Reviewerly/exHarmony huggingface dataset:
Graph Neural Network-Based Anomaly Detection in Blockchain Network

Long-term research has been done on anomaly identification. Its uses in the banking industry have made it easier to spot questionable hacker activity. However, it is more difficult to trick financial systems due to innovations in the financial sector like blockchain and artificial intelligence. Despite these technical developments, there have nevertheless been several instances of fraud. To address the anomaly detection issue, a variety of artificial intelligence algorithms have been put forth; while some findings seem to be remarkably encouraging, no clear winner has emerged. In order to identify fraudulent transactions, this article presented Inspection-L architecture based on graph neural network (GNN) with self-supervised deep graph infomax (DGI) and graph isomorphism network (GIN), with supervised knowledge methods, such as random forest (RF). The potential of self-supervised GNN in Bitcoin unlawful transaction detection has been demonstrated by the evaluation of the proposed technique on the Elliptic dataset. Results from experiments reveal that our approach outperforms existing standard methods for detecting anomalous events.
Opinion Mining-Assisted Intelligent Program Selection Employing Fuzzy SWARA Mechanism

Change is constant and reforms in systems are cyclic in nature, every domain is subject to the sequential time interval-based cyclic process, revolutions happen as the need arises resulting in changes in design, thought process, technology, algorithms, infrastructure, and applications. Every industrial revolution may have given global education patterns a new dimension. Primary, junior, middle, and senior grades, as well as undergraduate, postgraduate, and doctoral degrees, are the several levels of education offered in India. Students generally are less aware about domains and scope each program offers at degree level, while opting a particular program, parameters like specializations, scholarships, faculty resource, minimum eligibility criteria, placement records, and technical assistance. Work employs opinion mining-assisted mechanism to address the above-mentioned issue. For the problem discussed, a self-dependent automated opinion mining strategy is required to resolve this issue. The paper presents an attempt to find a fuzzy-supported pattern that will help the students choose the best program alternative for their better improvement. The study suggests a data analytics methodology for the student’s educated, intelligent decision-making while selecting the best program. A candidate should consider a variety of weighted criteria while choosing a certain domain as they contribute to the selection process. A SWARA-assisted approach has been employed to assess the viability of the presented method and to determine weights of criteria depending on experts’ choices. Data from a college of education, Bilaspur, Greater Noida, was utilized in the research project. An empirical case study was carried out on data set of 800 students selected from diversified courses like B.Ed., BCA, M.Ed., B.Sc. (Phy, Chem. Bio), LLB, BA, and MA. The organization focusses specifically on graduate courses after 10 + 2, and the data was acquired employing a questionnaire. Database used was heterogeneous and was optimum for the analysis owing to the diversity. The degree of utility for the proposed analysis was estimated (θi), and the priority order $$({\mathcal{S}}^{*})$$ was calculated. The degree of utility for ɸ2 was 97.4%, ɸ3 was 91.2%, and ɸ1 was 83.2%. It was determined using approximated values that aspirants preferred the parameters for opting program in the following order ɸ2 > ɸ3 > ɸ1 > ɸ5 > ɸ6 > ɸ4.
Performance Index of Anai I Irrigation-Area, Padang Pariaman

Abstract The irrigation area of Anai River I which has an area of 6,764 hectares is one of the main irrigation-area in West Sumatra Province. The objective of the present study is to determine the performance index of the main irrigation network (main building, primary canal and secondary canal) of the Batang Anai I Irrigation-Area based on the Minister of Public Works and Public Housing Regulation No. 12/PRT/M/2015, and to determine the level of service of it based on the MASSCOTE method. The method based on Permen PU No.12/PRT/M/2015 examines six aspects. They are physical infrastructure, planting productivity, supporting facilities, personnel organization, documentation and farmer association (WUA). The MASSCOTE method is based on the Rapid Appracial Procedure (RAP) which has four indicators in its assessment, namely; indicators of service, farmer association, human resources and of channel-operation modernization. Based on the Minister Regulation, the Batang Anai I is in the moderate category (76.85%) in the physical performance. Meanwhile, based on the MASSCOTE method, it is in the good category (3.22 in 4-scale) in the level of service. Based on both those categories, the management of the irrigation area only needs the operation and maintenance action.
Robust and Imperceptible Color Image Watermarking Using LWT, Schur Decomposition, and SVD in YCbCr Color Space

