Reviewerly/exHarmony huggingface dataset:
Research on Differential Signal Wideband Common-Mode Suppression Filtering Cable

In order to improve the reliability of electronic circuits, differential transmission technology is gradually applied to the transmission of high-speed digital signals. Compared with a single transmission line, the transmission of differential pairs has great advantages in anti-noise, anti-crosstalk, EMI. However, in the actual circuit, there will inevitably be some unbalanced factors, causing part of the differential signal to become common mode noise, which will affect the transmission of useful signals and destroy the signal integrity of the differential signal. How to effectively reduce the EMI of the differential signal and improve the anti-interference ability of the differential signal under the premise of ensuring the integrity of the differential signal is an urgent problem in the field of differential signal processing.compact planar dual-band filters.
Review on Sentiment Analysis of E-commerce Product Comments

Deeply mining and analyzing the comment data of e-commerce products is of great significance to reduce consumers' perceived risk and assist consumers in decision-making. Through literature research, this paper combs and summarizes the technical development trend and application scenarios of emotional analysis of e-commerce product comments. Firstly, the acquisition method, preprocessing method and text representation method of Chinese comment text data are described; Secondly, it introduces text emotion analysis based on different models and technologies; Then it analyzes the influence mechanism of comment text on e-commerce product sales, the information attribute and emotional attribute of comment, and the latest research results of the impact of comment contradiction on e-commerce product network sales; Finally, it is proposed that future research will further focus on improving the quality of comment data, integrating multimodal data in comments and studying the role of emoticons in emotional expression.
Phishing Detection with Browser Extension Based on Machine Learning

Social engineering has become an emergent field with the growth of network attacks. Phishing, is a popular social engineering attack types, has also become an important research topic in the network security field. In this study, we implemented a browser extension for detecting phishing websites based on a machine learning mechanism. The proposed front-end browser extension can detect phishing websites in real time and hence, prevent users from inputting their personal information to these sites. The experimental results show that the proposed detection mechanism can reach a high accuracy up to 97.5% within 4.5 s with 150 users on average.
Pengaruh Perpustakaan Digital Dalam Meningkatkan Minat Membaca

The purpose of this article is to provide a deeper explanation of the benefits of digital libraries in increasing the reading interest of the millennial generation in the era of globalization. The method used is a literature study in order to find out about digital libraries and the reading interest of the Indonesian people by utilizing library material sources to obtain research data. The low interest in reading for the Indonesian people has a negative impact on society, especially the rapid development of information technology and digitalization that has a considerable influence on society, especially for the millennial generation. Therefore, the presence of this digital library is expected to be put to good use in increasing the reading interest of the millennial generation more effectively and efficiently because they can directly access the digital library quickly. In the future, it is hoped that the millennial generation can take advantage of this digital library to increase their interest in reading. In order to compete with other countries and give birth to the next generation of the nation who are smart, competent, and qualified.
Removing Biased Data in Knowledge Graph with Ontological Reasoning

In this paper, we propose an approach that will help reduce the biases in the Knowledge Graph (KG). While understanding new knowledge formed by connecting previous KGs, it is a difficult task to notice in the KG whether it is biased or not and in which direction it leads to the biased, since KGs represent complex relationships between entities, and biases may be introduced through these relationships. Overall, this study highlights the importance of considering the potential way to understand the knowledge in a fair way from the KGs. By realizing responsible KGs and conducting further research into the underlying mechanisms, we can work towards ensuring the implementation of strategies to reduce biases and create KGs with fair results.
Text Summarization and Keyword Extraction

With the explosion of data in the digital age, it is an important yet challenging task to extract meaningful information from long texts. In this paper, a novel framework is presented to facilitate users in extracting summaries and keywords from long texts at real-time. It uses a hybrid approach based on feature extraction and unsupervised learning to generate quality summaries. In addition, it integrates machine learning with semantic methods to extract keywords and phrases from the source text. The framework is deployed as a mobile app that allows users to manage, share and listen to the extracted information to improve user experience. To test the effectiveness of the work, experimental and research evaluations are carried out on DUC 2002 dataset using ROGUE parameters. Results demonstrate a higher F1-score than the state-of-the-art methods used for extractive summarization on the same dataset. Experiment also shows an accuracy of 70% for the keyword extraction method, which is in par with other work in the field.
Designing a Macro-VBA Excel-based Kit List Printing Application for the Supporting Department of PT XYZ

