Reviewerly/exHarmony huggingface dataset:
Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP

In various computer vision applications, the moving object detection is an essential step. Principal Component Analysis (PCA) techniques are often used for this purpose. However, the performance of this method is degraded by camera shake, hidden moving objects, dynamic background scenes, and / or fluctuating exposure. Robust Principal Component Analysis (RPCA) is a useful approach for reducing stationary background noise as it can recover low rank matrices. That is, moving object is formed by the low power models and the static background of RPCA. This paper proposes a simple alternative minimization algorithm to fix minor discrepancies in the original Principal Component Pursuit (PCP) or RPCA function. A novel hybrid method of cartoon texture features used as a data matrix for RPCA taking into account low-ranking and rare matrix is presented. A new non-convex function is proposed to better control the low-range properties of the video background. Simulation results demonstrate that the proposed algorithm is capable of giving consistent random estimates and can indeed improve the accuracy of object recognition in comparison with existing methods.
Adaptive data-sharing methods for multi-agent systems using deep reinforcement learning

In general, the interaction between an agent and the environment can be described by a Markov decision process in a single-agent system (SAS). However, it is difficult to define them by a Markov decision process in a multi-agent system (MAS), and makes it difficult to learn appropriate action by the agents to avoid the difficulty. Lowe et al. have constructed data-sharing methods among the agents based on the actor-critic algorithm. This paper improves the data-sharing method by limiting the data-sharing rather than all the empirical data possessed by the other agents. This paper proposes three types of training data-sharing methods and conducts simulation experiments using multiple maze environments of different complexity to indicate the effectiveness of the proposed methods. Based on the experimental results, the proposed methods have better performance than the existing methods. In addition, this paper shows the appropriate method according to the characteristics of each target problem.
Productivity of Machine Translation

This article is about the field of technical translation which is called machine translation (MT), or machine-assisted translation. This method of translation uses various types of computer software to generate translations from a source language to a target language without the assistance of a human. There are different methods of machine translation. A plethora of machine translators in the form of free search engines are available online. However, within the field of technical communication, there are two basic types of machine translators, which are able to translate massive amounts of text at a time. There are transfer-based and data-driven machine translators. Transfer-based machine translation systems, which are quite costly to develop, are built by linguists who determine the grammar rules for the source and target languages. The machine works within the rules and guidelines developed by the linguist. Due to the nature of developing rules for the system, this can be very time-consuming and requires an extensive knowledge base about the structures of the languages on the part of the linguist; nonetheless, the majority of commercial machine translators are transfer-based machines. .
ANALISIS PERENCANAAN STRATEGIS TEKNOLOGI INFORMASI DENGAN PENDEKATAN SWOT PADA KOPERASI PROJAKOP

Investment in information systems and information technology is quite expensive. The value of benefits obtained through investment in information systems and information technology must be measurable. Not a few organizations investing in information technology are not able to show the benefits of information systems and information technology both visible (tangible) and invisible (intangible). ProjakopMitra Sejahtera Cooperative as a producer cooperative places information technology as part of its business strategy. Information systems and information technology strategies at the Projakop Cooperative are needed to plan the use of technology and information systems that are in line with the cooperative's business objectives. Strategic planning of Information Systems/Information Technology (IS/IT) uses one of the internal business analysis approaches and IS/IT is carried out using SWOT tools. The results of the SWOT analysis can identify potential weaknesses, strengths, opportunities, and threats. The results of the SWOT analysis show that strength is at the very high level of management and management. But on the same side, there are staff/employees who have a weakness in the lack of creativity in optimizing the existing IS/IT facilities. This can be seen in the activities of staff/employees who use social media more during working hours instead of working on the business activities of the Projakop Cooperative. The results of other SWOT analysis have four categories as indicators by having each SWOT matrix for information systems and information technology strategies, including governance, infrastructure, human resources, and applications.
A Study on Secure Wireless Mobile Data Exchange System in Healthcare Using RFID (Radio-Frequency Identification)

IoT provides a unified framework for coordinating the use of diverse real-world and digitally- enhanced healthcare assets. Telemedicine is appealing in poor countries because of a lack of access to healthcare services, an aging population with chronic diseases, rising healthcare expenditures, and a need to monitor patients remotely. The Internet of Things can provide individualized health services that enhance people’s quality of life while also reducing the burden on public health infrastructure. Therefore, the purpose of this work is to catalog, compare, and categorize previous research into Healthcare IoT (HIoT) systems. In order to better understand the backdrop, we looked at research articles on RFID-based Secure Wireless mobile data exchange systems in healthcare. There was also discussion of other crucial issues including security and interoperability. At the conclusion of this report, we briefly discuss the main advantages and disadvantages of each study. Lastly, there is a dearth of research into the security and interoperability concerns of IoT design in healthcare. Important outcomes of IoT in healthcare include improved information sharing, shorter hospital stays, and lower overall healthcare expenses. IoT in healthcare faced major obstacles due to concerns over patient privacy and data security.
GESTURE CONTROL MENGGUNAKAN METODE COMPLEMENTARY FILTER

