Reviewerly/exHarmony huggingface dataset:
Machine Learning Models for Detecting Anomalies in Online Payment: A Comparative Analysis

With the growing use of online payment systems, the necessity for strong security measures to defend against fraudulent activity has become critical. Machine learning algorithms-based anomaly detection approaches have developed as efficient solutions for spotting aberrant patterns and detecting fraudulent transactions in online payment systems. It offers efficient and effective online payment monitoring, protecting against fraudulent activity. In the present study, the applications of machine learning techniques for anomaly detection in online payment system is investigated. In conclusion, results provided by four models namely, Logistic Regression, Decision Tree, Random Forest and Extreme Gradient Boosting (XGB) Classifier can be preferred for anomaly detection in online payment. Among the four models, the XGB Classifier provided the highest model accuracy.
The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity.
Printing Document Security Based on Bit Mapping Technique on Character American Standard Code For Information Interchange (ASCII)

Document forgery is an illegal activity that alters official documents by adding, subtracting, or replacing certain letters or numbers, resulting in an invalid document. The proposed method to overcome this problem is by inserting secret words into printed text documents based on the American Standard Code for Information Interchange (ASCII) characters. In this study, bit mapping was done by inserting secret words into text documents and changing each character to a binary value of 7 (seven) bits long. The position of each bit of the secret word is determined by each bit of the text document character, which has the same value; for example, bit 0 of the secret character is mapped to bit 0 of the text character of the document, as well as bit 1 and is repeated until all the characters of the text document are mapped with the secret word character. So, every time one bit is mapped, the system records the position of the bit sequence, and this set of bit sequences is converted into a QR code to be pasted on the document to be printed. Testing of printed documents is carried out by comparing the QR Code value with digital text on printed documents obtained through the Optical Character Recognition process (OCR)
A Literature Review of Challenges and Solutions in Cloud Security

Cloud computing has emerged as a revolutionary paradigm that provides on-demand access to shared computing resources over the internet. However, it also poses significant security and privacy challenges, particularly in terms of data protection and incident management. This literature review explores the challenges and solutions to achieve a secure cloud computing ecosystem, such as natural disasters, system failures, hacking/malicious activities, and human errors, and discusses potential solutions, including network redundancy, intrusion detection, and disaster recovery. It also discusses common best practices, incident management, and disaster recovery models to ensure availability and data reliability. These practices ensure that the cloud ecosystem will be resilient to attacks and disasters from external and internal factors. As cloud technology continues to grow, we are likely to see improvement in cloud security practices and technologies like integration with machine learning and deep learning models, advanced patterns, or blockchain utilization. These improvements will help patch current known vulnerabilities and challenges in hope of a better and more secure cloud solution and ecosystem.
Systematic Literature Review: Automated Text Summarization for Indonesian Language

Text and document summarization machines is becoming more and more indispensable following the increase in the amount of digital information available both online and offline. Text summarization machines are needed to obtain shorter text without losing the essence of the document. This paper provides an overview of recent research on automatic text summarization for Indonesian documents. This review presents various text summarization techniques, datasets, categorization, challenges, and evaluation methods that can be used as a baseline in the development of an appropriate method and evaluation measure. This study is conducted with Systematic Literature Review (SLR) to get a more systematic and structured results with a measurable approach. The results of this paper can be used as a tool to help identifying opportunities to contribute within text summarization field.
Performance Comparison of AES, Grain V1, and RC4 Algorithms on the MQTT Protocol

MQTT is a communication protocol used by IoT devices. In its default configuration, it has no security layer, making it vulnerable to data theft or tapping attacks. Therefore, efforts must be made to maintain data security, which is achieved through cryptography. In this study, AES, Grain V1, and RC4 algorithms were tested for passive sniffing attacks and data processing time performance. Based on the experiment results, it can be concluded that three algorithms can overcome passive sniffing attacks. In addition to testing its resistance to the cryptanalysis attack, the security of the algorithm can be tested by comparing the time needed to break its key. Based on the experiment results, it was discovered that it took the longest time to break the AES algorithm, so it is safer than Grain V1 and RC4 algorithms. Furthermore, testing was carried out to test the performance of the encryption and decryption processing times for each algorithm, and it was found that the RC4 algorithm was faster than the AES and Grain V1 algorithms. The average encryption time performance of the RC4 algorithm is 20.4 microseconds, the Grain V1 algorithm is 763.4 microseconds, and the AES algorithm is 796.84 microseconds. Meanwhile, the average decryption performance for the RC4 algorithm is 0.13 microseconds, the Grain V1 algorithm is 0.18 microseconds, and the AES algorithm is 1.16 microseconds.
Investigating Convolution-Attention Model for Bone Scan Image Segmentation

