Reviewerly/exHarmony huggingface dataset:
Competent Energy Executing Structure in the Solar Sheet in the IoT Environment

AbstractA desirable array of electrical generating techniques reserves, utilizing renewable energy sources such as solar power, wind, and tidal energy. Many research companies have thought about primitive power generation technology, but electricity produced from these resources is not adequate and satisfactory and the power demands are that effective and intelligent systems are required to deliver the high energy level and the proper power delivery. A variety of other improved approaches can be used for successful manufacturing and delivery processes, although certain additional functionality would be needed to improve the operation. This article, thus, introduces an uncommon management framework and discusses efficiently the effects of the control of energy generation and delivery from renewable resources. Mainly for enriching the features Internet of Things (IoT) gets implemented for doubling the process output. IoT is a class of worldwide global neural networks in the cloud that acquaintances various things. The IoT is an elegantly connected device and framework which contain gleaming machine connecting and communicating with different machines, environments, and matter and will rise to meet a new challenge. Furthermore, this investigation here depends on the empowered solar panel monitoring and search for the proficient way to expand more power from solar radiation.Keywords: sunlightsolar panelIoT ACKNOWLEDGMENTThere is no acknowledgment involved in this work.ETHICS APPROVAL AND CONSENT TO PARTICIPATENo participation of humans takes place in this implementation process.HUMAN AND ANIMAL RIGHTSNo violation of Human and Animal Rights is involved.DATA AVAILABILITY STATEMENTData sharing is not applicable to this article as no datasets were generated or analyzed during the current study.Disclosure statementNo potential conflict of interest was reported by the author(s).AUTHORS’ CONTRIBUTIONSAll authors are contributed equally to this work.Additional informationFundingNo funding is involved in this work.Notes on contributorsSeema MuthiruluSeema Muthirulu received her M.E (Embedded System Technologies) from Anna University, Chennai in 2008, B.E (Electronics and Communication Engineering) from Madurai Kamaraj University in 2004. Currently she is working as an Assistant Professor in Department of Information Technology at Velammal College of Engineering and Technology and Pursuing Ph.D (Electrical Engineering) in Anna University, Chennai. She has published 45 papers as in journals, Conferences, books etc as author and co author.Subramaniyan VenkatanarayananSubramaniyan Venkatanarayanan has completed his Master Degree in Applied Electronics in Mohamed Sathak Engineering College during 2008. He has completed PhD during 2015 in power Electronics Converters, he is working as a Professor in EEE department, K.L.N College of Engineering, Sivagangai District, Tamilnadu, India. He has published 124 papers as in journals, conferences, books etc as author, co author. He has produced 4 PhDs, guiding 7 PhDs now. He is the Senior Member of IEEE.
Virtual Exercise in Medicine: A Proof of Concept on Healthy Population (Preprint)

Science is beginning to establish the benefits of the use of virtual reality (VR) in health care. This therapeutic approach may be an appropriate complementary treatment for some mental illnesses. It could prevent high levels of morbidity and improve the physical health of patients. For many years, the literature has shown the health benefits of physical exercise. Physical exercise in a VR environment may improve the management of mild to moderate mental health conditions. In this context, we developed a virtual environment combined with an ergocycle (the augmented physical training for isolated and confined environments [APTICE] system).This study aims to investigate the impact of physical exercise in a VR environment.A total of 14 healthy participants (11 men and 3 women; mean age 43.28, SD 10.60 years) undertook 15 minutes of immersive physical exercise using the system. Measures included mindfulness and immersion disposition, subjective perceptions of sensory information, user experience, and VR experience (ie, psychological state, flow, and presence).First, the APTICE system appears to be a useful tool because the user experience is positive (subscales in the AttrakDiff questionnaire: pragmatic quality=0.99; hedonic quality-stimulation=1.90; hedonic quality-identification=0.67; attractiveness=1.58). Second, the system can induce a positive psychological state (negative emotion, P=.06) and an experience of flow and presence (P values ranging from <.001 to .04). Third, individual immersive and mindful disposition plays a role in the VR experience (P values ranging from <.02 to .04). Finally, our findings suggest that there is a link between the subjective perception of sensory information and the VR experience (P values ranging from <.02 to .04).These results indicate that the device is well accepted with positive psychological and exteroceptive outcomes. Overall, the APTICE system could be a proof of concept to explore the benefits of virtual physical exercise in clinical medicine.
Toward Designing Haptic Displays for Desired Touch Targets: A Study of User Expectation for Haptic Properties via Crowdsourcing

