Reviewerly/exHarmony huggingface dataset:
Incorporating Siamese Network Structure into Graph Neural Network

Abstract Siamese network plays an important role in many artificial intelligence domains, but there requires more exploration of applying Siamese structure to graph neural network. This paper proposes a novel framework that incorporates Siamese network structure into Graph Neural Network (Siam-GNN). We use DropEdge as graph augmentation technique to generate new graphs. Besides, the strategy of constructing Siamese network’s paired inputs is also studied in our work. Notably, stopping gradient backpropagation one side in Siam-GNN is an important factor affecting the performance of model. We equip some graph neural networks with Siamese structure and evaluate these Siam-GNNs on several standard semi-supervised node classification datasets and achieve surprising improvement on almost every original graph neural network.
On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control

This paper represents the next step in the development of the recently proposed single objective metaheuristic algorithm - Self-Organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The CEC 2021 single objective bound-constrained optimization benchmark testbed was used for the performance evaluation of the modifications of the algorithm. The presented modifications were invoked by the results of CEC 2021 competition, where the SOMA-CLP ranked 7th out of 9 competing algorithms. This paper introduces three modifications of population organization process focusing on one particular phase of the SOMA-CLP algorithm aimed at exploitation. All results were compared and tested for statistical significance against the original variant using the Friedman rank test. The algorithm modification and analysis of the results presented here can be inspiring for other researchers working on the development and modifications of evolutionary computing techniques.
Joint Topology and Computation Resource Optimization for Federated Edge Learning

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices caused by the heterogeneity of wireless channels and edge devices' resources. In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning, so as to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the gradient aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel penalty-based successive convex approximation method, which converges to a stationary point of the primal problem under mild conditions. Simulation results demonstrate that the proposed TOFEL scheme remarkably accelerates the federated learning process, and achieves a higher energy efficiency.
Short Video Copyright Protection Based on Blockchain Technology

The advent of the 5G era has promoted the rapid development of the short video industry. As the short video industry has become more prosperous, short video copyright disputes have occurred frequently, seriously jeopardizing the healthy development of the short video industry. How to protect the copyright of short videos has become an urgent problem to be solved. In order to solve the problems of long period of short video copyright identification, difficulty in rights protection, difficulty in infringement supervision, and complicated copyright transfer process, this paper combines blockchain technology and designs a short video copyright protection system based on blockchain. The system uses a multi-channel architecture, combined with the non-tamperable and traceable characteristics of the blockchain, and has designed a variety of functions to provide solutions to the above problems. In this paper, the system is actually built. Related functional tests and analysis of the system, have verified the feasibility of this scheme.
Security of the Cryptosystem GPT Based on Rank Codes and Term-rank Codes

This paper investigates public key cryptosystems (PKC) in the rank metric and term-rank metric. For security analysis of cryptosystems, we introduce a GPT system. Recently, respective structural attacks this system were proposed by Over-beck. For GPT system, Overbeck's attack succeeds in polynomial time. We will discuss the new construction of Shiki system. This paper describes Overbeck's attack to the new Shiki system. Also, investigation of the possibility of using term-rank codes in public key cryptosystems.
S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System

Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the accuracy of FDS depending on various environmental changes. Given these facts, in this study, we newly propose an FDS method based on the squeeze and excitation (SE) 3D convolutional neural networks (S3D). In our method, keyframes are extracted from input thermal videos using the optical flow vectors, and the fall detection is carried out based on the output of the proposed S3D, using the extracted keyframes as input. Comparative experiments were carried out on three open databases of thermal videos with different image resolutions, and our proposed method obtained F1 scores of 97.14%, 95.30%, and 98.89% in the Thermal Simulated Fall, Telerobotics and Control Lab fall detection, and eHomeSeniors datasets, respectively (the F1 score is a harmonic mean of recall and precision; it was confirmed that these are superior results to those obtained using the state-of-the-art methods of a thermal camera-based FDS.
Synthesizing Privacy-Preserving Location Traces Including Co-locations

