Reviewerly/exHarmony huggingface dataset:
Factors on Mobile Application User Satisfaction in the Largest Indonesian Internet Service Provider (ISP)

Many users complain about using the largest mobile Internet Service Provider (ISP) application in Indonesia, MyIndihome, such as difficulties in verifying, logging in, and changing cell phone numbers and emails. With these complaints, the satisfaction of the MyIndihome application users decreases. The research aims to determine the effect of information quality, system quality, service quality, ease of use, usefulness, and chatbot effectiveness on user satisfaction with MyIndihome. Chatbot effectiveness is a novelty of the research because it has not been studied in previous research. The research applies a quantitative approach. Then the sampling technique used is probability sampling, and the method is simple random sampling with 417 respondents. Data collection techniques are carried out by distributing online questionnaires, and the data are statistically processed with SmartPLS and analyzed by Structural Equation Model (SEM). After carrying out several stages of testing from validity tests, reliability tests, and structural models, the results show that information quality, system quality, ease of use, usability, and chatbot effectiveness have a significant effect on user satisfaction. However, the service quality has no effect. These results can help companies to increase user satisfaction with the MyIndihome application. They can increase the variables that influence user satisfaction with the MyIndihome application.
Classification of Deepfake Images Using a Novel Explanatory Hybrid Model

In court, criminal investigations and identity management tools, like check-in and payment logins, face videos, and photos, are used as evidence more frequently. Although deeply falsified information may be found using deep learning classifiers, block-box decisionmaking makes forensic investigation in criminal trials more challenging. Therefore, the research suggests a three-step classification technique to classify the deceptive deepfake image content. The research examines the visual assessments of an EfficientNet and Shifted Window Transformer (SWinT) hybrid model based on Convolutional Neural Network (CNN) and Transformer architectures. The classifier generality is improved in the first stage using a different augmentation. Then, the hybrid model is developed in the second step by combining the EfficientNet and Shifted Window Transformer architectures. Next, the GradCAM approach for assessing human understanding demonstrates deepfake visual interpretation. In 14,204 images for the validation set, there are 7,096 fake photos and 7,108 real images. In contrast to focusing only on a few discrete face parts, the research shows that the entire deepfake image should be investigated. On a custom dataset of real, Generative Adversarial Networks (GAN)-generated, and human-altered web photos, the proposed method achieves an accuracy of 98.45%, a recall of 99.12%, and a loss of 0.11125. The proposed method successfully distinguishes between real and manipulated images. Moreover, the presented approach can assist investigators in clarifying the composition of the artificially produced material.
Implementation of Face Recognition in Presence System with Convolutional Neural Network Algorithm

The problem of resource efficiency is the main topic that arises as a result of the application of a paper-based presence system, in Penguatan Pendidikan Karakter (PPK) activities at SMA Negeri 1 Baleendah. Under ideal conditions, the existing presence system requires 336 pieces of paper per month, 25 officers per day, and takes days to complete the recapitulation of presence data per month. Face recognition implementation in presence systems using the Convolutional Neural Network (CNN) algorithm can have a significant impact on reducing the amount of paper used, reducing the need for human resources, and speeding up the process of writing and recapitulating presence data per month. This study compares two model architectures with different numbers of filters on the convolution layer, namely model 1 and model 2. The model with the best model accuracy performance will be applied to the face recognition presence system. The best model result from this study belongs to model 2, with 97% and a live-test accuracy of 100%. By applying the model to the presence system that has tested on March 1, 2, and 8, 2023, paper usage can be reduced by 83.33%, the number of human resource needs can be reduced by 63.63%, and the data recapitulation process in one month can be completed in 1.85 seconds. In addition, the attendance recording process can be done in under 1 second per person. So, it can be applied to a real-time system.
Blockchain Technology Framework for Universities

In recent years, the concept of blockchain technology has become increasingly popular. It serves as the foundation for cryptocurrency systems, distributed storage and smart contracts, which help to improve decentralization, security, and privacy. As a result, blockchain has emerged as a trend-setting technology which enhances the performance of other fields, including education, healthcare and national security. This article presents a framework that universities apply to implement blockchain technology. The paper first examines the structure, processes and related entities of universities. Then, it explains the means and applications of blockchain technology. After that, it proposes a framework that universities can use to adopt blockchain technology. The branched ledger is a new technique to maintain immutability and respond to the need of changes, in which it creates a new block of corrections and links it as a branch to the block that stores incorrect data. Finally, the paper analyzes the benefits and difficulties of implementing this framework.
Development of a Website-Based Financial Transaction Management System Using Agile with the Scrum Framework

