Reviewerly/exHarmony huggingface dataset:
Optimization study of convolutional neural network model based on computer simulation context

Convolutional Neural Network (CNN) is the most successful mathematical model of an artificial neural network used in the fields of computer vision, image recognition, and classification. High-performance neural network structures are large in size, and a large number of multiplicative-additive calculations are required for a complete inference process. Training out a high-performance convolutional neural network model requires tens of times more computation than the inference process. The current theoretical and technical level cannot achieve a model that can be universally applied to all domains, and different application scenarios require designing specific neural network structures and collecting specific data sets. Large computing power requirements and high-quality data acquisition are two key components for training to obtain high-performance convolutional neural networks.
Surface garbage target detection technology based on deep learning

Aiming at the problem that a large amount of water surface garbage causes serious pollution to the environment and is difficult to clean up in time, artificial intelligence equipment and machine vision target detection are combined. A deep learning-based lightweight floating object detection algorithm MobileNetV3-YOLOv4. MobileNetV3-YOLOv4 proposes to use a depth wise separable convolution method to replace the 3 × 3 conventional convolution in the network, replaces the original CSPDarknet53 backbone feature extraction network with MobileNetV3 network, and proposes to use a synthetic fusion pyramid network (SFPN) in the neck network of the model. A synthetic fusion Pyramid Network can reduce the gap between the detection accuracy and the original model while reducing the number of parameters and calculations. The number of parameters and calculations is only 20% of the original model, and the detection speed reaches 96 frames per second. Compared with the original YOLOv4 model, it is 69 frames per second, an increase of 39.1%.
E2LSH based on Gaussian kernel function optimization

How to perform similarity search on high-dimensional data is a great challenge, especially for Euclidean distance, although the traditional E2LSH algorithm can cope with this problem well, the value of the parameter has a great influence on its effect. In this regard, a hash bucket parameter selection algorithm incorporating the Gaussian kernel function is proposed for its parameter selection problem, which improves the sensitivity to the data to obtain more accurate and efficient nearest neighbor search results in multi-distributed high dimensional data. Meanwhile, experimental comparisons with other similar algorithms are conducted on a common dataset, and their results show that the optimized E2LSH algorithm demonstrates improved performance in terms of speed and efficiency.
A Sketch Framework for Fast, Accurate and Fine-Grained Analysis of Application Traffic

Abstract Nowadays, with the continuous increase in internet traffic, the demand for real-time and high-speed traffic analysis has grown significantly. However, existing traffic analysis technologies are either limited by specific applications or data, unable to expand for widespread implementation, or in offline mode are unable to keep up with dynamic adjustments required in certain network management scenarios. A promising approach is to utilize sketch technology to enhance real-time traffic analysis. Unfortunately, existing technologies suffer from defects, such as overly coarse-grained statistics that cannot perform precise application-level traffic analysis, and irreversibility, which cannot support real-time queries in a friendly way. To achieve real-time fine-grained application traffic analysis in general scenarios, we propose AppSketch, a real-time network traffic measurement tool. AppSketch adopts a one-pass approach to classify and label the application information of each packet in the network flows. It then hashes the flow, identified with the application tag, into a carefully designed multiple-key sketch, for gathering application-specific statistics. We conducted extensive experiments using a real-world network traffic dataset collected on a university campus. The results showed that AppSketch achieved high accuracy while requiring less update time than other alternatives. Moreover, AppSketch occupies limited memory ($ {\leq }$64KB), making it suitable for online network devices.
Two‐stage architectural fine‐tuning for neural architecture search in efficient transfer learning

Abstract In many deep neural network (DNN) applications, the difficulty of gathering high‐quality data in industry fields hinders the practical use of DNN. Thus, the concept of transfer learning (TL) has emerged, which leverages the pretrained knowledge of the DNN which was built based on large‐scale datasets. For this TL objective, this paper suggests two‐stage architectural fine‐tuning for reducing the costs and time while exploring the most efficient DNN model, inspired by neural architecture search (NAS). The first stage is mutation , which reduces the search costs using a priori architectural information. Moreover, the next stage is early‐stopping , which reduces NAS costs by terminating the search process in the middle of computation. The data‐intensive experimental results verify that the proposed method outperforms benchmarks.
Coordination of Cyclic crossover and Bat Algorithm for the Travelling Salesman Problems in Different Environments: A Simulation Approach