The recording of raw material orders in PT.XYZ must be done through the embedded ERP system in the company, Microsoft Dynamics GP. However, the system is not perfect as it is not suitable for workers who follow traditional practices. To avoid losing goods, PT XYZ has started implementing kit recording, but a shortage of workers who enter KIT data can lead to human error. To overcome this, PT XYZ can automate the printing of KIT using VBA in Microsoft Excel. VBA allows applications to work more efficiently by easily manipulating object models. Excel macros support VBA programming and enhance the flexibility of the programming language. A well-organized VBA Excel application can produce a KIT list by pulling data from the production order sheet attached to the bus chassis as it moves on the production line. Once the specifications for a bus body to be produced are obtained, the user can edit and click one button to get the KIT that is suitable for that bus. Therefore, VBA in Excel can help PT XYZ improve the recording of raw material orders and reduce human errors that can cause financial losses.
A Comparative Study of Stemming Techniques on the Malay Text

Text stemming, an essential preprocessing step in the development of Natural Language Processing (NLP) applications, involves the transformation of various word forms into their root words. Stemming plays a critical role in decreasing the volume of text, thereby enhancing the efficiency of various computational tasks such as information retrieval, text classification, and text clustering. Stemming is a rule-based approach. On the other hand, it frequently suffers affixation errors that result in under-stemming, over-stemming, or both, as well as unstemmed or spelling exceptions. Every language has different stemming techniques, and among the most well-known Malay stemming algorithms are the Othman and Ahmad algorithms. Therefore, this study aims to compare the performance of the stemming errors between the Othman and Ahmad algorithms in stemming Malay text, particularly on two different domains of textual datasets, which are the course summaries of the education domain and housebreaking crime reports of the crime domain. The Othman algorithm presents a set of 121 stemming rules (set A). In the meantime, Ahmad's algorithm proposes two distinct sets of stemming rules, comprising 432 (set B) and 561 rules (set C), respectively. Based on the experiment results with 100 course summaries, the Ahmad algorithm (Set B) obtained a higher accuracy rate of 93.61%. The second highest is the Ahmad algorithm (Set C) with 93.53%. The Othman algorithm achieved the lowest accuracy with 86.04% compared to the other two algorithms. Meanwhile, findings from the experiment with 100 housebreaking crime reports show similar results, with the Ahmad algorithm (Set C) achieving the highest stemming accuracy of approximately 93.80% and the Othman algorithm producing the lowest stemming accuracy (83.09%). The result indicates that stemming accuracy is consistent across different types of datasets.
Robust Extreme Learning Machine with Exponential Squared Loss via DC Programming

Extreme learning machines (ELM) have recently attracted considerable attention because of its fast learning rate, simple model structure, and good generalization ability. However, classical ELM with least squares loss function is prone to overfitting and lack robustness in dealing with datasets containing noise and outliers in the real world. In this paper, inspired by the maximum correntropy criterion, an exponential squared loss function is introduced, which is nonconvex and insensitive to noise and outliers. A robust ELM with exponential squared loss (RESELM) is presented to overcome the overfitting problem. The proposed model with nonconvexity is difficult to be directly optimized. Considering the superior performance of difference of convex functions (DC) programming in solving nonconvex problems, this paper optimizes the model by expressing the objective function as a DC function and employing DC algorithm (DCA). To examine the effectiveness of the proposed algorithm in noisy environment, different levels of outliers are added to the training samples in the experiments. Experimental results on benchmark data sets with different outliers levels illustrate that the proposed RESELM achieves significant advantages in generalization performance and robustness, especially in higher outliers levels.
A Hybrid Deep Learning Framework for Efficient Sentiment Analysis

In the era of Microblogging and the rapid growth of online platforms, an exponential rise is shown in the volume of data generated by internet users across various domains. Additionally, the creation of digital or textual data is expanding significantly. This is because consumers respond to comments made on social media platforms regarding events or products based on their personal experiences. Sentiment analysis is usually used to accomplish this kind of classification on a large scale. It is described as the process of going through all user reviews and comments that are discovered in product reviews, events, or similar sources in order to look for unstructured text comments. Our study examines how deep learning models like LSTM, GRU, CNN, and hybrid models (LSTM+CNN, LSTM+GRU, GRU+CNN) capture complex sentiment patterns in text data. Additionally, we study integrating BOW and TF-IDF as complementing features to improve model predictive power. CNN with RNNs consistently improves outcomes, demonstrating the synergy between convolutional and recurrent neural network architectures in recognizing nuanced emotion subtleties.In addition, TF-IDF typically outperforms BOW in enhancing deep learning model sentiment analysis accuracy.
Research on Qubit Mapping Technique Based on Batch SWAP Optimization

