Reviewerly/exHarmony huggingface dataset:
Modern Systems for Working with Scientific Information and Conducting Research Projects

It is impossible to imagine modern scientific activity without the use of intelligent information systems. Modern scientific information systems include electronic libraries, scientific project management systems, smart search engines, scientific information social networks, etc. In this article, we reviewed the most popular and widely used electronic libraries today, as well as some other scientific information systems. A number of shortcomings in the organization of classical electronic libraries based on relational databases have been identified: the relational schema is rigid and its change leads to the need to change a large amount of program code; on the basis of relational schemas it is impossible to draw any logical conclusions; description of the content using the code of the system of universal decimal classification of information and a set of terms has very limited possibilities for assessing the semantic proximity between objects of the same type and, moreover, of different types; the use of relational schemas significantly limits the interaction between different electronic libraries. As an alternative model of electronic libraries organization, ontological models are considered, which are considered more perfect and expressive. They solve the problems of describing the semantics (meaning) of the content of electronic libraries and implementing work with them using such knowledge representation models as semantic networks, frames and ontologies. The analysis of the problems and prospects of intellectual information systems in science has been carried out. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Targeted Defense Against Neuron-Exclusivity-Based Gradient Inversion Attack in Federated Learning

As a distributed machine learning approach, federated learning enables multiple clients to collaboratively train a deep learning model for a common artificial intelligence task and share their gradients only. However, recent gradient inversion attacks demonstrate the possibility to reconstruct clients' training data from shared gradients and thus pose a severe threat to the privacy of federated learning. In this paper, we focus on the neuron-exclusivity-based gradient inversion attack, which is the first analytic attack based on the neuron exclusivity state. Since the key condition of sufficient exclusivity is required to construct the neuron-exclusivity-based attack, we propose a batch-perturbation-based targeted defense aiming to eliminate the exclusivity state of the training batches. The batch perturbation algorithm can be modeled as an optimization problem that aims to find the optimal perturbation on an input batch to satisfy the secure boundary condition. Then we transform the optimization problem into a linear programming problem and solve it with PuLP. We evaluate our proposed defense on two datasets: MNIST and OrganAMNIST. The experiment results demonstrate that our proposed defense can effectively prevent the neuron-exclusivity-based attack while having almost no negative impact on model training and model performance.
Deep Learning-Enabled Efficient Storage and Retrieval of Video Streams in the Cloud

In recent years, the rapid growth of video data has presented challenges for efficient storage and retrieval in cloud-based systems. This research proposes a novel approach that utilizes machine learning techniques to address these challenges. The objective is to develop a robust framework that optimizes storage utilization, enhances retrieval efficiency, and maintains video quality with minimal latency. Machine learning algorithms are employed for tasks such as video compression, content analysis, and metadata extraction, enabling effective storage and retrieval mechanisms. The framework leverages advanced video analytics algorithms to extract relevant features, incorporating them into the storage and retrieval process for efficient indexing and organization of video content. The framework adapts to changing workloads and dynamically allocates resources for optimal performance in cloud environments. Extensive experiments and comparisons with existing methods validate the proposed framework, demonstrating significant improvements in storage utilization, retrieval performance, and scalability. Specifically, our framework achieves an average improvement of 25% in storage efficiency compared to another method. It also demonstrates faster retrieval speeds, with an improvement of approximately 10-20%, and achieves higher video quality ratings, with an improvement of approximately 10-20% compared to existing methods.
$\mu$ PrintGen: Supporting Workflow Logs Analysis Through Visual Microprint

A microprint is a visualization that condenses the contents of a text file into a small space. This is done by using colored scaled -down characters or pixels to represent each character of the text. The colors aim to highlight information relevant to the user. We have created μPrintGen, a framework for creating custom microprints by associating colors to rules that specify what is included or excluded from the line, with plain text or regular expressions. At the same time, the user can customize the look and structure of the created microprint for ease of use. We also created <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> PrintVis, a website for viewing the created microprints that supports various navigation features, like zoom and search. Feedback from the Rust and Julia open-source communities shows that μPrintGen lets users navigate through large log files, while easily highlighting reported errors. Two experts agree on the benefits of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> PrintGen, compared to pure textual-based searching features commonly offered by text editors. Video URL: https://youtu.beIlJIZHLQdXiO
Examining Anticipated Massive MIMO 5G Network Attacks Through Adversarial Examples