In this study, the features of cyclic crossover process and K-opt are incorporated in the bat algorithm (BA) to solve the Travelling Salesman Problems (TSP) in different environments. Swap operation and swap sequence are applied for the modification of the different operations of the BA to solve the TSPs. The cyclic crossover operation is applied in a regular interval of iterations on the best found solution and each solution of the final population of BA for the enhancement of the exploration as well as exploitation of the search process. K-Opt operation is applied on the population in each iteration of the BA with some probability for the exploitation. The algorithm is tested with a set of benchmark test instances of the TSPLIB. The algorithm produces exact results for a set of significantly large size problems. For the TSPs in fuzzy environment, a fuzzy simulation approach is proposed to deal with the fuzzy data having linear as well as non-linear membership functions. Also, a rough simulation process is proposed to deal with the TSPs in the rough environment where rough estimation can be done following any type of rough measure. The performance of the algorithm is compared with the state-of-the-art algorithms for the TSPs with crisp cost matrices using different statistical tools.
Detection of E-Commerce Fraud Review via Self-Paced Graph Contrast Learning

Abstract Recently, graph neural networks (GNNs) have been widely used for e-commerce review fraud detection by aggregating the neighborhood information of nodes in various relationships to highlight the suspiciousness of nodes. However, existing GNN-based detection methods are susceptible to sample class imbalance and fraud camouflage problems, resulting in poor quality of constructed graph structures and inability to learn reliable node embeddings. To address the above problems, we propose a novel e-commerce review fraud detection method based on self-paced graph contrast learning (SPCL-GNN). Firstly, the method constructs a subgraph by initially selecting nodes through a labeled balanced extractor. Secondly, the subgraph connections are filtered and complemented by combining self-paced graph contrast learning and an adaptive neighbor sampler to obtain an optimized graph structure. Again, an attention mechanism is introduced in intra- and inter-relationship aggregation to focus on the importance of aggregation under different relationships. Finally, the quality of the node embedding representation is further improved by maximizing the mutual information between the local and global representations. Experimental results on the Amazon and YelpChi datasets show that SPCL-GNN significantly outperforms the baseline.
Polytope compatibility—From quantum measurements to magic squares

Several central problems in quantum information theory (such as measurement compatibility and quantum steering) can be rephrased as membership in the minimal matrix convex set corresponding to special polytopes (such as the hypercube or its dual). In this article, we generalize this idea and introduce the notion of polytope compatibility, by considering arbitrary polytopes. We find that semiclassical magic squares correspond to Birkhoff polytope compatibility. In general, we prove that polytope compatibility is in one-to-one correspondence with measurement compatibility, when the measurements have some elements in common and the post-processing of the joint measurement is restricted. Finally, we consider how much tuples of operators with appropriate joint numerical range have to be scaled in the worst case in order to become polytope compatible and give both analytical sufficient conditions and numerical ones based on linear programming.
Object Oriented Design Systems for WebGIS Flood Risk Classification in Samarinda, Indonesia

Abstract Floods are a prevalent environmental concern that frequently impacts urban regions. In conjunction with the growth of urbanization, the expansion of urban areas frequently gives rise to issues related to flooding. Samarinda, the administrative center of East Kalimantan Province, is intrinsically linked to the problems of flooding and waterlogging. The occurrence of floods in this urban area is frequently observed during periods of heavy rainfall and elevated water discharge from the Mahakam River and its associated tributaries. The utilization of the Web Geographics Information System to present maps depicting areas susceptible to flooding serves as a digital instrument and informational resource for urban communities. This research aims to design and implement WebGIS software for the classification of flood-prone regions in Samarinda City, employing the Object-Oriented Design System (OODS) approach. The process of flood classification modeling involves the utilization of various factors, including rainfall data, road kinds, slope gradients, and land use types. The design of this WebGIS system incorporates OODS principles, utilizing the Unified Modeling Language (UML) methodology. This approach encompasses many UML techniques, including Use Case Diagrams, Class Diagrams, and Entity Relationship Diagrams. The outcome of this study resulted in the design and implementation of the WebGIS program, which serves the purpose of classifying flood-prone areas in Samarinda City. This result encompasses object designs and interfaces that visually represent flood zones on a map. The system validation test was conducted by comparing the classification results and historical flood data, with a result of 90%.
P-Alert earthquake early warning system: case study of the 2022 Chishang earthquake at Taitung, Taiwan

