Reviewerly/exHarmony huggingface dataset:
MSDeepNet: A Novel Multi-stream Deep Neural Network for Real-World Anomaly Detection in Surveillance Videos

Anomaly detection in real time surveillance videos is a challenging task due to the scenario dependency, duration and multiple occurrences of anomalous events. Typically, weakly supervised video anomaly detection that involves video-level labels is expressed as a multiple instance learning (MIL) problem. The objective is to detect the video clips containing abnormal events, while representing each video as a collection of such clips. Existing MIL classifiers assume that the training videos only have anomalous events of short duration. However, this may not hold true for all real-life anomalies and it cannot be dismissed that there may be multiple occurrences of anomalies in the training videos. In order to detect such anomalies, a novel multi-stream deep neural network (MSDeepNet) is proposed by employing spatio-temporal deep feature extractors along with weakly supervised temporal attention module (WS-TAM). The features extracted from the individual streams are fed to train the modified MIL classifier by employing a novel temporal loss function. Finally, a fuzzy fusion method is used to aggregate the anomaly detection scores. To validate the performance of the proposed method, comprehensive results have been performed on the large-scale benchmark UCF Crime dataset. The suggested multi-stream architecture outperforms state-of-the-art video anomaly detection methods with the frame-level AUC score of 84.72% for detecting anomalous events and lowest false alarm rate of 0.9% for detecting normal events.
Graph Neural Networks for Circuit Diagram Pattern Generation

Graph neural networks (GNNs) have found majority of applications in multiple domains including physical science, molecular biology, etc. However, there is still scope of research in the application of GNNs in electrical domain. This research work tries to generate the circuit graphs that analyzes the GNNs. The end goal is to create a mechanism to iteratively predict the graph structure and thus complete a incomplete circuit diagram. The research work firstly tries to find out how well GNN can be used for predicting the missing node label or the missing node geometric features in the subset of the graph. Then, application of GNNs to anomaly detection problem is investigated. Next, GNN architecture is used to accurately predict the node label and approximate geometric features of the missing node in the circuit graph. Furthermore, a Graph Autoencoder (GAE) model is created and used for pruning the wrong edges in the circuit graph. The GNN model created for the purpose of anomaly detection problem gave around 90% accuracy. The GNN model used for missing node feature estimation gave around 89% accuracy to predict the correct label for missing node and it also performed effectively in approximating the geometric features of the missing node correctly. The link prediction model is able to classify the correct edges 92% of the times. Finally, a mechanism is provided that iteratively predicts graph structure using the anomaly detection model, node feature prediction model and link prediction model in a cycle.
A Survey on Reinforcement Learning and Deep Reinforcement Learning for Recommender Systems

Recommender systems are quickly taking over our daily lives. By suggesting and customizing the recommended items, they play a significant part in solving the information overload issue. Traditional recommender systems used for simple prediction issues include collaborative filtering, content-based filtering, and hybrid techniques. With new techniques used in recommender systems, such as reinforcement learning algorithms, more difficult problems can be resolved. These issues can be resolved using Markov decision processes and reinforcement learning techniques. It is now possible to employ reinforcement learning techniques to address issues with the huge environment and states, thanks to recent advancements in the field. The development of traditional and reinforcement learning-based methods, their appraisal, difficulties, and suggested future research will be followed by a discussion of the reinforcement learning recommender system.
Generative Adversarial Networks for Domain Translation in Unpaired Breast DCE-MRI Datasets

Generative Adversarial Networks (GAN) are more and more gaining attention in the computer vision domain thanks to their ability to generate synthetic data, in particular in the context of domain adaptation and image-to-image translation. These properties are attracting the medical community too, in order to solve some complex biomedical challenges, such as the translation between different medical imaging acquisition protocols. Indeed, as the actual acquisition protocol is strongly dependent on factors such as the operator, the aim, the centre, etc., gathering cohorts of patients all sharing the same typology of imaging is an open challenge. In this paper, we propose to face this problem by using a GAN to realise a domain translation architecture in the case of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI), considering two different acquisition protocols, in the context of automatic lesion classification. Despite this work wanting to be a first step toward artificial data generation in the medical domain, the obtained results have been analysed from both a quantitative and qualitative point of view, in order to evaluate the correctness and quality of the proposed architecture as well as its usability in a clinical scenario.
Improve Knowledge Graph Completion for Diagnosing Defects in Main Electrical Equipment

