Reviewerly/exHarmony huggingface dataset:
Informal Indonesian Part-of-Speech Tagger Using Hidden Markov Model and Normalization Algorithm

Twitter is a social networking platform that can be used as resources to find information in real time. Twitter is one of the social media that does not have writing criteria, users are free to express their thoughts and send messages. There are many messages and tweets that use abbreviations, foreign words, local and mixed languages, which are characteristic of informal language patterns. Informal language patterns do not rely on Indonesian, making the process of annotating word classes less precise and influential on the level of precision. The use of POS Tagging in previous research can annotate word classes well in formal sentences. The problem in informal sentences is solved by the process of word normalization after the preprocessing stage, where informal words are converted into formal words then Part-of-Speech Tagging is carried out on each word in the sentence such as nouns, verbs, adjectives, etc. The dataset used was 846 words, with a informal word count of 99 words. With the Hidden Markov Model commonly used in machine learning as well as the word normalization process, the system is able to increase the resulting precision value, there is a significant increase of up to 10% in the precision produced.
Comparative Analysis of Diverse Classification Algorithms of Machine Learning by Using Various Quality Metrics

Machine learning classification algorithms and their applications are becoming increasingly popular in today's era of information science. Choosing an algorithm that is appropriate for the problem and application is a challenging task. The quality of the prediction model is significantly influenced by selecting the right algorithm. The purpose of the study and comparative analysis highlights the benefits and drawbacks of various classification algorithms from all perspectives. The Machine Learning (ML) based classification algorithms such as Logistic Regression (LR), K-Nearest Neighbour(KNN), DT (Decision Tree), SVM (Support Vector Machine), and Naïve Bayes (NB) are assessed for comparative analysis. These algorithms were tested and analysed using various datasets acquired and used from the UCIML repository. Algorithms are evaluated using well-established effective measures for accuracy, recall, and precision. A variety of datasets are used to monitor, measure, and compare each algorithm's performance in terms of metrics. Each algorithm's effectiveness and efficacy are analysed and compared from all aspects. Based on overall performance, the algorithms are ranked in the following order: logistic regression, decision tree, SVM, KNN, and Nave Bayes.
Design of Cyber Risk Calculation Methods Using Monte Carlo Simulation in Marketspace

Cyber risk assessment in marketspace necessitates comprehensive calculations that rely solely on data, reducing subjective expert opinions. This research proposes a methodology that utilizes Monte Carlo simulation to achieve objective risk calculations. This research identifies various causes of cyber risk in marketspace, including human error and third-party intention. By developing a Monte Carlo simulation program, risk quantification is simulated, allowing for a thorough understanding of the relationship between input variables and resulting risk values. The results show that the probability distribution of risk values can be obtained using data on cyber events that have occurred. The resulting output can provide a wide and varied range of risk values using only data and estimated values of influential factors. The Monte Carlo simulation developed can also provide similar outputs when there are data limitations with their own restrictions.
Design and Evaluation of a Real-Time Stock Inventory Management System

An efficient stock inventory management is a vital part of a company, stores, laboratory, hospitals etc to balance stock with the level of demand and reduce the amount of manpower and money required for manual management. In addition to this, safety of the inventory also becomes important to reduce any risks of theft leading to potential loss to the owners. In this paper, design of a real-time stock inventory management system is proposed. An application is also developed for the end user to monitor and access the real-time inventory management system. The proposed system is implemented using three machine learning models - You Only Look Once (YOLO), Single Shot Detector (SSD) and Faster Region-based Convolutional Neural Network (Faster-RCNN) and their performances are compared in the proposed work. The performance evaluation of the proposed system is carried out using real-time videos captured from a laptop camera. The proposed system yielded an average precision of 0.92, 0.893 and 0.89 with an average recognition time of 1.33s, 0.33s, and 1.81s on using YOLO, SSD, and Faster-RCNN algorithm respectively. The proposed system also allows the end user to stream the real-time video remotely along with the details of objects detected and notifies the user immediately on detecting an intruder in the environment. The proposed system can be employed in showrooms for electronic gadgets, hypermarkets and retail stores to track the stock and detection of intruders at odd times.
Enhancing Hierarchical CNN Via Pathway Loss and Reference Forwarding Synergy

