Reviewerly/exHarmony huggingface dataset:
A Critical Literature Review for Equal Participation in Human-Animal Interactions in Design

Animals have been studied in the CSCW, such as in studies about animal welfare, pet-advocacy groups, pet video chat, and multispecies interaction. Animal-Computer Interaction (ACI) is the field where studies with animals and technology are at the centre. However, within the CSCW and the ACI field, the equal participation from the animals’ viewpoint remains relatively human-centric, and how humans can collaborate with nonhuman animals remain underexplored. Research beyond human-centrism in other fields puts equal participation of nonhuman animals at the centre with the intention of equal inclusion. Thus, this poster introduces the initial results from a literature review on the previously published work in animal-inclusive and equity-oriented research fields with the purpose of opening a discussion on equity perspectives and equal participation of nonhuman animals in the CSCW work.
Solidarity and Disruption Collective Organizing In Computing II

This workshop responds to the incredible growth of corporate tech power - including during a deadly pandemic - and the growing need for tech workers of all stripes (including researchers/academics) to build grassroots power. This workshop’s twinned themes of solidarity and disruption acknowledge that solidarity is vital but not sufficient to enact the structural changes we need. Disruption— in the form of sit-ins, strikes, refusal, and direct action—has become a necessary condition in the face of technology corporations’ greed-driven expansion towards militaristic, techno-totalitarian futures. In this one-day workshop, we will bring together tech workers, researchers and activists from academia, industry, and community-based organizations to extend conversations that we began in two workshops last year. We will further explore avenues and approaches for action, particularly to support practitioners’ and activists’ objectives, and connect participants with concrete opportunities for on-the-ground action.
Exploring the Community of Model Publishers on TensorFlow Hub

We explore the community of AI model publishers on TensorFlow Hub (TF Hub). While researchers identified the challenges AI model publishers and AI model users faced, little is known about how they interact with each other in an online community. The analysis of the metadata recorded on TF Hub revealed the models that the AI model publishers uploaded. Also, we found out how the models published by the AI model publishers were shared with other people on TF Hub. To our knowledge, this is the first attempt to explore the online community of AI model publishers sharing their models with each other.
Korean Emoticons: Understanding How Subtle Emotional Differences Are Evoked Online

Online conversations through text have limitations in expressing emotions that can cause miscommunications across cultures. In this work, we study the Korean emotional expressions in text focusing on how people perceive emotional intentions through the use of emotion-expressing Korean characters. We define them as Korean emoticons (‘ㅋ’, ‘ㅎ’, ‘ㅠ’), onomatopoeic characters often used to express emotions for text-based communication. We examine the participants’ understanding and usage of Korean emoticons by conducting an online survey asking to evaluate emotional contents of given sentences and interviews to explain personal experiences. We found that the different numbers of Korean emoticons used evoke different emotions, and that negative emoticons amplify positive emotions in positive contexts and positive emoticons alleviate negative emotions in negative contexts, while emoticons in neutral contexts have varying impacts depending on the context. We further discuss design implications on how text suggestion tools can support users taking emotional intentions into account.
Arabic Sentiment Analysis Based Machine Learning for Measuring User Satisfaction with Banking Services' Mobile Applications : Comparative Study

Understanding the quality of the application and the user's needs are important tasks in application development. To understand user requirements to increase application quality, application review-based sentiment analysis (SA) can be used. This research aimed at determine customer opinions toward mobile banking services' applications, then update and maintain these applications. Banking services' mobile apps became a significant component of people life. So, these mobile apps user comments have targeted in this study for SA tasks. Utilized dataset is collected from banking mobile apps user reviews on Google Play Store. Labeling process is performed manually, it generate three main classes: positive, negative and neutral classes. Arabic sentiment analysis process is achieved using machine learning techniques, namely Naïve Bayes (NB), K-nearest neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM) models. The NB model has gained a better level of evaluation comparing to other DT, KNN and SVM algorithms. NB model has achieved the highest accuracy, recall, precision and F-score, which were 89.65% in accuracy, 88.08% in recall, 88.25% in precision, and 88.25% in f-score evaluation measure.
Finite Field Multiplication for Supersingular Isogeny Diffie– Hellman in Post-Quantum Cryptosystems

A quantum The computer's operating speed significantly faster than modern computers, posing a challenge to existing cryptosystems. Post-Quantum Cryptosystems (PQC) is a type of cryptography that focuses underlying mathematical problems deemed intractable. supersingular isogeny Diffie-Hellman (SIDH) key protocol is a modern post-quantum cryptosystem provides benefits in terms of secret key length and Super Singular Isogeny Diffie–Hellman, research and current and potential future challenges.
Camera based Activity Recognition for Assisted Living Applications