Location traces are useful for various types of geo-data analysis tasks, and synthesizing location traces is a promising approach to geo-data analysis while protecting user privacy. However, existing location synthesizers do not consider friendship information of users. In particular, a co-location between friends is an important factor for synthesizing more realistic location traces. In this paper, we propose a novel location synthesizer that generates synthetic traces including co-locations between friends. Our synthesizer models the information about the co-locations by two parameters: friendship probability and co-location count matrix. The friendship probability represents a probability that two users will be a friend, whereas the co-location count matrix comprises a co-location count for each time instant and each location. Our synthesizer also provides DP (Differential Privacy) for training data. We evaluate our synthesizer using the Foursquare dataset. Our experimental results show that our synthesizer preserves the information about co-locations and other statistical information (e.g., population distribution, transition matrix) while providing DP with a reasonable privacy budget (e.g., smaller than 1).
Privacy-Preserving Online Mirror Descent for Federated Learning with Single-Sided Trust

This paper discusses how clients in a federated learning system can collaborate with privacy guarantee in a fully decentralized setting without a central server. Most existing work includes a central server that aggregates the local updates from the clients and coordinates the training. Thus, the setting in this existing work is prone to communication and computational bottlenecks, especially when large number of clients are involved. Also, most existing federated learning algorithms do not cater for situations where the data distribution is time-varying such as in real-time traffic monitoring. To address these problems, this paper proposes a differentially-private online mirror descent algorithm. To provide additional privacy to the loss gradients of the clients, local differential privacy is introduced. Simulation results are based on a proposed differentially-private exponential gradient algorithm, which is a variant of differentially-private online mirror descent algorithm with entropic regularizer. The simulation shows that all the clients can converge to the global optimal vector over time. The regret bound of the proposed differentially-private exponential gradient algorithm is compared with the regret bounds of some state-of-the-art online federated learning algorithms found in the literature.
Transform-based tensor singular value decomposition in multidimensional image recovery

Due to the limitation of imaging conditions, observed multidimensional images (e.g., color images, video, and multispectral/hyperspectral images) are unavoidably incomplete or corrupted, hindering subsequent applications. Multidimensional image recovery, which infers the underlying multidimensional image from the degraded observation, is a fundamental problem in low-level vision. Recently, tensor singular value decomposition (t-SVD) emerged as a powerful multilinear framework for preserving the intrinsic structure of multidimensional images. In this chapter, we revisit the establishment of the t-SVD framework and some recent advances based on this approach. Next, the recent development of transform-based t-SVD for multidimensional image recovery is reviewed. Additionally, some numerical examples are provided. Finally, we summarize the trend of the developments for multidimensional image recovery within the t-SVD framework and suggest possible directions for future research.
Improving Pre- Trained Weights through Meta - Heuristics Fine- Tuning

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bioinspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.
Autonomous Learning and Navigation of Mobile Robots Based on Deep Reinforcement Learning

Abstract Aiming at the problems of convergence difficulties faced by deep reinforcement learning algorithms in dynamic pedestrian environments, and insufficient reward and feedback mechanisms, a data-driven and model-driven navigation algorithm which named GRRL has been proposed. In order to enrich and perfect the reward feedback mechanism, we designed a dynamic reward function. The reward function fully considers the relationship between the robot and the pedestrian and the target position. It mainly includes three parts. The experimental results show that the autonomous learning efficiency and the average navigation success rate of the mobile robot driven by the GRRL algorithm are improved, the average navigation time is shorter. The dynamic reward function we designed has a certain improvement effect on robot navigation.
DhakaNet: Unstructured Vehicle Detection using Limited Computational Resources