This study will discuss the use of the Complementary Filter method in hardware development that use gyroscope and accelerometer sensors, in order to see the ability of the sensor to read movement and assess how effective it is when used under movements with varying levels of aggressiveness. The complementary filter is a method that as the name suggests is to filter input data taken from the gyroscope and accelerometer sensors, this method is able to take data from both sensors and combine them into a single data that is more accurate by ignoring the shortcomings and using the advantages of each sensors The device that will be used is the MPU6050 GY521, this is a device that has both the two sensors on a single PCB so it is easier and more efficient to use, it has 3 axes meaning that this device can read motion in 3-dimensional space, the microcontroller used is a a variant of Arduino with an ATmega processor. After doing the test it was found that the use of the complementary filter method will greatly help the accuracy of the data because the filtered data contains very small noise with a significant difference when compared to raw data without using the filtering method, but because delay still can happens, wich cannot be helped by using this filtering method, this device can only be used with certain levels of movement
ANALISIS TATA KELOLA SISTEM INFORMASI MANAJEMEN RUMAH SAKIT UMUM DAERAH PROF. DR. W. Z. JOHANNES KUPANG MENGGUNAKAN COBIT 5 FRAMEWORK

It is needed an audit system control that does not only provide an evaluation of the Analysis Information System Management of Regional General Hospital Prof. Dr. W. Z. Johannes Kupang Using the Cobit 5 Framework, but also can provide input for the improvement of IT management in the future. This research is qualitative using interview and observation methods to assess the processes of several domains contained in the Cobit 5-based Capability Level standard. Researchers used the Cobit 5 framework with a focus on the MEA domain (Monitor, Evaluate, and Assess) as a reference. The focus is on the domain based on the problems that exist in the Regional General Hospital Prof. Dr. W. Z. Johannes Kupang to monitor, evaluate and assess the suitability of the domain with the hospital strategy in assessing the needs of the hospital and whether the current system still meets the objectives that have been designed and controls needed to meet regulatory requirements or not. This research is expected to provide good results so that it can be used as material or a reference in the development of Analysis Information System Management in the Regional General Hospital Prof. Dr. W. Z. Johannes Kupang Using the Cobit 5 Framework.&#x0D;
Unsupervised 3D Animal Canonical Pose Estimation with Geometric Self-Supervision

Although analyzing animal shape and pose has potential applications in many fields, there is little work on 3D animal pose estimation. This can be attributed to two aspects: the lack of large-scale well-annotated datasets, and perspective ambiguities which make it difficult to map 2D space to 3D space. To address data scarcity, we propose an unsupervised method to estimate 3D animal pose, given only 2D poses. To deal with perspective ambiguities, we introduce a canonical consistency loss and a camera consistency loss to impose geometric priors in the training process, and combine the reprojection loss and the 2D pose discriminator to enable self-supervised learning. Specifically, given a 2D pose, the pose generator network generates a corresponding 3D pose and the camera network estimates a camera rotation. During training, the generated 3D pose is randomly reprojected onto camera viewpoints to synthesize a new 2D pose. The synthesized 2D pose is decomposed into a 3D pose and a camera rotation, based on which consistency losses are imposed in both 3D canonical poses and camera rotations for self-supervised training. We evaluate the proposed method on real and synthetic datasets, i.e., SMAL and AcinoSet. The experimental results demonstrate the effectiveness of the proposed method and we achieve state-of-the-art performance among unsupervised algorithms for 3D animal canonical pose estimation.
Decentralized Optimization II: Primal-Dual Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Exponential Distributions

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Lipschitz Conditions

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Subgradient Method

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
A Study on Student Cyber Safety Consciousness in the Light of Online Learning

Our world online and networked is immersed under a wave of populism; populism spreads on the wings of internet. The recent technological advancements like the use of social media platforms and different applications made the information exchange faster and more efficient making the information access easier. To keep our information, gadgets such as cell phones, laptops, desktops, and tablets and also the internet safe, knowledge of cybersecurity is vital everywhere. In many colleges and Universities who are in to interconnected complex systems, data privacy is a huge challenge among their users. In most of the situations, due to lack of knowledge and awareness, users may engage in data breaches knowingly or unknowingly and the complete interconnected systems among the users may have a consequence of a cybercrime. This article seeks to unpack the rise of cyber-crimes and its relationship to cyber security among student groups during the pandemic where much of their interaction is online. The research aims to inquire in to the level of knowledge and awareness on cybersecurity among students during their online learning interaction using a well-structured questionnaire. The questionnaire will be focused on five parts: Awareness and Knowledge, Monitoring and Privilege, Security and Prevention, Protection from malware s and usage of removable Devices. The study is conducted using quantitative research methodology to quantitatively evaluate the knowledge of cybersecurity and inculcate an awareness against Cybercrime protection among the students. Finally, based on the analysis of collected data we present recommendations which will not forego the safety concerns for e mails, viruses, phishing, pop-up windows and forged ads which is a common problem. Some technological solutions and paths for the regulation of the cybercrimes are suggested to the respondents at the end.
TSAN et: Two-subnet Attention Network for Single Image Dehazing