Activity recognition in the assisted living environment could detect dangerous and anomalous activities. One such activity is the act of falling, which is very dangerous and can cause injuries and death. Falling is a leading cause of unintentional injury and death worldwide. The unintentional deaths caused by falls can be prevented to a great extent by its early detection and providing subsequent medical help. Although several fall detection systems exist, they lack speed, most of which are slow in inference time. We propose a single-shot fall detection model as part of camera-based activity recognition for assisted living applications which is fast and accurate. The proposed fall detection model uses a deep learning-based object detection framework called YOLOv5. Instead of following a two-step process for fall detection, namely, subject of interest detection (object detection) and activity recognition (fall activity recognition), we detect falls in a single stage by virtue of object detection and activity recognition merged into a one-step process. This approach introduces novelty by using an object detection framework for activity recognition and fall detection. The proposed model is implemented in the PyTorch framework. We train different variants of YOLOv5 (small, medium, large) differentiated by their depth. The best result obtained shows an inference speed of 32 frames per second. This fall detection model detects falls accurately in challenging conditions with noise and background clutter.
Comparative Analysis on the Image Steganographic Algorithms

As a result of recent technological advancements in digitalization, a vast amount of data is generated every day. With the increased usage of the Internet, it is becoming increasingly difficult to prevent black hat hackers from disclosing critical and secret information. As a result, this research contributes to identifying the best and most efficient algorithm from among the three opted image steganographic algorithms, which are Pixel Value Differencing (PVD), Optimum Pixel Value Adjustment Procedure (OPAP), and Discrete Cosine Transform (DCT), as well as the image format pairing that can facilitate the most data capacity while maintaining the highest image quality, resilience, and undetectability. The purpose of this research is to apply the three image steganographic algorithms to coloured and grayscale images in the PNG, JPG, and BMP formats. The research technique for the intended study activity is divided into three parts. Each step is intended to accomplish a certain goal. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were the two performance metrics utilized to evaluate the algorithms. For the experiment, seven (7) separate standard pictures of 512 x 512 pixels and 1024 x 1024 pixels in colour and grayscale are obtained and employed. The three algorithms were analysed, assessed, and compared at the end of this study in terms of robustness, capacity, and imperceptibility. In a nutshell, OPAP is the best and most efficient algorithm which can be used to conceal the secret text without affecting the image quality while OPAP works best with grayscale images of 1024 x 1024 px to produce an excellent output in terms of high robustness, imperceptibility, and capacity.
Virtual Reality as a Teaching Aid Material for Kindergarten Teachers in Early Mathematics

Virtual reality becomes a common thing in this technologized world, where human and technology worked together. This study investigated the effectiveness of virtual reality as teaching aid material in teaching Early Mathematics among kindergarten teachers in Malaysia. In this research, a quantitative study with the use of descriptive analysis is conducted to investigate the satisfaction of the users on using virtual reality application as teaching aid material in facilitating the teaching session. This application is built by Mobile Application Development Life Cycle (MADLAC) and National Preschool Standards-Based Curriculum as instructional design. The proposed system showed that kindergarten teachers that used the application is satisfied and having good user experience while using the application. The findings from this project suggested that the kindergarten teachers perceived VR application as a good teaching aid material and the score of System Usability Scale (SUS) score shows that the respondents show good feedback towards the usability of the application, along with their personal experience with the application itself.
DEADC: Density Extending Algorithm for Data Clustering

Data clustering is a very active research area in machine learning and knowledge discovery. Generating clusters of different densities is a challenging task. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm that has problems discovering clusters of varied density since it uses a fixed radius and has quadratic time complexity making it difficult in real applications with large datasets. In this paper, a Density Extending Algorithm for Data Clustering (DEADC) is proposed to cluster datasets with different densities, sizes, and noise with better accuracy and less execution time. DEADC uses a dynamic radius variable called ϵ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Extended</inf> based on statistical analysis that assigns a regional density value for each cluster by extending the data points with the ϵ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Extended</inf> neighborhood. DEADC implicitly needs to compute the empirical density for each created cluster, leading to linear time complexity. Experimental results showed the effectiveness of the DEADC for identifying clusters with varied densities from synthetic and real-world datasets with a significant improvement in clustering accuracy and execution time.
Proposal for an Individually Adapted Video Viewing System Linked to the User’s Feeling of Fear

