Reviewerly/exHarmony huggingface dataset:
PEMANFAATAN INTERNET OF THINGS PADA SMARTHOME DENGAN MODEL SIMULASI PROTOTYPE

Authors:
raw_author_name: Mahdianta Pandia
raw_author_name: Berti Sari Br Sembiring

Abstract: Internet of Things has been a recent focus in technology discussion. This tecnology enables every devices we own to connect to the internet, allowing remote control throught smartphones or even voice commands. The development of iot extends beyond large industries like power grids and factories, reaching into everyday household electronics. For instance, light can now be connected to the internet. The final project involves designing and creating an internet controlled relay system with comamands sent throught a telegram bot. This project aims to implement iot tecnology in a simple and affordable manner. The control system utilizes the nodeMCU microcontroller with a two -channel relay output to remote turn light on and off via telegram.
The Implementation of Bottom-Up in Reading Comprehension for Vocational School Students

Authors:
raw_author_name: Dwi Fita Heriyawati

Abstract: This research aims to investigate the improvement reading comprehension of SMK PGRI 2 Mojokerto by using Bottom-Up approach in reading comprehension of recount text. The research was conducted in two Cycles of Classroom Action Research (CAR) where each Cycle was conducted in 4 (four) phases, they were: planning, action, observation, reflection. Each Cycle was applied in (four) meetings. The subjects of the research were 35 students of tenth grade (X-C Mechatronics). In collecting data, the researcher applied three instruments: observation, questionnaire and reading test. There were 2 (two) observations in field note which conducted by the researcher: the first observation which was done before implementing Bottom-Up to find the problem during reading activity in English lesson. Meanwhile, the second observation was to find the improvement in having Bottom-Up that conducted by English teacher during the approach applied. The questionnaire was delivered to students in the fourth meeting of the second Cycle to find out the responses of the students in learning reading by using Bottom-Up. The data was analyzed through descriptive and statistical method. In this research, the researcher acted as a teacher and the English teacher as an observer. The research findings showed that the students reading comprehension improved from Cycle I to Cycle II. Furthermore, the data from the observation and questionnaire indicated that the majority of the students gave positive response toward the use of Bottom-Up approach in teaching reading. It emphasized that the approach could improve students’ ability in reading, effective, interesting, and grab the students’ enthusiasm in reading activity.
Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Authors:
raw_author_name: Muhammad Hasan Irshad
raw_affiliation_string: Department of Computer  Science, Faculty of Engineering  Science & Technology  Iqra University Main Campus,  Karachi, Pakistan
raw_author_name: Mansoor Ebrahim
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.
raw_author_name: Abdul Ahad Abro
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.
raw_author_name: Kamran Raza
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.
raw_author_name: Syed Hasan Adil
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.

Abstract: The abstract outlines a novel MATLAB-based technique for enhancing shadow detection accuracy in computer vision applications. This MATLAB-based method uses thresholding and color analysis to improve the accuracy of shadow identification. Luminance (L*) is distinguished from chromaticity (a* and b*) in digital photographs by converting them into the Lab color space. This makes it easier to identify possible shadowed areas. The luminance channel's thresholding makes it possible to distinguish between regions that are well-lit and those that are shadowed. The computation of chromaticity distance also helps in detecting modest color changes that are suggestive of shadows. The culmination of the suggested technique is the creation of a binary shadow mask, which isolates the image's shaded regions. The research addresses limitations of existing approaches by maintaining robustness in diverse lighting conditions and complex scenarios, thereby enhancing image comprehension and the precision of shadow detection. The proposed technique holds promise for improving object recognition and scene analysis in computer vision applications, offering a robust and reliable solution for identifying and isolating shadows in digital images.
CRIISL: Convert Rasterize Image Into SVG Layers

Authors:
raw_author_name: Ahmed Hassan
raw_affiliation_string: Department of Computer Science, & Faculty of Engineering Science & Iqra University Main Campus, Karachi, Pakistan 	Karachi, Pakistan.
raw_author_name: Mansoor Ebrahim
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan
raw_author_name: Abdul Ahad Abro
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.
raw_author_name: Kamran Raza
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.
raw_author_name: Syed Hasan Adil
raw_affiliation_string: Department of Engineering Science & Technology Iqra University Karachi, Pakistan.

