Reviewerly/exHarmony huggingface dataset:
Exploring the human digestive system through ARSIPERSIA augmented reality insights

Authors:
raw_author_name: Timothy John Pattiasina
raw_affiliation_string: Department of Information System, Faculty of Information Technology, Institut Informatika Indonesia Surabaya, Indonesia
raw_author_name: Raymond Sutjiadi
raw_affiliation_string: Department of Informatics, Faculty of Information Technology, Institut Informatika Indonesia Surabaya, Indonesia

Abstract: As times evolve, numerous advanced technologies emerge to support human activities. Augmented reality, a technology that integrates virtual objects with real-world elements in the physical environment, is particularly noteworthy. In the field of education, augmented reality is widely applied to enhance the interactivity of the teaching and learning process. This study endeavours to create an application focused on the human digestive system, with the aim of presenting digestive organs in three dimensions. Unlike traditional learning media, which typically feature two-dimensional images or videos of digestive organs, the Augmented Reality Human Digestive System (ARSIPERSIA) application addresses the challenge of visualizing authentic human digestive organs. Unity 3D and Vuforia were employed in the development of this application. The application underwent comprehensive testing through two methodologies: black box testing and usability testing. The results of the black box testing affirm the effective functionality of the application, covering tasks such as image target scanning, information display, and interactive quizzes. Usability testing findings indicate that ARSIPERSIA facilitates students in visualizing human digestive organs, provides relevant information, enhances comprehension of the digestive system, and sparks students' interest and motivation through captivating learning media.
Analysis of Autism Children's Classifications in Elementary School

Authors:
raw_author_name: Fitriyani Fitriyani
raw_affiliation_string: Universitas Pelita Bangsa, Indonesia
raw_author_name: Rina Rosdiana
raw_affiliation_string: Universitas Pelita Bangsa, Indonesia

Abstract: This research was conducted with the fundamental aim of carrying out a careful analysis of the classification of children with autism in the elementary school environment. Using the case study method, the research approach includes consideration of various aspects, including cognitive characteristics, behavior and social interactions. Data was collected through observations, interviews with teachers, as well as examination of related documents, providing a strong basis for in-depth analysis. With a focus on a deeper understanding of the varied characteristics of children with autism at the elementary school level, the main aim of the research was to classify these children based on their cognitive abilities, behavior and level of social interaction. It is hoped that the results of this research can be used as a basis for designing educational programs that are more appropriate to the individual needs of each autistic child. The benefits are not only limited to improving learning effectiveness, but also include supporting social integration in the elementary school environment. This research involved subjects of children who had autism and attended elementary school. Study participants included children with varying cognitive characteristics, behavior, and levels of social interaction. The results highlight significant variation in the characteristics of children with autism in elementary school, allowing classification based on their cognitive abilities, behavior and level of social interaction. Some children show a positive response to certain learning methods, while others require a more individualistic approach. It is hoped that the classification of research results will provide a solid basis for improving educational programs, increasing learning effectiveness, and supporting the social integration of children with autism in the elementary school environment.
PERBANDINGAN KRITERIA DECISION TREE PADA PENGETAHUAN MASYARAKAT PADA PEMILIHAN UMUM PRESIDEN INDONESIA

Authors:
raw_author_name: Lestari Yusuf
raw_affiliation_string: Universitas Nusa Mandiri

Abstract: In the Presidential election, gen z as a new voter, must know in advance who the presidential candidate will be in the 2024 election as well as the election process, because if voters do not know and do not understand, it will cause the wrong choice will result in their votes cannot be used even to abstain. What factors cause milennials and gen z generations to not know about elections can be determined using a decision tree. Therefore, in this study, a questionnaire was given to millennials and gen z generation to find out whether voters know the presidential candidate to be elected. The data from the questionnaire is processed to become training data and testing data with a ratio of 70:30. Then measure the accuracy level using the C4.5 algorithm with a comparison of splitting criteria, namely gain ratio, information gain and gini index. By knowing the right splitting criteria, the decision tree model can help overcome the problem of overfitting in the data. Overfitting occurs when the model is too complex in memorizing training data, thus failing to generalize well to read new data. The calculation results show the difference in accuracy values between Gain ratio, Information gain and Gini index, namely 81.67%, 83.33% and 83.33%. It can be concluded that for the use of Algorithm C4.5 splitting criteria Gain ratio and Gini index have the same accuracy value for accuracy measurement in this study.
Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

Authors:
raw_author_name: Devasis Pradhan
raw_affiliation_string: Department of Electronics and Communication Engineering, Acharya Institute of Technology, Bangalore -560107, Karnataka, India
raw_author_name: Manjusha Behera
raw_affiliation_string: School of Electronics Engineering Campus 12, KIIT deemed to be University, Bhubaneswar, Odisha
raw_author_name: Mehdi Gheisari
raw_affiliation_string: Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering,
Saveetha Institute of Medical and Technical Sciences, Chennai, India

Abstract: Abstract: The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.
Does board structure influence employee retention?: A systematic literature review

Authors:
raw_author_name: Haryanto Haryanto
raw_affiliation_string: Accounting Department, Sekolah Tinggi Ilmu Ekonomi Mulia Singkawang
raw_author_name: Amelia Setiawan
raw_affiliation_string: Accounting Department, Faculty of Economics and Business, Parahyangan Catholic University

Abstract: &lt;p&gt;Employee Engagement (EE) has become a crucial element for the success of any organization. While psychology has conducted extensive research on EE, accounting has yet to give it the attention it deserves. This systematic literature review (SLR) was conducted to explore the linkage between corporate governance (CG) practices and factors that drive employee engagement (EED) in greater detail. The review identified several key factors influencing EED: board composition, audit committees, top leadership expertise, network power, other stakeholders’ supervision, and above-board application. In particular, the study revealed that effective CG practices have positive EE consequences, such as increased staff morale, job satisfaction, and productivity, and decreased employee turnover rates. The study underlined the importance of an employee-centric culture that encourages empowerment and fosters a sense of belonging in achieving maximum business success. It suggests that companies must create a work environment that promotes EE, provides opportunities for growth and development, recognizes employee contributions, and fosters open communication and collaboration. By leveraging these insights, companies can enhance their business operations and achieve maximum success by fostering a culture of EE and empowerment. The study concludes that businesses prioritizing EE as a core value can create a sustainable competitive advantage, increase their reputation, and attract and retain top talent.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Fostering Responsible Behavior Online- Relevance of Cyber Ethics Education

Authors:
raw_author_name: Santhosh T
raw_affiliation_string: Central University of Kerala, India

Abstract: The role of ethics becomes even more significant given the huge advent and increase of cyber-criminal activities in cyberspace. It has become an urgent necessity to curb the menace of unethical practices in cyberspace and contribute ethical guidelines to a safe and secure digital environment. To minimize the growth of cybercrimes and unethical issues online, users must understand how existing societal norms and ethics can be extended according to the challenges posed by ethical dilemmas in cyberspace. Hence, understanding the importance of cyber ethics in daily life has become essential than ever before. Cyber ethics implies the rules of social engagement and responsibility in cyberspace; it is really about social responsibility in cyberspace and it is aimed at inculcating knowledge of responsible behavior in man when using the online environment. This paper is an attempt to provide an overview of the relevance of cyber ethics education in fostering responsible behavior online.
The optimal scaling factor based on Secant Method for image watermarking using the hybrid DTCWT-DCT domain

Authors:
raw_author_name: Mohamed Lebcir
raw_affiliation_string: Phd student Malysia
raw_author_name: None Suryanti Awang
raw_affiliation_string: 1 Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia.  2 Center of Excellence for Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah,    Lebuh Persiaran Tun Khalil Yaakob, 26300, Kuantan, Pahang, Malaysia.
raw_author_name: None Ali Benziane
raw_affiliation_string: Faculty of Science and Technology, University of Djelfa, Algeria.

Abstract: This paper proposes an implementation of the secant optimization method on the watermarkembedding in DTCWT domain. An objective function combining PSNR and NC (The Normalized Correlation)values calculated for a set of common attacks is used in the optimization process. The aim is to enhance the tradeoff between robustness and imperceptibility against the various image processing attacks via an automatic andcareful selection of the scaling factor using optimization. The watermark is inserted in the high energy bands ofDCT-transformed sub-vectors, which were generated from the DTCWT coefficient decomposition of the hostimage. Experimental results performed on a set of 512×512 greyscale images, show better results than theexisting schemes for most common image attacks such as compression, low-pass filtering and noise addition.
ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0

Authors:
raw_author_name: Siti Nurlela
raw_affiliation_string: Universitas Nusa Mandiri
raw_author_name: Supriatna Supriatna
raw_affiliation_string: Universitas Nusa Mandiri

Abstract: Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536&gt; F table 2.81 and the significance value is 0.0000011 &lt;0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.
AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a  Single High-Resolution Image

Authors:
raw_author_name: Amirkolaee, Hamed Amini
raw_author_name: Shi, Miaojing
raw_author_name: He, Lianghua