Things that people desire to touch in daily life are known to be limited to a number of specific targets (e.g., cats). The utilization of haptic displays to provide the experience of touching such desired targets is expected to enhance people's quality of life. However, it is currently unclear which haptic properties (e.g., hardness and weight) of desired targets should be rendered with haptic displays, and how they should be rendered. To address these issues, we conducted an experiment with 600 Japanese participants via crowdsourcing. Among the 600 participants, we identified potential users of haptic displays and analyzed their responses for each target. For each desired target, we identified the haptic properties in relation to which a “need for consistency” was felt by potential users between their expectations and actual impressions during touching. We also identified the haptic properties in relation to which a “biased impression” was held by potential users for each target. For example, potential users responded that cats were soft and that the actual impression of softness during touching needed to be consistent with their impression. Our results provide insights into the design of haptic displays for realizing desired touch experiences.
Deep Neural Networks Overview

The following Chapter introduces the reader to basic DNN concepts. It provides the definition of the basic DNN operations which are directly related to the underlying numbering system. It also decibels popular DNN models used in modern AI systems. Finally, an overview of the standard training procedure (gradient descent, backpropagation) is given.
DFXP for DNN Architectures

This chapter examines the use of dynamic fixed point number systems for deep neural networks. We introduce the concept of DFXP and compare it to block floating point systems highlighting their similarities and differences. In addition, we review existing DNN architectures that use DFXP and compare their performance. Additionally, we discuss the various factors that impact DNN performance when using DFXP and explore different approaches for determining the optimal settings of these factors.
BFP for DNN Architectures

This chapter explores the use of the block floating point number system as an alternative to traditional fixed point and floating point number systems in deep neural network implementations. We begin by providing an overview of BFP and explaining how it differs from FLP and FXP. Next, we discuss the factors that can impact the performance of BFP in DNN acceleration, drawing from existing literature to identify the best practices for optimizing BFP performance. Finally, we present quantitative comparisons of various works that have utilized BFP for DNNs, demonstrating its potential as an effective tool for accelerating DNN computations.
PSAbot: A Chatbot System for the Analysis of Posts on Stack Overflow

With the progressive development of technology, programming learners have significantly increased. However, the lack of human tutors and the rapidly updating information cause the learners to spend a considerable amount of time browsing and filtering authentic online resources, and decrease learning efficiency. Although many coding websites and programming communities can provide credible advice, it is still a challenge for learners to figure out their accurate questions. Therefore, we devised a Chatbot system, named PSAbot, to consider the above issue. PSAbot supports keyword extraction and analysis for multiple posts to guide the users through questions. PSAbot applies word embedding, sentence similarity, LDA (Latent Dirichlet Allocation) topic modeling, and weighting functions to help filter out redundant information and decrease the time cost of browsing, and further improve the learning efficiency. The conducted experiments show that about 80% of the Top1 answers recommended by PSAbot can largely meet the user expectations.
Value Based Prioritization of Requirements in Software Engineering Education

Both physical and software products have a functional use for which they were designed. Putting a new project idea into action necessitates a clear, holistic vision of the expected gain and costs. However, gain (or value) can have many different manifestations that often go far beyond pure functionality. For one thing, it can be about purely monetary value, but it can also be about expanding the company’s reach, improving customer loyalty, displacing competitors, or generating other additional benefits for the company or the user. Cost and Value Engineering is a promising approach for addressing such a value-centered perspective without losing sight of expenses. In typical Software Engineering courses Software Engineering students learn how to deal with requirements in general, as well as user stories, epics, and other requirements artifacts in the context of agile software development projects. However, aside from rudimentary planning activities for user stories or epics, they rarely learn to apply good value prioritization techniques. The primary goal of this paper is to present a validation experiment for two cost and value engineering methods in order to determine whether these methods are appropriate and valuable in a student context. The results are promising, indicating that these methods should be used in software engineering courses on a regular basis. In order for these methodologies to be properly incorporated into Software Engineering courses, an integrated tool support is required.
Development of a script to aggregate document inspection comments across different platforms to GitHub in a software development PBL