Bone scan image segmentation is a crucial step in the early detection of a tumor spreading across the human body. By dividing each bone region, the Bone Scan Index can be analyzed for further follow-up against cancer. Much research has been done in this field, including the uses of Computer Aided Diagnosis (CAD) in an application developed by EXINI, the development of Active Shape Model (ASM), and Constrained Local Model (CLM) as a further development of ASM. However, these models still rely on landmark points for their training phase instead of using masks for the annotations. A recent convolution model, DeepLabv3+, relies on a convolution mechanism to extract local features. A new approach using a pure transformer, Segmenter, can extract global features in a parallel process. A combination of convolution and attention, Dual Attention Network (DANet) uses a similar backbone from DeepLabv3+ and implements attention modules to capture long-range contextual information from the input image. In this paper, a bone scan image segmentation system using DANet will be proposed. All models are trained using the bone scan dataset divided into anterior and posterior groups. The annotation is composed of 12 different classes of bone regions. The results show that the convolution-attention approach of DANet outperformed existing models in both the anterior and posterior sections. A performance of 76.85% mIoU is achieved in the anterior section, and 80.99% mIoU is achieved in the posterior section.
Development of Internet of Things Technology on Monitoring the Process of Poultry Feed and Supplement Management in Indonesia

This journal discusses how Internet of Things (IoT) technology has overseen the management of poultry feed and supplements in Indonesia. By looking at the new findings proposed by the author, as well as the completeness of the explanation and analysis provided, this study aims to determine the level of scientific contribution of the paper. The authors of this study suggest using an Internet of Things (IoT) system to enable farmers to monitor feed and supplement conditions remotely. Using an average ping data of 64.8 milliseconds to the Blynk cloud server, notifications about the availability of feed and supplements are sent to farmers. In addition, the machine is automatically activated according to the feeding schedule at 08.00 and 16.00 and supplementation at 19.00. The controller technology uses the HC-SR04 ultrasonic sensor to measure the surface height of feed and supplements. These sensors have very high correlation coefficients (r), which are 0.998 and 0.99, respectively. The research results show that this paper makes a scientific contribution by proposing and implementing an Internet of Things system that can help farmers monitor and manage the process of providing poultry supplements and feed. The new findings that combine controller technology with ultrasonic sensors to detect feed and supplement surfaces with high detection rates demonstrate good research quality. Overall, this paper makes a significant research contribution to the advancement of Internet of Things technology in the field of poultry farming, particularly in terms of automatic control and management of feed and supplements.
Improved Immersive Virtual Reality (VR) using Image Enhancement Method

The research aims to enhance immersive VR through improved design display quality in virtual environments. It is grounded in the image problem because in the virtual environment, there is a difference in color contrast 3D design interior and exterior environments of residential with a real view of the interior and external environments of buildings. So the results of the design of the interior and exterior environments of housing are still not immersive and do not look natural or real. The method used is image enhancement consisting of Histogram Enhancement (HE), Contrast-Limited Adaptive Histogram (CLAHE), and Fuzzy Contrast Enhancement (FCE). The data used in this study totaled 3.157 images in PNG format with a resolution of 512 x 512. The stages of this study start with data finalization, the image enhancement process, and evaluation. Through the investigative process, CLAHE method is able to outperform HE and FCE with an average of Structural Similarity Index (SSIM) 0.959, Image Quality Index (IQI) 0.959, through Mean Squared Error (MSE) 0.003, Root Mean Squared Error (RMSE) 0.055, and Peak Signal-to-Noise Ratio (PSNR) 25.705. Additional parameters, such as tiles generation and clip limit, on CLAHE can improve the image quality of the virtual environment without causing over-enhancement.
Investigation into Massively Parallel MIMD Architecture based IPU System through Application Benchmarking