Abstract: Raster images in multiple layers or vector format can be challenging for digital artists. However, converting them into Scalable Vector Graphics (SVG) format, such as Adobe Illustrator, Inkscape, or CorelDRAW, offers advantages. SVG files allow for easy resizing without loss of quality, making them ideal for creating logos, icons, and other graphics that need to be scaled to different sizes. They are lightweight and can be easily edited using code, providing more control over design elements. This versatile and flexible tool streamlines the design process and provides greater flexibility for artists in their creative projects. SVG files are smaller in size compared to raster images, making them easier to work with and share. Developers can also use code-based approaches to further manipulate and customize SVG images as needed.
Review on green resources and AI for biogenic solar power

Authors:
raw_author_name: Subhasis Roy

Abstract: The need for clean and renewable energy has grown dramatically during the past few years. As potential candidates for producing green energy in this region, photovoltaic and bio-solar energy technologies have arisen. This review presents a novel approach for designing and developing photovoltaics and bio-solar cells using eco-friendly materials and artificial intelligence (AI) techniques. An intriguing architecture is outlined for a bio-solar cell that fuses photovoltaic electronics with photosynthetic organisms. A recyclable thin-film solar cell serves as the basis of our photovoltaic system. To further maximize the effectiveness of the device, we use AI algorithms. According to statistical calculations, the proposed bio-solar cell can produce a sizable amount of electricity while being ecologically sound. This paper outlines significant advances in developing solar cells and photovoltaics using green nanomaterials and AI, which provide exciting potential for improving energy harvesting capacity. This review also presents an overview of the effects of the potential commercialization of our strategy, its social and environmental benefits, and its pitfalls.
Efforts to improve nutritional knowledge in adolescent girls through contemporary video-based education

Authors:
raw_author_name: Feranita Utama

Abstract: Adolescent girls belong to a vulnerable age group that experiences nutritional problems with various risks of health problems such as anemia, chronic energy deficiency, and overnutrition. Increasing the nutritional knowledge of adolescent girls can be an opportunity to improve their nutritional status throughout the initial 8000 days of life. This community service activity uses contemporary video media to expand the knowledge of adolescent girls related to nutrition. Community service is integrated with life-cycle nutrition courses. The target audience was 30 adolescent girls. This activity is located at SMA Negeri 4 Palembang for 1 month. The activity method was carried out by designing and making contemporary videos by 3rd-semester nutrition students, followed by distributing educational videos to participants. Feedback from participants as well as pre- and post-test results were used in the evaluation process. Results from community service projects show that 30% of targets have anemia based on Hb examination with the GC-Hb tool, and 30% of targets have malnutrition based on IMT/U assessment. The target's knowledge increased after providing education with contemporary videos (p0.0001). After completing this community service project, it was concluded that the knowledge of adolescent girls increases by providing contemporary video-based nutrition education.
Developing community technology: A profile website for SDI Miftahus Sholihin

Authors:
raw_author_name: Eka Kusumawardhani
raw_author_name: Leonardus Sandy Ade Putra

Abstract: The Community Service Program titled "Developing Community Technology: A Profile Website for SDI Miftahus Sholihin" aims to address various challenges associated with school accreditation through the utilization of information and communication technology (ICT). The program's objectives are to enhance the quality of education and facilitate better access to information. It entails the design, development, and management of a profile website for SDI Miftahus Sholihin, alongside training sessions focused on website management and promotion strategies. These efforts are geared towards achieving improvement targets in school accreditation. The program timeline spans six months, encompassing phases of preparation, coordination, website development, training, and evaluation. Anticipated outcomes include heightened information quality and accessibility pertaining to school-related matters, as well as the enhancement of website management skills among school staff. Ultimately, the program aspires to elevate the educational quality of SDI Miftahus Sholihin, foster greater community engagement in educational initiatives, and facilitate the attainment of higher accreditation standards for the school.
Microcontroller based food dehydrator for Rengginang crackers micro businesses in Prambontergayang Village, Tuban District

