Reviewerly/exHarmony huggingface dataset:
Real-Time Hand Gesture Recognition Using YOLO and (Darknet-19) Convolution Neural Networks

There are at least three hundred and fifty million people in the world that cannot hear or speak. These are what are called deaf and dumb. Often this segment of society is partially isolated from the rest of society due to the difficulty of dealing, communicating and understanding between this segment and the rest of the healthy society. As a result of this problem, a number of solutions have been proposed that attempt to bridge this gap between this segment and the rest of society. The main reason for this is to simplify the understanding of sign language. The basic idea is building program to recognize the hand movement of the interlocutor and convert it from images to symbols or letters found in the dictionary of the deaf and dumb. This process itself follows mainly the applications of artificial intelligence, where it is important to distinguish, identify, and extract the palm of the hand from the regular images received by the camera device, and then convert this image of the movement of the paws or hands into understandable symbols. In this paper, the method of image processing and artificial intelligence, represented by the use of artificial neural networks after synthesizing the problem under research was used. Scanning the image to determine the areas of the right and left palm. Non-traditional methods that use artificial intelligence like Convolutional Neural Networks are used to fulfill this part. YOLO V-2 specifically was used in the current research with excellent results. Part Two: Building a pictorial dictionary of the letters used in teaching the deaf and dumb, after generating the image database for the dictionary, neural network Dark NET-19 were used to identify (classification) the images of characters extracted from the first part of the program. The results obtained from the research show that the use of neural networks, especially convolution neural networks, is very suitable in terms of accuracy, speed of performance, and generality in processing the previously unused input data. Many of the limitations associated with using such a program without specifying specific shapes (general shape) and templates, hand shape, hand speed, hand color and other physical expressions and without using any other physical aids were overcome through the optimal use of artificial convolution neural networks.
Building Resilience in the future workforce: The Role of Continuous Learning and Transferable Skills

As rightly said by Robert Greene, “The future belongs to those who learn more skills and combine them in creative ways” (Greene, n.d.), the future of the workforce and learning is rapidly evolving due to technological advancements, changing global demographics, and the growing need for skills in new and emerging industries. To meet these challenges, it is essential for the workforce and education systems to adapt and stay ahead of the curve. The role of continuous learning and transferable skills in the future workforce cannot be overstated. These will be essential in preparing individuals for the future of work. With the increasing pace of technological change, it’s likely that many jobs will become automated in the coming years. In order to remain relevant and competitive in the job market, individuals must be able to adapt to new technologies and continuously learn new skills. The key to success will be a focus on lifelong learning and the development of transferable skills that can be applied in a variety of contexts. Besides this, interdisciplinary approaches, inclusive and diverse learning environments, and the integration of technology in education will be crucial in preparing individuals for the workforce of the future. The future of workforce and learning is an exciting and dynamic field, and it is important for individuals, organizations, and governments to actively engage in shaping its trajectory.
Towards Machine Perception Aware Image Quality Assessment

Over the years, the objective of image and video compression has been to preserve perceived quality according to the Human Visual System (HVS) with minimal rate. Traditional encoders achieve this with the use of Rate-Distortion Optimization (RDO) techniques along with Image Quality Assessment (IQA) metrics that are correlated with human perception. Nowadays, a fast-growing number of applications fall within the realm of Video Coding for Machines (VCM), where the final recipient of compressed data is not a human but a machine performing a vision task. Recently, the lack of correlation between existing distortion measures and machine perception has been revealed, especially for RDO algorithms where distortion measures are computed on a local scale. In this paper, we propose a machine perception-aware metric designed to be incorporated into a standard-compliant Versatile Video Coding (VVC) encoder. Our proposed metric relies on a supervised training procedure as well as additional information available on the encoder side. In terms of correlation with machine perception, our metric significantly outperforms existing distortion measures in the literature.
Workshop on Context-Aware Recommender Systems 2023

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2023 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.
Pengaruh Media Google Classroom terhadap Hasil Belajar Kognitif Siswa