As we explore further into this relm of 5G technology, the importance of Massive Multiple-Input Multiple-Output (MIMO) systems stands out as the pivotal force propelling the advancement of mobile networks. The surging demand for data and the exponential proliferation of connected devices have ushered in a new era marked by the escalating complexity and cost of managing these extensive MIMO systems. One promising avenue of research addresses this complexity by integrating Artificial Intelligence (AI) models designed to gracefully accommodate the increasing demands of these systems. However, this fusion of AI technologies also introduces a host of novel security concerns and threats, most notably adversarial attacks. These malicious tactics exploit inherent vulnerabilities within AI models, casting a shadow of uncertainty over the performance and integrity of large-scale MIMO 5G and beyond networks. The integration of AI and its attendant challenges within the context of 5G networks remains conceptual, with early-stage implementation efforts underway. In this paper, we aim to offer a comprehensive analysis of the current landscape, shedding light on emerging trends and potential future threats, explicitly focusing on adversarial example-based attacks that target AI models within expansive MIMO 5G networks.
Phishing Detection Based on Webpage Content: Static and Dynamic Analysis

Phishing attacks present a formidable cybersecurity problem, that continue to grow despite efforts to counter. This research investigates the potentials of dynamic analysis of webpages for detecting phishing behavior compared to static analysis. We performed automated analysis on a large dataset of 30,000 samples. Findings show that dynamic analysis achieved a higher accuracy rate, demonstrating a significant potential in page content analysis for phishing detection. Dynamic analysis reduced uncertainty in difficult-to-classify samples by 16%, enhancing the algorithm's resilience. Leveraging webpage content offers promising prospects for phishing detection, providing a valuable complement to existing systems and aiding in detecting sophisticated attacks.
Gargoyle Guard: Enhancing Cybersecurity with Artificial Intelligence Techniques

Common user authentication methods have limitations that pose significant cybersecurity risks today. Users may inadvertently or intentionally leave their devices and sensitive applications vulnerable to attackers. Although timing-based and dedicated hardware solutions reduce this risk, they do not guarantee foolproof user authentication. To address this security gap, we introduce realtime user activity fingerprinting. This technique continuously verifies users based on their unique behavior patterns. In this paper, we introduce Gargoyle Guard, a smart software solution, and provide insights into our comprehensive software development process, including requirement analysis, software architecture, and design. Gargoyle Guard employs a machine learning model we developed, trained, and tested to dynamically evaluate its authentication system's performance, error rates, and accuracy.
iTrace-Visualize: Visualizing Eye-Tracking Data for Software Engineering Studies

iTrace is community infrastructure that allows software engineering researchers to conduct eye-tracking studies on large realistic code bases. The iTrace infrastructure consists of a set of tools that assist with gathering, processing, and evaluating eye-tracking data on large software projects within an Integrated Development Environment (IDE). A typical eye-tracking study results in millions of raw gazes that are overwhelming to view and sort through. To help researchers view and comprehend this data, iTrace-Visualize is presented. This tool integrates information produced by the iTrace infrastructure into a dynamic video recording of the eye-tracking session. Eye fixations and the scan path between fixations are overlayed on the video. Additionally, the line being examined can be highlighted in the video. iTrace-Visualize lets a researcher replay eye fixations via a video overlay immediately after a study. This serves as quick validation of what was done during the study and can also provide quick insights into what the participants looked at. To illustrate iTrace-Visualize's capabilities, a small preliminary study is performed. Demo Video-https://youtu.be/c1hUFDmBM50
Comparative Analysis Of Rainfall Value Prediction In Semarang Using Linear And K-Nearest Neighbor Algorithms

Indonesia has a tropical climate with erratic weather changes. It is necessary to conduct a study to predict rainfall as decision-making on weather information that will occur in the future. Rainfall is one of the factors that cause changes in weather in an area. This research was conducted on the climate in Semarang which is part of the Central Java region with geographical and topographical conditions in the form of mountains and lowlands which cause changes in rainfall. Variables that are used as parameters and affect rainfall are temperature, humidity, wind speed and length of sunlight. These variables are processed using data from BMKG Semarang. The data were analyzed in this study to find out the comparison of research results with pre-existing data. The research was conducted using Multiple Linear Regression and K-Nearest Neighbor comparison algorithms by making the rainfall data the dependent variable and other parameters as independent variables. This study uses climate data from the BMKG Semarang Station for the period January 2021 to June 2023. The evaluation was carried out with the aim of estimating the magnitude of the prediction error value for total rainfall per month against observational data for total rainfall for that month so that rainfall levels can be predicted in the next six months. Realization of the actual rainfall data is 17.89, the amount of actual data is compared with the results of the evaluation using the Linear Regression equation the MAE acquisition value is 16.59 and calculations using the K-NN algorithm MAE = 18.75.
Elephant Herding Optimization Algorithm for the Operation of a Hydropower Plant