Computer vision applications have been widely used in critical areas however its reliability is hampered by low accuracy when dealing with small objects. As the internal information for small objects is limited by its pixels, addition of external information from the object context is important to determine object identity. Context features that are shared among objects can be used to group and label them hierarchically. It will then be used to improve classification by reducing the search space and linking the object's general feature or function. Hierarchical-based CNN uses a hierarchy of labels with multi-level classifiers in classification of simpler coarser level before tackling more complex classification on finer level. However, the current implementation of hierarchical CNN keeps information between levels isolated from each other thus limiting the potential of its structure. Improvement is made towards the loss function with the addition of pathway loss where the hierarchy pathway from coarse to fine is considered on each classifier branch. Additionally, we introduce the reference forwarding function, which transfers crucial information learned from previous layers to the subsequent finer ones, thereby further improving the classification accuracy. Proof of concept experiments on CIFAR-100 dataset shows each of the proposed innovations improve accuracy compared to baseline model and the overall combinations synergize well with each other, achieving the best result with improvement of almost 8%.
Domain-Specific Language Model Post-Training for Indonesian Financial NLP

BERT and IndoBERT have achieved impressive performance in several NLP tasks. There has been several investigation on its adaption in specialized domains especially for English language. We focus on financial domain and Indonesian language, where we perform post-training on pre-trained IndoBERT for financial domain using a small scale of Indonesian financial corpus. In this paper, we construct an Indonesian self-supervised financial corpus, Indonesian financial sentiment analysis dataset, Indonesian financial topic classification dataset, and release a family of BERT models for financial NLP. We also evaluate the effectiveness of domain-specific post-training on sentiment analysis and topic classification tasks. Our findings indicate that the post-training increases the effectiveness of a language model when it is fine-tuned to domain-specific downstream tasks.
The Method of Negative Correlation as an Effective Means of Processing an Experiment to Determine the Frequency Characteristics of an Object

The article poses and solves the problem of creating an experiment processing method that is compact, high-precision and convenient for use as part of an adaptive controller. These requirements are met by the author's method of negative correlation. The method uses the property of effective ASC. It consists in the fact that the frequency of natural oscillations of the system is significantly higher than the cutoff frequencies of the disturbing influences. Taking into account the low frequency, it turns out to be sufficient to measure only two ordinates for estimating the amplitude of oscillations and two ordinates for estimating their phase, followed by averaging over a set of periods. Each pair is separated by an interval equal to the half-period of the harmonic, and as a result, by a sign change. The sign change causes the use of the subtraction operation when averaging over the set, instead of the addition operation, the correlation moment is subtracted from the noise variance, and due to which it is accompanied by a significant decrease in the influence of the noise on the accuracy estimate. Analytical expressions are obtained for the absolute maximum amplitude and phase errors for a given confidence level. The experiment planning procedure was designed to obtain the desired a priori information about the perturbation and system properties. The program was developed in the Mathcad environment. Researches of the methods offered in work are carried out. They showed high efficiency, as well as the possibility of their practical use as part of an adaptive controller.
H.264/AVC Semi-Fragile Water Print Algorithm Based on Cost Strategy

Aiming at the current H.264/ A VC standards, the problems exist in the semi-fragile water printing algorithm, and propose a H.264/ A VC semi -fragile water algorithm based on coststrategy. By eliminating the correlation between content -based characteristic codes, it improves the stability of content certification watermark. In order to achieve semi -fragile watermarks, it can accurately position the tampering area, but also has a minimum impact on video quality and code rate. A cost function is introduced. Through this function, select a best coefficient in the DCT coefficient of 4 x 4 blocks of the residual 4x4 blocks embedded in the d for embedding the best coefficient watermark. Experiments show that the algorithm embedded in watermarks has a small impact on video quality and code rate, and semi -vulnerable watermarks can be effectively and accurately detected and located on the image area of malicious tampering.
Regex Parsing in Hybrid and Pure Approaches of Text Summarization

Hybrid automated text extraction refers to a combination of extractive and abstractive techniques in text summarization. Instead of relying solely on one approach, these methods leverage the strengths of both to generate more accurate and coherent content. The proposed model is a direct combination of direct extraction of sentences and Regex based parsing abstraction of document. In this paper, the best of the results based on Accuracy, Recall and Precision metrics are taken to select the Automated Text Summarization method of enhanced pure abstractive summarization with a regex parser and pure extractive summarization through sentence score assignment, getting compared with the result of hybrid automated text summarization.
Hamiltonian Minimization in the NISQ Era