The conventional approach for initial qubit mapping in the Noisy Intermediate-Scale Quantum (NISQ) era typically uses a static heuristic strategy, overlooking insufficient qubit neighborhood in subsequent operations, resulting in excess additional SWAP gates. To address this, we introduce a multifactor interaction cost function considering qubit distance, interaction time, and gate operation error rates, enhancing SWAP gate selection in the traditional strategy. Considering quantum hardware constraints, we propose Batch SWAP Optimization Strategy (BSOS). BSOS tackles qubit mapping challenges by leveraging optimal SWAP gate selection and a SWAP-based batch update technique, effectively minimizing SWAP gates throughout circuit execution. Experimental results show that BSOS significantly reduces additional gates by intelligently selecting SWAP gates and using batch updating, with a 38.1% average decrease in inserted SWAP gates, leading to a 12% reduction in hardware gate counting overhead.
Privacy-Preserving Location-Based Advertising via Longitudinal Geo-Indistinguishability

As location data have been increasingly adopted in location-based advertising (LBA), revealing locations to untrusted service providers has raised severe privacy concerns. Recent studies propose obfuscation mechanisms built upon geo-indistinguishability (geo-IND) to provide formal privacy guarantee. Unfortunately, due to the high degree of spatiotemporal regularity in human mobility pattern, the privacy cost will be unacceptably high in this situation, leading to accurate inference of user real locations. In this study, we identify this privacy risk in LBA scenarios under long-term and multi-platform assumption. We demonstrate an attacker can infer 75%∼90% of top-1 locations within a range of only 200 meters. To address it, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PrivLocAd</i> , a novel system which can provide longitudinal privacy guarantee. The novelty of PrivLocAd stems from a novel surrogate-based obfuscation, which generates multiple surrogate locations to improve the privacy-utility trade-off. In addition, two novel obfuscation mechanisms, the two-stage Gaussian and multi-level surrogate generation mechanism in charge of surrogate generation can achieve the longitudinal privacy guarantee in intra- and inter-platform condition respectively. Our experimental results demonstrate PrivLocAd is able to defend against the attack, which reduces the inference rate to less than 1% of user top-1 locations in the 200 meter range.
An Efficient and Accurate A-star Algorithm for Autonomous Vehicle Path Planning

Efficient and accurate path planning algorithm of unmanned vehicles for unstructured road is of great significant for field disaster relief. An improved A–star algorithm for unmanned vehicle path planning is proposed in this paper to achieve optimized path with shorter length and less inflection point. Redundant safety spaces are set up in the improved algorithm to filter out impassable narrow roads and to avoid collisions between vehicles without obstacles. The prejudgment planning strategy and redundant inflection point elimination strategy are integrated into the algorithm to obtain a better driving path. Furthermore, a safe corridor approach was used in the trajectory optimization to avoid repeated checks and corrections and save computer time. The simulation results show that the path planned with the improved algorithm has shorter length, less inflection point and smoother than the path planned with the initial algorithm, and can avoid collision between vehicle contours and obstacles.
Invisible and Steganalysis-resistant Deep Image Hiding Based on One-way Adversarial Invertible Networks

Deep image hiding is a challenging image processing task that aims to hide a secret image into a cover image of equal size perfectly. How to improve the imperceptibility of deep image hiding while ensuring high computational efficiency is a primary challenge. Where imperceptibility means not being visually perceived while not being perceived by the steganalysis model. In this paper, we propose a novel deep image hiding framework called DIH-OAIN (Deep Image Hiding based on One-way Adversarial Invertible Networks) to address it. Firstly, an image cascade framework is introduced to extract image semantics and details with dual-resolution branches, and reduces computation complexity by balancing image resolution and model complexity. Secondly, a hidden probability guided module is designed to constrain the secret image to be hidden in the texture region, utilizing the image texture complexity as prior knowledge. The above two points can effectively improve visual imperceptibility. Finally, a one-way adversarial training strategy is proposed to enhance the model imperceptibility. A series of experimental results show that the proposed method is significantly improved in imperceptibility comparing to state-of-the-art deep image hiding algorithms, while maintaining a low computation complexity.
A General Multi-Scale Pyramid Attention Module for Ship Detection in SAR Images

