camera. Firstly, AKAZE algorithm was combined with Tanimoto similarity estimation to find the optimal pair of feature points, and the relative depth of feature points on adjacent frames was calculated by using the triangulation method in SFM algorithm. Then, the real depth value was obtained by combining the translation scale. Finally, the depth value of virtual and real objects was compared to realize the virtual and real occlusion of monocular camera. The experimental results show that the virtual-real occlusion based on monocular camera is realized and the occlusion accuracy is high.
Object Detection using You Only Look Once (YOLO) Algorithm in Convolution Neural Network (CNN)

A useful tool to utilize would be a computer vision technique that enables us to recognize and find items in an image or video. To meet the demand, many algorithms, including Region based Convolution Neural Network (RCNN) Fast Region based Convolution Neural Network (FRCNN), are available. However, in this instance the You Only Look Once (YOLO) V3 technique is suggested to be used for object detection. This program can instantly find and identify different objects. The class probabilities of the discovered photos are provided by the object identification process in YOLO, which is conducted as a regression problem. Both Python and OpenCV used for implementation of the work. The observation of the work results more accuracy of object detection using YOLO algorithm.
GTReZ - Gujarati Text Recognition using Zone Segmentation

Many ancient books and parchments are available in various parts of India which are deteriorating with time. Preserving these manuscripts and writings is equal to preserving the heritage of our country. Digitization increases the efficiency and protects the records no matter what natural disaster, robbery, or loss happens. According to the statistical data collected by the National Mission for Manuscripts, around 30% of the manuscripts available in India are written in Gujarati language. The proposed project represents an effective, robust, and highly accurate system to carry out this task with convenience. It consists of a model that will convert the typed newspaper text into online machine format that can be edited easily. The traditional approaches required each and every combination of consonants and modifiers to be considered. This would result into a large number of classes [47*12 = 564]. Therefore, an approach is implemented to reduce the number of classes where the text line is divided into three parts horizontally on the basis of word alignment. The words are divided into vowel diacritic above the base letter, the base letter and the vowel diacritic below the base letter. After the post-processing is done, the final output will be the combination of the three zones. This will result in drastic reduction in the number of classes involved in training the model from 564 to 79. Along with inference, the proposed model will also be able to detect text blocks from columns of text and will be highly efficient for Gujarati text mainly focusing on vowels.
Classification of Orange Fruit Using Convolutional Neural Network, Support Vector Machine, K-Nearest Neighbor and Naive Bayes Methods Based on Color Analysis

Citrus fruit is a fruit that has good vitamins and is popular with the public. This fruit also has various types with different benefits. Each type of orange also has a variety of colors. Types of oranges can be checked manually by looking directly at the color and texture of the fruit. This manual method is very simple but also very subjective because of the different understanding of each person about the types of oranges. Therefore, this research discusses and explains how to determine the type of fruit by comparing several methods, namely using the SVM method (Support Vector Machine), the CNN method (Convolutional Neural Network), the K-NN method (K-Nearest Neighbor), and the Naïve Bayes method by taking several samples of citrus fruit images consisting of sweet oranges, lemons and limes using a mobile phone camera. The total dataset used in this study is 90 datasets consisting of 30 sweet orange images, 30 lime images and 30 lemon images. Of the 90 datasets are divided into training data and test data. From the results of the study, it was obtained that the accuracy of compatibility with a percentage of 100% using the CNN method (Convolutional Neural Network).
THE IMPORTANCE OF POSITIVE REINFORCEMENT IN TODAY’S TEACHING