Abstract A series of earthquakes that struck Taiwan's southern Longitudinal Valley on September 17 and 18, 2022 severely damaged several buildings in Taitung and Hualien. The Chishang earthquake, which had a magnitude of M L 6.8 and a large foreshock with a magnitude of M L 6.6 the day before, was the mainshock in this sequence. The strongest intensity reported in the epicentral region during this earthquake sequence, which was 6 + , is the highest ever recorded since the Central Weather Bureau (CWB, renamed as the Central Weather Administration since September 15, 2023) revised its seismic intensity scale. National Taiwan University (NTU) has operated a low-cost earthquake early warning (EEW) system known as the P -Alert for a decade. In this study, we demonstrate the performance of the P -Alert network during the 2022 Chishang earthquake and the largest foreshock. The P -Alert network plotted shake maps during these earthquakes that displayed various values within 5 min. The high shaking areas on these maps were in good agreement with observed damages during this earthquake, providing valuable insights into rupture directivity, a crucial component of earthquake engineering. Individual P -Alert stations acted as on-site EEW systems and provided a lead time of 3–10 s within the blind zone of CWB. For the M L 6.8 mainshock, there was a lead time of at least 5 s, even up to 10 s, demonstrating their effectiveness in the blind zone. The P -Alert regional EEW system provided the first report about 9 s and 7 s after the mainshock and the largest foreshock occurrence, respectively, with estimated magnitudes of 5.74 and 5.67. The CWB system estimated magnitudes of 6.72 and 6.16 in the first report, respectively, about 7 s and 9 s after the earthquake occurrence. The timeliness of the two systems were not significantly different. Despite the effectiveness of the P -Alert network, data loss due to connection interruptions prompted us to develop a new compact data logger for improved data availability.
Software system for automatic reading, storing, and evaluating scanned paper Evaluation Sheets for questions with the choice of one correct answer from several offered

Abstract Although the technology to automatically score multiple-choice tests has been around for several decades, it is still not as widely available or affordable, especially for paper-based test processing. The main reasons that hinder these processes are the lack of software systems capable of working with contents that are not filled optimally and do not require expensive scanners or other costly equipment. In this study, we present a software system for the automatic reading, storing, and evaluation of scanned assessment sheets. This software system offers a solution that only requires the usual scanned Evaluation Sheets in the form of a not high-resolution image and with the help of specific markers, the system performs reading, storage, and scoring. The user interface is designed to read and display the data from the Evaluation Sheets in detail so that they are very understandable to the user and allow him to quickly spot any errors. The tool has been validated over six years of use and has been continuously improved throughout that period. Thus, this software system achieves a high level of reliability and sensitivity to different levels of the quality of filling out Evaluation Sheets by students.
Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection

Occupational Safety and Health (OHS) is an effort to guarantee and protect the safety and health of every worker through efforts to prevent work accidents and work-related diseases. Safety Helmet is one of the components that must exist and be used in accordance with Occupational Safety and Health standards. Detection of safety helmets usage is one of the efforts to support these activities. The application of Arc Circularity Naive Bayes is used to detect whether an object meets the ratio of a circle by utilizing RGB and HSV image filtering and classification using Naïve Bayes. That method is used to detect whether a worker uses a safety helmet or not, it also detects helm color. The average value of accuracy is 50.8, precision is 58.3%, recall is 66.0%, and f1-score is 59.5% which are calculated using the Confusion Matrix
Forward Chaining Method in a Web-Based Bipolar Disorder Expert System

Bipolar disorder is a psychiatric disorder. At Argodadi Pinilih Disability Secretariat, Sedayu District, Yogyakarta Region, around 60% of people with disabilities are affected by psychiatric disorders and one of them is bipolar, one of them is depression, and suicide if initial treatment is not given to the sufferer. In addition, the cost of doing consultation with the medical specialist is not cheap.An expert system is an artificial intelligence system that is useful for diagnosing an error and as a decision-maker with the knowledge rules applied by an intelligent system that can solve problems like an expert. In making an expert system, the forward chaining method is used which aims to be able to diagnose bipolar disorder with accurate results and its utilization can be used by experts and laypeople to make an initial diagnosis of bipolar disorder.The results of this study are in the form of an expert system program that is used to diagnose bipolar disorder which can provide information related to the disease and can provide information on initial treatment of the disease. The information obtained by the consultant from the expert system is in the form of a percentage.
Implementation of the Decission Tree Algorithm to Determine Credit Worthiness

Credit is a loan from a bank that needs to be repaid with interest. In practice, problematic credit or bad credit often occurs due to less thorough credit analysis in the credit granting process, or from bad customers. This research aims to predict creditworthiness using the Decision Tree Classification Algorithm and find a solution for determining it. This research uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. This research method tests the effects of using the decision tree, Support Vector Machine, and Naïve Bayes model with the Decision Tree Classification Algorithm. The decision tree classification algorithm accurately analyzed problem loans and non-problem debtors at 93.49%. The decision tree algorithm test results are better than the support vector machine by 3.45%, and naïve bayes by 13.03%. The results of our study were also 4.16% better than the previous study. This research has also implemented the selected model in the form of website application deployment.
Information Security Policies and Procedures Guidance for Agencies