This study aims to determine the effect of google classroom media on cognitive learning outcomes of class X students of SMA Negeri 1 Batulayar for the academic year 2021/2022. The type of research used is a quasi-experimental research. The population in this study were all students of class X MIPA 1 Batulayar Academic Year 2021/2022 class X MIPA 1 Academic Year 2021 as a control class with a total of 24 people and class X MIPA 1 Academic Year 2022 as an experimental class with 20 students. Research data were taken from pre-test and post-test to both samples. The data collection technique used 15 multiple choice questions and 5 essay questions. Based on data analysis, the average value of the multiple choice pre-test experimental class 41, essay 23 and multiple choice pre-test control class 35.00, essay 14 with homogeneous categories and normally distributed. The results of the post-test showed that the experimental class average for multiple choice essays was 70 and 71.5 and the control class for multiple choice essays was 80.00 and 74.8. The final value data for the two classes was tested using the Critical one-tail t test, it was obtained tcount &gt; ttable with a tcount of -4.00 and ttable of 1.685 at a significant level of 5%, so that Ha was accepted and Ho was rejected. It can be concluded that there is no influence of google classroom media on cognitive learning outcomes of class X students of SMA Negeri 1 Batulayar.
Sensing in the wild: A DCODE DRS Lab exploring a more-than-human approach to distributed urban sensing

The Sensing in the Wild Lab is a speculative experiment in designing a decentralised urban sensing system from a more-than-human perspective.It is part of DCODE, an H2020-ITN project that explores the future of designing with AI.During the Lab participants assume different identities -roleplaying as children but also as moss, as municipal authorities, as CCTV cameras, as pigeons, and as undocumented immigrants trying to evade the authorities -and are asked to feed into the sensing system data that reflects their particular perspectives and interests.The data participants share, in the form of an image and text uploaded to a dedicated WhatsApp channel, helps to reveal both frictions and alignments among actors.In this, the Lab offers municipalities an opportunity to shift their thinking about the future smart city from a "system of systems" that is optimised for a few city dwellers to a much more distributed, inclusive meshwork in which data is contributed, circulated, and negotiated by humans and nonhumans alike.
Efficient end-to-end failure probing matrix construction in data center networks

Data centers play an essential role in the functioning of modern society. However, failures are unavoidable in data center networks (DCN) and will lead to negative impact on all applications. Therefore, researchers are interested in the rapid detection and localization of failures in DCNs. In this paper, we present a theoretical model to analyze the end-to-end failure detection methods in data center networks. Our numerical results verify that the proposed theoretical model is accurate. In addition, we propose an algorithm to construct probing matrices based on an enhanced probing path selection indicator. We also introduce deep reinforcement learning (DRL) method to solve the problem and propose a DRL-based probing matrix construction algorithm. Our experimental results show that both of the proposed algorithms for constructing probing matrices achieve better performance in detection accuracy than existing methods. We discussed different scenarios that the algorithms are applicable to that can improve detection accuracy or construction speed performance.
Chua’s Oscillator-Based RSA Algorithm With Authenticated Masked Public Key for Secure Communication

This article proposes Chua’s oscillator-based RSA algorithm for secure communication. The prime numbers, required in the RSA algorithm, are generated with the help of different output parameters of the Chua’s oscillator. These prime numbers are highly random due to the non-linear and unpredictable (or chaotic) behaviour of Chua’s oscillator. With this improved version of RSA, a highly reliable and secure system has been achieved. Additional security has been provided in the communication system by making the authenticated masked public key. The synchronization is also done with the help of Chua’s oscillator to enhance security and to avoid receiving the wrong messages. The masked public key with one output parameter of Chua’s oscillator is incorporated to provide the authentication in the communication system. The four algorithms are discussed in this paper to make secure, reliable, and authenticated communication system.
Learning Speaking Problems in Online Learning During Covid-19 Outbreak for ESP Learners