This paper outlines the use of noisy intermediate-scale quantum (NISQ) computers for Hamiltonian minimization problems. We delve into the mathematical formulation of Variational Quantum Eigensolver (VQE), Quantum Annealing (QA) and Quantum Approximation Optimization Algorithm (QAOA), with computational results for a 3-qubit minimization problem and its extension to 6-qubit, 13-qubit and 140-qubit. We show how different initial parameters leads to optimal, less accurate, or no satisfactory solutions using the considered versions of VQE and QAOA, a well known challenge. For all problems, the optimal solution was found using QA and hybrid solvers. This work serves as a hands-on approach to understand quantum annealing, variational quantum algorithms, quantum hardware limitations and current landscape.
SG-CIM Model Verification and Validation Framework in Business Middle Platform

With the continuous development of State Grid middle platforms and expansion of power grid applications, SG-CIM model verification has received increasing attention. It plays a crucial role in ensuring the data accuracy and consistency for information applications tailored to the needs of different professional departments in the power industry. This paper provides an overview of the SG-CIM middle platform, emphasizing its crucial role in supporting data sharing and analysis applications between different professional and operational units. We outline key steps involved in the verification and validation process for SG-CIM models, including input checks, model validation, sensitivity analysis, and model evaluation modules. Furthermore, the paper also highlights how this framework fosters collaboration between different parts of an organization by ensuring that data used in these applications is accurate and consistent across different business processes.
The Algorithm for Constructing the UAV Route Based on the Combined Method

This article addresses one of the navigation challenges of unmanned aerial vehicles (U A V s) in hostile environments - the construction of a safe route from the starting point to the destination point. To solve this problem, we propose initially defining threat points using a normal distribution, determining candidate points for constructing a preliminary route, and then using the Voronoi diagram vertices to construct a Delaunay triangulation for the obtained points. The shortest path is then determined from the “safe” edges of the triangulation based on Dijkstra's algorithm. Unlike existing route construction algorithms for UAVs, the developed original algorithm improves the promptness of determining the shortest and safest route during UA V navigation. The developed algorithm was implemented programmatically using the Python programming language and graphical tools of Matlab. The results of the computational experiment confirmed the functionality and effectiveness of the propose.
Big Data Complexity, Dependability and Emerging Applications

In order to improve the accuracy and dependability of results in big data applications, numerous models, programs, software, hardware, and technologies have been developed and proposed. However, picking the right technology in this situation can be difficult and time-consuming. Technical compatibility, deployment complexity, costs, effectiveness, performance, dependability, help, and potential security issues are just a few of the factors that need to be carefully considered.
Surveillance Application Analysis of Beihang Aviation Satellite-1

With the rapid development of air traffic control (ATC) globalization, the space-based ADS-B system has attracted extensive research in the field of ATC because of its global coverage characteristics to overcome the blind spots of existing ground-based systems. Beihang Aviation Satellite-1 is China's first ADS-B technology verification satellite for ATC surveillance requirements and has conducted in-orbit technology verification of space-based ADS-B. This paper discusses the availability of Beihang Aviation Satellite-1 in various flight phases for civil aviation and general aviation surveillance applications with its in-orbit data, and analyzes the surveillance coverage radius and the position message update interval. The results show that Beihang Aviation Satellite-1 has the capability to provide global surveillance of civil aircraft in all phases of takeoff/landing, departure/approach and cruise, as well as the capability to track general aviation aircraft on a global scale. The maximum surveillance coverage radius is more than 2,800 km and the update interval is less than 8 seconds in different-density airspace.
Smart-Kube: Energy-Aware and Fair Kubernetes Job Scheduler Using Deep Reinforcement Learning

One of the most challenging problems in the popular orchestration framework Kubernetes is assigning sufficient resources to containers to operate at a required level while also avoiding excessive resource allocation which can delay other jobs in the cluster. A variety of heuristic approaches have been proposed to tackle this problem but these require considerable manual adjustments which can be laborious. Reinforcement learning approaches have been proposed to address this issue but these proposals do not consider the energy consumption of the cluster. This is an important component of the problem due to the commitments of large cloud operators to carbon neutrality. We have proposed a system called Smart-Kube to achieve a target utilization on nodes while maintaining energy consumption at a reasonable level. An experimental framework is designed on top of real-world Kubernetes clusters and real-world traces of container jobs are used to evaluate the framework. Experimental results show that Smart-Kube can approach the target utilization and reduce energy consumption in a variety of ways depending on the preferences of the cluster operator for a variety of cluster sizes.
Modification of the Canny Edge Detector for Recognition of Image Markers of Architectural Plans