We present a performance benchmarking analysis of Intelligent Processing Unit (IPU). IPU is a new architecture developed by Graphcore, specifically designed for handling Neural Network (NN) and Graph Computing (GC) workloads. IPU-POD16 is a 5U server solution, from Graphcore, consisting of four IPU servers and one host server. Each IPU server (IPU M2000) consists of 4 MK2 GC200 IPU. Our performance evaluation is focused on two aspects. First -To measure throughput, considering IPU-POD16 as a single machine, Second-To assess the scalability, by varying the number of IPUs assigned to current workload. For compute workload we used the Tensorflow implementation of ResNet-50 and performed training and validation with the Imagenet dataset. Experiments are conducted to measure the performance of the IPU system with respect to scaling, precision, network latency by devising three separate strategies. We reported throughput numbers (images/second) for training and validation, with both real data and synthetic data. Our initial conclusion is that applications are easy to port and run in the IPU platform, due to availability and support of popular frameworks like tensorflow and pytorch. In order to fully utilize the capabilities of the IPU hardware platform, advanced engineering techniques are required. In this case, we measured the performance by benchmarking with limited application varieties. More study and experimentation are required to fully understand and realize the power of the architecture (computation, networking). It would also be interesting to see how applications from other domains (other than AI/ML/DL) take benefit of this new platform.
Individual Performance of Students in Online Learning Environments: Study of User Satisfaction

Investigation on e-learning is important, especially user satisfaction in support of student academic performance. Evaluation of user satisfaction can be measured by evaluating perceived quality on the user’s side, so this research aims to investigate the role of perceived quality over online learning on User satisfaction. Users of online learning targeted are students because students have experience and knowledge of the implementation of online learning at the university. Collected data and information from students related to satisfaction with online learning through online questionnaires. 615 students filled out questionnaires, then questionnaire data was processed quantitatively through SPSS with statistical testing based on research hypotheses. Three factors essentially increase user satisfaction in online learning, regarding the system, service, and information. Student satisfaction can be improved when supported by information on the platform. The role of a platform regarding information in the online learning context is important, a measure that explains the quality of information among other things useful, understandable, interesting, and reliable. Research findings provide important information for universities as organizers of education, aimed at attaining information quality related to online learning.
The Boundaries of Copyright Protection for Deep Learning Technologies of Artificial Intelligence

With the continuous progress in the field of artificial intelligence, the breadth and depth of deep learning technology is being strengthened. Although the technology itself is neutral, it is necessary to pay attention to the boundaries of the use of data and to comply with legal regulations in the process of practical application. Regarding the “data mining” and other types of behaviour in deep learning, a comprehensive judgment should be made based on the reproduction right in the field of copyright law, and at the same time, the dissemination effect formed by the cumulative technical characteristics of the reproduction of deep learning technology should be analysed, so that whether or not to obtain the reproduction legally should be taken as the basis of the infringement judgment. Ultimately, the prohibited areas of copyright use and the boundaries of protection should be clarified to avoid the risk of violation of law and promote the sustainable development of the industry.
Weight Distribution Based Model Compression Method for Object Detection System

Artificial intelligence-based object detection is an essential field in computer vision applications such as autonomous driving, security surveillance, medical image analysis, intelligent transportation, and human-computer interaction. However, such state-of-the-art algorithm is computing and resources intensive, which limits its deployment especially on resources constrained devices such mobile phones, drones, and autonomous vehicles. In this paper, we propose a compression method to overcome such bottleneck. Our approach reduces the computing complexity by automatically zeroing out certain percentage of weights in convolutional layers. Through extensive experiments on the well-known YOLOv5s model, 60% computing can be saved during the inference process without sacrificing the (mean Average Precision) mAP.
MonoDet-K: A Monocular 3D Object Detector on BEV with Keypoint Regression