Abstract: The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, ie Jiangsu, Yosemite, and London; and outperforms the state of the art methods significantly.
Training-Free Consistent Text-to-Image Generation

Authors:
raw_author_name: Tewel, Yoad
raw_author_name: Kaduri, Omri
raw_author_name: Gal, Rinon
raw_author_name: Kasten, Yoni
raw_author_name: Wolf, Lior
raw_author_name: Chechik, Gal
raw_author_name: Atzmon, Yuval

Abstract: Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy per-subject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multiple subjects. Here, we present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model. We introduce a subject-driven shared attention block and correspondence-based feature injection to promote subject consistency between images. Additionally, we develop strategies to encourage layout diversity while maintaining subject consistency. We compare ConsiStory to a range of baselines, and demonstrate state-of-the-art performance on subject consistency and text alignment, without requiring a single optimization step. Finally, ConsiStory can naturally extend to multi-subject scenarios, and even enable training-free personalization for common objects.
Video Super-Resolution for Optimized Bitrate and Green Online Streaming

Authors:
raw_author_name: Menon, Vignesh V
raw_author_name: Rajendran, Prajit T
raw_author_name: Bross, Benjamin
raw_author_name: Marpe, Detlev

Abstract: Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.
Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

Authors:
raw_author_name: Lu, Sijin
raw_author_name: Xu, Pengyu
raw_author_name: Liu, Bing
raw_author_name: Sun, Hongjian
raw_author_name: Jing, Liping
raw_author_name: Yu, Jian

Abstract: Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information directly from the post itself, neglecting the information from external knowledge sources. Therefore, we propose a Retrieval Augmented Cross-Modal (RACM) Tag Recommendation Model in Software Q\&A Sites. Specifically, we first use the input post as a query and enhance the representation of different modalities by retrieving information from external knowledge sources. For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code. In the fusion process, a gate mechanism is employed to achieve fine-grained feature selection, controlling the amount of information extracted from the submodalities. Finally, the fused information is used for tag recommendation. Experimental results on three real-world datasets demonstrate that our model outperforms the state-of-the-art counterparts.
Minds versus Machines: Rethinking Entailment Verification with Language  Models

Authors:
raw_author_name: Sanyal, Soumya
raw_author_name: Xiao, Tianyi
raw_author_name: Liu, Jiacheng
raw_author_name: Wang, Wenya
raw_author_name: Ren, Xiang

Abstract: Humans make numerous inferences in text comprehension to understand discourse. This paper aims to understand the commonalities and disparities in the inference judgments between humans and state-of-the-art Large Language Models (LLMs). Leveraging a comprehensively curated entailment verification benchmark, we evaluate both human and LLM performance across various reasoning categories. Our benchmark includes datasets from three categories (NLI, contextual QA, and rationales) that include multi-sentence premises and different knowledge types, thereby evaluating the inference capabilities in complex reasoning instances. Notably, our findings reveal LLMs' superiority in multi-hop reasoning across extended contexts, while humans excel in tasks necessitating simple deductive reasoning. Leveraging these insights, we introduce a fine-tuned Flan-T5 model that outperforms GPT-3.5 and rivals with GPT-4, offering a robust open-source solution for entailment verification. As a practical application, we showcase the efficacy of our finetuned model in enhancing self-consistency in model-generated explanations, resulting in a 6% performance boost on average across three multiple-choice question-answering datasets.
Automated Description Generation for Software Patches

Authors:
raw_author_name: Vu, Thanh Trong
raw_author_name: Do, Thanh-Dat
raw_author_name: Nguyen, Thu-Trang
raw_author_name: Vo, Hieu Dinh
raw_author_name: Nguyen, Son

Abstract: Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aiding comprehension and collaboration among developers. However, manual description creation poses challenges in terms of time consumption and variations in quality and detail. In this paper, we propose PATCHEXPLAINER, an approach that addresses these challenges by framing patch description generation as a machine translation task. In PATCHEXPLAINER, we leverage explicit representations of critical elements, historical context, and syntactic conventions. Moreover, the translation model in PATCHEXPLAINER is designed with an awareness of description similarity. Particularly, the model is explicitly trained to recognize and incorporate similarities present in patch descriptions clustered into groups, improving its ability to generate accurate and consistent descriptions across similar patches. The dual objectives maximize similarity and accurately predict affiliating groups. Our experimental results on a large dataset of real-world software patches show that PATCHEXPLAINER consistently outperforms existing methods, with improvements up to 189% in BLEU, 5.7X in Exact Match rate, and 154% in Semantic Similarity, affirming its effectiveness in generating software patch descriptions.