A robust and imperceptible color image watermarking scheme is presented to conceal the data by taking the QR code logo as the watermark. The YCbCr color space is used for watermarking. The proposed method uses the fundus image as the input host image. The fundus image is the image of the rear part of the eye. Fundus photography is done with the help of specialized cameras. The suggested method for adding a watermark uses the Lifting Wavelet Transform (LWT), Schur Decomposition, and Singular Value Decomposition (SVD). Even if the recovered QR code image is deformed, it is a resilient code from which contained data can be recovered. The proposed watermarking technique is robust against attacks like JPEG Compression, Region of interest filtering, Salt and Pepper noise, Average filter, Gaussian noise, and Median filter. To check the performance of the proposed technique, Peak signal-to-noise ratio (PSNR), Mean square error (MSE), Normalized correlation coefficient (NCC), and Structural similarity index measurement is used.
Single MCMC chain parallelisation on decision trees

Abstract Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, and boosted trees miss a probabilistic version that encodes prior assumptions about tree structures and shares statistical strength between node parameters. Existing work on Bayesian DT depends on Markov Chain Monte Carlo (MCMC), which can be computationally slow, especially on high dimensional data and expensive proposals. In this study, we propose a method to parallelise a single MCMC DT chain on an average laptop or personal computer that enables us to reduce its run-time through multi-core processing while the results are statistically identical to conventional sequential implementation. We also calculate the theoretical and practical reduction in run time, which can be obtained utilising our method on multi-processor architectures. Experiments showed that we could achieve 18 times faster running time provided that the serial and the parallel implementation are statistically identical.
Implementation of Virtual Reality for Photography Exhibitions

Art exhibitions are one way to communicate a work of art while displaying creations that can be enjoyed by visitors. However, it takes a very high cost to make a good exhibition hall, and many people are too lazy to go to the exhibition if it is just to look around. In this era of technology 4.0, many new innovations have emerged in human life, one of which is virtual reality. Virtual reality is increasingly being used as an alternative to visiting a place without having to come there directly. This research aims to create an Android system using virtual reality that provides an experience to users viewing works virtually. Virtual reality prepares a representative space to display works and also does not need to incur high costs while still providing quality and complete information from exhibition speakers. This research was built using Blender and Unity. In the application design analysis, there are four main stages: (1) analysis, (2) design, (3) product development, and (4) product testing. The results of the assessment conducted by the virtual reality lecturer obtained an average result of 89%. In addition, the results of trials for students obtained an average percentage of 94% and were included in the very good category. The results of this study show that AR has indeed proven feasible to be used as a learning medium in introducing photographic works with 2D and 3D Animation, and therefore the potential to make exhibitions in VR is also used for other courses and learning. The result of this research is that an Android-based virtual reality system can simulate the exhibition of artwork.
Application of Certainty Factor Method to Web-based Expert System for Chicken Disease Diagnosis

This research aims to design an expert system to diagnose diseases in chickens. This system is designed to assist farmers in identifying diseases in chickens accurately and quickly. This expert system was built using the Certainty Factor method. Chicken disease data is collected from trusted sources, and rules are made to support the diagnostic process. This application is used to assist users in identifying chicken diseases based on the symptoms they input. This expert system is tested to see its ability to provide accurate and useful diagnosis for users. Therefore, this expert system of chicken disease diagnosis can be a useful solution in the field of animal husbandry.
Fine‐grained assured insertion and deletion scheme based on onion encryption in cloud storage

