Reviewerly/exHarmony huggingface dataset:
Analysis of Machine Learning for Recognizing Hand Gestures

The medical field and other research areas fields heavily rely on artificial intelligence (AI) and machine learning (ML). Hand gesture recognition (HGR), which is a straightforward approach for interacting with machines, has drawn the attention of numerous researchers in the context of AI. Utilizing ML technique, HGR is carried out. Both the image technique and the design approach are parts of it. In the image technique, the hand picture is rebuilt utilizing the attributes of the images. However, when using the model-based approach, the image is recreated using many models, including volumetric, geometric, and others. The key difficulty in gesture control continues to be the ML individual's complexity and preparation time. Its accuracy is used to assess the HGR accuracy. Its precision is used to assess the HGR overall system performance. The many techniques and algorithms utilized in gesture control are covered in detail inside this work.
Resource Allocation Technology Mobile Edge Computing System Using Blockchain Technology

The notion of Mobile Edge Computing (MEC) is now being used as a response to the multiplicity of requests for high-quality computing services. It comprises a lot of crucial processes including task completion, data transport, and allocating resources. Also, scientists enhanced the MEC state's dependability using blockchain based. Allocating resources with in Mesh network is now a popular subject, but there remain a few problems with the procedure in the classic MEC architecture, such as security exposure and other difficulties. In addition, a blockchain-based MEC network, the resource allocation problem would be more sophisticated, and the mining process may have an influence on resource distribution policy. This study analyses the resource allocation problem using a blockchain-based MEC system architecture to handle this issue. To improve resource allocation strategy, the asynchronous advantage Actor-Critic (A3C) method is utilised. The findings reveal that the TCN-based A3C algorithm not only converges quicker but is also more stable.
Blockchain for Supply Chain Traceability: Opportunities and Challenges

Blockchain technology offers a promising solution for supply chain traceability by providing an immutable, decentralized ledger for recording transactions and tracking the movement of goods. This can improve transparency, accountability, and efficiency in supply chain operations. However, implementing blockchain for supply chain traceability also poses several challenges. One major challenge is the lack of standardization and interoperability between different blockchain platforms, which can make it difficult for different supply chain partners to communicate and share information. Additionally, there are concerns about the scalability and performance of blockchain networks, as well as the need for secure and reliable data management. Despite these challenges, the potential benefits of blockchain for supply chain traceability are significant, and research and development in this area is ongoing to address these challenges and unlock the full potential of blockchain technology for supply chain management.
Enhancing Few-Shot Image Classification With Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer
Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-Based Image Retrieval

Content-based image retrieval (CBIR) represents a class of problems that aims at finding relevant images in response to an image-based search query.The CBIR systems use similarity measures or distance metrics between a group of representative features in the query image and those in the image repository.Traditionally, these features were generated by hand, employing image features such as colour, texture, shape, and so on.Due to the fact that these methods do not provide a comprehensive perspective of the images, they cannot be widely utilized in contemporary CBIR systems.This is due to the so-called semantic gap between query intent and system perspective.The most recent advancements in deep learning offer a viable alternative to manually built features, leveraging the representational learning capability of deep neural networks.This paper presents a method of implementing a CBIR system using a multistage approach known as classify, differentiate, and retrieve (CDR).The first stage involves using a deep neural network to encode the images.Later, a custom-trained stacked Siamese Neural network (SSiNN) is employed to differentiate the latent space representation of the images obtained from the first stage.The experimental results for the CIFAR-10 dataset were presented, along with an algorithm for applying this strategy to any generic dataset.Experimental outcomes demonstrate that the proposed strategy is superior to the current best practices.
An Edge-Cloud Collaboration Framework for Graph Processing in Smart Society

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware way to provide the capacity-constrained portable terminals with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of the human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transportation, Internet of Things (IoT), etc. These application domains often manage a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems where graph-based data is involved. In consideration of the relatively insufficient resource provisioning of the edge devices, in this article, for the first time to our knowledge, we propose a reliable edge-cloud collaboration framework that facilitates the graph primitives based on a lightweight interactive graph processing library (GPL), especially for shortest path search (SPS) operations as the demonstrative example. Two types of different practical cases are also presented to show the typical application scenarios of our graph processing strategy. Experimental evaluations indicate that the acceleration rate of performance can reach 6.87x via graph reduction, and less than 3% and 20% extra latency is required for much better user experiences for navigation and pandemic control, respectively, while the online security measures merely consume about 1% extra time of the overall data transmission. Our framework can efficiently execute the applications with considering of user-friendliness, low-latency response, interactions among edge devices, collaboration between edge and cloud, and privacy protection at an acceptable overhead.
AI-Based Logistics Solutions to Tackle Covid-19 Pandemic and Ensure a Sustainable Financial Growth

