Reviewerly/exHarmony huggingface dataset:
Análisis de accesibilidad y visibilidad para los portales académicos de las universidades de Cartagena

Authors:
raw_author_name: Gabriel Elías Chanchí Golondrino
raw_author_name: Plinio Puello Marrugo

Abstract: Introduction: With the growing demand for applications in the market, companies have increasingly focused on meeting the technological needs of the average user, often overlooking those with functional diversity. Consequently, accessibility has emerged as a critical challenge across various contexts, particularly within the educational sphere, where the diverse range of individuals accessing web content is particularly pronounced. Objective: This paper presents a study centered on the accessibility inspection of six university websites located in the city of Cartagena. The primary aim is to ascertain the relationship between the integration of accessibility guidelines and web positioning factors as determined by Webometrics. Methodology: The methodology comprises five distinct phases: website selection, tool selection for inspection, execution of accessibility inspection, analysis of obtained results, and formulation of conclusions and recommendations. Results: The study revealed a correlation exceeding 0.8 between the local ranking of universities according to Webometrics and the ranking of accessibility issues identified through automated inspection tools. Conclusions: It is recommended that educational institutions adopt automatic web evaluation tools to enhance the accessibility of their institutional websites. This regular review and rectification of issues represent best practices for improving both local and international visibility and usability.
CT-GCN+: a high-performance cryptocurrency transaction graph convolutional model for phishing node classification

Authors:
raw_author_name: Yixuan Wang
raw_affiliation_string: School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, China
raw_author_name: Tao Feng
raw_affiliation_string: School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, China

Abstract: Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.
Persona/scenario (P/S) toolkit enhancing inclusive fashion design education

Authors:
raw_author_name: Jee Hyun Lee
raw_affiliation_string: Department of Human Environment & Design, Human Life & Innovation Design, Yonsei University, Lab 713, 7th Floor, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
raw_author_name: Jiwon Huh
raw_affiliation_string: Department of Human Environment & Design, Fashion-based Integrated Design Lab, Yonsei University, Samsung Bld. #709, 7th Floor, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
raw_author_name: Minji Lena Kim
raw_affiliation_string: Department of Human Environment & Design, Fashion-Based Integrated Design Lab, Yonsei University, Samsung Bld. #709, 7th Floor, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
raw_author_name: Jieun Kim
raw_affiliation_string: Department of Human Life & Innovation Design, Yonsei University, Lab 224, 2nd Floor, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea

Abstract: Abstract This study aimed to address the diversity issue in fashion design education by developing two prototypes for Inclusive Fashion Design (IFD) education. The prototypes were constructed based on the 3C3R model of Project-Based Learning (PBL) and incorporated the Persona/Scenario (P/S) toolkit for IFD. Additionally, the study examined the effectiveness of the FEA (Function, Expressive, and Aesthetics) self-checklist and the P/S toolkit in the IFD process. The experiments involved two design instructors, two teaching assistants, and 12 senior-level students divided into four teams. Thematic analysis was conducted on the interview data collected from the participants. The results revealed that FEA factors were highly considered in both education prototypes, regardless of the presence of the P/S toolkit. Maintaining consistency of the IFD concept throughout each stage of the process was found to be crucial, and the use of the P/S toolkit played a significant role in achieving this consistency. Team communication emerged as an essential factor in IFD education, as team collaboration using the P/S toolkit triggered diverse perspectives on targets, facilitated design expansion, and extended individual competences. Overall, this study contributes to the understanding of the diversity issue in fashion design education and promotes the adoption of the IFD education methodology, emphasizing the significance of consistent concept development, effective toolkits, and team collaboration in achieving inclusive design practices.
Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Authors:
raw_author_name: Sazia Parvin
raw_affiliation_string: Information Technology, Melbourne Polytechnic, Melbourne, VIC 3072, Australia
raw_author_name: Sonia Farhana Nimmy
raw_affiliation_string: Faculty of Economics and Business, University of New South Wales, Sydney, ACT 2612, Australia

Abstract: Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.
Evaluation of Governance in Information Systems Security to Minimize Information Technology Risks

Authors:
raw_author_name: Yulia Darmi
raw_affiliation_string: Universitas Muhammadiyah Bengkulu
raw_author_name: Sandhy Fernandez
raw_affiliation_string: Institut Teknologi Telkom Purwokerto
raw_author_name: M Yoka Fathoni
raw_affiliation_string: Universiti Kuala Lumpur
raw_author_name: Sena Wijayanto
raw_affiliation_string: Institut Teknologi Telkom Purwokerto