Defects in main electrical equipment can potentially cause power grid collapses, leading to catastrophic risks and immense pressure on dispatch operations. We adopt a Knowledge Graph Completion (KGC) approach to diagnose defects in main electrical equipment. The conventional approach for knowledge graph completion involves randomly initializing the feature representation of nodes and edges. In the context of main electrical equipment, we propose an innovative method based on ChatGPT to generate a corpus for fine-tuning the pre-trained model. We then employ the pre-trained model as a feature extractor for nodes and edges, enhancing the relationships between nodes and improving the initial embedding quality of both nodes and edges. Our pre-trained model fine-tuning process can be efficiently executed on a CPU with less than 10% of the parameters required for fine-tuning the entire pre-trained model. Our experimental results show that our model effectively improves the performance of KGC in diagnosing defects in main electrical equipment.
A Novel Probabilistic Approach for Detecting Concept Drift in Streaming Data

Concept drift, which indicates data-distribution changes in streaming scenarios, can significantly reduce predictive performance. Existing concept drift detection methods often struggle with the trade-off between fast detection and low false alarm rates. This paper presents a novel concept drift detection algorithm, called SPNCD*, based on probabilistic methods, particularly Sum-Product Networks, that addresses this challenge by offering high detection accuracy and low mean lag time. Based on three benchmark datasets, the proposed method is evaluated against state-of-the-art algorithms, such as DDM, ADWIN, KSWIN, and HDDM_A. Our experiments demonstrate that SPNCD* outperforms the existing algorithms in terms of true positive rate, recall, precision, and mean lag time while improving the performance of the base classifier. The SPNCD* algorithm provides a reliable solution for detecting concept drift in real-time streaming data, enabling practitioners to maintain their machine learning models’ performance in dynamic environments.
RE-EVALUATING USER MOTIVATION IN USING ARCHITECTURAL SOFTWARE IN EACH DESIGN PHASE IN INDONESIA

Abstract: Digitalized era has caused design software to develop so rapidly. As a result, architects and designers are offered various choices of design software to choose from, and sometimes designers use different software on different design phases. However, the motivation of choosing a specific software in specific phases is still unknown. Therefore, this paper aims to investigate the frequencies of certain architectural software usage in different design phases, along with the user's motivation in choosing the software. Qualitative method with grounded theory approach are used in the study. Data is then analysed with directed content analysis extracted from software external and internal quality in ISO/IEC 25010:2011. The result of this study reveals that in earlier (exploratory) design phases, media with high efficiency (such as SketchUp) are preferred, and later phases show a significant increase in the need for precision. Efficiency and output suitability are highly valued in architectural visualisation and presentation needs.Abstrak: Era digitalisasi telah menyebabkan software desain berkembang sangat pesat. Akibatnya, arsitek dan desainer disuguhkan dengan berbagai pilihan software desain dan seringkali desainer menggunakan software yang berbeda pada setiap fase desain. Namun, alasan memilih software tertentu dalam fase tertentu masih belum jelas diketahui. Oleh karena itu, makalah ini bertujuan untuk meneliti frekuensi penggunaan software arsitektur dalam fase desain yang berbeda, sekaligus alasan pengguna dalam memilih software tersebut. Penelitian ini menggunakan metode kualitatif dengan pendekatan grounded theory. Data yang didapat dianalisis dengan directed content analysis dari ISO/IEC 25010:2011. Hasil penelitian ini mengungkapkan bahwa pada fase desain awal (eksplorasi), media dengan efisiensi tinggi lebih disukai. Sedangkan fase akhir menunjukkan peningkatan yang signifikan terhadap kebutuhan kepresisian. Efisiensi dan kesesuaian outputjuga sangat dicari dalam fase visualisasi dan presentasi arsitektur.
Improve QoS for multi-body sensor analytics in smart healthcare system using machine learning algorithm

Embracing significant learning methods for human lead affirmation has shown suitable in taking out discriminants from the coarse information packs obtained from body-mounted sensors. But human headway is ideal coded in a movement of moderate models, the standard AI strategy is to finished certification obligations without taking advantage of the normal relationship between analysis information tests. This paper proposes the use of (DRNN) to manufacture a psychological model that can get critical distance conditions with factor-length input position. We present unidirectional, bidirectional, and comfortable models concerning DRNNs with LSTM and finding parameters using sporadic benchmark datasets. Exploratory results show that the proposed model is superior to a standard AI-based system. SVM and Nearest Neighbour Method (KNN). Moreover, In this Paper implementation smart system runs in tendency to other significant learning techniques like Deep Trust Organization (DBN) and CNN. Human Action Acknowledgment (HAR) assignments were consistently made using arranged highlights got by heuristic cycles.
Efficient cost evaluation of real time applications using mathematical model in computing environment