In this paper, the efficiency of a metaheuristic optimization algorithm, Elephant Herding Optimization (EHO), is tested for the operation of a Hydro-Power Plant (HPP). The optimization problem is to impose the realization of the monthly energy generation planned for one year of a HPP and to compare the obtained results with the exact numerical solution (obtained using the fsolve built-in function of MATLAB). As in most algorithms that are inspired by nature, EHO manages through simple operations of sorting, keeping the most suitable solution, removing the worst solution and repopulating with modified solutions, to iteratively lead to optimal solutions for engineering problems. With a reduced calculation effort and with the right choice of the coefficients involved in the algorithm, results were obtained with good accuracy, so EHO can be used in more complex optimization problems, which cannot be analytically solved.
Anomaly Detection in Crypto Currency

Dating back to several years ago, the crypto-currency market has been a volatile, but still growing in transaction services. Though many do not fully appreciate much more than investment opportunities due to lack of familiarity with the challenges behind tyrannical commerce regulations, the crypto-currency market allows many peoples to truly own something which can't be tracked, taxed, or levied by financial institutions. However, it can still be possibly stolen, hacked or manipulated. Because the Bitcoin Exchange lives on ledgers which are com-puted through block chains, once the transactions are computed into the history of the network, they are irreversible. Fraud and hacking in this environment do not occur by cracking cyphers, but more likely by social engineering, one-sided transactions and stealing credentials. Because the block chain technology has as much interest as crypto-currency, it is practical to build cyber defenses such as anomaly detection and possibly even predictors against these newer transactions and sharing networks.
Machine Learning Models for Classification of Sensitive Financial Documents

This research proposes a machine learning-based approach for the automated classification of sensitive financial documents. The proposed method utilizes Support Vector Machines (SVM), Naive Bayes, and a rule-based classifier to classify categorized financial documents accurately. The system extracts relevant features from the documents and trains the models to classify them into predefined categories. The performance of each model is evaluated, and the results are compared to identify the best-performing model. The result shows that all methods achieved satisfactory results in terms of accuracy, F-Score, and execution time. The experimental results show that the Naïve Bayes classifier outperforms the other models, achieving an accuracy of 95% in classifying the financial documents. The total time consumption, which takes into account manual handling of missorts and classification of files that could not be sorted, was significantly lower for the Naive Bayes implementations. The proposed method can significantly reduce the time and effort required for manual classification and can improve the accuracy of the classification process. The version with a tolerance level performed best and showed higher precision, but lower accuracy. This research can have significant implications in the financial industry, where the automated classification of sensitive financial documents can improve operational efficiency and reduce the risk of errors.
DCE: A High-Performance, Scalable, Enterprise-Level Cloud Native Operating System

The cloud native methodology has emerged as an optimized solution to meet the ever-evolving demands and challenges of modern application deployment and management. DaoCloud Enterprise (DCE), a high-performance and scalable cloud native operating system, developed by Shanghai DaoCloud Network Technology Co., Ltd. has experienced rapid growth over the past eight years, gaining valuable experience in various fields, including finance, automotive, manufacturing, smart city and retail etc. The company has been dedicated to exploring cloud native technology, such efforts fruited as eight major components offered by DCE, including multi-cloud orchestration, microservice governance, container management, insight, global management, storage, service mesh, and workbench. DCE empowers the enterprises to embrace their digital future, and efficiently increase their business agility.
A Lightweight Privileged Account Management System for Develpoment and Operation

Privileged accounts are used in both software and hardware during the DevOps process. However, hard-coded privileged accounts in development may not be deleted before submitted to customers, which leads to a greater risk of leakage and results in a larger attack surface. Furthermore, privileged accounts are closely related but widely distributed across the DevOps project, meaning that a leak of any privileged account could result in the entire project being paralyzed. Therefore, privileged account management systems that span the entire development and operation process are needed to address the issue of hard-coded accounts. This paper proposes a privileged account management solution to prevent hard-coded privileged accounts and uniformly manage privileged accounts in DevOps projects. The solution is designed for DevOps scenarios based on Zero Trust thinking, which includes two working stages: development and operation & maintenance. In the development stage, developers apply for encryption keys from the management center and use them to encrypt privileged accounts in the source code. In the operation & maintenance stage, users set their own passwords, which are stored locally, and the management center periodically updates the encryption keys. Finally, this paper discusses the defensive capabilities of the proposed solution against several security risks.
Multi-Camera Object Fusion Tracking Model for Autonomous Driving