The process of managing financial transactions, especially in the education sector, is still mostly carried out conventionally, so it has the potential to cause many risks, such as loss or damage to documents related to difficulties in searching for data as needed, transaction processes that take quite a long time, financial records that are not properly recorded. Well, it is prone to discrepancies between the nominal filing and the nominal disbursed conventional representations of financial statements that make the stakeholder evaluation process uncomfortable, and many other things. Therefore this research was conducted to build a website-based information system that can prevent this from happening in the future. The method used in the development of this information system is the Agile method with the Scrum framework. The Scrum framework consists of five stages, starting from Initiate, Plan, and Estimate, Implement, Review and Retrospect, and Release. This website was built using the Javascript programming language using the Vue JS framework for the Front-end. With this website-based application, it is hoped that transaction activities can be carried out better and correctly without being limited by space and time.
DETERMINING THE IMPACT OF TOURISM ON THE ENVIRONMENT BY EXTRACTING THE CARBON FOOTPRINT OF ROAD INFRASTRUCTURE IN NATURAL PROTECTED AREAS – CASE STUDY OF THE UČKA NATURE PARK

Purpose – The research is based on the thesis that if the impacts of tourism on the environment were reduced to a local carrying capacity of the environment, then the global impact of tourism on the environment would be fully sustainable. In this light, the purpose of this research is to measure the local impact of tourism related road traffic on the environment on the example of the Učka Nature Park. Methodology – The carbon footprint of road traffic in the Učka tunnel was calculated using the carbon footprint methodology. The footprint was measured in the period from 2015 to 2020 on a monthly basis in order to gather database for analysing the seasonality of CO2 emissions, taking into account the local biocapacity of the environment. Findings – The total carbon footprint of traffic in the Učka tunnel from 2015 to 2020 is the result of an average volume of 3,204,375 vehicles per year. This amount of road traffic emitted an average of 2934.3 tons of CO2 per year. On a yearly basis 4.45% of the total biocapacity of the Učka Nature Park or 687.9 lha is needed to absorb carbon emissions from the Učka tunnel. The share of tourism in the total carbon footprint of road traffic in the Učka tunnel during the observed period at the annual level is 30.5%. Contribution – The paper contributes to the discussion of the local impact of tourism related to road traffic. Specifically, the paper aims to raise awareness and encourage the scientific community to research more local case studies that will measure the concrete impact of tourism on the environment. The applied contribution of the work is expressed through the measured value of the total and specifically separated tourist carbon footprint and contributes to the expansion of the database that would enable objective, measurable and sustainable spatial management.
Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark

In this article, we provide a comprehensive study of a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8528 high-quality and elaborately selected images with object mask annotations, covering five superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection (COD) in given images. This is implemented by an interimage collaborative feature exploration (CFE) module, an intraimage object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art (SOTA) models, including 12 COD algorithms and six CoSOD algorithms, on the proposed CoCOD8K dataset under five widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet-CoCOD.
TCTL-Net: Template-free Color Transfer Learning for Self-Attention Driven Underwater Image Enhancement

Vision is an important source of information for underwater observations, but underwater images commonly suffer severe visual degradation due to the complexity of the underwater imaging environment and wavelength-dependent absorption effects. There is an urgent need for underwater image enhancement techniques to improve the visual quality of underwater images. Due to the scarcity of high-quality paired training samples, underwater image enhancement based on deep learning has never achieved success similar to other vision tasks. Instead of learning complicated distortion-to-clear mappings with deep networks, we design a template-free color transfer learning framework for predicting transfer parameters, which are more easily captured and described. In addition, we add attention-driven modules to learn differentiated transfer parameters for more flexible and robust enhancement. We verify the effectiveness of our method on multiple publicly available datasets and show its efficiency in enhancing high-resolution images. The source code and the trained models are available on the project homepage: https://trentqq.github.io/TCTL-Net.html.
Multi-Query Vehicle Re-Identification: Viewpoint-Conditioned Network, Unified Dataset and New Metric

Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks. In this paper, we propose a more realistic and easily accessible task, called multi-query vehicle Re-ID, which leverages multiple queries to overcome viewpoint limitation of single one. Based on this task, we make three major contributions. First, we design a novel viewpoint-conditioned network (VCNet), which adaptively combines the complementary information from different vehicle viewpoints, for multi-query vehicle Re-ID. Moreover, to deal with the problem of missing vehicle viewpoints, we propose a cross-view feature recovery module which recovers the features of the missing viewpoints by learnt the correlation between the features of available and missing viewpoints. Second, we create a unified benchmark dataset, taken by 6142 cameras from a real-life transportation surveillance system, with comprehensive viewpoints and large number of crossed scenes of each vehicle for multi-query vehicle Re-ID evaluation. Finally, we design a new evaluation metric, called mean cross-scene precision (mCSP), which measures the ability of cross-scene recognition by suppressing the positive samples with similar viewpoints from the same camera. Comprehensive experiments validate the superiority of the proposed method against other methods, as well as the effectiveness of the designed metric in the evaluation of multi-query vehicle Re-ID. The codes and dataset are available at: https://github.com/zhangchaobin001/VCNet.
Research on Real-time Recognition and Classification of Human Gesture Based on BiLSTM-CNN