This article is devoted to the importance of creating positive atmosphere in the teaching process.In the given work trustworthy relationship between teachers and students is considered to be a link that leads to the desire of acquiring knowledge.In the studies, conducted by both Ukrainian and foreign scientists, it has been proven that positive behavior, reinforced and praised, tends to be repeated and students will choose more subconsciously to do well.At the same time their self-esteem is raised and better connection can be set among the students.It is impossible to avoid negative behavior, especially nowadays when we feel so much disoriented and exhausted, but is in our power to redirect it, making sure the desired behavior is repeated.Teachers can always respond positively, by providing redirection, or refocusing their energy they will be able to direct students toward the wanted aim.Moreover, accentuating the positive, building trust makes it possible to develop meta skills in our students and sincerity is seen as the most important one among them.That is why remembering our values and goals is the base of the responsible behavior of our students in the future.Constructive praise is closely connected with motivation and involvement of students in the lesson activities.They see that teachers show genuine interest in their performance and it will definitely increase productivity.The relevance of the article lies in the need for the teachers to adjust to the modern realia so that students can become involved in the learning process.In the light of the current situation, the necessity of encouragement helps our students to adapt better, they feel appreciated and try even harder despite the obvious obstacles in their way.
A Multi-agent Based Satellite Health Management System Architecture and Implementation Scheme

Health management is one of the important contents of satellite intelligent and autonomous management. Its goal is to ensure the normal operation of satellite system on orbit, improve system reliability and safety, and reduce the dependence on ground and human resources. In this paper, we analyze the task requirements faced by satellite intelligent health management, and propose a satellite distributed health management system architecture based on Multi-Agent System. Combined with the layered model and system characteristics of the satellite on-board integrated electronics system, the scheme design of the health management system is completed, and a health management system architecture conforming to the integrated electronic standard is established, which provides a feasible scheme for future engineering design.
THE ROLE OF CONSUMER EDUCATION IN STRENGTHENING CONSUMER PROTECTION LAWS

Consumer education significantly strengthens consumer protection laws by empowering consumers to make informed decisions about their purchases and increasing their awareness of their legal rights. This research paper explores the connection between consumer education and consumer protection laws and highlights the benefits of educating consumers about their rights and responsibilities. The study examines the historical evolution of consumer protection laws and the emergence of consumer education as a key strategy to promote consumer protection. The study finds that consumer education can increase consumer knowledge of their rights and obligations, improve their ability to identify and report fraudulent or deceptive business practices and enhance their confidence in the marketplace. The research also analyses the effectiveness of different consumer education programs and initiatives in improving consumer awareness and protection. Moreover, the research examines the impact of technology and digital platforms on consumer education and protection, highlighting the need for tailored and innovative approaches to consumer education in the digital age. The study discusses the challenges and opportunities of consumer education in the context of emerging technologies, such as artificial intelligence and blockchain, and the implications for consumer protection laws. Keywords: Consumer, Consumer Protection Laws, Technology Impact, Fraudulent, Deceptive Business.
Numerical and Evolutionary Optimization 2021

This Special Issue was inspired by the 9th International Workshop on Numerical and Evolutionary Optimization (NEO 2021) held—due to the COVID-19 pandemic—as an online-only event from 8 to 10 September 2021 [...]
Social Sphere: A MERN Stack Social Media App

Abstract: In today's ever-evolving tech industry, there is a significant surge in the demand for full-stack developers. Reports suggest that skilled professionals in this field, particularly in the United States, can expect remarkable average profits, with figures reaching as high as $110,770, according to Real. A full-stack developer is an individual who possesses the technical expertise to handle both the front-end and back-end aspects of dynamic websites and internet-based applications. Web development often revolves around frameworks like the LAMP stack (Linux, Apache, MySQL, PHP/Perl) and Java (Java EE, Spring), which incorporate a combination of programming languages. Additionally, JavaScript plays a crucial role in enhancing web experiences, enabling developers to leverage the MERN stack (MongoDB, Express.js, React.js, and Node.js). This comprehensive technology stack empowers developers to effectively address user interface requirements and server-side operations
Text Summarizer Using SpaCy in NLP

Abstract: Using machine learning and natural language processing techniques to summarize the huge volume of text data and give a short summary. To develop a system which identifies contexts of a document and give the best possible summarized text with the help of SpaCy in natural language processing.