Abstract: Information system security within XYZ University constitutes a vital component of its IT framework, exerting significant influence over security levels across all facets of the information systems. Among the numerous implemented information system services at the university, a considerable portion lacks active security measures within operational systems. In pursuit of achieving uniform governance, this study adopts the most recent COBIT 2019 framework. The primary objective of this research is to evaluate the degree to which current information system security management aligns with the process achievement values stipulated in the COBIT 2019 standard. This evaluation entails the calculation of maturity level values that gauge performance levels in managing information system security. Findings from the COBIT 2019 Design assessment conducted at XYZ University's LTIK reveal that individuals scoring above 80 or those requiring Capability Level 4 include APO12 and BAI10. Moreover, the calculation outcomes for each subdomain reveal the presence of 2 subdomains at Level 4, 4 subdomains at Level 3, 15 subdomains at Level 2, and 19 subdomains at Level 1. The identification outcomes underscore the existence of gaps within each domain. Particularly, the APO12 and BAI10 domains exhibit a gap spanning 2 levels.
The Empirical Study On Algorithm Optimization In Expert Systems For Diagnosing Rice Plant Diseases

Authors:
raw_author_name: Ahmad Habib
raw_affiliation_string: Universitas 17 Agustus 1945 Surabaya
raw_author_name: Elsen Ronando
raw_affiliation_string: Kyushu Institute of Technology Japan

Abstract: Rice is one of the most important cultivated plants for human survival. The activity of cultivating rice plants becomes a livelihood for most of these residents, so the success rate of the amount of rice harvested becomes very important because they depend on how much rice can be harvested, disease diagnosis is very important for farmers, this is very important to reduce economic losses due to diseases that cause crop failure. Therefore, when dealing with rice diseases, an expert is needed to make diagnoses or solutions to rice diseases. However, an expert does not know when to come to the village, and farmers also do not understand all rice diseases. Therefore, a web-based expert system application using the forward chaining method is proposed to represent an expert to help farmers diagnose and solve diseases of rice plants with existing symptoms.
A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

Authors:
raw_author_name: Shogo Okada
raw_affiliation_string: Japan Advanced Institute of Science and Technology, Ishikawa, Japan

Abstract: This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.
Usage of Multiplane Image Information SEI Message for Distribution of Lightweight 3DoF+ Immersive Content with Conventional 2D Decoder

Authors:
raw_author_name: Taoran Lu
raw_affiliation_string: Dolby Laboratories Inc., Sunnyvale, CA USA
raw_author_name: Peng Yin
raw_affiliation_string: Dolby Laboratories Inc., Sunnyvale, CA USA
raw_author_name: Sean McCarthy
raw_affiliation_string: Dolby Laboratories Inc., San Francisco, CA USA

Abstract: In this presentation, we describe a new SEI message under consideration for VSEI/H.274 - the multiplane image information SEI message (MPII SEI) [1]. The motivation to design this MPI is to establish open non-proprietary standards to enable distribution of lightweight 3DoF+ immersive consumer experiences encoded in a single bitstream compatible with already deployed smartphones, laptops, etc. equipped with a conventional 2D decoder.
Overview of Visual Signal Compression towards Machine Vision

Authors:
raw_author_name: Shurun Wang
raw_affiliation_string: Damo Academy, Alibaba Group, Beijing, China
raw_author_name: Shiqi Wang
raw_affiliation_string: City University of Hong Kong, Hong Kong, China
raw_author_name: Yan Ye
raw_affiliation_string: Damo Academy, Alibaba Group, Sunnyvale, USA

Abstract: Machine analysis of visual signals has been providing fundamental infrastructure support for artificial intelligence (AI) applications. As such, the compact representation of visual signal towards machine analysis is critical in current AI systems. A straightforward and general pipeline of visual signal compression for machines is to compress the visual signal, then perform the machine analysis on the reconstructed visual signal for ultimate results. This pipeline naturally supports multiple machine vision tasks and human perception, and is denoted as visual signal compression towards machines (VSCM). This paper provides an overview to summarize the VSCM related technologies in terms of various modules, and envision the future development of VSCM.
No-Reference Quality of Experience Model for Dynamic Point Clouds in Augmented Reality

Authors:
raw_author_name: Minh Nguyen
raw_affiliation_string: Alpen-Adria-Universität (AAU), Klagenfurt, Klagenfurt, Austria
raw_author_name: Shivi Vats
raw_affiliation_string: Alpen-Adria-Universität (AAU), Klagenfurt, Klagenfurt, Austria
raw_author_name: Hermann Hellwagner
raw_affiliation_string: Alpen-Adria-Universität (AAU), Klagenfurt, Klagenfurt, Austria

Abstract: Point cloud streaming is becoming increasingly popular due to its ability to provide six degrees of freedom (6DOF) for immersive media. Measuring the quality of experience (QoE) is essential to evaluate the performance of point cloud applications. However, most existing QoE models for point cloud streaming are complicated and/or not open source. Therefore, it is desirable to provide an open-source QoE model for point cloud streaming.
Accelerated and Adaptive Non-local Mean Temporal Filtering for Improved HEVC Coding Efficiency