To improve the classification accuracy of real-time gesture recognition by surface electromyography (sEMG), an inertial measurement unit (IMU) is added to assist the IMU sensor in acquiring the mixed signals of IMU and sEMG. The proposed algorithm is based on a combination of Bi-directional long short-term memory (BiLSTM) and convolutional neural networks (CNN) for real-time gesture recognition and classification. The first layer of the BiLSTM uses the gesture data collected by the IMU for arm orientation recognition. The second layer of the CNN uses the energy kernel phase diagram features for real-time recognition and classification of six gestures with mixed signals of motion states. By optimising the parameters of the model, the BiLSTM-CNN classification algorithm has a real-time gesture recognition time of less than 35ms, an average recognition accuracy of 90.6% for arm direction and 87.3% for gesture classification, which is advantageous in terms of recognition speed and accuracy compared with other machine learning classification algorithms.
La traduzione pedagogica: una risorsa motivazionale nell’insegnamento della LS/L2

The work focuses on pedagogical translation as motivational tool.In particular, as a tool that works on the motivation of the beginner L2 student.The specific working language is Czech, but we believe that what has been observed may also apply to other languages, whose teaching is characterized by a small number of students.The aim is to offer a theoretical and practical framework to better frame translation as a teaching tool even for beginners L2 at the University.After presenting what we mean with lower-frequency language, pedagogical translation and beginner group, we move into the work with translation and how this activity fits into the investigation of motivations in the LS.
Proxy design: a method for involving proxy users to speak on behalf of vulnerable or unreachable users in co-design

Purpose Designing digital artifacts is not a linear, straightforward process. This is particularly true when applying a user-centered design approach, or co-design, with users who are unable to participate in the design process. Although the reduced participation of a particular user group may harm the end result, the literature on solving this issue is sparse. In this article, proxy design is outlined as a method for involving a user group as proxy users to speak on behalf of a group that is difficult to reach. The article investigates the following research question: How can roleplaying be embedded in co-design to engage users as proxies on behalf of those who are unable to represent themselves? Design/methodology/approach The article presents a design ethnography spanning three years at a cancer rehabilitation clinic, where digital artifacts were designed to be used collaboratively by nurses and patients. The empirical data were analyzed using content analysis and consisted of 20 observation days at the clinic, six proxy design workshops, 21 telephone consultations between patients and nurses, and log data from the digital artifact. Findings The article shows that simulated consultations, with nurses roleplaying as proxies for patients ignited and initiated the design process and enabled an efficient in-depth understanding of patients. Moreover, the article reveals how proxy design as a method further expanded the design. The study findings illustrate: (1) proxy design as a method for initiating design, (2) proxy design as an embedded element in co-design and (3) six design guidelines that should be considered when engaging in proxy design. Originality/value The main contribution is the conceptualization of proxy design as a method that can ignite and initiate the co-design process when important users are unreachable, vulnerable or unable to represent themselves in the co-design process. More specifically, based on the empirical findings from a design ethnography that involved nurses as proxy users speaking on behalf of patients, the article shows that roleplaying in proxy design is a fitting way of initiating the design process, outlining proxy design as an embedded element of co-design.
Demonstration of A Figma Plugin to Simulate A Large-Scale Network for Prototyping Social Systems

Prototyping novel social computing systems is a challenge in the field of Social Computing. Rapid experimentation with novel social network sites can offer valuable insights into their pro-social benefits before their public release to a large audience. In this demo, we present SocialSketch, a Figma Plugin to simulate a crowd in a social network. This demo introduces a plugin for Figma, a no-code interactive prototyping tool, enabling the creation of profile frames and prototype links for large crowds based on realistic network models. Privacy-protective profile content is generated using AI. The plugin aids UX designers, researchers, and students in prototyping social apps and exploring social system design.
Can GPT models Follow Human Summarization Guidelines? Evaluating ChatGPT and GPT-4 for Dialogue Summarization

This study explores the capabilities of prompt-driven Large Language Models (LLMs) like ChatGPT and GPT-4 in adhering to human guidelines for dialogue summarization. Experiments employed DialogSum (English social conversations) and DECODA (French call center interactions), testing various prompts: including prompts from existing literature and those from human summarization guidelines, as well as a two-step prompt approach. Our findings indicate that GPT models often produce lengthy summaries and deviate from human summarization guidelines. However, using human guidelines as an intermediate step shows promise, outperforming direct word-length constraint prompts in some cases. The results reveal that GPT models exhibit unique stylistic tendencies in their summaries. While BERTScores did not dramatically decrease for GPT outputs suggesting semantic similarity to human references and specialised pre-trained models, ROUGE scores reveal grammatical and lexical disparities between GPT-generated and human-written summaries. These findings shed light on the capabilities and limitations of GPT models in following human instructions for dialogue summarization.
Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA handshapes, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.
LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs. We also present a pre-processed version of the dataset, from which we computed statistics of movement, position and handshape of the signs.