Smart technologies are profoundly changing the economy and the global market, especially to overcome emergencies such as Covid-19. Logistics systems have new objectives: to satisfy the various economic requirements in crisis times, to meet the possible upcoming challenges, and to ensure sustainable financial Growth. This economic transformation is taking an increasing part in all companies and modifies the axis of the businesses. Using Artificial intelligence-based solutions is about meeting the challenges of tomorrow and satisfying the various involved economic stakeholders. Today, the customer is at the center of this financial sector. It is necessary to consider his expectations, which require the revision of the logistic services during the pandemic to concretize a propensity towards individualism, safety, as well as financial progress. Companies must therefore improve customer satisfaction through increasingly connected and intelligent technology. Artificial intelligence (AI) innovation is at the core of the business and offers a wide range of solutions to address the complexity of activities. Innovation and business sophistication are the two major elements in the attractiveness of smart solutions. This chapter aims to highlight the importance of AI-based logistics to tackle the Covid-19 Pandemic and ensure sustainable Financial Growth. This chapter provides potential financial insights through systematic analysis and synthesis of the importance of AI-based logistics solutions.
Trajectory Design and Bandwidth Allocation Considering Power-Consumption Outage for UAV Communication: A Machine Learning Approach

Recent research has demonstrated that the heat induced by high data rate transmission could cause low-temperature burns. As a promising application for future wireless networks, unmanned aerial vehicle (UAV) communication is capable of providing high data rate transmission for ground users. Inspired by this progress, this article focuses on the resource allocation for the UAV scenario and proposes a novel framework to consider the newly mentioned phenomenon named by power-consumption outage (PCO). Specifically, we give the analysis of heat transfer model in the smartphone based on which we initially integrate the influence of PCO into the optimization problem of the UAV scenario. Furthermore, to solve the problem with joint optimization of bandwidth allocation and trajectory design, we propose a machine learning model consisting of the position prediction based on echo state network and the joint optimization based on deep reinforcement learning (DRL). Due to the continuity in action space, DRL optimization is specifically implemented by the normalized advantage function algorithm. Besides, considering the restriction for the implementation of machine learning, we propose a digital twin-enabled architecture to provide a virtual environment for the training. Simulation results show the advantage of the proposed scheme in total throughput and the adaptability for trajectory design in the presence of PCO.
Privacy-Preserving Large Language Models (PPLLMs)

Recently large language models (LLMs) have gained significant attention as they have shown surprising signs of artificial general intelligence (AGI). Artificial intelligence and large language models can be used for various good purposes, such as digital assistants for knowledge creation. However, such powerful models can have potential risks as well. Among other concerns and risks are security and privacy risks that AI models can pose to data as well as users. In this article, we discuss how mathematical structures, such as polynomial and vector spaces, and privacy-preserving delegation of polynomial and matrix-vector functions can be used for transforming a computational model (including LLMs) to a privacy-preserving computational model.Furthermore, we highlight some well-known cryptographic constructions along with some solutions by which LLMs can be improved, in the sense that they can preserve the privacy and security of data and thus users. Overall, privacy-preserving and zero-knowledge LLMs, that we introduce in this article, could be potential solutions for preserving the privacy of data and users to some good and reasonable extent. More importantly, perhaps AI models should be trained on publicly available trustworthy data; and the trained models should be compressed and used by users locally.
Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model

In the case of low-resource language, there is still the requirement for developing more efficient Automatic Speech Recognition (ASR) systems. In the proposed work, the ASR system is developed for the publicly available Tamil language dataset. The approach in this paper applies the combination of Gammatone Cepstral Coefficients (GTCC) with Constant Q Cepstral Coefficients (CQCC) based on integrated frontend feature extraction techniques. To implement the backend part of the system hybrid acoustic model is applied. Two-dimensional Convolutional Neural Network (Conv2D) with Bidirectional Gated Recurrent Units based (BiGRU) Backend Model is used as the model. To build the ASR system, Connectionist Temporal Classification (CTC) loss function, CTC, and prefix-based greedy decoder are also used with the acoustic model. The proposed work shows that the joint GTCC and CQCC feature extraction techniques show the 9–16% improvement in Word Error Rate (WER) compared to isolated delta-delta features with the available integrated model.
Sentiment Analysis of Twitter Data by Natural Language Processing and Machine Learning

Twitter, a social media site is a rich source of user generated text. So there must be a way to analyze and classify the tweets posted on Twitter. Twitter data set has been used for the sentiment analysis. Sentiment analysis of Twitter data is carried out by natural language processing, and the classification is performed by machine learning. Various machine learning classifiers are implemented in Python to make confusion matrix, and performance measure is calculated. Accuracy, F1-score and misclassification error performance measure by different machine learning classifier is calculated. Finally, the research paper concludes with the best machine learning classifier.
Efficient Quality-Diversity Optimization through Diverse Quality Species