In recent years, watching movies and videos have become increasingly diverse. Some movie theaters offer aromas and vibrations in addition to sound and 2D/3D images that accompany the movie. However, most movies provide a single same experience to all the viewers. Another way to watch movies is through video distribution services that allow users to enjoy them anywhere without having to specify the location. In this study, we proposed to automatically modulate the video playback speed according to the user’s feeling of fear estimated from pulse-rate variability measured with a smartwatch. Ten subjects watched a 10-minutes horror short movie in the standard way and with feeling-controlled playback speed modulation. After each viewing, they answered an impression evaluation questionnaire. The responses revealed that the modulation of playback speed amplified subjects’ feeling of fear.
Design And Development Of Manga Mobile Application Recommender System

Reading an e-book has become common in modern society due to its convenience. Manga, another sort of e-book, has developed a devoted following over the years. As a result, when selling products online, most businesses have a recommendation system in place. The majority of websites, however, are not made with the customer in mind; rather, the corporations' force add-ons sell to clients by making recommendations. As a result, our research primarily focused on a machine learning strategy known as a clustering-based method to address this constraint. The user's preferences have been taken into account by the clustering-based method. The agile methodology has been used in our study. A mobile application has been used to assess how effective this strategy is. Following evaluation, the utility of the system is assessed at 85.71 percent, followed by satisfaction at 93.20 percent, ease of use at 88.53 percent, ease of learning at 95.83 percent, and usefulness at 85.71 percent. Overall, the system performed well in the usability assessment with a score of 85.08 percent.
Vehicle Trajectory Planning for Collision Avoidance with Mobile Obstacles

Travelling on crowded roads, vehicles usually move in a dynamic environment where, fixed and mobile obstacles can be detected. This paper proposes a solution for a trajectory planner that can be used to compute paths so that, both fixed and mobile obstacles can be overpassed. The trajectory planner, based on the information from sensors regarding the distance to the other vehicles, computes the time moments when the vehicle has to overpass the obstacles. The generated trajectory is a time function that, based on these time moments, leads the vehicle to avoid a collision with the obstacles. The proposed trajectory planner is tested in three scenarios, where the obstacles are fixed or mobile. The results prove the effectiveness of the proposed trajectory planning solution.
Escape from the Hamster Wheel: Exploring the Role of Joy of Movement in Movement-based Games

Movement-based games often leverage digital games as an opportunity to mask physical activity, neglecting the perspective that movement can be inherently enjoyable. In the work presented here, we examine joy of movement through Puzzle Ball Hamster, a game in which players need to move and bounce on a large sensor-equipped gymnastic ball to make game input. We invite the attendees of CHI PLAY to engage with our game, explore their own experience of joy of movement, and to safely lead a little hamster to escape.
Twitter Sentiment and Bitcoin Price – Is there a connection?

Still in the inception of Web 3.0, the present study stands as an important incursion into the future of the Internet and displays how different technologies, Social Media and Cryptocurrencies, can influence each other and the world around them in these new, exciting, times. This paper seeks to find if there is a relationship between Twitter Sentiment and Bitcoin price. To this end, data is collected and preprocessed to construct a dataset consisting of two time series, one for Twitter Sentiment regarding Bitcoin and one for Bitcoin Price. The relationship between the two is studied through a Vector Autoregressive model. The results show that each exerts influence on the other. Twitter Sentiment is quicker to absorb the information, while Bitcoin Price takes longer to intake the most recent events. Future avenues of study are identified, with topic analysis of Bitcoin discourse being highlighted due to its potential to unlock new knowledge.
Automotive Tracking with High-speed Stereo Vision Based on a Spatiotemporal Shared Filter

In this study, we propose a robust method for automotive tracking using high-speed stereo vision. The tracking is achieved using a shared correlation filter for stereo images that can be captured at high speed using the features of high-framerate images. In addition, by adjusting the tracking window for each automobile, robust tracking can be performed even in the case of partial occlusion with respect to visually overlapping automobiles. Experiments on in-vehicle camera images were performed at a frame rate of 500 fps to validate the efficacy of the proposed approach.
Social Medical Network