Summary With the current explosion of data volume and the rapid development of cloud computing, cloud storage is one of the most important services of cloud computing. It greatly reduces the cost of users and improves the convenience of use, but the separation of data control and ownership has led to a series of security problems. This article proposes a fine‐grained assured deletion scheme based on onion encryption (ADOE) to ensure the security of user data and achieve assured deletion of data. In the past, the solution was limited by the credibility of the third party, and due to the limitation of the administrator's computing power, there would be a window period for processing high concurrency. In this article, the third party is abandoned, and most of the work is handed over to the CSP. Through multilayer encryption of data and role‐based access control, flexible and fine‐grained access and deletion are realized. At the same time, with this scheme, Merkle hash tree (MHT) is improved, and a new data verification structure Merkle index hash tree (MIHT) is designed to quickly retrieve data blocks and provide deletion proof. Finally, through experimental comparison and theoretical demonstration, it is proved that ADOE is a safe, efficient, and usable solution.
Analisis Menurunnya Kualitas Air Sumur Akibat Pembuangan Limbah Rumah Tangga Yang Tidak Tepat

Water is a basic need that must be fulfilled in everyday human life so that the quality of clean water must be used as a need for bathing, drinking, cooking, and so on. Apart from that, we want to reveal things that can contaminate this basic human need by conducting a literature review. The purpose of this literature analysis is to find out how many residents in this research area still dispose of household waste in an inappropriate manner or inaccurately in the placement of liquid and solid waste materials which will later have an impact on the surrounding environment. The research was conducted using a literature review. Construction of wells must comply with safe and good characteristics so that the quality of well water does not threaten human health. The standard distance of the well from the source of pollution that is generally used is 10 meters.
Object Detection for Mixed Traffic under Degraded Hazy Vision Condition

Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source dataset of footage obtained from security cameras installed on traffic signals is used for this study to evaluate the performance of these algorithms. The dataset includes various traffic objects under varying haze levels, providing a diverse range of atmospheric conditions encountered in real-world scenarios. The experiments illustrate that the YOLO-based techniques are effective at detecting objects in degraded hazy conditions and give information about how well they perform in comparison. The findings help object detection models operate more accurately and consistently under adverse weather conditions.
Comparing Sensor-Based Computing and Predictive Data Analytics for Usage in Context-Aware Applications

Context-aware information systems count on sensors for establishing the user situation and adjusting their behavior accordingly. Nevertheless, there are quite some situations where sensors would be insufficiently effective: (a) Sometimes it is physically challenging to mount sensors at the optimal position, resulting in lower quality of the gathered data; (b) Mental aspects, such as user intentions, emotions, arousal, motivation, and engagement, are not easy to capture by means of sensors; (c) Data fusion concerning data from different sensors could result in wrong inferences. We propose combining / augmenting sensor-based approaches with data analytics that can make predictions about larger groups of users. When the focal user is a member of that larger group and when there are more users with a similar profile, predictions about the focal user can be made. We combine context awareness conceptualizations and relevant data analysis techniques, and we reflect on these choices in a discussion concerning the usefulness of applying data analytics in facilitating context-aware computing. The paper illustrates the concepts with an example from the transport domain.
Storing and Accessing Medical Information Using Blockchain for Improved Security

Medical information is highly sensitive and needs to be protected. Medical data should be accessible only to those who need to access it and for reasonable periods of time. Storing medical records securely is a highly complex task. Thus, given the increasing complexity of digital transactions, the need for security across different channels is evident and is a key factor affecting the readiness of healthcare organizations to meet the challenges of security. This paper utilizes Blockchain innovation to make a patient-centric electronic well-being record while keeping a solitary genuine variant of the client's information. Store the electronic health records or medical records of the patient using Blockchain technology for security. Hyper ledger fabric with smart contracts issued so that it allows only the permissible user to access the records. All medical records of the user are stored in a single account and made accessible to the patient and the doctor through the hospital network. The deployment of Blockchain is done utilizing Amazon-managed Blockchain services through AWS (Amazon web services) Blockchain enables high security and a dependable interface. The performance of the Blockchain is measured in terms of latency, and throughput. The implementation results are analyzed for the secure, reliable, and fast interactions of data across the Blockchain platform.
A Video-Based System for Vehicle Tracking Based on Optical Flow and Shi-Tomasi Corner Detection Algorithm