In order to solve the problems such as vehicle trajectory interruption and frequent ID switches caused by the limited perception range of a single camera in autonomous driving, a real-time multi-camera object fusion tracking algorithm is proposed, which combines multiple cameras' detected objects, and automatically extends the autonomous vehicle's perception range to 230 meters, providing a larger perception range and more stable and continuous historical trajectory input of surrounding vehicles for the downstream trajectory prediction module in autonomous driving. The experimental results on the public and real-vehicle datasets show that our method can effectively realize the real-time fusion tracking of multiple cameras' multiple objects under the perspective of the autonomous driving vehicle, and achieve high accuracy and robustness under the premise of satisfying the real-time requirements of autonomous driving.
Research on the Intelligent Acquisition and Mining Technology of Metadata Based on Electric Power Database

The intelligent metadata acquisition algorithm and the power industry database-based K-Means clustering algorithm can enhance the efficiency of data acquisition and processing, providing effective support and guidance for data analysis and applications in the power industry. The experimental results demonstrate that the K-Means clustering algorithm achieves the best clustering performance and high accuracy when the number of clusters is set to 3. The research findings of this paper offer valuable support and references for data management and applications in the electric power industry. Furthermore, they are relevant and significant for data analysis and applications in other fields.
Handling of Indonesian-English Codeswitching Speech in End-to-End Indonesian Speech Recognition System Using Connectionist Temporal Classification Model

Code-switching in speech poses a significant challenge to Automatic Speech Recognition (ASR) systems. When left unaddressed, code-switching between languages can lead to decreased speech recognition accuracy. With advancements in technology, ASR systems have evolved into two main architectures: conventional and end-to-end (e2e). Compared to conventional architectures, e2e architectures are more commonly used due to their simplicity and superior performance. A widely used modeling technique in e2e ASR is the Connectionist Temporal Classification (CTC), which combines Recurrent Neural Networks (RNN) with the CTC loss function to handle situations where the alignment between speech and transcription is unknown. This study focuses on handling the code-switching phenomenon between Indonesian and English in e2e ASR using the CTC model. The proposed code-switching handling involves pre-training and transfer learning. Pre-training is performed on Indonesian speech data, resulting in an average error rate of 13.23% (WER) and 4.13% (CER) on Indonesian test data. However, the error rate remains high for code-switched test data. Transfer learning is then conducted on code-switched speech data by fine-tuning the model. This results in improved performance, with an average error rate of 48.115% (WER) and 16.8% (CER) on code-switched test data. Nonetheless, the error rate on Indonesian test data increases. To address this, a two-model CTC system is developed, capable of distinguishing between Indonesian-only and code-switched data using confidence values. The 2-model system successfully reduces the average error rate to 24.6625% (WER) and 7.0525% (CER) for Indonesian data and achieves 53.852% (WER) and 20.7675% (CER) for code-switched data.
Smartphone-Integrated Reverse Lens Hemocount Microscope for Red Blood Cell Imaging

Anemia, a prevalent global medical condition affecting approximately 33% of the world's population in 2015 and 32% of Indonesian adolescents in 2018, is characterized by a reduction in red blood cell count or hemoglobin levels, resulting in diminished oxygen-carrying capacity within the bloodstream. The conventional method for detecting anemia involves Complete Blood Count (CBC), which demands specialized hematology analyzers and substantial time. In response to these constraints, the Hemocount, a portable microscope utilizing the reverse lens technique, was developed to capture high-quality images of red blood cells. This innovative approach requires blood sample standardization for optimal Hemocount performance. The technique involves inverting the mobile camera lens, aligning the sensor focus with the sample, while a preparation table and light source illuminate from below. Experiments with various lens, table, and light source configurations identified the optimal setup as employing the front camera lens from an iPhone 5s, a transparent table, and a cool white light source, yielding a digital image resolution of 42.19 nm/px. This resolution allows for blood cell detection within the size range of 165.92 px to 189.62 px. For optimal results when using Hemocount, a recommended blood concentration of 50% minimizes standard deviation, while concentrations of 30% maximize the correlation coefficient R, and 25% yield clear images by preventing red blood cell enlargement due to osmosis.