Inefficient traffic signal control system is one of the most important causes of traffic congestion in the cities of developing countries such as Bangladesh, India, Kenya, etc. This can be mitigated by adopting a decentralized traffic-responsive signal system, where vehicle detection is performed on the road through different image-based deep learning architectures amenable to limited-resource embedded platforms as available in developing countries. Deep learning architectures currently available in this regard demand high computational resources to achieve higher inference speed and better accuracy. Besides, the few existing limited-resource deep learning architectural alternatives neither attain higher inference speed nor substantial accuracy due to not overcoming the inherent limitations. To this extent, in this study, we propose a novel limited-resource deep learning architecture, namely DhakaNet, for real-time vehicle detection in on-road (street-view) traffic images. Our proposed architecture leverages enhancing Cross-Stage Partial Network and Path Aggregation Network to build the backbone and head networks, respectively. Besides, we develop a novel multi-scale attention module to extract multi-scale meaningful features from the images, where the developed multi-scale attention module boosts the detection accuracy at the cost of small overhead. Rigorous experimental evaluation of our proposed DhakaNet over three benchmark street-view traffic datasets such as DhakaAI, IITM-HeTra-A, and IITM-HeTra-B shows up to 51% faster inference speed at a similar accuracy, or up to 13% higher accuracy at a similar inference speed compared to other state-of-the-art limited-resource deep learning architectural alternatives.
Research on Obstacle Avoidance Method of Robot Based on Region Location

In order to solve the problem of environment generalization in continuous state space, an obstacle avoidance method based on region location is proposed. The method is divided into three steps: (1) Using Region Proposal Network (RPN) to localize the obstacle area; (2). The environment map is established by the regional position mapping relation; and (3). The Deep Q-Learning Network (DQN) is used to realize collision detection of the robot, then pixel collision detection module is introduced and finally the pixel collision simulation distance sensor is combined to obtain the distance between the robot and obstacle and whether the collision or not. In this paper, the experiments were carried out in static obstacle environment and in dynamic and static obstacle environment for robot obstacle avoidance tasks. Experimental results show that the problem of environment generalization can be effectively solved by introducing pixel collision detection in the process of robot obstacle avoidance, and the network model trained in a dynamic environment has some generalization ability.
Blockchain-based Scalable Ubiquitous Code Allocation Method Resilient to Congestion

Recently, the demand for global identifiers has rapidly increased since IoT devices are widely used around the world. This indicates that we need to build a scalable and secure identification system. A blockchain is effective to resolve global identifier conflicts and to prove the legitimacy of the identifiers. However, the blockchain replication cost may become a bottleneck when allocation requests increase. Here, we propose a blockchain-based scalable allocation system for a ubiquitous code (ucode), which is a standardized global numerical identifier. In our system, a registrar bundles ucode allocation requests into a single transaction to reduce the replication cost. Based on the blockchain data generated from the transaction, the owner of each ucode space can provide a proof to claim his ownership. The proof mechanism requires registrants to wait for receiving proof data from the registrar. We analyze the trade-off between the waiting time and ucode allocation scalability. We also compare our proposal with the conventional methods and find that the proposed method enables more registrants to claim ucode ownerships.
A Consensus Rewarding Protocol based on Two-Dimensional Contract for Blockchain Networks

A consensus rewarding protocol compensates decentralized miners for participating in the block mining process. Existing consensus mechanisms are always composed of elaborate mining protocols with simply fixed rewarding halved every four years and voluntary transaction fees. However, with the increasing proportion of free packaging transaction fees, strategic miners can entice others miners to follow their blocks by leaving more transaction fees, so as to obtain benefits and subvert the whole chain. This study presents a contract theory-based consensus rewarding protocol to prevent such behavior by considering two dimensional types of miners: relative hash rate and transaction fee preference. The utility of both miners and blockchain system are formulated considering TPS miner provided and corresponding rewards. The real transaction data of Bitcoin is used to carry out the experimental simulation, and the results show that our proposed incentive mechanism effectively prevents miners’ strategic behavior and improves system utility.
Inclusiveness in decision making and avoiding unnecessary costs for successful decision making