The article presents a prototype social media network dedicated to the medical staff of Romania, from the architectural and functional points of view. The medical social media network is implemented as a web application for the time being, followed by the future implementation of a mobile application as well. This network allows for the medical staff in Romania to register, log in and virtually connect, with the purpose of sharing special medical cases or getting advised opinions for setting diagnostics. Any doctor in Romania will be able to create an account, add their information about their medical department/specialty in which they activate, their former studies, obtained diplomas/specializations, former or current occupied positions, resume etc. They will have the possibility to create posts, in the form of text, accompanied by medical images if needed. Oher users will be able to comment on the added posts. Each post or comment is considered a document, stored in the database. One of the most useful functionalities of the web application is the possibility of search - simple, based on the name of a doctor; or advanced - which implies searching in posts and comments using information retrieval algorithms. The web application is based on a MongoDB database, using MongoDB Atlas search, that is an embedded full-text search, build on Apache Lucene. The medical social media network is now in the testing phase, being utilized by a group of doctors from a private clinic in Romania. The feedback gathered from this usage is taken into consideration to improve and further develop the application.
Comparative Analysis of Deep Semantic Segmentation Networks Sensitivity to Input Noise

In this paper we are analyzing the sensitivity of deep semantic segmentation networks output with respect to input image augmentations. Our goal is to introduce a new method for measuring the sensitivity of deep neural networks doing semantic segmentation and thus determine how stable these networks are when image alterations are encountered during real life image capturing. To achieve our goal, we construct a sensitivity analysis model and apply it to some commonly used semantic segmentation CNN architectures (PSPNet, ICNet, DeepLabV3) across a couple types of image degradations. Extrapolating the results we obtain would allow for estimating various CNN models performance for new domains comprised by images of lower quality (captured with different types of camera or light conditions). Our specific experiments for semantic segmentation task revealed that DeepLabV3is more stable to input image degradations than PSPNet and ICNet. However, for some object classes even DeepLabV3is seriously affected by the input noise.
A Multi-Layer Feed Forward Neural Network for Breast Cancer Diagnosis from Ultrasound Images

Diagnosis of breast cancer from ultrasound images (USIs) and images processing are two main stages of medical computing field. In this paper, we propose a Multi-Layer Feed Forward Neural Network (MLFNN) for classification of benign and malignant breast tumors by using a Python based implementation. The neural model is trained using the preprocessed regions of interests (ROIs) from USIs that belong to the Breast Ultrasound Dataset (BUSI dataset). The preprocessing procedure includes extracting the ROIs, resizing, normalizing, and flattening. The ROIs are obtained with our own algorithm that overlaps the original image with its corresponding ground truth image. More images and tumor shapes employed in the training stage of the neural network can lead to better prediction performances. In this study, the binary classification of tumors into benignancy or malignancy gives 99% training accuracy, 86% validation accuracy and 71.43% test accuracy.
Using Knowledge Transfer for Neural Network Architecture Optimization with Improved Training Efficiency

Neural networks have demonstrated their potential for solving a wide range of real-world problems. While capable of learning correlations and relationships among complex datasets, neural network training and validation are often tedious and time-consuming, especially for large, high-dimensional datasets, given that sufficient models should be run through the validation pipeline so as to adequately cover the hyperparameter space. In order to address this problem, we propose a weight adjustment method for improving training times when searching for the optimal neural network architecture by incorporating a knowledge transfer step into the corresponding pipeline. This involves learning weight features from a trained model and using them to improve the training times of a subsequent one. Considering a sequence of candidate neural networks with different hyperparameter values, each model, once trained, serves as a source of knowledge for the next one, resulting in improved overall training times. We test our approach on dataset of various sizes, where we obtain reduced total training times, especially when small learning rates are used for finely-tuned convergence.
End-to-End Single Image Super-Resolution Based on Convolutional Neural Networks

Single image super-resolution algorithms aim to increase the resolution of an input image without deteriorating its visual perception. With a strong ability to understand the structure of an image, convolutional neural networks (CNNs) are successfully applied to this problem. Previous studies have shown that human perception is mainly influenced by variations in luminance. In this regard, this paper introduces two CNNs that operate only on the luminance channel, at low computational costs. Each model provides an end-to-end mapping between a low-resolution (LR) and a high-resolution (HR) map. Because upsampling is integrated into CNN, the design allows the control of HR image quality. In addition, the neural architectures can be configured with many layers operating with small LR feature maps, to provide fast run-time image processing. The approach is exemplified in two cases: generate the HR map or a residual map for the luminance channel; the residual map should be added to the map upsampled by interpolation. Besides having improved time performance, the two models can produce HR images with high NIQE scores, as shown experimentally.
From Pepper's Photographic Lessons to Appointment Application

The significant evolution of perception over humanoid robots had led to a great development of applications in different areas. The most engaging interactions between humans and robots involve visual and auditive communication which can be achieved using cameras due to their rich sensory information. The main focus of the Pepper robot is to amuse users and make their daily routines better. The objective of the application would be to observe Pepper's behavior in the scenario of a humanoid-robot appointment system and to develop a corresponding optimal approach.