The cloud real time services are provided in an efficient manner now a days in rapid way. The Cloud computing objective is an on-request administration model for end clients. The on-request administration model comprises memory stockpiling, equipment on the board, data handling, and different IoT applications utilizing numerical programming rationales with the upgraded fluffy model. Money saving advantage analysis is the thing that everybody should do as such as to consider a strong or a proficient framework. In any case, while thinking out on cost and advantage examination, we additionally need to figure out factors that truly influence the advantages and expenses of the framework. In creating quotes for a framework, we really want to think about some of the cost components. A few components among them are equipment, staff, office, working, and supply costs. By utilizing the fluffy model, the end clients get the best ideal cloud administrations. The proposed system provides mathematical model of rough set theory. The proposed model provides an adequate cost to the end users in terms of cloud services. For giving cloud administrations in a streamlined manner the fluffy rationale is assuming a significant part. Our proposed calculation is performing better under pressure from different calculations. The proposed algorithm finds the proposed system most suitable w.r.t cost. The reproduction of our calculation is finished in simulation system of the cloud environment.
Rig Implementation Using Duik Bassel Plugin In 2d Animation "Terjebak Hoaks"

The method of making the appearance of moving pictures from a huge number of objects is known as 2D animation. To generate positions and movement of objects without having to sketch them one at a time, the procedure of supplying bones (rigging) is necessary (frame by frame). The Duik Bassel plugin w
Peer Tutoring on Fish and Vegetables-Based Diet Education to Prevent Anemia and Hypoalbuminemia in Adolescents

Early detection of nutritional problems is a vital skill for individuals as it enables timely prevention before any symptoms arise. This skill can be fostered through enhancing knowledge, practicing skills via educational activities and mentoring, and seeking support from peers who share similar challenges. This study aims to investigate the impact of peer tutoring on the development of a balanced menu and healthy snack consumption based on fish and vegetables among 11th-grade female students at a state high school in Singosari, Malang Regency. This study examined the effect of these interventions on the intake of energy, protein, and iron among the participants. Data were gathered by collecting food consumption data using the 24-hour food recall method and measurement of weight, height, Hb, and Albumin levels. The study employed a purposive sampling technique by conducting pretest, intervention, and post-test. The results showed significant improvements in the health indicators of the participants, including knowledge, intake, Hb, and Albumin levels (p=0.000; α=0.005). These findings demonstrate the potential of education and mentoring through peer tutoring in promoting healthy eating habits and preventing nutrition-related problems among female adolescents.
Design Process of Smart Home Product Service System (SH-PSS) in Indian Context

Designing for the smart home space is a challenge for design community because of the complexity of Internet of Things (IoT), which combines the tangible products and the intangible services and networks. Many IoT design methods exist to support design activities within this space. To that end, we have been interested in how smart home ideation design methods relate to each other along with user-centric design methods. Further, we invited the designers of smart homes products service system (SH-PSS) to a semi-structured interview and open discussion workshop. We assembled the smart home product designers for a collaborative landscaping exercise using these design methods. The designers tested out each of these IoT design strategies and mapped them into a design process model. In the interview, there was an understanding of the design process followed and challenges faced by these designers during the process of designing smart home. Interview questions were formulated to understand the journey of designers and various challenges faced by them. We had targeted for qualitative analysis of the information, so the order and formations of the questions need not to be definite in our case. Interview questions were face validated by three research scholars. A pilot study had been conducted to validate the questions. We could identify the ideation toolkits and user centric design methods followed while designing SH-PSS. Following this, we propose that collaborative approaches to understanding each method, that may help the design research community. We conclude that the design research community may benefit from more accurate mappings when selecting the best suitable smart home design method for a particular task in a specific context.
Analysis of Postgraduate Studies on Learning Disability in Preschool Period

The current study aims to review the postgraduate studies on learning disabilities in preschoolers in Turkey. In the study, document analysis was employed as a qualitative research method. The sampling was determined based on the criteria that the studies were focused on the pre-school period only, there was no interval between the years of the study, and the full texts of the studies were included in the Council of Higher Education Thesis Center. Nine postgraduate studies that were conducted in the preschool period were included in the sample. Results show that most of the postgraduate studies were master’s theses and most of the studies utilized quantitative methods as research designs. The studies were mostly conducted in educational sciences institutes and children made up the majority of the participants in these studies. The results showed that the first research on the subject was completed in 2005 and there was only one study in the years 2010, 2012, 2017, and 2022. There were two studies regarding the learning disability in 2018 and 2019. It can be said that learning disability studies needs more attention in preschool age.
Sistem Keamanan Pintar Menggunakan Kamera Berbasis OpenCV