A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred. Most conventional QD approaches, for example, MAP-Elites, explicitly manage a behavioral archive where solutions are broken down into predefined niches. In this work, we show that a diverse population of solutions can be found without the limitation of needing an archive or defining the range of behaviors in advance. Instead, we break down solutions into independently evolving species and use unsupervised skill discovery to learn diverse, high-performing solutions. We show that this can be done through gradient-based mutations that take on an information theoretic perspective of jointly maximizing mutual information and performance. We propose Diverse Quality Species (DQS) as an alternative to archive-based QD algorithms. We evaluate it over several simulated robotic environments and show that it can learn a diverse set of solutions from varying species. Furthermore, our results show that DQS is more sample-efficient and performant when compared to other QD algorithms. Relevant code and hyper-parameters are available at: https://github.com/rwickman/NEAT_RL
Automated Algorithm Configuration and Design

tutorial Free Access Share on Automated Algorithm Configuration and Design Authors: Leslie Pérez Cáceres Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile https://orcid.org/0000-0001-5553-6150Search about this author , Manuel López-Ibáñez University of Manchester, Manchester, UK University of Manchester, Manchester, UK https://orcid.org/0000-0001-9974-1295Search about this author , Thomas Stützle Université libre de Bruxelles, Brussels, Belgium Université libre de Bruxelles, Brussels, Belgium https://orcid.org/0000-0002-5820-0473Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 2438–2463https://doi.org/10.1145/3583133.3595046Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
An emerging and urgent need for transdisciplinary research on Bitcoin

Bitcoin's diverse impacts point to the urgent need for transdisciplinary research to support evidence-based policy-making. Although anecdotal evidence suggests potential benefits to human and societal well-being, rigorous peer-reviewed research on Bitcoinâ€™s impacts remains scarce, creating knowledge gaps. A Bitcoin science roadmap could identify research priorities and capacity-building opportunities across environmental, social, financial, economic, and security dimensions. Collaboration among researchers, industry, government, and investors is essential to pinpoint research needs, secure funds for high-impact projects, and advance societal interests. This proactive, evidence-based policy approach will help navigate the complex Bitcoin space, ensuring well-informed decisions that balance potential risks and rewards.
Stationary quantum memory effects induced by a periodic time-dependent system-environment coupling

Periodical driving often leads to nontrivial steady states. In the context of open systems, such states may have nonvanishing currents. In this work, we experimentally study the time-discrete open system dynamics of a two-level system where the system-environment coupling is varied periodically. The two-level system is decoded on the transverse polarization and its environment on the frequency degrees of freedom of a single photon. The dynamics is implemented using linear optics. We find that the periodic driving leads to nonvanishing bidirectional information flow between the open system and the environment. We develop a comprehensive theoretical model which explains the experimental findings.
Intersecting Positionalities and the Unexpected Uses of Digital Crime and Safety Tracking in Brooklyn

Citizen is a live crime and safety tracking app in New York City that uses AI to monitor police scanners for incidences that are relevant to “public safety,” whilst also utilizing user‐recorded footage, as users near a crime, fire, or accident are encouraged to “go live” and film unfolding events. Users comment additional information and post expressive emojis as incidences unravel. In sharing information across a digital network, Citizen functions as both a form of social media and a peer‐to‐peer surveillance app. Through this lens, my ethnographic research investigates the impact of the digitization of crime and safety as an everyday experience in increasingly gentrified neighbourhoods in Brooklyn. The question of whether technology is a marker of simultaneous inclusivity and exclusivity speaks to the dialectical nature of digital technology, as producing concurrent “good” and “bad” effects. This article explores the ways that Citizen exemplifies these tensions: The app makes users feel safer but also more anxious; Citizen is a place for community information sharing to both productive and pejorative effects, it is used to both surveil one’s neighbourhood, instilling fear and mistrust, and to sousveil law enforcement and circumnavigate the NYPD at protests, producing accountability and a sense of safety. Through ethnographic examples, this article further navigates the cultural and local specificities of use, the complex positionalities that are mediated by the app and the consequences this has for those who experience social inclusion and exclusion.
Spatio-Temporal Fusion of LiDAR and Camera Data for Omnidirectional Depth Perception

Object recognition and depth perception are two tightly coupled tasks that are indispensable for situational awareness. Most autonomous systems are able to perform these tasks by processing and integrating data streaming from a variety of sensors. The multiple hardware and sophisticated software architectures required to operate these systems makes them expensive to scale and operate. This paper implements a fast, monocular vision system that can be used for simultaneous object recognition and depth perception. We borrow from the architecture of a start-of-the-art object recognition system, YOLOv3, and extend its architecture by incorporating distances and modifying its loss functions and prediction vectors to enable it to multitask on both tasks. The vision system is trained on a large database acquired through the coupling of LiDAR measurements with complementary 360-degree camera to generate a high-fidelity labeled dataset. The performance of the multipurpose network is evaluated on a test dataset consisting of a total of 7,634 objects collected on a different road network. When compared with ground truth LiDAR data, the proposed network achieves a mean absolute percentage error rate of 11% on the passenger car within 10 m and a mean error rate of 7% or 9% on the truck within 10 m and beyond 10 m, respectively. It was also observed that adding a second task (depth perception) to the modeling network improved the accuracy of object detection by about 3%. The proposed multipurpose model can be used for the development of automated alert systems, traffic monitoring, and safety monitoring.