Because of the pandemic, Indonesian government implemented new policies in the education system. Previously, education and training took place in the luring situation on a regular basis, particularly in higher education institutions, and now it is entirely daring. The current study aims to examine students' issues in speaking for ESP contexts during the COVID-19 circumstance when they were learning the English language by the online situation. The descriptive qualitative design was used in this study. The information was gathered using questionnaire in the help of Google form media and also interviews with 40 students from the fourth semester of the Management Informatics Diploma Major at Piksi Input Serang Polytechnic in Banten. Based on this study, the research findings highlighted a variety of issues for students. Six issues emerged during distance learning in the Speaking course. The first issue that arose was students' learning equipment and supporting facilities. The second issue was dealing with students' accessibility restrictions to Online Video Conference. The third issue was students' concerns on the lecturer's materials during an online lesson. The fourth issue was the students' approach to completing the lecturer's task or assignment. The use of online streaming videos as a media of learning resources also had a role in the fifth and final issue that emerged during distance learning amid COVID-19 pandemic situations is students' limited cell signal and internet quota. The findings can be used to further enhance and improve online teaching and learning techniques. Further research into how students deal with obstacles is encouraged.
A quantum annealing approach with a problem specific initial state for the combinatorial selection of catalog lenses for the automated optics design

Using quantum annealers within tools for the automated optics design promises to yield advantages regarding computational costs and design quality. These advantages can be enhanced by using problem specific anneal schedules and initial states.
Multi-agent learning via gradient ascent activity-based credit assignment

We consider the situation in which cooperating agents learn to achieve a common goal based solely on a global return that results from all agents' behavior. The method proposed is based on taking into account the agents' activity, which can be any additional information to help solving multi-agent decentralized learning problems. We propose a gradient ascent algorithm and assess its performance on synthetic data.
KBO Constraint Solving Revisited

Abstract KBO constraint solving is very well-known to be an NP-complete problem. Motivated by the needs of the family of SCL calculi, we consider the particular case where all terms occurring in a constraint are bound by a (single) ground term. We show that this problem and variants of this problem remain NP-complete even if the form of atoms in the constraint is further restricted. In addition, for a non-strict, partial term ordering solely based on symbol counting constraint solving remains NP-complete. Nevertheless, we provide a new simple algorithm testing KBO constraint solvability that performs well on benchmark examples.
Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images

Recent studies have shown that deep-learning-based models for processing Unmanned Aerial Vehicle (UAV) remote sensing images are vulnerable to artificially designed adversarial examples, which can lead to incorrect predictions of deep models when facing adversarial examples. Previous adversarial attack methods have mainly focused on the classification and detection of UAV remote sensing images, and there is still a lack of research on adversarial attacks for object tracking in UAV video. To address this challenge, we propose an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline. First, we employ an attention feature loss to make the original frame’s features dissimilar to those of the adversarial frame, and an attention confidence loss to either suppress or enhance different confidence scores. Additionally, by forcing the tracker to concentrate on the background information near the target, a background distraction loss is used to mismatch templates with subsequent frames. Finally, we add total variation loss to generate adversarial examples that appear natural to humans. We validate the effectiveness of our method against popular trackers such as SiamRPN, DaSiamRPN, and SiamRPN++ on the UAV123 remote sensing dataset. Experimental results verify the superior attack performance of our proposed method.
A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation

We present a template matching algorithm based on local descriptors for aligning two geospatial products of different modalities with a large area asymmetry. Our system is generic with regards to the modalities of the geospatial products and is applicable to the self-localization of aerial devices such as drones and missiles. This algorithm consists in finding a superposition such that the average dissimilarity of the superposed points is minimal. The dissimilarity of two points belonging to two different geospatial products is the distance between their respective local descriptors. These local descriptors are learned. We performed experiments consisting in estimating a translation between optical (Pléiades) and SAR (Miranda) images onto vector data (OpenStreetMap), onto optical images (DOP) and onto SAR images (KOMPSAT-5). Each remote sensing image to be aligned covered 0.64 km2, and each reference geospatial product spanned over 225 km2. We conducted a total of 381 alignment experiments, with six unique modality combinations. In aggregate, the precision reached was finer than 10 m with 72% probability and finer than 20 m with 96% probability. This is considerably more than with traditional methods such as normalized cross-correlation and mutual information.
Analysis of the current situation of university- city integration development based on data mining technology and exploration of the optimization path

University is an inevitable product of a city's development to a specific stage. In different social environments and historical stages, universities always develop symbiotically with cities, and the integration of the higher education system and emerging technologies can accelerate the regional economic development of universities and cities. Based on data mining technology, this study uses a neural network algorithm to establish an algorithmic model and sigmoid function as the incentive function to analyze the integration development of emerging technology industry and universities in Dongguan city and provide an optimization path for the integration development of city and universities. The research results show that in the field of scientific and technological research results, the universities in Dongguan City applied for 49,726 patents in 2021 and authorized 25,523, with an efficiency rate of 51.33%. In the area of urban economic development, Dongguan's GDP in 2021 showed strong momentum, achieving a regional GDP of 108.554 billion yuan, an increase of 8.2% over the previous year.
The application of big data technology in online subject education innovation research