Authors:
raw_author_name: Dror Porat
raw_affiliation_string: NVIDIA, Yokneam, Israel

Abstract: As modern video codecs exploit at their basis temporal and spatial correlations in video signal for compression, and such correlations are corrupted by noise that is present in the video signal, temporal filtering has been shown to be effective for improving coding efficiency in various encoder implementations [1], [2], [3]. In particular, the non-local mean approach in [1] has been applied in AV1, an open video coding format competing with HEVC (aka H.265), which includes built-in mechanisms that allow effective temporal filtering using non-displayable reference frames (aka ALTREF frames).
E-WISH

Authors:
raw_author_name: Daniele Lorenzi
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
raw_author_name: Minh Nguyen
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
raw_author_name: Farzad Tashtarian
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
raw_author_name: Christian Timmerer
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

Abstract: HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT's carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device's battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.
H/W Accelerated Unsupervised Codec Modernization

Authors:
raw_author_name: Tamar Shoham
raw_affiliation_string: Beamr, Herzliya, Israel

Abstract: Following a decade where AVC/H.264 was the dominant codec in many video applications, the last years have finally seen more modern video codecs gain traction. This raises the need for cost effective, fast, fully automatic transcoding into modern codec formats which will preserve the input perceptual quality, at the lowest possible bitrate. The availability of such a solution makes the switchover to newer encoders a smooth, easy, and low-cost process accelerating adoption of HEVC, AVI or VVC.
Combining Deep Learning and Feature Engineering for No-Reference Video Quality Assessment

Authors:
raw_author_name: Axel De Decker
raw_affiliation_string: Synamedia Ltd., Kortrijk, Belgium
raw_author_name: Jan De Cock
raw_affiliation_string: Synamedia Ltd., Kortrijk, Belgium
raw_author_name: Glenn Van Wallendael
raw_affiliation_string: Ghent University, Ghent, Belgium

Abstract: With the ever-growing demand for high-quality video content, methods for evaluating the aesthetic quality of digital videos have become invaluable tools to ensure a satisfying viewing experience for the end-user. Over the years, many accurate and reliable Full-Reference (FR) video quality assessment (VQA) metrics [4, 5, 10, 16, 17] have been developed that assess quality degradation by comparing the processed video to its original source. However, in scenarios where the reference video is unavailable or impractical to obtain, No-Reference (NR) metrics have to be used, which assess quality solely on the distorted video. Despite intensive research, no NR method so far has been found accurate enough to be widely adopted by industry [9].
Inferring Video Streaming Quality of Experience at Scale using Incremental Statistics from CDN Logs

Authors:
raw_author_name: Christoph Neumann
raw_affiliation_string: Broadpeak, France

Abstract: With video streaming applications, end-user Quality of Experience (QoE) is generally estimated using metrics provided by the video players. While QoE monitoring is critical for Content Delivery Networks (CDNs), player metrics are not always provided due to technical, contractual or regulatory limitations. We propose to leverage CDN access logs to infer five common QoE player metrics, with the help of machine learning models. Our approach is based on constant-memory statistic accumulators, allowing large-scale analysis of video streams. We evaluated our implementation with more than 100 000 concurrent streaming sessions on a single CPU core, showing good correlation with QoE ground truth (ρ > 0.7, R2 > 0.5).
A New Blockchain-Based Medical Model for Smart Hospital Integrity Management of the Drug Supply Chain

Authors:
raw_author_name: Narendra Kumar Dewangan
raw_affiliation_string: Dept. of CSE (AI and ML), Shri Shankaracharya Institute of Professional Management and Technology Raipur.
raw_author_name: RPS Chauhan
raw_affiliation_string: Dept. of CSE (AI and ML), Shri Shankaracharya Institute of Professional Management and Technology Raipur.

Abstract: Counterfeit medications are currently one of the biggest issues in pharmacology. According to a research from the Health Research Funding organization, between 10–30% of pharmaceuticals in underdeveloped nations are counterfeit. The primary problem is not in counterfeiting per such, but rather in the fact that these pharmaceuticals have distinct adverse consequences on human health when compared to regular drugs. The WHO estimates that over 30% of all medications sold in Latin America, Asia, and Africa are fake. This is the main issue facing the entire world, and things become worse in underdeveloped nations where one in ten medications are either counterfeit or do not adhere to drug standards. It is becoming increasingly challenging to standardize drug safety due to the growth of online pharmacies. It is challenging to identify fakes. The safety of the pharmaceutical supply chain is becoming a serious public health concern, a process that affects all of us. In this work, we present a new approach to medication supply chain management that handles safe drug supply chain data using Hyperledger Fabric, a blockchain-based platform. By implementing drug record transactions on a blockchain, a drug supply chain and smart healthcare ecosystem may be constructed. The suggested method addresses this issue. To allow for time-limited access to patient health information and electronic prescription records, a smart contract has been introduced.