Reviewerly/exHarmony huggingface dataset:
JORA: Blockchain-based efficient joint computing offloading and resource allocation for edge video streaming systems

Edge computing has been widely applied in video streaming systems, aiming to provide low-latency video streaming services. However, limited resources and a lack of incentivization affect the performance of edge video streaming systems. In this paper, we propose an efficient Joint computing Offloading and Resource Allocation (JORA) method for blockchain-supported edge video streaming systems (BEVS). Specifically, we develop a novel incentive mechanism based on the blockchain smart contract to enable automatic incentivization. Meanwhile, we customize a system prototype of BEVS on Amazon EC2 using Apache Storm. Preliminary experiments with the customized prototype reveal the coupling impacts of the offloading mode, the allocation scheme, and video redundancy on system performance and resource demand. Motivated by the results, we formulate joint computing offloading, resource allocation, and video adaptive compression as an optimization problem. We transform the original problem into a convex problem and a mixed-integer programming problem (MIP). We then develop an algorithm based on the generalized Benders decomposition (GBD) to solve the original problem in an iterative way. Extensive experiments demonstrate that the proposed scheme achieves full resource utilization and an effective trade-off between energy consumption and accuracy.
Meta-learning-based adversarial training for deep 3D face recognition on point clouds

Recently, deep face recognition using 2D face images has made great advances mainly due to the readily available large-scale face data. However, deep face recognition using 3D face scans, especially on point clouds, has been far from fully explored. In this paper, we propose a novel meta-learning-based adversarial training (MLAT) algorithm for deep 3D face recognition (3DFR) on point clouds. It consists of two alternate modules: adversarial sample generating for 3D face data augmentation and meta-learning-based deep network training. In the first module, adversarial samples of given 3D face scans are dynamically generated based on current deep 3DFR model. In the second module, a meta-learning framework is designed to avoid the performance decrease caused by the generated adversarial samples. Overall, MLAT algorithm combines the adversarial sample generating and meta-learning-based network training in a uniform framework, in which adversarial samples and network parameters are optimized alternately. Thus, it can continuously generate diverse and suitable adversarial samples, and then the meta-learning framework can further improve the accuracy of 3DFR model. Comprehensive experimental results show that the proposed approach consistently achieves competitive rank-one recognition accuracies on the BU-3DFE (100%), Bosphorus (99.78%), BU-4DFE (98.02%) and FRGC v2 (98.01%) database, and thereby substantiate its superiority.
ARES: Adaptive Resource-Aware Split Learning for Internet of Things

Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Split-learning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing model convergence and accuracy.
Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Metaheuristic algorithms are widely used in solving optimization problems.In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types.The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appropriate solutions for optimization problems.In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.
Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.
Detect Phishing Website by Fuzzy Multi-Criteria Decision Making

Phishing activity is undertaken by the hackers to compromise the computer networks and financial system. A compromised computer system or network provides data and or processing resources to the world of cybercrime. Cybercrimes are projected to cost the world $6 trillion by 2021, in this context phishing is expected to continue being a growing challenge. Statistics around phishing growth over the last decade support this theory as phishing numbers enjoy almost an exponential growth over the period. Recent reports on the complexity of the phishing show that the fight against phishing URL as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. Previous research have proposed different methods including neural network, data mining technique, heuristic-based phishing detection technique, machine learning to detect phishing websites. However, recently phishers have started to use more sophisticated techniques to attack the internet users such as VoIP phishing, spear phishing etc. For these modern methods, the traditional ways of phishing detection provide low accuracy. Hence, the requirement arises for the application and development of modern tools and techniques to use as a countermeasure against such phishing attacks. Keeping in view the nature of recent phishing attacks, it is imperative to develop a state-of-the art anti-phishing tool which should be able to predict the phishing attacks before the occurrence of actual phishing incidents. We have designed such a tool that will work efficiently to detect the phishing websites so that a user can understand easily the risk of using of his personal and financial data.
Dashboard Design Patterns

This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in "dashboard genres" such as narrative, analytical, or embedded dashboard. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github.io.
NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks

Convolution Neural Network (CNN) models have gained ground in research activities particularly in medical images used for Diabetes Retinopathy (DR) detection.X-ray, MRI, and CT scans have all been used to validate CNN models, with classification accuracy generally reaching that of trained doctors.It is mandatory to evaluate the strength of CNN models used in medical tasks against adversarial attacks especially in healthcare; that is to say, the security of such models is becoming extremely relevant to the diagnosis as this latter will guide highstakes decision-making.However, little study has been conducted to better comprehend this issue.This paper focuses on MobileNet CNN architecture in order to investigate its vulnerability against fast gradient sign methods (FGSM) adversarial attacks.For this end, a Neural Structure Learning (NSL) and a Multi-Head Attention (MHA) have been used to effectively reduce the vulnerability against attack by end-to-end CNN training with adversarial neighbors that produce adversarial perturbations on optical coherence tomography (OCT) images.With suggested model NSL-MHA-CNN, there has been an ability to maintain model performance on adversarial attack without increasing cost of training.Through theoretical assistance and empirical validation, it was possible to examine the stability of MobileNet architecture and demonstrate its susceptibility, particularly to adversarial attack.The experiments in this paper show that indiscernible degrees of perturbation ε < 0.01 were sufficient to cause a task failure resulting to misclassification in majority of the time.Moreover, empirical simulation shows that the proposed approach advanced in this paper can be an effective method to defense against adversarial attack at level of CNN model testing.
Partial Convolution for Padding, Inpainting, and Image Synthesis

Partial convolution weights convolutions with binary masks and renormalizes on valid pixels. It was originally proposed for image inpainting task because a corrupted image processed by a standard convolutional often leads to artifacts. Therefore, binary masks are constructed that define the valid and corrupted pixels, so that partial convolution results are only calculated based on valid pixels. It has been also used for conditional image synthesis task, so that when a scene is generated, convolution results of an instance depend only on the feature values that belong to the same instance. One of the unexplored applications for partial convolution is padding which is a critical component of modern convolutional networks. Common padding schemes make strong assumptions about how the padded data should be extrapolated. We show that these padding schemes impair model accuracy, whereas partial convolution based padding provides consistent improvements across a range of tasks. In this article, we review partial convolution applications under one framework. We conduct a comprehensive study of the partial convolution based padding on a variety of computer vision tasks, including image classification, 3D-convolution-based action recognition, and semantic segmentation. Our results suggest that partial convolution-based padding shows promising improvements over strong baselines.
Quantum vs. Classical Algorithms for Solving the Heat Equation

Abstract Quantum computers are predicted to outperform classical ones for solving partial differential equations, perhaps exponentially. Here we consider a prototypical PDE—the heat equation in a rectangular region—and compare in detail the complexities of ten classical and quantum algorithms for solving it, in the sense of approximately computing the amount of heat in a given region. We find that, for spatial dimension $$d \ge 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>d</mml:mi><mml:mo>≥</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> , there is an at most quadratic quantum speedup in terms of the allowable error $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math> using an approach based on applying amplitude estimation to an accelerated classical random walk. However, an alternative approach based on a quantum algorithm for linear equations is never faster than the best classical algorithms.
Simulating key properties of lithium-ion batteries with a fault-tolerant quantum computer

There is a pressing need to develop new rechargeable battery technologies that can offer higher energy storage, faster charging, and lower costs. Despite the success of existing methods for the simulation of battery materials, they can sometimes fall short of delivering accurate and reliable results. Quantum computing has been discussed as an avenue to overcome these issues, but only limited work has been done to outline how they may impact battery simulations. In this work, we provide a detailed answer to the following question: how can a quantum computer be used to simulate key properties of a lithium-ion battery? Based on recently-introduced first-quantization techniques, we lay out an end-to-end quantum algorithm for calculating equilibrium cell voltages, ionic mobility, and thermal stability. These can be obtained from ground-state energies of materials, which is the core calculation executed by the quantum computer using qubitization-based quantum phase estimation. The algorithm includes explicit methods for preparing approximate ground states of periodic materials in first quantization. We bring these insights together to perform the first estimation of the resources required to implement a quantum algorithm for simulating a realistic cathode material, dilithium iron silicate.
Enabling Blockchain with IoMT Devices for Healthcare

Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.
A New Neural Network Training Algorithm Based on Artificial Bee Colony Algorithm for Nonlinear System Identification

Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types of problems. A successful training process is required to create effective models with ANN. An effective training algorithm is essential for a successful training process. In this study, a new neural network training algorithm called the hybrid artificial bee colony algorithm based on effective scout bee stage (HABCES) was proposed. The HABCES algorithm includes four fundamental changes. Arithmetic crossover was used in the solution generation mechanisms of the employed bee and onlooker bee stages. The knowledge of the global best solution was utilized by arithmetic crossover. Again, this solution generation mechanism also has an adaptive step size. Limit is an important control parameter. In the standard ABC algorithm, it is constant throughout the optimization. In the HABCES algorithm, it was determined dynamically depending on the number of generations. Unlike the standard ABC algorithm, the HABCES algorithm used a solution generation mechanism based on the global best solution in the scout bee stage. Through these features, the HABCES algorithm has a strong local and global convergence ability. Firstly, the performance of the HABCES algorithm was analyzed on the solution of global optimization problems. Then, applications on the training of the ANN were carried out. ANN was trained using the HABCES algorithm for the identification of nonlinear static and dynamic systems. The performance of the HABCES algorithm was compared with the standard ABC, aABC and ABCES algorithms. The results showed that the performance of the HABCES algorithm was better in terms of solution quality and convergence speed. A performance increase of up to 69.57% was achieved by using the HABCES algorithm in the identification of static systems. This rate is 46.82% for the identification of dynamic systems.
An emoji-aware multitask framework for multimodal sarcasm detection

Sarcasm is a case of implicit emotion and needs additional information like context and multimodality for better detection. But sometimes, this additional information also fails to help in sarcasm detection. For example, the utterance “Oh yes, you’ve been so helpful. Thank you so much for all your help”, said in a polite tone with a smiling face, can be understood easily as non-sarcastic because of its positive sentiment. But, if the above message is accompanied by a frustrated emoji , the negative sentiment of the emoji becomes evident, and the intended sarcasm can be easily understood. Thus, in this paper, we propose the SEEmoji MUStARD , an extension of the multimodal MUStARD dataset. We annotate each utterance with relevant emoji, emoji’s sentiment, and emoji’s emotion. We propose an emoji-aware-multimodal multitask deep learning framework for sarcasm detection (i.e., primary task) and sentiment and emotion detection (i.e., secondary task) in a multimodal conversational scenario. Experimental results on the SEEmoji MUStARD show the efficacy of our proposed emoji-aware-multimodal approach for sarcasm detection over the existing models.
From co-design to the construction of a metaverse for the promotion of cultural heritage and tourism: the case of Amiais

Co-design is characterized as a key process so that researchers can count on the collaboration of those who are bearers of knowledge and wisdom of a certain subject, so that together they can work towards a common end. This paper focuses on the case study of Amiais, a small village located in the central region of Portugal in which a project is being developed that intends to replicate the village in the Second Life metaverse so that visitors can get to know the place virtually. To make this possible, several visits to the place were made and co-design sessions were developed so that there would be a participative and ethnographic proximity research. In this way, the inhabitants also became members of the research team, and their knowledge was central to the project. Thanks to the sharing of knowledge, experiences and stories, the contribution of these inhabitants will have an impact on the preservation and dissemination of the cultural heritage of Amiais. This article focuses on the two co-design sessions held by the project, which gave voice to the inhabitants of Amiais, making them also members of the team. The main objective of these co-design sessions was to create brainstorms with the purpose of sharing the knowledge and life experiences of those who live in the village. Thanks to the sessions, it was possible to recreate and share the cultural heritage of Amiais in the Second Life metaverse.