Compared with large scale ships, small scale ships occupy few pixels and have low contrast, so it poses a great challenge to detect multi-scale ships in SAR images. In order to improve the accuracy of multi-scale ship detection in SAR images, this paper designs a general multi-scale pyramid attention module (MPAM), which is a plug-and-play lightweight module that can adapt to many ship detection networks. In MPAM, a deep feature extraction sub-module (DFES) is first designed to use the multi-scale pyramid structure to divide the feature map into different levels, extracting rich features with resolution and semantic information for multi-scale ship detection. The channel multi-layer attention fusion sub-module (CMAFS) and spatial multi-layer attention fusion sub-module (SMAFS) are then designed to fuse the channel and spatial attention blocks on different level feature maps, which could better learn the dependent features from the channel and spatial dimensions, to enhance the feature representation. Finally, the fused feature map is input into the existing ship detection networks to obtain the detection result. Experiments on SAR datasets containing multi-scale ships show that the effectiveness of MPAM in improving the accuracy of the existing ship detection networks.
Evaluating Non-Functional Requirements Classification for Spanish Text: Traditional vs. Deep Learning Approaches

The automatic classification of non-functional requirements helps to reduce time and effort for the stakeholders. Several techniques have been used for this task, including the latest techniques in Machine Learning (ML) and Natural Language Processing (NLP), such as pre-trained models, with promising results. This research aims to analyze the performance metrics to classify requirements into sub-classes of non-functional type. Six distinct algorithms, including both traditional machine learning (ML) and deep learning (DL), were trained using a Spanish-translated PROMISE NFR dataset to assess and compare their performance outcomes. The findings reveal that Support Vector Machine (SVM) with Bag of Word technique, and fastText overperformed the other algorithms. However, fastText stands out between the two due the ease of implementation and the absence of data preprocessing.
Identifying the Potential Geothermal Zones Based on Probability Mapping of Radon Gas Concentration, Lineaments Density, Geochemistry, and Alteration Data

The issue of global warming must be immediately responded to by reducing carbon emissions. However, in Indonesia, the main energy needs are still met by using fossil fuels which produce quite large carbon emissions. The solution to this problem is to use a clean alternative energy such as geothermal energy. Indonesia’s potential geothermal resource is very large, which is around 27,500 MWe or about 30–40% of the world's geothermal potential. Wayang Windu geothermal field in West Java is one of the areas that show a large geothermal potential in Indonesia. Further study was carried out to extend the geothermal potential area by identifying and modeling the permeability zone from the results of Rn gas concentration, lineament density, geochemistry, and alteration data using geostatistical methods. The probability map of Rn gas concentrations, trace elements of alkali metal, pH, temperature, and alteration index are generated using sequential Gaussian simulation (SGS). Meanwhile, for the lineament density data, the spatial distribution of lineament was extracted based on digital terrain model (DTM) using segment tracing algorithm (STA). After that, the probability map was modeled using SGS as well. The result of this study was a spatial distribution of geothermal potential zones based on the permeability index.
Conversation Design for Raising Awareness on the Responsible Use of the Internet

Abstract The rapid process of digital transformation and servitization experienced on a global scale in recent decades, further accelerated by the pandemic crisis, has radically altered the life experience of societies, with technological innovations leading to the emergence of new ethical and legal challenges. The issue of digital literacy and the acquisition of basic skills for responsible use of the internet has become one of the most urgent prerogatives in international government programs to address psychosocial phenomena such as those related to online grooming, cyberbullying, cybersuicide, cyber racing, or online scamming. The proposed work introduces the use of participatory game design as an empowerment tool for young students. The case study of the NetWalking project, developed by an interdisciplinary team of practitioners and researchers in several secondary schools in the city of Palermo (Italy), describes the experimentation of co-design activities of an edugame with a conversational interface (chatbot), which exploits the logic of nonlinear storytelling to actively engage students in playful learning activities, contributing to the development of hard skills (STEM) and soft skills (self-assessment, teamwork and information management).
Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, this makes developers must have sufficient fundamentals and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. The use of NaÃ¯ve Bayes and C 4.5 algorithms into a dataset that has 9 programming languages related to mobile app development. Results obtained in This research is Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native apps frameworks is a technology that will have the opportunity in the future with an accuracy level above 90% with NaÃ¯ve Bayes and C 4.5 algorithms. These results are obtained by considering the results of testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. Expected this researchÂ Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology.