Reviewerly/exHarmony huggingface dataset:
An Interactive Framework of Cross-Lingual NLU for In-Vehicle Dialogue

As globalization accelerates, the linguistic diversity and semantic complexity of in-vehicle communication is increasing. In order to meet the needs of different language speakers, this paper proposes an interactive attention-based contrastive learning framework (IABCL) for the field of in-vehicle dialogue, aiming to effectively enhance cross-lingual natural language understanding (NLU). The proposed framework aims to address the challenges of cross-lingual interaction in in-vehicle dialogue systems and provide an effective solution. IABCL is based on a contrastive learning and attention mechanism. First, contrastive learning is applied in the encoder stage. Positive and negative samples are used to allow the model to learn different linguistic expressions of similar meanings. Its main role is to improve the cross-lingual learning ability of the model. Second, the attention mechanism is applied in the decoder stage. By articulating slots and intents with each other, it allows the model to learn the relationship between the two, thus improving the ability of natural language understanding in languages of the same language family. In addition, this paper constructed a multilingual in-vehicle dialogue (MIvD) dataset for experimental evaluation to demonstrate the effectiveness and accuracy of the IABCL framework in cross-lingual dialogue. With the framework studied in this paper, IABCL improves by 2.42% in intent, 1.43% in slot, and 2.67% in overall when compared with the latest model.
Rancang Bangun Sistem Informasi Penjualan Berbasis Web Pada UMKM ZXID Di Kota Batam

The development of technology is currently growing, one of them is the online development, which is very fast, many companies have started to offer their spare parts online in order to make the most profit, so the competition between companies is growing "spare parts" services. Company. This is why UMKM ZXID needs to create a web marketing information system so that consumers can easily access spare parts information as they can order directly և pay for spare parts through the website. The method used is the SDLC waterfall model, which consists of needs analysis, design, software coding, testing, marketing, and maintenance. The result of this research is a web-based marketing information system that allows consumers to easily order products to order spare parts and saves employees time and money.
Phishing URL Detection Using Machine Learning

Phishing attacks pose a significant threat in the digital landscape, requiring effective detection of phishing URLs. This paper explores machine learning techniques for phishing URL detection, including feature extraction and model training using algorithms such as Logistic Regression, Random Forest Classifier, Decision Tree, Support Vector Classifier, K-Neighbors Classifier, and MLP Classifier. The models were evaluated using labeled datasets and achieved promising accuracy, with the Random Forest Classifier performing best. Deployment of these models in real-time systems enhances protection against phishing attacks. Continuous monitoring, feedback collection, and model improvement contribute to staying ahead of emerging threats. By combining machine learning with other cybersecurity measures, users can safeguard their sensitive information.
Research on Eye-Movement Control System Based on Eyeglass-Based Eye-Tracking Device

With the rapid development of computer hardware and software technology, eye movement technology has gradually become a major research hotspot. Eye movement control through eye tracker detection and processing application plays an important role in many fields. This paper mainly focuses on the use of eye trackers, development of gaze positioning and tracking algorithms, and design of eye movement control system. The system is mainly based on the principle of the pupil-cornea reflection method, including procedures such as extracting the pupil center and cornea reflection coordinates from the eye diagram, forming the pupil-cornea reflection vector to represent the direction of sight, and then establishing a gaze point mapping model through the calibration link. By using this model, the sight of eyes is converted into the gaze point on the display screen, and the state of eyes is detected to realize the control of gaze target.
AI and Blockchain-based Secure Message Exchange Framework for Medical Internet of Things

With the advent of revolutionary technologies, the Internet of Things (IoT) has proved its effectiveness in the medical sector. The inclusion of sensors in IoT devices enables connectivity and facilitates the collection of valuable data for patient monitoring and effective treatment methods. Sensors are tagged with the medical equipment and foster real-time tracking of medical devices such as oxygen pumps, nebulizers, wheelchairs, etc. It collects medical data that has the patient's personal and sensitive information regarding medical history, such as heart rate, diagnosis information, drug prescription, body temperature, etc. However, the security of medical data has become a significant concern. Therefore, this paper proposes an artificial intelligence (AI) and blockchain-enabled scheme that offer cyber security to medical IoT data. Here, different machine learning (ML) classifiers, such as decision tree (DT), K-Nearest neighbor (KNN), Naive Bayes (NB), support vector machine (SVM), and gradient boosting classifier (GBC) are used to classify medical IoT data as an attack (1) and normal (0) class. Then, an IPFS-based blockchain network is used to offer security and transparency to the medical IoT data. Moreover, for evaluation, different performance metrics are considered, such as accuracy, training time, receiver operating characteristic (ROC) curve, and scalability. Among all ML classifiers, KNN achieves the highest accuracy of 95.4%.
Vision Based Relative Position Estimation in Surgical Robotics

Teleoperation-based Robotic-Assisted Minimally In-vasive Surgery (RAMIS) has gained immense popularity in medical field. However, limited physical interaction between the surgeon and patient poses a significant challenge. In RAMIS, the surgeon operates the robotic system remotely, which can diminish the personal connection and raise concerns about immediate responsiveness to unforeseen situations. Additionally, patients may perceive RAMIS as riskier due to potential technological failures and a lack of direct surgeon control. Surgeons have identified accidental clashes between surgical instruments and tissues as a critical issue. This work presents a technique that measures the distance between a surgical tool and tissue by extracting feature points from a Static Virtual Marker (SVM) and employing a classic feature detection algorithm Fast Oriented and Rotated Brief (ORB). Using a customized surgical robot and a ROS-based transform measurement system, this approach was successfully validated in the Gazebo simulation environment, offering safer surgical operations.
Based on the Virtual Reality of Automatic Warehouse Training Simulation System Research

With the development of virtual reality technology and the increasing number of practitioners in automatic warehouse, virtual training has achieved great development. Due to the dense structure, compact equipment and narrow space of automatic warehouse, on-site training is difficult to observe and learn, and easy to bring loss of life and property due to illegal operation. In order to solve these problems and enable trainers to master related technologies faster, an automatic warehouse simulation training system based on virtual reality is created. This training system uses the handle and head-mounted virtual reality display as the human-computer interaction window to realize learning and roaming in the full virtual scene. It is verified by experiments that the SUS score of the system is 78, which is within the acceptable range, the System Usability performance reaches about 80%, and the usability rating is between Good and Excellent. In addition, the SSQ score is 14.4 points, which is at a relatively low level. In a word, this system simulates the real environment of automatic warehouse, improves the learning flexibility, security, and training efficiency of trainees through virtual training and has good usability.
Establishment and solution of a pure azimuth passive positioning model in different formation flights of UAVs

Currently, the cooperative passive positioning of UAVs is developing rapidly and has become the mainstream positioning method for UAVs nowadays. This paper focuses on the modeling establishment and algorithm solving of the UAV passive positioning problem. For the problem of how to determine the position information of each UAV in circular and rhombic UAV formations and how to adjust them to the ideal trajectory, an ideal passive triangular positioning model, a target optimal planning model based on iterative adjustment in the ideal direction and a rhombic passive 3D positioning adjustment model are established, and through the establishment and solution of these models, the positioning and position of UAVs under different circumstances are solved By building and solving these models, we solve the UAV positioning and position adjustment problems under different situations.
Pengembangan Media Pembelajaran Handout Berbasis Discovery Learning Di SMAN 17 Luwu

This study aims to determine the results of the development of Discovery Learning-based Handout learning media at SMAN 17 Luwu. This type of research is R&amp;D (Research and Development) which refers to the ADDIE development research design which consists of five stages, namely analysis, design, development, implementation and evaluation. The population of this study were all students of class XII SMAN 17 Luwu and the sample of this study was class XII MIPA 2 who were selected using a purposive sampling technique. The research instruments were in the form of validation sheets, questionnaires and learning achievement tests. Data analysis techniques used in this study are validity test, practicality test and effectiveness test. The results of research on the development of Handout learning media based on Discovery Learning at SMAN 17 Luwu show that: (1) media expert validation test data obtained a validity percentage of 87.5% (very valid) while material experts obtained 84.3% (valid) (2) test the practicality of the teacher's response to the product was 95% (very practical) while the student's response to the product was 71.05% (practical) (3) the effectiveness data was obtained 85% (very effective). The conclusion from the development of Discovery Learning-based Handout learning media can be used in Metabolism material at SMAN 17 Luwu.Keywords: development; handout learning media based on discovery learning
Joint Extraction of Organizations and Relations for Emergency Response Plans With Rich Semantic Information Based On Multi-Head Attention Mechanism

At present, deep learning-based joint entity-relation extraction models are gradually able to accomplish complex tasks, but the research progress in specific fields is relatively slow. Compared with other fields, emergency plan text has the characteristics of high entity density, long text, and many professional terms, which make some general models unable to handle the semantic information of emergency plan text well. Therefore, this paper addresses the problem of complex semantics of emergency plan text, and proposes a joint extraction model of emergency plan organization and relationship based on multi-Head Attention Mechanism (MA-JE) to enrich semantic information, starting from multiple perspectives and different levels to obtain contextual information, aiming to deeply mine and use sentence semantic information through deep feature extraction of emergency plan text. The proposed model and the baseline model are experimented separately on the Chinese emergency response plan dataset, and the results show that the proposed approach outperforms existing baseline models for joint extraction of entity and their relations. In addition, ablation experiments were performed to verify the validity of each module in the model.
Digital Culture in Higher Education: Lost in Digital Transformation

The transition from traditional pedagogy into modern pedagogy in the higher education environment shows that higher education institutions are capable of moving toward digital transformation. However, the shift of education to utilizing technology in higher education will never be easy and perfect,
Making the Most of Existing Data: Challenges and Opportunities for Geothermal Brine Resource Exploration

Summary With increased focus on green energy upscaling and diversification, geothermal energy is expected to play an increasingly significant role in the sustainable energy mix. As an energy source that is available virtually everywhere, geothermal resources can be used for power and heat for a range of applications. Additionally geothermal fluids can host high-demand elements, such as lithium, required for energy transition technologies. As part of Global Geothermal Resource Assessment and Global Lithium Brines projects, we collated, integrated, and analysed numerous publicly available datasets for geothermal exploration insights, including temperature, fluid chemistry and production data. An extensive amount of data is available for geothermal exploration and development. Integrating disparate datasets through robust data transformation and quality control processes allows us to construct comprehensive databases for analysis. Leveraging data-driven approaches on properly formatted data can provide valuable insights, validate models, and explain relationships and phenomena that may otherwise be poorly understood, for example when investigating temperature at depth in different areas, or predicting fluid properties. However, collating and using diverse datasets comes with inherent challenges concerning discoverability, accessibility, and transformation. Understanding and addressing these challenges, and establishing best practices for data-handling, can unlock the value of public data for geothermal developments.
Streamlining Tax Calculations: An Automated Approach for Individual Taxpayer

In this paper, we provide an innovative tax computation method that intends to automate and simplify the calculation of taxes for Indian citizens. The method that we suggest in this work uses machine learning (ML), natural language processing (NLP), optical character recognition (OCR), and OCR technologies to accurately interpret financial data from a user's bank statement. The method we provide here leverages and employs data extraction, cleaning, sorting, and categorization using NLP techniques, as well as tax computation based on the current Indian tax framework. Our approach tackles both the shortcomings of current automated tax computation systems as well as the difficulties Indian taxpayers encounter when navigating the complicated tax structure of their nation. With the help of automation of the tax calculation process, we aim to reduce the likelihood of errors and minimize the time and effort required by taxpayers while preventing fraudulent activities. Although our approach has shown promising results, there are limitations and areas for further development, such as improving OCR accuracy, adding support for different languages, managing complex tax scenarios (for people owning business), enhancing scalability, and addressing privacy and security concerns. With further research and development, our proposed tax computation method has the potential to streamline and enhance the tax filing process in India, making it more efficient, accurate, and user-friendly.
Temporal Distance Issues and their Mitigation Strategies in GSD: An Empirical Study

GSD has several advantages that make it easier for software companies to follow this trend, like skillful workers, lower costs, and an improved marketplace. To achieve the benefits mentioned above, distributed teams must communicate effectively. Communication is a major problem for development, especially in GSD. Communication issues are classified based on temporal, geographical, and sociocultural distances. The temporal distance problem affects more GSD operations than the other hazards combined. Organizations can mitigate these obstacles and attain the desired quality by employing mitigation strategies while adhering to budget and time constraints. The authors of this study conducted a systematic literature review to identify temporal distance issues and their respective mitigation strategies to develop a conceptual framework. Additionally, a questionnaire interview with closed-ended questions was conducted. Without an empirical study, it is difficult to determine the precise relationship between temporal distance issues and mitigation strategies. The primary objectives of this study are to experimentally evaluate the proposed conceptual framework and the effects of selected risk mitigation measures. This study identified forty-four mitigation techniques and nine temporal distance challenges with a focus on a GSD-based environment, empirical research was conducted using data collected from Pakistani software companies. The findings of this study indicate, with a p-value of 0.01, that various mitigation techniques significantly impact various temporal distance issues. Based on the findings, software organizations can employ appropriate mitigation techniques to address temporal distance issues and reduce the failure rate of software projects.
AI-Explained (Part I) [Guest Editorial]

As we witness the remarkable progress of artificial intelligence (AI) over the last decade, its potential to address real-world complexities and revolutionize various fields has become evident. From image processing to natural language translation, AI solutions have accomplished significant milestones. However, the growing importance of AI in diverse domains has raised the need for making this complex technology accessible to a broader audience. In this issue, we embark on an educational enterprise, as we endeavor to bridge the knowledge gap between experts and non-experts in the field of AI. Our mission is to present AI concepts and methods in a comprehensible and interactive manner, allowing readers to explore, experiment, and understand the magic behind AI techniques. For this purpose, we are leveraging the recent introduction of immersive articles in IEEE Xplore and the Computational Intelligence Magazine, which allows us to not just present static content, but let readers interactively engage with the articles themselves.
Modification of Demonstrative Methods: Increased Understanding of the Value of Place in Children with Special Needs

Improving Students' Understanding of the Value of Places Through Demonstration Methods in Students of Class IV-C Sekolah Luar Biasa Negeri Doloksanggul School Year 2022/2023. The purpose of this study is to describe the improvement of students' understanding of the material of place values through the Demonstration method so as to improve student learning outcomes in Sekolah Luar Biasa Negeri Doloksanggul, Humbang Hasundutan Regency. This class Action Research was conducted in Sekolah Luar Biasa Negeri Doloksanggul Humbang Hasundutan Regency. The type of action in this study is in the form of concrete actions, namely guiding students in the implementation of learning activities through the Demonstration method. The research was conducted in semester I, precisely in September-November 2022. This classaction research sub j ek is students in Sekolah Luar Biasa Negeri Doloksanggul Humbang Hasundutan Regency with a total of 4 students. The results showed that students' understanding of the venue value material increased after applying the Demonstration method thereby improving student learning outcomes. The average student learning outcomes increased after applying the Demonstration method to the Place Value material, where the average student learning outcomes in the first cycle of 45.00 increased to 80.00 in cycle II.
Etkin Bir Pazarlama Stratejisi Oluşturmak için Apriori Algoritması Temelinde Müşteri Satın Alma Davranışının Ölçülmesi

Due to technological developments and competitive conditions, it is becoming more and more important to gain customers and keep them. Association rules are one of the methods used effectively to determine which products and services are more important and preferred by customers together. The aim of this study is to determine the types of patterns purchased by a company operating in the forest products sector, using the data of decor paper, which affects the cost the most, is used extensively in production and is difficult to plan. This study was carried out with the Apriori algorithm in WEKA, which is widely used in association analysis. It is thought that the association rules obtained from this study will enable the firm to determine the purchasing behaviors of its customers by revealing the overseas model trends and associations. In addition, this study will shed light on the company at the stage of determining the marketing strategy of the company.
Iterative Back Projection Network Based on Deformable 3D Convolution

Video super-resolution technology enhances the display quality of videos by obtaining high-resolution videos from low-resolution videos. Unlike single-image super-resolution, utilizing information between adjacent video frames is crucial in video super-resolution. To improve the performance of video super-resolution reconstruction, a model combining deformable 3D convolution and iterative back projection is proposed to fully exploit the temporal-spatial correlation of video frames. The model takes multiple consecutive video frames as input and outputs the super-resolution reconstruction of the middle frame, including three modules: multi-scale feature extraction, feature fusion, and high-resolution reconstruction. Firstly, multi-scale 3D convolution is used for preliminary feature extraction. Then, deformable 3D convolution and iterative back projection are combined for feature fusion. Finally, multiple residual dense blocks and sub-pixel convolution are used for high-resolution reconstruction, and global residual connections are utilized to obtain the reconstructed high-resolution video. Experimental results on the Vid4 dataset demonstrate that compared to existing methods, this method can effectively improve the peak signal-to-noise ratio and structural similarity performance and achieve better visual effects with 4x super-resolution magnification.