Haze restricts visual quality and degrades the qual-ity of captured images. The aim of single image dehazing is to recover a haze-free image from a hazy one. However, most present image dehazing methods treat different feature information in channels and pixels evenly, which may influence the dehazing result because of the uneven distribution of haze. To address hazy uneven distribution, we propose an end-to-end two-sub net attention network (TSANet), which consists of attention-recurrent (AR) and asymmetric u-shaped dehazing refinement (AUDR). In addition, a feature residual attention (FRA) block is designed to focus on thick-hazy regions and high-frequency regions of a hazy image when dehazing. In the model, the input image is first fed into the AR sub-network to extract feature information like thick hazy regions and high-frequency regions. For further feature refinement, we propose the AUDR sub-network to further process feature information from the AR sub-network. The AUDR sub-network adopts an encoder-decoder module containing FRA and transformer blocks to further process feature information of high-frequency regions and filter hazy feature information, and uses skip connections to enhance the representation of our TSANet. The extensive experimental results demonstrate the effectiveness of our method and outperform other dehazing methods on synthetic and real-world hazy datasets.
DARK: Deep automatic Redis knobs tuning system depending on the persistence method

Database Management System (DBMS) runs the server with numerous knobs having different roles, which are set in the configuration file, and the performance of the DBMS can easily depend on the configuration. It could derive greater performance by finding the appropriate values of knobs, but there is a limitation for an expert who seeks to ascertain the most effective parameter combination directly due to the diversity of knobs and the wide range of values that each parameter could have. In this study, we propose the DARK tuning system to improve the performance of Redis, an in-memory key-value store. Since the two persistence methods provided in Redis are not designed to operate simultaneously, the tuning was performed by classifying knobs related to persistence methods. Also, we propose the Cross-GA, a method of conducting the prediction and alignment alternately of Genetic Algorithm to improve throughput and latency simultaneously. To verify the effectiveness of the proposed method, we carried out performance evaluations through Memtier-benchmark, a benchmark program for in-memory databases. As a result of performing Redis knobs tuning via DARK, the optimal configuration was derived to improve throughput up to 39.8% and latency up to 71.3% compared to the existing configuration.
Network Communication and Electronic Control Strategy of New Energy Vehicles Based on Cloud Platform in the IoT Environment

Aiming at the low accuracy of network intrusion detection (In-De) in the traditional network communication strategy of new energy vehicles (NEVs), this paper proposes an electronic control (E-C) strategy for network communication of NEVs based on cloud platform in the internet of things (IoT) environment. First, based on the cloud platform and deep learning (D-L) algorithm, the E-C system model including sensor, actuator, gateway, and cloud platform is constructed, and on this basis, the edge computing model is introduced to efficiently handle information interaction and computing tasks. Then, by using Bi-LSTM neural network to train historical data in the cloud center layer of the system, a D-L method combining cloud and edge nodes is proposed. Finally, by introducing the AlexNet network into the model, the problem of gradient vanishing when the network is deep is solved and the training speed is accelerated.
Construction method of urban low-altitude UAV route network based on demand

In response to the increasing demand for urban low-altitude Unmanned Aerial Vehicle (UAV) operation, this paper comprehensively considers the constraints of urban area traffic demand, airspace environment and UAV performance, with the goal of minimizing network nodes and maximizing coverage demand points, this paper proposes an urban low-altitude UAV route network construction method. First, the network node generation model and the route network construction model are established; secondly, the improved greedy algorithm is used to generate network nodes; then, k-Nearest Neighbor(K-NN) and Prim's algorithm are combined to construct the UAV route network. Finally, simulation verification is carried out based on the demand data of logistics business in Shanghai and the low-altitude operating environment, and the performance of the constructed route network is analyzed by using the complex network-related indicators. The results show that the urban low-altitude UAV route network construction method proposed in this paper can realize the construction of trunk and branch route networks at the same time, and the trunk route network realizes full network connectivity. In addition, the network nodes can be set according to the UAV mission requirements to achieve flexible planning of the route network.
Implementation of Moving Average for Forecasting Inventory Data Using CodeIgniter

The purpose of this study is to be able to apply a manual inventory forecasting system with the moving average method in order to speed up the decision-making process and be able to minimize the error rate in calculating stock in the warehouse. The results of inventory forecasting in 2023 have an average of 192 for 116 BR animal feed items. Based on the results of calculating the average amount, a satisfaction level of 3,825 was obtained and if you follow the reference of Kaplan and Norton satisfaction level, the conclusion that can be drawn is that the level of user satisfaction with the inventory application is included in the satisfied category. So that the indications shown are positive from the PIECES model, namely users are satisfied with this inventory application.