The objective of this study is to examine the effects of inclusiveness in decision making on the costs of decision making and its implementation. This study is based on multiple regression model and the individual effects of each of the explanatory variables will be evaluated by applying partial derivatives. The findings of this study suggest that inclusiveness in decision making brings unity, cohesion, and team spirit and thus reduces the cost of correct decision making and its implementation. In addition, if decisions are made on the basis of consensus of all stakeholders as well as the employees, the cost of correct decision making will decrease, and it is most likely that correct decisions will be made because of the consensus and presence of democratic institutions. Listening to the practitioners and seeking expert advice may also reduce cost and it is most likely that correct decisions will be made. Maintaining goodwill and harmony will also promote cooperative attitudes and thus will reduce the cost of correct decision making and its implementation. Empowering, honoring, providing incentive bonus and recognizing the great contributions of the employees will truly motivate the employees and cost of decision making will decline and most likely correct decisions will be made which will eventually place the firm or the corporation or the state on the road to success and prosperity. This study will be immensely useful to decision makers, top managers in private and public enterprises, policy makers, research scholars, and academicians across the globe.
Improving Word Alignment by Adding Gromov-Wasserstein into Attention Neural Network

Abstract Statistical machine translation systems usually break the translation task into two or more subtasks and an important one is finding word alignments over a parallel sentence bilingual corpus. We address the problem of introducing word alignment for language pairs by developing a novel neural network model that can applied to other generative alignment models. We use Multi-layer attention model and multi-layer model with multi-head-attention mechanism on each layer provides superior translation quality. It can be trained on bilingual data without relying on word alignment. In this paper, we cast the correspondence problem directly as an optimal distance problem. We use the Gromov-Wasserstein distance to calculated how similarities between word pairs are related across languages. The resulting alignments dramatically outperform the GIZA++ and FastAlign approach, these alignments are comparable on public data sets.
Hybrid Spaces for designing and making

This paper discusses five patterns to create more hybrid learning opportunities on campuses by designing and connecting available spaces appropriately. A HYBRID DESIGN ENGINEERING STUDIO provides a digitally enhanced environment for rich collaboration among different locations. HYBRID INNOVATION SPACES support the design thinking process on-site with digital tools. MAKER GARAGES provide space for students to work transparently on their projects. An OPEN EDUCATION POP-UP SHOP makes it possible to reach out and present results off-campus. SHOW CASE GALLERIES invite the public on-campus and transforms the university into a museum to engage citizens and external stakeholders.
End-to-end Saliency Face Detection and Recognition

Abstract Face recognition is a long-lasting hot topic in compute vision. The face recognition system mainly includes face detection, alignment and feature extraction. In the forward task, the extracted features are used to measure the similarity between faces, and outputs whether those are same person or not or which person it is in the registered set. Typically, the three stages of recognition system training independently of each other have the following shortcomings: 1) redundant calculation of feature maps; 2) unable to end-to-end optimization; 3) detecting an extracting so much useless face. A lightweight model for saliency face detection and recognition that can be optimized end-to-end is proposed. While maintaining accuracy, it meets the real-time and memory limitation requirements in embedded devices or terminals.
Network AI Management &amp; Orchestration: A Federated Multi-task Learning Case

6G treats artificial intelligence (AI) as the corner-stone and fundamental paradigm shift for providing inclusive intelligent services, which requires to natively support the training and reasoning of AI and provide a comprehensive network AI management & orchestration (NAMO) solution. However, NAMO faces many practical challenges like multi-tenant multi-task coordination, heterogeneous resource scheduling, and security & privacy concerns. In this paper, we take the federated multi-task learning as a starting case to demonstrate a promising NAMO solution. In particular, we propose a resource-aware method which leverages a primal-dual relationship to allow no direct up-loading of local data to the edge server and maintain synchronous updates with straggler tolerance. Also, the proposed method could dynamically tune the learning accuracy at devices and the number of federated iterations to obtain a satisfactory training accuracy. Extensive simulation results have demonstrated the effectiveness of the proposed method.
An empirical study on COVID-19 for social contact tracing on classification perspective

The COVID-19 is a pandemic and irresistible one without the antibody and cure. This issue needs preventive controls through the creation of awareness and implementation of the contact tracing process. This device has its usage to lessen the infections with the information described at the infectious disease and reduce the spreadsheet of infections and inflamed human beings. In contrast, social media networking is a superb knowledge set of nowadays market coverage of maximum sociology of the planet to see, analyse and interpret. AI has been used to determine the infected COVID-19 and infers the good action and preventive measures to reduce the expansion of COVID-19. The authors have derived the available data with its analysis and contract tracing through the usage of social media datasets.