Reviewerly/exHarmony huggingface dataset:
Mailing Pigeon: A Comprehensive Platform for Matching Users Based on Intellectual Interests and Natural Language Processing *

Mailing Pigeon is an integrated software platform that connects users with similar intellectual interests and facilitates meaningful conversations. Using natural language processing (NLP) techniques and algorithms such as K-means clustering and Nearest Neighbours the platform analyses user profiles and messages to provide effective matching based on compatibility. Users can engage in discussions, improve their English proficiency, and gain insights into their writing patterns. Mailing Pigeon categorizes users' texting personalities and recommends matches using machine learning models and algorithms. By utilizing text similarity-based approaches like Cosine users can join communities and have conversations with like-minded individuals. The platform also allows users to analyze their profiles, including writing patterns and vocabulary, using algorithms like K-means, Nearest Neighbours, and the NLTK library. Mailing Pigeon aims to enhance social experiences and well-being by addressing the challenges of finding motivated communities in a vast online network. It provides a comprehensive solution for connecting individuals, fostering meaningful conversations, and promoting personal growth in language and communication skills.
VisualEyez – Assisting The Visually Impaired with Barbadian Banknote Detection

The International Agency for the Prevention of Blindness's Vision Atlas reports that there are 1.1 billion people globally with vision loss and this is expected to rise to 1.7 billion by 2050. Moreover, much of this vision loss is driven by inequality with higher rates occurring in low- and middle-income countries such as those within the Caribbean region. Four percent of the Barbadian population is estimated to be visually impaired (VI) and of this, one percent is totally blind. Although this is a small percentage, it represents over ten thousand people who have the right to live independently in an inclusive society. Until December 05, 2022, this right was inhibited by the lack of a mechanism for members of the VI community to distinguish among the various local banknotes. The introduction of unique tactile symbols on each denomination was welcomed by the community. However, as the older notes are still widely circulated, the issue persists. VisualEyez, an artificial intelligence (AI) mobile application was developed to address the matter. This application identifies the different denominations of Barbadian banknotes and communicates the detected value using either speech or vibration modes. In this work, the Systems Usability Survey (SUS) instrument was employed on a purposive sample of 6 members from the Barbados Association for the Blind and Deaf (BABD) to quantify the user experience (UX). Results showed an average rating of 79.6 which indicates that users are very satisfied with the application. Speech was the generally preferred operating mode, with vibration mode being preferred in public spaces. This research shows that the VisualEyez application will be beneficial to those who are blind or visually impaired.
Development of a Safe Gaming and Anti-Hate Serious Game

Online gaming culture is a fertile ground for hate groups to exploit for the recruitment and promotion of hateful narratives. The widespread adoption of video games and the global interconnectedness provided by online platforms enable hate groups to gather and coordinate violence. This paper presents a multi-stakeholder approach to develop an anti-hate serious game designed to educate youth about the nature and implications of hate by employing a series of coping mechanisms, relatable experiences, and decision-making strategies woven into a compelling narrative guided by a curriculum focused on understanding emotion, increasing digital literacy, and familiarizing with stereotypes. A preliminary exploratory study was undertaken, incorporating both pre- and post-test assessments alongside in-session assessments, to discern the perceived impact and effectiveness of the experience. The constraints imposed by the COVID-19 pandemic necessitated a limited sample size, however, preliminary findings demonstrate a promising trend with both the youth and facilitators positively welcoming the game and highlighting the value of the game in providing tangible, actionable tools for combating the pervasive issue of online hate narratives. These initial findings underscore the need for a larger study to further investigate the game's potential in fostering a more tolerant and understanding digital society.
On Performing Vestibular Damage Assessment and Therapy Using Virtual Reality: Lessons Learned

Virtual and augmented reality-based devices have been proposed for a range of assessment and treatment tasks, but how well are they accepted by clinicians and their patients? To investigate this question a prototype VR-based tool was developed for vestibular damage assessment and treatment. Designed to be used primarily within an in-person clinical setting, this tool was developed with the long-term goal of also supporting in-home independent and supervised treatment. Mock treatment and assessment sessions were held with non-clinical patients and the operational and patient experiences evaluated qualitatively through post-session questionnaires. Participants found the process engaging although there were concerns over hygiene, especially in light of the COVID pandemic. Clinicians felt that a VR- or AR-based approach could be effective, especially if it engaged patients in supervised, at-home exercises.
Exploring the Impact of Menu Systems, Interaction Methods, and Sitting or Standing Posture on User Experience in Virtual Reality