The article describes an algorithm used for recognition of image markers of design and estimate documents of the developer's architectural plans using computer vision technology. The main goal of the study is to propose a pre-processing method capable of improving and revealing the features of the given images and solving the problem of noise reduction. The article describes the analysis of the Canny operator and considers its modification, where the Chambollele algorithm-based Rudin-Osher-Fatemi model is used for image pre-processing. The method described is effective in detection of edges on images of architectural plans. An approbation of the developed software modules and a comparative analysis of the results of the algorithms were carried out.
Improved Random Forest Based Anomaly Detection for Urban Rail Transits

As the construction of urban rail transit is rapidly expanding, the maintenance costs associated with it are also increasing. However, traditional anomaly detection methods have limitations as they require setting thresholds for various indicators to monitor rail transit status. To address this issue, there is a pressing need for utilizing machine learning methods to detect possible anomalies in trains. This paper proposes an anomaly detection method based on random forest, which can estimate the abnormality score of the train's traction motor based on low-dimensional sensor data and insufficient labels collected by the motor sensor. This approach can identify potential anomalies of the motor and help maintenance personnel develop specific plans to address them. In addition to the proposed anomaly detection method, we have designed a train traction motor anomaly alarm system that effectively detects potential anomalies in the train traction motor covering data pre-processing and anomaly warning. Compared to deep learning models that have high training costs, our proposed method requires less training data, making it suitable for situations with limited training data. The results demonstrate that our method can significantly improve the accuracy of anomaly detection compared to traditional machine learning methods.
Analysis of the Demodulation Performance of Space-Based ADS-B Signals

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology has become a new research hotspot in the field of aviation surveillance due to its global coverage capability. While the space-based ADS-B system can solve the blind spots of the terrestrial ADS-B system, it also brings new problems such as weak ADS-B reception energy and increased signal conflicts. Aiming at the demodulation of weak signals for space-based ADS-B, this paper derived the signal-to-noise ratio (SNR) threshold for the demodulation of space-based ADS-B signal and analyzed the BER distribution as well as the relationship between SNR and correct decoding probability of the coherent demodulation method. Through simulation experiments and in-orbit data verification, the theoretical analysis of this paper met the actual situation, and the coherent demodulation method can meet the requirements of space-based ADS-B weak signal reception, which provides a direction for the improvement of the sensitivity of space-based ADS-B receivers.
Twitter Account Analysis for Drug Involvement Detection

Social media is integral to modern communication, connecting billions of individuals globally. However, this connectivity has also led to negative consequences such as violence, cyber-bullying, cybercrime, and even the promotion of illicit drug-related activities. Among these problems, this paper focuses on an analysis of how drug dealers advertise and drug buyers request drugs using Twitter. For this purpose, we first collect data from users involved in (assumed) illicit drug trade as well as regular users. After monitoring and collecting user data, we store them with our data hierarchy approach and then apply preprocessing, which applies some data cleaning as well as the introduction of additional data from emojis and shared images. This helps better understand and analyze the data. We also apply n-gram analysis to identify patterns and commonalities between sellers and buyers. The results reveal the characteristics of Twitter accounts with (assumed) illicit drug trade including the use of specific words, emojis, and types of photos in their tweets. We observe that Twitter lacks a robust system for consistently monitoring drug-related accounts, with some being suspended while many remain active. Our research and results represent a crucial step toward the development of systems that can effectively identify and address drug-related activities on social media platforms.
An Analysis and Comparison of the Development Status of Green Data Centers in China

In the context of the dual carbon policy, various industries in China have started their own low carbon reform journey. As a major power consumer, data centers have received widespread attention for their progress in energy saving and emission reduction. In this paper, we investigate the current industry and market status of green data centers, focusing on the technology and construction in China. We also analyze and compare typical green data centers in China. Finally, we conclude with an outlook on green data centers, and an analysis of the future direction of development.
An Electric Power Data Privacy Protection Algorithm Based on Multiple Composite Functions

To address the problems of existing privacy computing technologies, this paper analyzes the characteristics of privacy information protection, and proposes an algorithm framework for power data privacy protection based on multiple Function composition, which integrates the existing privacy computing technology. This algorithm framework divides the process of protecting privacy information into three stages: “privacy identification, privacy data protection, and privacy logic protection”, and establishes the basic function operation logic. The corresponding Function composition of each stage is designed, the specific algorithm operation function parameters and processes are given, and the overall technical scheme of power data privacy information protection is provided. The rationality of the algorithm framework was demonstrated based on the principles of “on-demand, moderation, and scenario differentiation”, and its advantages were pointed out. Finally, experiments are given to prove the effectiveness of the approach proposed in this paper.