Authors:
raw_author_name: Eko Wahyu Abryandoko

Abstract: The micro-business of Rengginang crackers with the Urnika brand in Prambontergayang Village, Tuban District, is an industry in the food sector. The process of drying Rengginang crackers in partner’s still uses conventional methods or under sunlight to carry out the drying process. Hence, it takes quite a long time to dry Rengginang, namely around 6-7 days. This community service program aims to develop and train the use of alternative technology for drying Rengginang crackers to Rengginang crackers micro-entrepreneurs in Prambontergayang Village, Tuban District. The method used for implementing PKM is a participatory approach (participatory action research/PAR). The results of the PKM carried out have had a positive impact on partners, namely more optimal and efficient drying. Drying using a microcontroller-based food dehydrator can speed up drying up to 120 minutes with a capacity of 5000 pcs of wet Rengginang. With this technology, partners can increase their production capacity for Rengginang crackers so that they can increase the expected number of sales.
A Study on the Priority Derivation of Implementation Strategies for Resource Management and Operation Technology Development in the Logistics Industry

Authors:
raw_author_name: Seeun Jeon
raw_author_name: Jaesung Choi

Abstract: Globally, the industrial structure is changing to respond to digital transformation and climate change, and the logistics industry is also experiencing unprecedented upheaval, away from the existing labor-intensive industry. Currently, 10 high-value-added convergence logistics and delivery infrastructure innovation technology development-related R&amp;D projects are underway under the leadership of the convergence logistics group of the Korea Agency For Infrastructure Technology Advancement to change the status of the domestic logistics industry’s technology development from follower to first mover. However, in this study, a basic study on the priority of implementation strategies for the development of detailed technologies (policy) was conducted based on a survey of logistics experts in relation to the “logistics industry resource management and operation technology development” research project to be additionally carried out. The Importance-Performance Analysis (IPA) results of the average of importance and preparedness were placed in four quadrants to analyze the meaning of each sector’s alternatives, and the need to improve items related to the basic foundation such as private data opening, infrastructure construction, legal system improvement, and standardization was derived in the areas of concentration where the importance was high but the preparation was insufficient. However, this study has limitations such as the composition and method of survey respondents due to limited time and budget, so it is inevitable that research will be advanced through follow-up studies in the future.
Empirical Study on the Impact of Smart Logistics Platform Utilization Capabilities in the Era of the Fourth Industrial Revolution on Data-Driven Demand Forecasting Performance

Authors:
raw_author_name: Chang-Bong Kim

Abstract: The rapid emergence of Industry 4.0 technologies, such as big data, IoT, and AI, has significantly transformed the distribution and logistics landscape. Smart logistics platforms are increasingly adopted in this sector, leading to improvements in various aspects, including demand forecasting accuracy. This study aims to investigate the mediating effects of data compatibility, real-time risk response, and product history tracking on the relationship between a company’s smart logistics platform utilization capability and its data-driven demand forecasting performance. Drawing upon theoretical insights from existing literature, this study develops a research model and corresponding hypotheses. Operationalization of key constructs is achieved through the development of a structured questionnaire based on established measurement scales. To ensure the representativeness of our sample, the survey is distributed to professionals utilizing smart logistics platforms in domestic distribution and logistics companies. Following a rigorous data collection process, a total of 280 valid responses are retained for further analysis. This study aims to highlight the growing importance of smart logistics platform utilization capability as smart technologies advance. Based on the research results, it presents practical situations in the industrial field and provides policy implications for supporting small and medium-sized enterprises (SMEs) to government agencies and local governments.
"Clustering Algorithm for the Connected Model of Repeated Measurements and Survival Data: Application to HIV Study"

Authors:
raw_author_name: Hissah Alzahrani
raw_affiliation_string: Mathematics Department, College of Applied Sciences, Umm AlQura University, Saudi Arabia

Abstract: Many clinical trials applications generate repeated measurements and time to event (survival data). In longitudinal studies the patients are followed over many occasions (repeated measurements) and their data indicates biomarkers. Sometimes these longitudinal data include time to event, for example time to death, Alzahrani [1].
Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment

Authors:
raw_author_name: Yifei Ding
raw_affiliation_string: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada; School of Mechanical Engineering, Southeast University, Nanjing, China
raw_author_name: Minping Jia
raw_affiliation_string: School of Mechanical Engineering, Southeast University, Nanjing, China
raw_author_name: Yudong Cao
raw_affiliation_string: School of Mechanical Engineering, Southeast University, Nanjing, China
raw_author_name: Xiaoan Yan
raw_affiliation_string: School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
raw_author_name: Xiaoli Zhao
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China
raw_author_name: Chi-Guhn Lee
raw_affiliation_string: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

Abstract: Fault detection or anomaly detection relies heavily on learning from datasets where only normal samples are available, resulting in the emergence of numerous one-class classification (OCC) methods. However, learning discriminative deep representatives with good generalization from cross-domain positive samples remains challenging. Therefore, this work proposes an end-to-end framework, deep transfer one-class classification (DTOCC) for unsupervised fault detection, which combines adversarial generative OCC and distribution alignment from the perspective of manifold learning. Specifically, pseudo-negative samples are generated outside the positive manifold, facilitating the model to learn discrimination with respect to normal and anomaly. Further, cross-domain positive samples are aligned in log-Euclidean manifold space to enhance representation learning. Then, we provide the specific implementations for fault detection and validate its superiority through case studies on multi-class and run-to-failure datasets, simulating both offline and online scenarios.
A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering

Authors:
raw_author_name: Xiaoxue Ma
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Jacky Keung
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Pinjia He
raw_affiliation_string: School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
raw_author_name: Yan Xiao
raw_affiliation_string: School of Cyber Science and Technology, Sun Yat-Sen University, Shenzhen, China
raw_author_name: Xiao Yu
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
raw_author_name: Yishu Li
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China

Abstract: With the rapid development of the Industrial Internet of Things (IIoT), log-based anomaly detection has become vital for smart industrial construction that has prompted many researchers to contribute. To detect anomalies based on log data, semi-supervised approaches stand out from supervised and unsupervised approaches because they only require a portion of labeled data and are relatively stable. However, the state-of-the-art semi-supervised approaches still suffer from two main problems: manual parameter setting and unsatisfactory performance with high false positives. We propose AdaLog, an integrated semi-supervised approach based on self-adaptive clustering, for industrial anomaly detection. In particular, the clustering step performs automatic label probability estimation by distinguishing twelve situations so that the label probability of each unlabeled data can be carefully calculated, leading to high accuracy. In addition, AdaLog employs a pre-trained model to learn contextual information comprehensively and a transformer-based model to detect anomalies efficiently. To alleviate class imbalance, an undersampling method is incorporated. The results on three popular datasets demonstrate that AdaLog significantly outperforms three state-of-the-art semi-supervised approaches by 17.8%–2,489.8% on average in terms of F1-score, and is even superior to two supervised approaches in most cases with average improvements of 10.9%–23.8%.
Reconstruction and Preservation of Feature Curves in 3D Point Cloud Processing

Authors:
raw_author_name: Ulderico Fugacci
raw_affiliation_string: Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, Via de Marini 6, 16149 Genova, Italy
raw_author_name: Chiara Romanengo
raw_affiliation_string: Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, Via de Marini 6, 16149 Genova, Italy
raw_author_name: Bianca Falcidieno
raw_affiliation_string: Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, Via de Marini 6, 16149 Genova, Italy
raw_author_name: Silvia Biasotti
raw_affiliation_string: Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, Via de Marini 6, 16149 Genova, Italy

Abstract: Given a 3D point cloud, we propose a method for suitably resampling the cloud while reconstructing and preserving the feature curves to which some points are identified to belong. The first phase of our strategy enriches the cloud by approximating the curvilinear profiles outlined by the feature points with piece-wise polynomial parametric space curves through the use of the Hough transform. The second phase describes how the removal of a point or its insertion can be performed without affecting the approximated profiles and preserving the enriched structure of the cloud. The combination of the two steps provides multiple possibilities for processing a point cloud by varying its size or improving its density homogeneity without affecting the retrieved feature curves. The various capabilities of our approach are investigated to produce simplification, refinement, and resampling techniques whose effectiveness is evaluated through experiments and comparisons.