Autonomous driving necessitates solving the challenging task of monocular 3D object detection, where the goal is to infer accurate 3D predictions solely from a single 2D image. Existing approaches typically rely on conventional 2D object detectors to localize objects based on their centers and predict 3D attributes using neighboring features around these centers. However, this local feature-based approach falls short in comprehending scene-level 3D spatial structures and neglects crucial inter-object depth relations inferred from contextual cues. At the same time, Transformer-based methods usually ignore geometry relationship. In response, this paper introduces MonoDet-K, a transformer based 3D detector with camera-based keypoint regression. In this network, 3D object candidates are represented as a set of queries, and a depth-aware encoder uses attention mechanisms to generate non-local depth embeddings of the input image. Our proposed depth-guided decoder incorporates depth cross-attention modules, facilitating both inter-query and query-scene depth feature interactions. Consequently, each object query can adaptively estimate its 3D attributes by leveraging depth-guided regions from the image, unbound by the limitation of relying solely on neighboring visual features. MonoDet-K is designed as an end-to-end network, eliminating the need for additional data. Remarkably, it achieves state-of-the-art performance on the KITTI benchmark, showcasing significant improvements. Through extensive ablation studies, we demonstrate the efficacy of our approach, underscoring its potential to serve as a transformer-based baseline for future research in monocular 3D object detection. The proposed framework represents a substantial step forward in addressing the challenges of monocular 3D object detection and opens new avenues for advancing this critical field in autonomous driving.
Research on Parameter Prediction of Heating System in a Middle School Based on Optimized Sample Features

In order to optimize the parameters of the secondary network process of the heating system, a data collection system for heating based on the Internet of Things was built in a middle school in Tianjin. After the system was built, heating season data from 2020, 2021, and 2022 was collected. After analysis, it was found that there were significant differences between the daily flow, supply and return water temperatures of the secondary network of the heating system and the curve obtained by the formula. Afterwards, the water temperature and daily flow of the three heating seasons were optimized in MATLAB by combining outdoor meteorological data. Multiple days and multiple meteorological factors were used as sample features, and a random forest was used to build a prediction model. The importance of sample features was highlighted using visualization tools, and they were screened. Then, according to the CS optimizttion, a BP neural network was established to predict the supply and return water temperatures and daily flow using the screened sample features. The results showed that in the random forest prediction model, the correlation coefficients (R2) of supply and return water temperatures and daily flow were improved from 0.46, 0.4, and 0.55 to 0.70, 0.69, and 0.64, respectively. In the BP neural network prediction model, the determination coefficients (R) of supply and return water temperatures and daily flow were 0.76, 0.83, and 0.91, respectively, indicating that this sample feature optimizttion method can effectively improve the prediction accuracy.
A WGAN-based Missing Data Causal Discovery Method

The state-of-the-art causal discovery algorithms are typically based on complete observed data. However, in reality, technical issues, human errors, and data collection methods among other reasons result in missing data. The methods for handling missing data mainly involve statistical and machine learning approaches, where statistical methods are simple and practical, while machine learning methods offer higher accuracy. The typical approach for causal structure discovery in the presence of missing data involves two steps: First, applying missing data imputation algorithms to address the issue of missing data, and then using causal discovery algorithms to identify the causal structure. However, this two-step approach is suboptimal because imputing missing data may introduce biases in the underlying data distribution, making it challenging to accurately assess causal effects between variables. This paper proposes an iterative approach based on generative models for both missing data imputation and causal structure discovery. This approach incorporates an architecture based on Wasserstein generative adversarial networks and autoencoders (AE) to respectively impute missing data and output the causal structure. Through extensive experiments comparing against various state-of-the-art baseline algorithms, the effectiveness and superiority of this method are validated, providing valuable insights for further research on causal structures in the context of missing data.
Design of Fall Detection System Based on Infrared -Visible Camera and Internet of Things Technology

According to statistics, the incidence of falls among the elderly in China is as high as 30%, and falls have become one of the most important reasons threatening the safety and health of the elderly. A deep learning-based infrared-visible fall remote alarm system is designed to address the problem that it is difficult to detect falls of elderly people in time, resulting in high death and disability rates. The system deploys infrared or visible light cameras for different indoor scenarios to balance “privacy” “accuracy” and “comfort” and is equipped with a voice recognition module. The basic information interaction function is realized by IOT technology. The experimental results show that the fall alarm system operates stably, with good performance and reliable data transmission.