In today's digital e-commerce and m-commerce world, the information itself acts as an asset and exists in the form of hardware, software, procedure, or a person. So the security of these information systems and management is a big challenging issue for small and large-scale agencies. So this chapter discusses the major role and responsibility of the organization's management in identifying the need for information security policy in today's world of changing security principles and controls. It focuses on various policy types suitable for all kinds of security models and procedures with the background details such as security policy making, functionality, and its impact on an agency culture. Information security policies are helpful to identify and assess risk levels with the available set of technological security tools. The chapter describes the management strategies to write a good policy and selection of the right policy public announcement. The agencies must also ensure that the designed policies are properly implemented and ensure compliance through frequent intermediate revisions.
Temporal Knowledge Graph Attention Network for Online Doctor Recommendation

This paper discusses challenges in online doctor recommendation services, emphasizing personalized recommendations based on patients' health needs and preferences. We address issues related to information asymmetry and time-related attributes of diseases (e.g., seasonal, recurring, and long-term) by proposing the use of knowledge graphs and introducing the temporal factor into the KGAT approach in the online doctor recommendation scenario. We construct knowledge graphs using interaction data, extract heterogeneous auxiliary information to accurately assess user preferences, and encode timestamps to cluster temporal dynamics. Using a real-world dataset, we validate our approach and demonstrate its effectiveness in enhancing the performance of online doctor recommendation systems when compared to classical models used as a baseline for comparison.
The Improved Inception Networks based on Attention Mechanism

Deep learning is a machine learning method and it have enabled major progresses in image, natural language process and other more various and wide fields. Inception is a classic deep learning module included in Google net and it has good performance in image recognition, classification and segmentation. Attention is essentially a distribution mechanism, with the core idea of highlighting certain important characteristics of the object. In this paper, we introduced attention mechanism into Inception net model. We applied our model on MNIST, CIFAR-10 and CIFAR-100 public dataset to train and test. The performance of the model based on Inception and Attention Mechanism is improved greatly compared to the other models. At the same time, we also apply SENet model with attention mechanism to the same datasets and the experiment results show our improved Inception model is superior to SENet. These demonstrated that our proposed attention mechanism has better effectiveness.
Research on the evaluation of general education courses based on neural networks

Students' evaluation of courses is a direct reflection of students' satisfaction and sense of gain from the course. It is also the basis for teachers to understand students' needs, care about students' emotions, improve teaching methods, and improve teaching quality. Traditional courses In order to evaluate the teaching quality of general courses more accurately and objectively, this article uses the real evaluation data of general courses by students in a university, uses text analysis, and uses neural network algorithms to analyze natural language and perform quantitative and qualitative processing. Data can be used to build a more applicable evaluation model to accurately describe teaching quality and teaching effects, and provide a reference for teachers to improve teaching methods.
A Medical Data Generative Model based on Knowledge Graph Attention Network

With the rapid growth of medical data, the challenges between enhancing the quality of medical data and ensuring patient privacy are becoming increasingly prominent. Conventional data anonymization methods often fail to effectively protect medical data, prompting us to seek advanced techniques like differential privacy and data synthesis. This study introduces the Knowledge Graph Attention Network (KGAT) to generate synthetic medical data, aiming to enhance the interpretability of the model. The methods include classifying data fields, preprocessing the data, and using KGAT and Decision Tree to generate core fields. The synthetic medical records are evaluated through JS divergence and Wasserstein distance metrics. Preliminary results indicate that the generation performance of KGAT on most core fields is close to the original data. This will provide a new direction for the field of medical data generation.
An Improved DMSQPSO Algorithm For 3D UAV Path Planning

Unmanned aerial vehicle (UAV) path planning problem requires obtaining an optimal path in complex terrains. To ensure smooth and stable paths, this study measures the stability of the horizontal direction by using the mean squared deviation of horizontal turning angles in the UAV path planning model, which is based on using height mean square deviation to measure vertical stability. Particle swarm optimization (PSO) is often used for UAV path planning due to its simplicity. However, PSO easily traps in local optima. Therefore, this study proposes dynamic multi-swarm quantum PSO (DMSQPSO) to address this problem which is based both on dynamic multi-swarm PSO (DMSPSO) and quantum PSO (QPSO). The proposed algorithm is as follows: we split the population into several sub-swarms and use the updating strategy from QPSO to improve the diversity of particle behaviors to avoid premature convergence to local optima. Additionally, we use a cubic B-spline curve to smooth the path, which makes it more suitable for real-world UAV flights. Simulation results demonstrate the superior performance of the DMSQPSO algorithm in obtaining feasible paths compared to other algorithms.