Reviewerly/exHarmony huggingface dataset:
An Unsupervised Approach to Bilingual Lexicon Induction with Comparable Corpora

The steady growth of globalization leads to an increasing requirement for translations among different languages and an urgent need to keep pace with the challenge of cross-lingual international exchanges in multiple fields. Bilingual lexica are important language resources that provide valuable information for semantic equivalence of words across Languages, which has been a research hotspot. However, under resource-poor scenarios, it is difficult to find sufficient cross-lingual knowledge for model training, which requires the exploration of unsupervised learning methods. Unsupervised BLI aims at obtaining the bilingual translation dictionary from monolingual corpora of source and target languages without any cross-lingual knowledge, which can bring huge benefits to NLP tasks in resource-poor fields. Based on previous research, this thesis proposes a neural bilingual lexicon induction (NBLI) method with comparable corpora. This unsupervised method jointly learns word embeddings of source and target languages in the same vector space based on a bidirectional Long Short-Term Memory (BiLSTM) network, and then matches translation pairs through the Cross-domain Similarity Local Scaling (CSLS) retrieval to implement the induction task. It performs well in the general domain and improves the induction performance in the optoelectronic domain between English and Chinese, which breaks the limitation of the pure linear transformation model based on isomorphic assumption in resource-poor scenarios and solves the hubness problem in the bilingual vector space. Furthermore, it requires only comparable corpora in the model training process, and is applicable to various language pairs and specific fields.
Advanced Encryption Standard information encryption in pipeline cutting and welding system based on Django framework

With the rapid development of encryption technology and intelligent management system, a large amount of information needs to be processed online. It contributes to the development of intelligent information encryption management system. This paper designs and implements a kind of Advanced Encryption Standard (AES) information encryption in pipeline cutting and welding system based on Django framework. After the experimental verification, the system can realize the information function of encryption and decryption. It is intuitive and convenient that it has statistical performance on the enterprise pipeline welding information. It plays an important role in promoting the efficiency of the production task.
Stellarium Virtual Environment as a Means of Implementing Interdisciplinary Connections During the Study of Astronomy

The article is devoted to the study of the problem of implementing interdisciplinary connections in the educational process. It is outlined that interdisciplinary connections are a system of relations between knowledge, abilities and skills that are formed as a result of the consistent reflection of objective connections with reality in the means, methods and content of educational disciplines. Significant opportunities for the implementation of interdisciplinary connections belong to astronomy. As an educational subject, astronomy has a significant worldview potential, which is realized through connections with other disciplines (physics, mathematics, information technologies, geography, chemistry, history, literature), which are revealed in the educational process when solving applied problems from various subject areas. A methodical approach to the implementation of interdisciplinarity was implemented in the process of solving the integrated practical task $\langle\langle$Verification of historical facts and events using the Stellarium virtual environment$\rangle\rangle$. Solving the tasks of integrated content ensures the formation of a unified natural and scientific picture of the world, intensify the research activities of students, promotes active mastery of modern technologies, the formation of professional interests, etc.
Lateral Ventricle MR Image Segmentation Based on Recursive Gated Convolution with Multi-Scale Feature Fusion

The lateral ventricle, a crucial component of the brain structure, holds immense significance in medical image analysis and clinical diagnosis. Nevertheless, accurately segmenting lateral ventricles remains a challenging endeavor due to variations in their shapes and sizes, as well as image noise. In response to this challenge, we propose a novel deep learning network architecture that combines recursive gated convolution and multi-scale feature fusion strategies. Recursive gated convolution, as an effective convolution operation, adeptly captures long-range dependencies in images, thereby enhancing the network's capacity to discern lateral ventricular regions. Concurrently, we introduce multi-scale feature fusion to improve the network's characterization ability and segmentation accuracy by integrating feature information from different scales. Subsequently, we conducted an extensive experimental evaluation utilizing the publicly available IBSR dataset. The results of our experiments demonstrate the superiority of our method in MRI lateral ventricle segmentation. Our model achieved outstanding Pre, Dice, and Jac results of 92.93%, 92.84%, and 89.39%, respectively. Compared to other algorithms, our method accurately localizes and segments the lateral ventricle region, yielding finer segmentation outcomes.
Voice Controlled Operations and Navigations for e-Commerce

Voice based operations are increasingly being used by the users in their day to day lives and are being used in various sectors for making their lives easier simply by speaking rather than typing keywords. Performing actions using voice is faster and accessible to the users who are not comfortable with the UI of the application. However, there are very few implementations of these voice-controlled systems on the web and e-commerce. This paper presents a browser extension which can be used on various e-commerce sites and get appropriate responses. This tool will allow users to perform human-to-machine interaction, anytime and anywhere using speech recognition and provide ease to the users without using their fingertips. This tool will be a chrome extension (add-on or plugin), that will take voice as an input and will convert it to text, extract meaning from the text, and then perform a wide variety of tasks including the product search, add to cart, in-app navigations, and other operations through speech. This extension will be a combination of technologies, such as automatic speech recognition, text-to-speech service, and natural language understanding, to engage in natural conversational interactions with humans.
Spatial Target Tracking Algorithm Based on Heterogeneous Sensors Data Fusion

The track of Spatial targets is basically non-linear, which makes the cost of using homogeneous sensors for space target tracking significantly increase. Data fusion using heterogeneous sensors can achieve information complementarity, thereby improving recognition ability. In this paper, two heterogeneous data of radar and infrared are fused at the measurement level, and then the data are extended Kalman filter. The simulation example is given. The results show that this is an effective method for heterogeneous data fusion to track space targets.
Implementing ownCloud for Scientific Project Coordination as Document Management System

This paper presents the theoretical background and implementation approach for utilizing ownCloud, an open-source cloud storage platform, as a document management system (DMS) for project reporting in an educational project. The document outlines the motivation for adopting ownCloud, discusses its key features and benefits and presents a step-by-step implementation process. The paper also highlights the potential impact of ownCloud as a secure and scalable solution for managing documents in project implementation, fostering collaboration, and enhancing data privacy.
Dependency on AI-Based Writing Tools in English Learning: Implications for Human-Computer Interaction

This study investigates the reliance of English learners on AI-based writing tools and the repercussions for human-computer interaction (HCI). Through survey and interview administered to a sample of English learners, this study investigates their reliance on AI tools, its impact on their writing skills, and their attitudes toward these technologies. The results disclose a significant reliance on AI tools among participants, with the majority relying on them for writing assignments and having doubts about their writing ability without them. The findings emphasize the potential benefits of AI tools for enhancing learners' writing skills and boosting their confidence. However, they also raise concerns about overdependence, decreased participation in face-to-face learning, and addiction to these technologies. The research highlights the need for a balanced incorporation of AI tools in language learning. The study contributes to the field of Human-Computer Interaction (HCI) by providing insights into user experiences and promoting informed decision-making regarding the incorporation of AI-based writing tools in language learning contexts.
The Ratio of the KNN Method with the SVM Method on Processing Unstructured Data in Social Media

The development of internet technology and social media has had a significant impact on people’s lives, including in Bandung City. Users share their opinions through comments on social media accounts. We conducted a study to classify these comments into eight categories, namely, health, education, administration, economy, infrastructure, citizenship, social, and environment. Our methodology involved several steps, starting with constructing an Indonesian-based social media data dictionary extracted from a news portal. We then collected 800 Instagram comments and performed data pre-processing, training, and classification on our dataset using two models: K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). We used randomized search and grid search approaches to find the best hyperparameter for our models. Our experimental results show that the SVM outperformed KNN, both on the training and testing datasets are 82.32% and 74.32%, respectively. The results indicate that SVM is more reliable than the KNN for our dataset. However, we identified that our model misclassified certain comments into different categories. The result needs further analysis and employs different strategies to improve the accuracy.
Comparative Analysis of Forensic for Whatsapp Desktop on Mac OS and Windows Using IDFIF V2

WhatsApp is the most widely used instant messaging application compared to other instant messaging platforms. According to a report from Business of Apps, the total number of WhatsApp Messenger users worldwide reached 2.41 billion in 2022. WhatsApp is available not only on mobile devices such as Android and iOS but also on desktop computers running Windows, Linux, and Mac operating systems. However, with the increasing number of WhatsApp users, it is undeniable that there is also a rise in criminal activities associated with the platform. From August 2018 to January 2019, the Indonesian Ministry of Communications and Informatics received reports of 43 hoax content disseminated through the WhatsApp Messenger application. The Integrated Digital Forensic Investigation Framework Version 2 (IDFIF V2) is an update to the IDFIF Vl model. IDFIF V2 is used in the digital evidence investigation process and is included in comprehensive stages that accommodate all investigation phases. This research compares the digital forensic results of the WhatsApp Messenger desktop application that is involved in fraud involving electronic transactions on different operating systems, specifically Mac OS and Windows. IDFIF V2 will serve as a reference for discovering digital evidence in the investigation process, and the Magnet Axiom tool will function in digital forensic analysis.
Realization of Ackermann robot obstacle avoidance navigation based on Multi-sensor fusion SLAM

The Ackermann kinematic structure has a wide range of applications in various fields. This system has been utilized to design an obstacle avoidance navigation system for Ackermann robots using a Multi-sensor fusion SLAM approach, enabling the fusion of data obtained from an inertial measurement unit and laser odometry. Additionally, the traditional A* algorithm is improved including the heuristic function and point selection strategy of the algorithm, utilizing a combination of improved A* algorithm and local planning algorithm TEB as the navigation strategy to achieve motion control, mapping, positioning, navigation, based on the characteristics of the Ackerman steering structure. To verify the system's performance, an experimental test was conducted in a laboratory environment, using the Ackermann robot hardware platform. The experimental results demonstrate that the system can achieve high map accuracy, reaching the level of cm, and the improved A* algorithm is superior to the traditional A* algorithm, indicating excellent navigation accuracy.
Comparative Analysis of Binary and Interpolation Search Algorithms on Integer Data Using C Programming Language

To perform data search, there are various algorithms that have been developed, such as binary search and interpolation search. There are various views on these two algorithms, regarding which one is better than the other. From the various views that have emerged, this study aims to find out which algorithm is more efficient in terms of time complexity and memory complexity using Big-O notation, as well as its execution time in the C programming language. This research was conducted with 6 datasets consisting of 3 datasets with uniform data types and 3 datasets with non-uniform data types. Each dataset type consists of 1 million, 10 million, and 100 million data. The results show that the binary search algorithm is more efficient to use rather than interpolation search on datasets that are not uniformly distributed or the data type is unknown. While the interpolation search algorithm is more efficient to use rather than binary search on datasets that are known to be uniformly distributed.
Classification of Clothing Quality Dimension Based on Consumer Review Using BERT and RoBERTa

Consumer feedback plays a crucial role in enhancing the quality of clothing products. However, effectively analyzing the vast and diverse range of reviews necessitates the application of Natural Language Processing (NLP) techniques and advanced language models. This research proposes the utilization of a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model and an optimized RoBERTa (Robustly Optimized BERT approach) model for multi-label classification tasks, with the objective of comprehending consumer reviews more efficiently. To conduct the study, a dataset was compiled by collecting reviews from Amazon.com using a dedicated scraping platform. Additionally, a Kaggle public dataset was incorporated to augment the dataset’s comprehensiveness. The fine-tuned BERT and optimized RoBERTa models were employed to analyze the collected data and extract meaningful insights. The experimental results indicate that the RoBERTa model exhibits superior performance compared to the BERT model, as evidenced by its macro-averaged F1 score of 0.87 and micro-averaged F1 score of 0.85. These results highlight the robustness and efficacy of the RoBERTa model in effectively comprehending consumer reviews for the given task of multilabel classification. By leveraging the proposed approach, which combines NLP techniques and advanced language models, the study contributes to enhancing the understanding of consumer feedback and enables more efficient analysis of clothing product reviews. The findings underscore the potential of leveraging state-of-the-art language models for improving the quality and relevance of consumer-driven insights in the fashion industry.
Research on Social Stability Coefficient based on GWO-XGBoost-LSTM

A “color revolution” achieves regime change by attacking the weakest link in society, but once the ratio of each link in society is out of balance, it will cause social deformation and thus affect social stability. To study the factors affecting social stability and avoid “color revolutions”, it is necessary to establish an effective social stability index system, and on this basis, to build a social stability coefficient model. To establish a complete social stability index system, this paper selects 44 indicators such as the total number of armed personnel, percentage of military funds, total population, per capita GDP, the average age of education, etc. of European and American countries and regions during the period of 1962–2022, and use AHP-EWM model to weight the summation under the premise of dimensionless dimensioning, and then obtain population, defense science and technology, environment, education, economy, and agriculture after dimensionality reduction. The six indicators were then downscaled to obtain population, national defense, science and technology, environment, education, economy, and agriculture, and the social stability coefficient was classified into five levels from 0 to 1 using the TOPSIS evaluation algorithm, with the higher the level the worse the social stability. Using Spearman and Akaike Causality to test the indicator system, it was found that there was a significant correlation and causality between the stability coefficient and each indicator, and the indicator system was well established. To establish a reasonable model of social stability coefficients, this paper optimizes the initial parameters of XGBoost by GWO, fits the prediction to the index system established in Problem 1, then uses CEEMD to process the residuals generated by the GWO-XGBoost model to decompose its nonlinear components, and uses the LSTM model to fit the residuals quadratically, to modify the GWO- XGBoost model. The modified GWO-XGBoost-LSTM model shows superior performance, and its average error is reduced from the initial 0.0218 to 0.0078. For regions that failed to launch “color revolutions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\prime\prime$</tex> , this paper takes Iran as an example, and after bringing its 44 indicators into the social stability coefficient model in 2018, it comes out that the social stability coefficient of Iran is 0.637, and the social stability level is in the second level of stability to the third level of stability, which is a more stable state at this time. There may be some precursors of social unrest. At the same time, Iran's population development and education level are at a high level, and the general education and knowledge of the Iranian people are high, so the number of people who participate in internal conflicts and revolutions is reduced, which leads to the failure of color revolutions. The article concludes with a summary of this paper's research and submits development proposals in order to prevent color revolutions from posing a threat to social stability.
Privacy and Security in the Use of Voice Assistant: An Evaluation of User Awareness and Preferences

Recently, voice assistants have become increasingly popular with millions of users relying on them to complete daily tasks and access information. While it may be convenient to use, some may question its security and are concerned about their privacy. While the users are attracted to the beneficial services provided by voice assistants, they are risking their personal information. This study aims to evaluate user awareness regarding the privacy and security aspects of voice assistants, and their preferences for future development of voice assistants. The literature review in this study obtained three vulnerabilities of privacy and security in the use of voice assistants and the countermeasures that can be taken. The user-centric countermeasures to mitigate the vulnerability of voice assistants were chosen for analysis by systematically evaluating 151 relevant journal and conference articles from 6 databases and 52 were chosen for analysis. To evaluate user awareness and preferences regarding the use of voice assistants, a questionnaire with a total of 37 participants who have experience in using voice assistants was done. The results showed that the participants were aware of the voice assistant's vulnerability and were concerned that their data might be misused.
Convolution Neural Network Approach for Satellite Object Detection and Recognition

Image processing researchers have improved remote-sensing item detection. Satellite image analysis can identify houses, farms, highways, ships, airports, and hangars. An example of a more focused remote sensing imaging-based object-identification approach for military usage is a stationary aircraft detection system. The model could identify airport-parked jets. Deep learning detects airport planes in the proposed research. Google Earth satellite images dominate this collection. Deep learning modeling uses cutting-edge RCNN (Regions with Convolution Neural Network) to perform well. The training uses a custom CNN architecture throughout the system's early development. After anchoring the bounding boxes of stationary aircraft objects, RCNN detects regions. CNN training requires a large dataset of airplane images. Turkish airport satellite images validate the system. The investigation proves the model's high-performance airplane recognition effectiveness. The YOLO-v3 came in second place, offering a good compromise between accuracy and quickness despite its somewhat lower capability. The recommended aircraft identification framework uses matching bounding boxes to identify airplanes in test photographs, although the classifier network configuration utilized in the early research phase has a 98.4% success rate.
DLT Based Smart Medical Ecosystem

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices.
Category Classification of Content from Instagram Business Pages

The paper shows the possibility of using texts related to hashtags for the automated classification of texts from business pages or organizations on the social network Instagram. Three groups of texts for neural network training related to the hashtags “IT”, “medicine” and “astronomy” have been identified on Instagram. Texts from Instagram posts of “microsoft”, “med.base” and “nasa” accounts are collected as a test data set, respectively. The best results of post-categorization are observed for the field of information technology, fewer texts are specified by words in the medical field with hashtags as trained data. The classifier can be used to automatically sort content or determine the probable belonging of pages to a defined set of categories.