Reviewerly/exHarmony huggingface dataset:
Online Art Gallery and Selling Platform (ARTOGRAM)

In the past, artists faced significant limitations in terms of how, when, and where they could sell their artwork. The absence of online platforms meant that artists had to physically reach potential buyers, often through art galleries, exhibitions, or spontaneous street sales. However, the advent of the internet has transformed this landscape, offering artists unprecedented opportunities to share and sell their creations to a global audience, thereby supporting their artistic endeavors. The artogram project capitalizes on the full array of applications and portals available on the internet. It takes the form of a dynamic web application, serving as a premier online platform for the discovery and display of creative work, fostering artist collaborations, and facilitating the purchase of art products. At its core, artogram offers a highly interactive and user-friendly interface, catering to art enthusiasts of all backgrounds. The website serves a multifaceted role, functioning as an artist's portfolio, a marketplace for the sale of artistic creations, and a hub for talent acquisition.
Ill-Founded Proof Systems for Intuitionistic Linear-Time Temporal Logic

Abstract We introduce ill-founded sequent calculi for two intuitionistic linear-time temporal logics. Both logics are based on the language of intuitionistic propositional logic with ‘next’ and ‘until’ operators and are evaluated on dynamic Kripke models wherein the intuitionistic and temporal accessibility relations are assumed to satisfy one of two natural confluence properties: forward confluence in one case, and both forward and backward confluence in the other. The presented sequent calculi are cut-free and incorporate a simple form of formula nesting. Soundness of the calculi is shown by a standard argument and completeness via proof search.
Interactive NeRF Geometry Editing With Shape Priors

Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it is not trivial to change their content afterwards. Previous methods to modify NeRFs provide some control but they do not support direct shape deformation which is common for geometry representations like triangle meshes. In this paper, we present a NeRF geometry editing method that first extracts a triangle mesh representation of the geometry inside a NeRF. This mesh can be modified by any 3D modeling tool (we use ARAP mesh deformation). The mesh deformation is then extended into a volume deformation around the shape which establishes a mapping between ray queries to the deformed NeRF and the corresponding queries to the original NeRF. The basic shape editing mechanism is extended towards more powerful and more meaningful editing handles by generating box abstractions of the NeRF shapes which provide an intuitive interface to the user. By additionally assigning semantic labels, we can even identify and combine parts from different objects. We demonstrate the performance and quality of our method in a number of experiments on synthetic data as well as real captured scenes.
Dynamic path planning based on the fusion of improved RRT and DWA algorithms

A hybrid algorithm combining improved RRT algorithm and dynamic window method is proposed to solve the path planning problem of robots in the environment of dynamic or unknown obstacles. Firstly, aiming at the problems of many redundant nodes and low efficiency in the RRT algorithm, the target bias sampling and parent node reselection strategies are used to improve them. Secondly, for the problems of poor environmental adaptability and difficulty in taking into account speed and safety in the dynamic window method, a DWA algorithm with fuzzy control of parameters is proposed, which dynamically adjusts the weight of the evaluation function according to the distance of the robot from the target and obstacle. Finally, the above improved RRT algorithm and fuzzy DWA algorithm are mixed to realize dynamic path programming. By comparing the simulation results, the effectiveness of the hybrid algorithm in the complex dynamic environment is verified.
Lightning Talk: Efficiency and Programmability of DNN Accelerators and GPUs

While GPUs have provided high computational power for AI applications, their power efficiency may not be satisfied for applications requiring a high-speed response within a low power budget. Specialized deep neural network (DNN) accelerators have been developed to address this challenge. However, there are still limitations and concerns surrounding the adoption of DNN accelerators as a viable choice for commercial products. This lightning talk aims to highlight the importance of fast and domain-specific DNN accelerators and discuss the additional features they should possess besides speed and power efficiency. By addressing these points, the talk will shed light on the necessity of specialized DNN accelerators and explore potential areas for improvement to meet the growing demands of efficient and high-performance AI computations.
Research on human following technology of robot based on ECO-HC

Due to the poor robustness of the following robot to stably follow the target and relocate the lost target in the dynamic environment, this paper proposes a mobile robot target following method combining YOLOv5 and ECO-HC tracking algorithm. It mainly uses the fast and accurate target detection ability of YOLOv5 to provide the necessary tracking target information for the ECO-HC tracking algorithm. By extracting the FHOG (Histogram of Oriented Gradient) feature and CN (Color Name) feature of the target to train the correlation filter, it is ensured that the following robot can follow the target for a long time. In order to avoid problems with the target loss caused by the block, using DIoU as a criterion for data matching, achieve target recognition. The target detection and tracking algorithm in ROS platform deployed to the mobile robot, and use the Kinectv2 depth camera to obtain follow real coordinates of target. The target following experiment is completed in the experimental scene, and the experimental results show that the mobile robot has strong robustness and can follow the target in real-time.
Development of 3D Scanned Environment in Virtual Reality

The advancement of 3D scanning technology creates new opportunity for the development of immersive digital environments in virtual reality (VR). The development of a VR application using phone LiDAR, Unity, and Oculus console is presented in this paper to visualise and navigate a 3D-scanned environment. The VR application enables users to navigate in the virtual spaces and to teleport between rooms or buildings using a remote controller. This study focuses on the application’s development process, technical limitations, and usability, demonstrating its potential use cases in fields such as engineering prototyping, construction, interior design, gaming, and virtual tourism. The viability of combining 3D scanning technology with VR tools can rapidly create an interactive and immersive experiences, thereby leading to fast deployment in VR applications.
Students’ Attitudes toward Using Chatbot in EFL Learning

The current study is an exploratory experiment on the attitudes of English as a Foreign Language students toward using chatbots in their learning. The study sheds light on the recent technical tools for language learning and finds out to what extent chatbots are effective in acquiring language skills, especially their usability, precision, assessment, and strengths. To answer the study’s main question, the researchers examined 64 English foreign language first-year secondary Egyptian students’ responses to a 20-item questionnaire through a survey research approach. The researchers chose students through purposive sampling. They used descriptive statistics to assess the data from the Likert-scaled items. The findings indicated that chatbots appeal to language learners because learners can utilize them without the help of the instructors, which in turn encourages them to become autonomous learners. They also believed that the Chatbot might simulate an interaction cycle so they could practice the target language. Additionally, the students felt that the Chatbot boosted their enthusiasm and confidence, which ultimately helped them feel active and more comfortable. Consequently, the data provide insightful information about properly integrating chatbots in English language learning inside and outside the classroom while considering its defects.
Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning

Due to the Ising model’s strong expressivity and Ising machines’ unique computational power, it is highly desired if Ising-based learning can be used in real-world applications. Unfortunately, the challenges in learning the Ising model and gaps between the practical accuracy of Ising machines and the theoretical accuracy of the Ising model impede the realization of Ising machines’ potential. Hence, we propose an Ising Machine Learning framework, Ising-CF, for collaborative filtering, a widely-used recommendation method. Specifically, Ising-CF uses Linear Neural Networks with Besag’s pseudo-likelihood and voltage polarization for fast, accurate Ising model learning and an Ising-specific logarithmic quantization for ns-level Ising machine inference with near-theoretical accuracy, 7.3% over SOTA.
Research on Mixed-Strategy Based Grey Wolf Optimization for Gene Expression Classification

With the advantages of easy implement and few adjustment parameters, grey wolf optimization (GWO) performs well in solving global optimization problems. However, the original GWO can suffer from slow convergence speed and plunge into local optimums in dealing with complex problems. To address these issues, improvement of mixed-strategy based grey wolf optimization (MGWO) is proposed in this paper. Firstly, we present chaos strategy in combination with quasi-opposition learning strategy for generating more high-quality population to improve convergence speed of the algorithm. Then, we embed sine cosine mechanism into the original GWO to jump out of local optimums. We utilize the interaction of sine and cosine to better balance the exploration and exploitation capabilities of the algorithm. Finally, we use benchmark functions for testing the performance of proposed algorithm. Experimental results demonstrate that proposed algorithm can improve search accuracy and convergence speed compared with other state-of-the-art algorithms. Simultaneously, to verify further the performance of proposed algorithm, classification problems on gene expression datasets are also evaluated. Both continuous and binary versions of MGWO are respectively used for completing parameters optimization and feature selection. Experimental results show that proposed algorithm has better performance than others in terms of Sensitivity, Specificity, G-mean, Accuracy and F-measure.
AI-assisted Improved Service Provisioning for Low-latency XR over 5G NR

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.
MP-HAR: A Novel Motion-Powered Real-Time Human Activity Recognition System

With the rapid advance of the Internet of Things (IoT), more and more wearable devices are being developed for real-time monitoring. Most of these existing monitors are powered by chemical batteries. Replacing and disposing batteries for an exponentially increasing number of IoT nodes prohibitively results in labor-intensive maintenance. It is also environmentally unfriendly. Energy harvesting (EH), reclaiming the wasted ambient energy, is a promising technology for battery-free IoT. This paper presents a novel motion-powered real-time human activity recognition (HAR) system called MP-HAR, where the harvester works as both an energy source and sensor. MP-HAR emphasizes low-power as well as low-cost characteristics, encompassing four necessary units: energy transduction unit (ETU), energy management unit (EMU), energy user unit (EUU), and edge computing unit (ECU). In particular, the unique intermittent operation based on the re-configurable on/off threshold voltages given by the well-rounded energy-aware circuit has been discussed in detail. The balance between energy supply and information demand in MP-HAR has been achieved by using a handy design. Utilizing the unique correspondence between human arm swing frequency and harvested energy, the information flows with energy inside the system. By knowing the interval between transmitted packets, MP-HAR has realized HAR in real time. Moreover, an all-in-one prototype has been fabricated to validate the performance of the proposed system. Lab and field tests have demonstrated that MP-HAR can reliably recognize different human activities, such as standing, walking, jogging, and running. As a cyber-electro-mechanical co-design, MP-HAR has brought a promising solution for pervasive HAR and ubiquitous IoT.
Helping Hands

"Helping Hands" is a student project designed to provide a platform for event organizers to connect with volunteers who are willing to donate their time and efforts for a good cause. The project is built on MERN stack and comprises of four modules - Admin, Sponsor, Volunteer, and Event Organizer. The Admin module controls the entire system and manages the overall overview of the platform. The Sponsor module allows sponsors to donate money for any event they wish to support. The Volunteer module enables users to register themselves as volunteers and search for events where they can help. The Event Organizer module is responsible for creating new events and managing them. Event organizers can post details of their event, such as date, time, location, and required volunteers. Volunteers can then browse through the available events apply for them, and confirm their participation. The project aims to provide a seamless experience for both event organizers and volunteers, making it easy for them to connect and work together. The platform encourages people to come forward and contribute to social causes by volunteering their time and resources. With the help of this platform, organizers can find the right volunteers for their events, and volunteers can find meaningful opportunities to make a positive impact on their community.
Implement Solutions Across Services

When taking the DP-420 exam, it is important to not only have a deep understanding of the inner workings of Azure Cosmos DB, but to also understand how Azure Cosmos DB integrates with other services in a production environment. This includes having familiarity with additional Azure services that are often integrated with Azure Cosmos DB.
Gesture-based Human-Computer Interaction using Wearable Devices

For decades, traditional computer interfaces such as keyboards and mouse have been the major way of interaction. These interfaces, however, can be restricted, particularly in situations when hands-free or realistic contact is sought. Gesture-based interactions are made possible by the use of wearable devices such as smartwatches or motion-capture sensors, which allow people to communicate with computers through natural hand and body gestures.
Postdigital Intercreative Pedagogies

Postdigital intercreative pedagogies understand collective creation as the key to social transformation and as an educational strategy. They reject technological determinism and instrumentalism and claim a posthumanist, non-dualist, and critical position in relation to the digital-analog hybridization of human condition. Postdigital intercreative pedagogies build upon the values of openness and collaboration and posit the making and dissemination of knowledge as mutually interdependent with educational practice.
A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Abstract: In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.
Music as an Element of Director’s Screen Language. Part 1. Sergio Leone and Ennio Morricone: Creating a Musical Image as Basis for Creative Tandem

The purpose of the article is to determine the peculiarities of using music in the screen plane through the study of artistic tandems of director-composer. Research methodology. The methods of scientific analysis, comparison, and generalisation were comprehensively applied in the development of the topic. Analytical and systematic methods in their unity were used to consider the art historical aspect of the problem. The scientific novelty of the research is that the director's creativity is studied in the context of composers’ practices of creating music for audio-visual art; the appropriateness of using a systematic method in studying the peculiarities of composers' creativity in the context of audio-visual art and production has been proven. Conclusions. The materials presented in the article expand the arsenal of knowledge regarding the specifics of co-creation of a director and a composer in the screen arts and enable their application in educational courses, creation of educational and methodological literature on the theory and history of music, film and television, directing, and composition.&#x0D; Key words: musical art, composer, screen art, directing creativity, musical image, creative tandem, musical instruments.
Introduction to the R environment

This chapter introduces the <italic>R</italic> programming language, which is freely available for all major operating systems. It explains the significance of <italic>Rstudio</italic> in the development of the core <italic>R</italic> language. <italic>RStudio</italic> has several customizable options to improve accessibility through the sections of <italic>Preferences and Appearance</italic>. Moreover, the help function is a great way of finding out more about a specific <italic>R</italic> function. The chapter then discusses the examples of <italic>R</italic> syntax for fundamental programming concepts such as scripts, data objects, functions, packages, conditional statements, and loops. It presents a table of the useful core <italic>R</italic> functions and their respective description.
Why low power is undesirable

This chapter addresses the questions why a low-powered study has a high probability of failing to detect genuine effects. It highlights how low power makes drawing useful inferences from failure to reject the null hypothesis more difficult. It shows that the risk that a significant result is a Type I error increases if an experiment is low-powered and analyses why low power inflates the effect sizes associated with statistical significance. The chapter clarifies that the statistical power of an experiment is one minus the probability of making a Type II error based on the outcome of that experiment. It confirms that low power is undesirable because it means an increased risk of a Type II error, emphasizing that the lower the power, the less likely it is that a significant result is actually triggered by a real underlying effect.
Transfer Learning and Ensemble Learning

In this chapter, we start from transfer learning and introduce the relationship between learners. We use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust classifier for pattern classification.
OISHI: An Ontology Integration Framework for Domains of Socio-Humanitarian Importance Incorporating Hybrid Machine Intelligence

There is a need for strategic ontology integration as the integration of ontologies that are cognitive entities and by themselves are knowledge representation, and description models based on certain logic is essential as the integral structure of the ontology concerning the context and reason it was created must be preserved. So, owing to this, there is a strategic need for automating the strategy of ontology integration, at least up to a certain extent, for that Semantically Inclined Knowledge Centric models and strategies are required as ontologies have to be conceptualized based on human interpretation and cognition and also have to be integrated without any loss or significant modifications concerning the associations between the ontology. In this paper, an ontology integration model for domains such as Mass Communications, Liberal Arts, Media and Journalism, and Public Policy is made use of where the ontologies are specifically generated from distinct heterogeneous document datasets to which the Term Frequency-Inverse Document Frequency (TF-IDF) is applied to identify the rare and yet the most informative terms and for topic modeling, Latent Dirichlet Allocation (LDA) is employed. Furthermore, the classification of the ontologies is achieved using the XGBoost classifier. The strategy involves the extraction of indices from E-books and formalized Taxonomy using the Renyi Entropy with a specific standard deviance measure. The Knowledge Stack achieves entity aggregation formalized using the LOD Cloud, Wiki Data, and Google KG API from that the Semantic Network is formalized. The classified ontologies are the formalized Semantic Network, that is further used to compute the Semantic Similarity under the Tabu search Metaheuristic optimization to formalize, finalize, and integrate the ontologies. The SemantoSim measure, that incorporates differential thresholds, is used in the Tabu search framework to compute semantic similarity. The proposed OISHI framework has achieved an overall Precision of 94.39%, Recall of 96.44%, Accuracy of 95.41%, F-Measure of 95.40%, and the lowest FDR of 0.06.