This study regards inspections conducted in project-based learning (PBL) for software development as learning feedback. To solve the problem that inspection comments are distributed separately across platforms, which is an obstacle to classification of comments, we develop a script, Comment Gatherer, which aggregates inspection comments between GitHub and Figma. Then, we describe the concept of a support application that enables the automatic classification of inspection comments by machine learning.
Designing a Multimodal Environment for Cognitive and Creative Activity in Pre-School Education – Competence of the Teacher

This article focuses on the formation of pedagogical competence for the design of multimodal educational environment, the functionality of which is the foundation of the cognitive and creative activity of the child in pre-school age. It problematizes the need for adequate professional reflection of the teacher in the context of the transgressive approach and highlights the parameters of organizing pedagogical interactions that utilize multimodality as a communication phenomenon. The understanding that the competence of the teacher for designing a multimodal environment as well as cognitive and creative activities develops as transgressive and is continuously created in the cultivation of new knowledge and skills, while expanding the cognitive and practical limits of the subject, is affirmed. Based on this affirmation, the thesis that if a child has entered the “communicative state” and has engaged in multimodal educational interactions, the child appropriates the social experience by forming a transgressive attitude of behavioral response.
Ring attractor bio-inspired neural network for social robot navigation

Introduction We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments. Methods The navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents. Results The results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model. Discussion Our approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment.
Fake Face Detection Based on Colour Textual Analysis Using Deep Convolutional Neural Network

Detecting fake faces has become a crucial endeavour within the realm of computer vision. The widespread availability of digital media has facilitated the creation and dissemination of deceptive and misleading content. A prominent strategy for identifying counterfeit faces employs advanced deep-learning methodologies that scrutinise both colour and textural attributes. This investigation is geared towards devising a method for discerning fake faces by leveraging the capabilities of convolutional neural networks (CNNs). These networks are trained to discriminate between authentic and forged images by discerning nuances in their colour characteristics. To achieve this, the MSU MFSD dataset will be harnessed, allowing for exploring colour textures and extracting facial traits across diverse colour channels, including RGB, HSV, and YCbCr.The proposed framework marks a notable stride in the realm of computer vision research, particularly given the prevalent employment of digital media, which has eased the generation and distribution of misleading or deceitful content. Developing dependable systems for identifying counterfeit faces holds immense potential in curtailing the proliferation of false information and upholding the integrity of digital media platforms.
Knee Injury Diagnosis with Data and Feature Fusion-Enhanced Multi-Label Classification Network

Knee injuries are among the most common human afflictions despite age, gender, or lifestyle. They are often detected using a non-invasive diagnostic tool such as Magnetic Resonance Imaging (MRI) of the knee. With the rising number of injuries yearly, the risk of diagnostic errors by radiologists increase exponentially. In order to mitigate this, deep learning methods have been used for designing an automatic MRI interpreter. MRNet public dataset is an established knee injury dataset consisting of multi-view MRI images with multi-label classification. The prior deep learning solutions utilizing this dataset including the MRNet model, ignore the relationship and dependency among the different injuries and views. Given the specific properties of this dataset and problem, we propose a tailored multi-label classification network with enhanced data and feature fusion. The designed model out-performs the MRNet baseline model in terms of complexity and performance reaching an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.925.
APLIKASI KELAYAKAN PENERIMA SUBSIDI KEPEMILIKAN RUMAH PADA PERUMAHAN GRIYA MURI UTAMA

Muri Utama Propertindo is a company engaged in the sale of subsidized housing,currently PT. Muri Utama Propertindo conducts housing marketing manually with promotional media with a system that does not yet use technology. By using the old system, of course, it will take a lot of time which can burden the company by distributing a brochure. By analyzing these problems, the author tries to design an information system according to current technological developments, namely a web-based information system. With this system, it is hoped that it will make it easier and save time and energy for employees in marketing housing because this system will display the location and type and price list of the housing offered. The benefits of making this system that can help the performance of employees of PT. Muri Utama Propertindo in obtaining the required information in the form of customers who make the ordering process or booking and stock unit availability and making it easier for customers to place orders or book houses at Griya Muri Utama.
COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations

&lt;strong&gt;COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations&lt;/strong&gt; &lt;b&gt;Author(s) &lt;/b&gt;Ivan Heibi – ivan.heibi2@unibo.it &lt;em&gt;Digital Humanities Advanced Research Centre (DHARC), Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy&lt;/em&gt; Silvio Peroni – silvio.peroni@unibo.it &lt;em&gt;Digital Humanities Advanced Research Centre (DHARC), Department of Classical Philology and Italian Studies, Univer&lt;/em&gt;
Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors

Abstract This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered excitable spike generator. The top contact layer of the device is designed with a nanopillar structure (500 nm in diameter) to restrain the injection current, yielding therefore lower energy operation for spike generation. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs. Moreover, we show the device’s ability to deliver spiking responses when biased in either of the two regions adjacent to the negative differential conductance region, the so-called ‘peak’ and ‘valley’ points of the current–voltage ( I – V ) characteristic. This work also demonstrates experimentally key neuron-like dynamical features in the nanostructure RTD-PD, such as a well-defined threshold (in input optical intensity) for spike firing, as well as the presence of spike firing refractory time. The optoelectronic and chip-scale character of the proposed system together with the deterministic, repeatable and well controllable nature of the optically-elicited spiking responses render this nanostructure RTD-PD element as a highly promising solution for high-speed, energy-efficient optoelectronic artificial spiking neurons for novel light-enabled neuromorphic computing hardware.
Proving Termination of C Programs with Lists

Abstract There are many techniques and tools to prove termination of programs, but up to now these tools were not very powerful for fully automated termination proofs of programs whose termination depends on recursive data structures like lists. We present the first approach that extends powerful techniques for termination analysis of programs (with memory allocation and explicit pointer arithmetic) to lists.
Bird’s-Eye View Semantic Segmentation for Autonomous Driving through the Large Kernel Attention Encoder and Bilinear-Attention Transform Module

Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether the crucial image encoder can construct long-range feature relationships. Hence, we redesign an image encoder with a large kernel attention mechanism to encode image features. Considering the performance gains obtained by the complex view transform module are insignificant, we propose a simple and effective Bilinear-Attention Transform module to lift the dimension completely. Finally, we redesign a BEV encoder with a CNN block of a larger kernel size to reduce the distortion of BEV features away from the ego vehicle. The results on the nuScenes dataset confirm that our model outperforms other models with equivalent training settings on the segmentation task and approaches state-of-the-art performance.
APLIKASI ZAKAT, INFAK, SEDEKAH PADA BAZNAS BATANG HARI

Zakat is maliyah ijtima'iyyah worship which has a very important, strategic, and decisive position both in terms of Islamic teachings and in terms of developing the welfare of the people. zakat, infaq, and alms that come in, and data management carried out through Microsoft Excel will certainly take relatively longer time. In addition, the limited budget for existing funds makes BAZNAS experience problems in managing application. One solution to this problem is the creation of an application for zakat, infaq, and alms. Making this application more leverage than writing all incoming zakat, infaq, and alms data, and managing data through Microsoft Excel will certainly take relatively longer time. This financial integration system was built in four stages, namely requirements analysis, system design, implementation, and system testing with the PHP programming language framework, namely Laravel and PostgreSQL database. The features in this application are zakat calculators, payments, registrations and reports. This application provides added value, namely the management of zakat, infaq, and alms data to be faster and more accurate and is expected to help the performance of BAZNAS officers in Batanghari Regency.