Security is a condition that is free from denger, every individual has the right to feel safe in daily life or activities. As time goes by, the security level can be disrupted or dangerous things can happen beyond prediction, in other words, it can happen suddenly, therefore of course there must be a system development as a medium for anticipating this. With the aim of develop the system, it is necessary to design a system that can reduce crimes that are beyond human expectations. This system is called the Smart Security System which is a development of CCTV in general, this system uses cameras and smartphones as information media that will provide notifications to each individual to get notifications regarding real-time conditions in areas under camera surveillance via telegram. This system is assisted by Machine Learning in the form of OpenCV which is programmed on a Raspberry pi4 device which is connected directly to the camera to detect people ormovement in areas under CCTV monitoring so that it can immediately provide information to users which is sent via telegram in the form of video. It is hoped that with this system the level of security can be more effective so as to reduce the risk of theft in a room. From the results of the tests that have been carried out, it is found that to determine the effectiveness of the work of this study, an optimal distance is needed with a minimum distance range of 2 meters - 10 meters from the front of the camera and bright lighting
Optimization of compression algorithm for snapshot mosaic hyperspectral images based on CCSDS 123 standard

In this paper, the compression algorithm recommended by the Consultative Committee on Space Data System (CCSDS) 123 standard is optimized according to the spectral correlation of snapshot mosaic hyperspectral images (SSM HSI) to achieve a higher compression ratio. A novel inter-spectral processing module is added to the predictor to calculate the inter-spectral correlation between each spectral band of the hyperspectral image. The required one-dimensional spectral neighborhoods for prediction are selected based on the highest to lowest correlation order, which allow more accurate prediction of the current sample. This approach improves the efficiency of compression. The optimization was performed for Fast Lossless (FL) predictor with sample adaptive encoder recommended by CCSDS-123.0-B-1 standard, FL Extended (FLEX) predictor with sample adaptive encoder, and hybrid encoder recommended by CCSDS-123.0-B-2 standard, respectively, to compare the compression ratio before and after optimization. The results showed that the compression performance was improved by 0.015%-3.1%, 0.01%-2.29% and 0.01%-1.95% respectively. Further optimization with larger compression ratio could be realized by parameters adjustment of the algorithm proposed in this paper.
Backlit traffic scene image enhancement based on foreground and background characteristics

Due to the low foreground brightness and distorted background brightness of traffic backlight images, a technique of fusing LIME and improved histogram equalization is proposed to eliminate the backlight phenomenon in images. Firstly, the input image is segmented into two parts, foreground and background, using the maximum interclass variance method. Then LIME method is used globally for the backlit image to enhance the foreground brightness while maintaining the color distortion, and only the foreground part of the processed image is retained. Then the pixel value distribution of the background part is calculated separately, and the global histogram equalization results on the three RGB channels are mapped one by one to the corresponding limited interval, which improves the contrast of the background. Finally, the Canny operator is used to detect the black edges at the front background stitching, and three adaptive filtering templates are generated based on the black edges to perform step-by-step mean filtering on the black edges, eliminating the black edges and improving the visual quality of the image. The average metrics of the proposed method on the laboratory selfconstructed CHD_B dataset are, respectively, NIQE of 5.37, BRISQUE of 47.74, average gradient of 0.79, information entropy of 6.61, and average running time of 11.38s, which are synthetically better than the current backlight image enhancement methods and have better visual quality and visibility
Medical Image enhancement based on frame accumulation and registration technology

Medical images serve as an important carrier and auxiliary tool for modern medical workers to analyze and study health conditions. However, the fact that many medical images have unclear features, and low definition makes it impossible to provide the necessary support for subsequent detection. Therefore, image enhancement based on frame accumulation and centroid registration is put forward in this paper. First of all, in this paper, the experimental equipment for collecting simulated breast images is briefly introduced, followed by the analysis of the collected image data. It has been found that the image presents insufficient feature information due to the scattering characteristic. According to this feature, it is suggested to use the centroid of the image to match the micro-motion images between the frame sequences, and then employ the frame accumulation technique to synthesize, and linearly stretch the frame-accumulated image to improve the imaging clarity. The results show that the information entropy value of the image has been increased from 4.5780 to 6.2734 with an increase of 1.6954. To further prove the effectiveness of the algorithm, comparisons with other algorithms from both subjective and objective perspectives are conducted in this paper to show the superiority of the algorithm.
An algorithm of facial expression classification based on deep learning

Facial expression classification is widely used in various research fields. This paper putts forward to an algorithm based on deep learning to solve the problem. Firstly, it segments the facial region and transforms it to gray image, then constructs a convolutional neural network which includes convolution layers and max-pooling layers and full connection layers. The network can classify the facial expression to two categories which are happiness and sadness. Experiments are used to test and verify the network. And it has achieved good results