In the era of upgrowing technologies with automation, building a smart city is the need of the country. Goal of smart city is to develop smart technologies that can be helpful to the day-to-day routine life. At that time, surveillance plays an important part in development of smart city. In surveillance systems, vehicle surveillance is very needy and important for intelligent transportation system. To develop a system for vehicle monitoring, vehicle speed measurement is very useful parameter that can be useful to detect over speed in speed limit area or detection of possibly accident due to over speed. To reduce the cost of Speed gun or sensors, computer vision technologies are very useful to develop a system for vehicle speed measurement that takes CCTV footage as input. To calculate a speed efficiently, tracking of vehicle from video is crucial part. The paper proposes a system for tracking a vehicle based on optical flow and Shi-Tomasi corner detection. Additionally, the edge-detected frame after thresholding is applied for Shi-Tomasi corner detection that derives a novel approach to track a vehicle precisely.
Creative Hub Planning in East Jakarta with Contemporary Tropical Architecture Approach

Abstract The achievements of the world’s greatest economic performance and great prospects due to the rapid development of the Indonesian economy can be realized through the creative economy sector. Creative economy is an economic concept whose products are produced from creativity, ideas, and human knowledge as its main assets. The government also contributed by issuing PERPRES RI No. 6 of 2015, which calls for encouraging, strengthening, and promoting the creative economy in an effort to increase national economic growth. Based on these problems, it is important that there is a creative hub as a center for creative economic activities in each community activity center that is able to lift and develop economic potential in each region, including East Jakarta. As one of the cities with the largest area in DKI Jakarta, East Jakarta has various potentials that should be developed. In planning and designing this architecture, the primary data collection methods were observation and interviews, as well as secondary data collection by document/literature studies. The data obtained will then be processed by descriptive analysis and data synthesis is carried out to develop problem solving in the design of various aspects of the approach. The creative hub plan that will be made in the form of a single building on an area of 17,874 m2 in East Jakarta with a contemporary tropical architectural design approach. Contemporary tropical architecture is oriented to the tropical climate, but has freedom of expression in its design to keep up with the times. This approach aims to achieve user convenience that supports the productivity of the creative community. One application is by using sustainable materials such as wood, natural stone, brick, concrete, bamboo, and so on. Thus, the planned creative hub will be able to provide solutions to all community needs while at the same time having a good impact on the surrounding environment.
IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography

Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality.
The Bottle Caps (Crown Cork) Waste’s Treatment Innovation as a Solonese Bridal Accessories Product to Supports the Creative Industry

Abstract Bottle cap waste’s (crown/cork) pollutes the environment, a breakthrough solution is needed for recycling innovation to become a Solonese women’s bridal accessories product that has aesthetic value and adds its selling value. The objective of the study was to determine the innovation of bottle cap waste treatment as a product of the Solonese women’s bridal accessories. Data collection techniques using observation, documentation and online questionnaires. The design of this research use the R&amp;D the descriptive analysis with product validity and feasibility tests. The results of the validity test of all accessories innovation products in the form of (cunduk mentul, cunduk comb, centung, earrings, brooches, rings, men’s necklaces, tretes bracelets, panetep, tretes necklaces) show an average of 98.46% with very valid criteria, and the preference test results for ten accessories products is obtained by an average of 96.61% with the criteria of very much likeness. The Solonese women’s bridal accessories product as an innovation for bottle cap’s treatment waste is very feasible and can be developed as a home industry to support the creative craft commodities.
Concentration or distraction? A synergetic-based attention weights optimization method

Abstract The attention mechanism empowers deep learning to a broader range of applications, but the contribution of the attention module is highly controversial. Research on modern Hopfield networks indicates that the attention mechanism can also be used in shallow networks. Its automatic sample filtering facilitates instance extraction in Multiple Instances Learning tasks. Since the attention mechanism has a clear contribution and intuitive performance in shallow networks, this paper further investigates its optimization method based on the recurrent neural network. Through comprehensive comparison, we find that the Synergetic Neural Network has the advantage of more accurate and controllable convergences and revertible converging steps. Therefore, we design the Syn layer based on the Synergetic Neural Network and propose the novel invertible activation function as the forward and backward update formula for attention weights concentration or distraction. Experimental results show that our method outperforms other methods in all Multiple Instances Learning benchmark datasets. Concentration improves the robustness of the results, while distraction expands the instance observing space and yields better results. Codes available at https://github.com/wzh134/Syn .