This paper applies big data technology to promote the innovative development of online subject education and improve students' self-learning efficiency to promote innovative management interaction in the education industry. In the design process, big data technology is used to mine and analyze massive educational data, combine the learning needs of online subjects, and provide technical support for six application levels of the online subject education system. Document transformation of education data is performed through the DCF mechanism, and the synchronization time slot is divided into a safe time slot and a reservation time slot. Then, the adaptive recommendation function is used to extract valuable information from behavioral data for personalized learning resource pushing. To verify the practical application effect of big data technology in the online subject education innovation system, the simulation analysis results show that after applying big data technology, the recommended resources preference of the education system is above 86%, the subject coverage rate is 90.48%, and the performance of test scores is improved by 17.5% relative to Class C. This shows that big data technology optimizes the application mode of online subject education and can provide students with better-quality educational resources.
Object Fetching UAV using Autonomous Flight and Object Detection Algorithms

Abstract: Autonomous UAVs (Unmanned Aerial Vehicles) are being used in various applications around the world like surveillance and aerial security, construction, agriculture, delivery, etc. These applications require heavy duty UAVs with high payload capacity and long battery life. The aim of this project is to use the principles of autonomous flight and object detection for indoor autonomous flight with the target to fetch and deliver lightweight objects with minimum energy and time consumption to create a viable prototype for indoor applications like delivering objects to bedridden patients, smart home applications, warehouse management and more.
An Evaluation of Mobile Banking Revolution in India: A Study of Behavioural Matrix through Utaut2 Model

Aims: The study aims to comprehensively explores Mobile Banking Revolution in Odisha by utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to analyze the behavioral matrix of individuals' adoption and usage patterns. It also aims to provide insights on what motivates people to use mobile banking services and what keeps them using it.&#x0D; Sample: Data on individuals’ demographic, including gender, age, and education, were recorded using descriptive parameters. 200 from 220 questions that were handed out were returned in complete form. But 192 surveys were reliable enough to use in the statistical analysis.&#x0D; Study Design: The study employs a quantitative research approach to investigate the adoption of mobile banking services in the context of India's financial sector.&#x0D; Place and Duration of the Study: Sample: Residents of Odisha. Between February 2023 to June 2023.&#x0D; Methodology: The study sampled 192 Odisha residents who used m-banking services as respondents using a questionnaire survey. SmartPLS software was used to analyse the primary data that had been gathered.&#x0D; Results: All the factors have significant relation with intention in adoption. The application of the UTAUT2 model was demonstrated within the framework of the investigation.&#x0D; Conclusion: This study focused on the state of Odisha and explored the rapidly changing environment of mobile banking usage in the context of India's financial industry. The study investigated the complex web of factors that influence consumers' behavioural intents and actions in accepting mobile banking services via the perspective of the expanded Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model.&#x0D; Recommendation: Through the UTAUT2 model, this study greatly advances our knowledge of the behavioural aspects of India's mobile banking boom. Its recommendations can help researchers, financial institutions, and policymakers maximise mobile banking's potential for widespread adoption and greater financial inclusion.
Ontology Engineering to Model the European Cultural Heritage: The Case of Cultural Gems

Cultural gems is a web application conceived by the European Commission’s Joint Research Centre (DG JRC), which aims at engaging people and organizations across Europe to create a unique repository of cultural and creative places. The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cultural realm. Cultural gems maps more than 130,000 physical places in over 300 European cities and towns, and since 2020 it also lists online cultural initiatives. The new release aims, among other, to increase the interoperability of the application. At this purpose, we provide an overview on the current development of an ontology for Cultural gems used to map cultural heritage in European cities by using Linked Open Data (LOD) standards and making the data FAIR, that is Findable, Accessible, Interoperable and Reusable. We provide an overview of the methodology, presenting the structure of the ontology, and the services and tools we are currently building on top.