Virtual Reality (VR) has become an increasingly crucial aspect in both commercial and industrial settings. However, the user experience of the user interfaces and interaction methods in the VR environment is often overlooked. This paper aims to explore different menu systems, interaction methods, and the user's sitting or standing posture on user experience and cybersickness in VR applications. An experiment with two menu systems and two interaction methods in an implemented VR application was conducted with 20 participants. The results found that traditional, top-down, panel menus with motion controls are the best combination regarding the user experience. Sitting posture provides less severe simulator sickness symptoms than standing.
Introducing Inclusive Design by Showing the Effects of Impaired Environments on Neurodiverse Users

This paper focuses on a study to determine the feasibility of the use of serious games, in a user-interface design course, to remove any barriers that students may initially have towards inclusive design by introducing accessibility issues that affect neurodiverse users. Neurodiverse users are often overlooked in the creation of software because of the stigma and misinformation that exists within society. These games highlight difficult to understand experiences that these users undergo when using an impaired user-interface. Each one requires the completion of a single task in an inaccessible environment enabling students to experience the frustrations neurodiverse users experience when common accessibility mistakes are made.
An Effectual Methodology to Objective Quality Assessment for Noisy and Compressed Images Using Full and Blind Reference Quality Metrics

The proposed work elucidates the role of metrics to decide the superiority of noisy and compressed images. The metrics approximate the superiority of degraded image (noisy or compressed) with or without the reference images. The computed numerical value based on this comparison finalizes the excellence of the image under test. The degradation in any stage of image processing (acquisition, transmission and reception) deteriorates the eminence of an image. It is challenging to understand the source and reason for low quality of the image. The suggested work analyzes the quality of salt and pepper noisy image and compressed image using blind and complete reference metrics. The analysis using full reference metrics is an unpretentious process. However, they do not related to human perception. In contrast, blind quality metrics demonstrated its hegemony in this facet.
Automated Difficulty Assessment Model for Platformer Games: A Comprehensive Approach

In general, a video game offers a seamless progression in gameplay difficulty, starting with easy levels that allow players to grasp the basic mechanics of the game, and gradually introducing more challenging obstacles as they progress. The success of a game title heavily relies on its ability to provide a wellbalanced difficulty curve and a satisfying sense of progression. Designing a game entails a complex and time-consuming process that involves extensive playtesting. One promising approach to address this challenge is the utilization of software tools capable of automatically evaluating the difficulty of game levels. In this paper, we present a comprehensive model for automatically assessing the difficulty levels of platformer games. Our model is based on the formal calculation of static danger zones within levels and the analysis of enemy movement patterns using simulated pheromones. To validate our model, we implemented it and conducted tests using the complete set of levels from the original Super Mario Bros. game. The paper includes a detailed presentation of the model, the tools developed, and a comparative analysis showcasing the computed results.
Analysis of Instructional Techniques Influence on a Robot Assembly Task

As virtual reality (VR) has grown to become a common medium for training and entertainment, the techniques for communicating and instructing the user to perform a task within VR have been diverse and varied. Some of these techniques utilize familiar patterns such as dialogs and textual instructions, while others provide more VR-native interactions such as environmental cues or holograms, In this study, each subject was tasked with assembling two robots from their constituent parts one piece at a time. The subject received the instruction through one of the following in-tructional techniques: an unchanging set of generic instructions, a dynamic line of text per part, an image of the robot highlighting each part's destination, and a hologram in the destination of where each part should be placed. We conclude that no instructional technique offered a statistically significant increase in performance. While the hologram instructional technique was the overall preference of the population. Finally, the analysis of feedback shows promise that holograms would solve issues demonstrated in other instructional techniques.
An Exploratory Study of Vulnerabilities in Big Data Systems

The use of big data systems has become prevalent across sensitive domains, including health, defense and finance, among others. These big data systems are often complex and with complexity often comes vulnerabilities. Most big data systems also have Application Programming Interfaces (API) where these vulnerabilities can be present. This results in introducing security risks into the projects using these big data tools. To address this issue, this paper presents and uses a research process that can be used to assess the state of big data security in open source projects. The study examines tool versioning in various open source projects to uncover the vulnerabilities present and then assess the significant factors of these vulnerabilities. Furthermore, we provide insights into the vulnerabilities of different big data system APIs and how each of the vulnerabilities can manifest in a project using the specific tool. This study serves as a guideline for big data system developers to develop highly secure data-intensive systems. CCS CONCEPTS • Security and privacy → Database and storage security; Software and application security
Report on the 11th International Workshop on News Recommendation and Analytics (INRA 2023) at ACM RecSys 2023

News remains a challenging domain for personalization. INRA'23 allowed experts and practitioners to get together and discuss recent trends and future directions. Peer review selected six contributions for presentation covering a wide array of topics including beyond accuracy evaluation, the use of large language models, misinformation, and emotions in news. Date : 18 September 2023. Website : https://research.idi.ntnu.no/NewsTech/INRA/.
Rethinking E-Commerce Search

E-commerce search and recommendation usually operate on structured data such as product catalogs and taxonomies. However, creating better search and recommendation systems often requires a large variety of unstructured data including customer reviews and articles on the web. Traditionally, the solution has always been converting unstructured data into structured data through information extraction, and conducting search over the structured data. However, this is a costly approach that often has low quality. In this paper, we envision a solution that does entirely the opposite. Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs. Then, search and recommendation can be performed through a Q/A mechanism through an LLM instead of using traditional information retrieval methods over structured data.
Interpreting High Order Epistasis Using Sparse Transformers

Genome-Wide Association Studies aim to identify relations between Single Nucleotide Polymorphisms (SNPs) and the manifestation of certain diseases, which is an important challenge in biomedicine and personalized healthcare. However, most genetic diseases are explained by the interactions between several SNPs, known as epistasis. Detecting epistasis is a very computationally demanding task, due to the sheer number of SNP combinations to analyze. Recently, deep learning has emerged as a possible solution for genomic prediction, but the black-box nature of neural networks and the lack of explainability is a drawback yet to be solved. In this paper, a new, flexible framework for interpreting neural networks for anyorder epistasis detection is presented. Using sparse transformers, a technique not yet employed for epistasis detection, different SNP representations are explored and attention scores are assigned to each SNP to quantify its relevance for phenotype prediction. The results on simulated datasets show that the proposed framework outperforms state-of-the-art methods for explainability, identifying SNP interactions in diverse epistasis scenarios. The proposed framework is validated on a real breast cancer dataset, identifying second to fifth order interactions in the top 40% most relevant SNPs.
Short: Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises

At-home exercising strongly predicts physical therapy patient outcomes, underscoring the need for analyzing patient behaviors at-home via remote patient monitoring. Contemporary methods for remote patient monitoring rely on specialized sensors, i.e., Inertial Measurement Units, RGB-Depth cameras, motion capture systems, or stereo vision which are costly and not scalable to all physical therapy patients. Here, we observe a lack of literature using only a monocular RGB camera. In this paper, we demonstrate a skeletal feedback model for at-home exercises using only video acquired from a smartphone camera. We propose models for (i) Patient Performance Evaluation - which classifies the correctness of exercises, and (ii) Guidance - which identifies why the exercise went wrong so the patient can correct themselves. We use these models on our dataset of four common physical therapy exercises labeled by a physical therapist. Our results demonstrate the feasibility of using skeletal data from state-of-the-art 3D human pose estimation models for physical rehabilitation exercise evaluation and guidance. Thus, we enable remote patient monitoring and guidance from a single camera - making it highly cost-effective and scalable.
Demo: P-Fall: Personalization Pipeline for Fall Detection

We present an edge-cloud collaborative personalized fall detection pipeline called P-Fall. A personalized fall detection model requires real-time adaptation of a pre-trained model using real-time feedback data provided by the user. We, herein, highlight the design of the software architecture for a collaborative framework, the smart-watch's UI for the ease of collecting a user's feedback data, and the automation of the personalization process.
Exploring Earables to Monitor Temporal Lack of Focus during Online Meetings to Identify Onset of Neurological Disorders

This paper presents a framework called enGauge that leverages ear-based inertial sensing to continuously monitor listener focus levels in online meetings and provide feedback to the speaker about audience engagement. This allows for the identification of the onset of several neurodevelopmental disorders, including attention deficit hyperactivity disorder (ADHD), and can help to improve the effectiveness of online meetings by allowing speakers to adjust their speaking pace and style based on audience engagement. We explore a contrastive learning-based approach coupled with a judicial selection of anchor events from the meeting contents to model the system. enGauge can detect patterns or shifts in behavior and focus levels of passive listeners to accurately identify changes in focus. Results from a user study with 38 participants showed an overall F1-score of 0.89 for detecting passive listeners' focus levels. Our study suggests that ear-based inertial sensing has the potential to be a valuable tool for the early detection and monitoring of several neurodevelopmental disorders among individuals.
The Relationship Between Colour and Music in the Art of Composer Ede Terényi

"From his early youth, the composer lived his whole life under the spell of colors and color combinations. His talent as an artist was already apparent at the age of six, when, in addition to his piano studies, he enjoyed drawing plants and flowers in school notebooks. He kept his first vase, on one side of which he drew a bouquet of flowers sprouting from a heart on a bright pale green background when he was in high school, and on the other side a deer flying through a bush and a pine tree. She always felt a special fascination for the pine tree, seeing it as a symbol, its scent, and its green color as magical. He was similarly attracted to green grassy lawns and the changing colors of autumn leaves, which he saw as a symphony. These childhood experiences are also reflected in his colorful graphics. This paper presents the relationship between color and music in Ede Terényi’s compositions, and the composer’s conception of tonal aesthetics, the relationship between tonalities and colors. Keywords: Ede Terényi, composer, music compositions, colors, tonalities, graphics"
In Memoriam Viorel Cosma (1923-2017): Outstanding Personality for Romanian Musicology

"This year marks a century since the birth of the musicologist and enescologist Viorel Cosma (1923-2017). The purpose of this article is to recall the impact that Viorel Cosma had in musicological research on a national and international level through the thousands of signed articles and through the approxi¬mately 100 books published on themes that highlighted Romanian music and musi¬cians. The significant contribu¬tions of Viorel Cosma are recognized in particular for 22 volumes dedicated to George Enescu, for 12 biobibliographical lexicons with personalities of Romanian music, for musical criticism signed throughout 40 years at concerts as well 60 years at opera performances. Viorel Cosma remains in the history of Romanian musical culture as an emblematic personality who successfully combined the role of musicologist, teacher, enescologist, lexicographer and music critic. Keywords: Viorel Cosma, Romanian Musicology, George Enescu, Lexicographer, Music critic."
Interpretative Elements of György Kósa’s Chinese Song Series From an Art Psychology Perspective

"I got to know the art of György Kósa (1897-1984) during my performing career. During the performances of the first Chinese Songs series, the songs inspired me to learn more about the composer. Delving into the literature shed light on the composer’s artistic concept, elements of which I can identify with also as a performer. It can be said that, even if we focus only on his songwriting, his most important role as a songwriter is to communicate values. His choice of lyrics is based on a profound knowledge of twentieth-century Hungarian and world literature, and his literary themes represent both the cultural and mental-spiritual well-being of the time, and satisfy the musician’s need for expression, both on a personal and a communal level. György Kósa’s oeuvre as a songwriter is a densely woven structure, consistently and organically formed. The songs are each a microcosm of their own, linked to a particular state of being, time and emotional mood of the individual, and in portraying this, the literary and musical tools employed can all be accurately mapped. Keywords: Chinese songs, György Kósa, Po Chü-i’s poetry, Sándor Weöres, performing arts, creative arts, singing voice, Stella Ferch"
About Electroacoustic Music: From the Beginning Until Our Days

"In this article, we will follow the development of electroacoustic music. This article focusses especially on the instruments that contributed to the apparition of this genre of music, starting with the beginnings, before the twentieth century. This will help us understand the evolution of musical instruments and how important they are when it comes to making music. What is more fascinating about electro acoustic music will be presented in the article, focusing on some of the pioneers of this type of music, and how electro acoustic music turned into what nowadays we call techno music. Also, we will follow the development of the instruments used back in the days to the processor which we use in the creation of electronic music. Keywords: electroacoustic, technology, musique concrète, Theremin."
Smart Contracts for Enhanced Water Resource Management

Water management is critical for long-term development and the preservation of essential natural resources. Traditional water management strategies are becoming increasingly ineffective in dealing with developing water shortages and the need for efficient resource allocation. The incorporation of smart contracts, which are supported by blockchain technology, presents a viable answer for reshaping water management techniques. When combined with internet of things (IoT) devices that give real-time data on water use, quality, and environmental conditions, a powerful alliance is formed that has the potential to alter traditional water management practices. The concept of smart contracts self-executing agreements with established rules that trigger automatic activities upon the fulfillment of specific criteria lies at the heart of this fusion. The use of blockchain technology has emerged as a disruptive option to alleviate water scarcity and ensure equal distribution in the goal of sustainable water management.