Reviewerly/exHarmony huggingface dataset:
A novel real-time multiple objects detection and tracking framework for different challenges

Recently, there was a lot of researches on real-time detection and tracking algorithms, as the frequent use of surveillance cameras and the expansion of its applications, especially in security and surveillance. However, many challenges have emerged that hinder monitoring systems' work, whether in the detection or tracking stage. We propose a robust new algorithm to detect and track objects from natural scenes captured with real-time cameras to achieve this. This work aims to create a detection and tracking algorithm that is responsive to actual and fundamental changes. This algorithm is characterized by the detection of multiple moving creatures, limited resources, and different challenges. This algorithm combines principal component analysis and deep learning networks to make the most of these two approaches' advantages to achieve an intelligent detection and tracking system that works in real-time. It is done adaptively between the two approaches to enhance performance compared to the existing detection and tracking algorithms. The experimental results showed the new algorithm's effectiveness and efficiency by comparing it with other detection and tracking systems and obtaining good detection and classification accuracy.
Feelings of Being for Mobile User Experience Design

Creating emotionally engaging mobile applications requires consideration of constantly evolving technology, content richness, usability, and user experience (UX). UX plays an important role in promoting long-term usage. We focused on the emotional aspects of UX design of mobile applications. In particular, we adopted the concept of feelings of being (FoB) – also known as existential feelings – in the context of mobile UX design. We presented an in-depth literature review covering 112 articles (2005–2021) on human-computer interaction, UX, mobile application development, mobile learning, and emotional engagement. Of these articles, 16 discussed FoB in the context of mobile applications. Building on the results of literature analysis and other previous research, we presented a FoB model for mobile application design comprising 13 FoB (ownership, engagement, contribution, security, trust, adjustability, enjoyment, empowerment, effectiveness, frustration, excitement, gratification, and needs fulfilment) and the hedonic and eudaimonic aspects of UX. Finally, we validated the FoB model through nine design projects, proposed design recommendations based on the model, and presented considerations on extending the model with additional elements.
<scp>ShareChain</scp>: Blockchain‐enabled model for sharing patient data using federated learning and differential privacy

Abstract Every individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as ‐anonymity, ‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model.
Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network

Graph convolutional networks (GCNs) are important techniques for analytics tasks related to graph data. To date, most GCNs are designed for a single graph domain. They are incapable of transferring knowledge from/to different domains (graphs), due to the limitation in graph representation learning and domain adaptation across graph domains. This paper proposes a novel Graph Contrastive Learning Network (GCLN) for unsupervised domain adaptive graph learning. The key innovation is to enforce attraction and repulsion forces within each single graph domain, and across two graph domains. Within each graph, an attraction force encourages local patch node features to be similar to global representation of the entire graph, whereas a repulsion force will repel node features so they can separate network from its permutations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> domain-specific graph contrastive learning). Across two graph domains, an attraction force encourages node features from two domains to be largely consistent, whereas a repulsion force ensures features are discriminative to differentiate graph domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> cross-domain graph contrastive learning). The within- and cross-domain graph contrastive learning is carried out by optimizing an objective function, which combines source classifier and target classifier loss, domain-specific contrastive loss, and cross-domain contrastive loss. As a result, feature learning from graphs is facilitated using knowledge transferred between graphs. Experiments on real-world datasets demonstrate that GCLN outperforms state-of-the-art graph neural network algorithms.
Artificial Intelligence, Employee Engagement, Experience, and HRM

At the end of this chapter, you should be able to:
DRL-Based Joint Resource Allocation and Device Orchestration for Hierarchical Federated Learning in NOMA-Enabled Industrial IoT

Federated learning (FL) provides a new paradigm for protecting data privacy in Industrial Internet of Things (IIoT). To reduce network burden and latency brought by FL with a parameter server at the cloud, hierarchical federated learning (HFL) with mobile edge computing (MEC) servers is proposed. However, HFL suffers from a bottleneck of communication and energy overhead before reaching satisfying model accuracy as IIoT devices dramatically increase. In this article, a deep reinforcement learning (DRL)-based joint resource allocation and IIoT device orchestration policy using nonorthogonal multiple access is proposed to achieve a more accurate model and reduce overhead for MEC-assisted HFL in IIoT. We formulate a multiobjective optimization problem to simultaneously minimize latency, energy consumption, and model accuracy under the constraints of computing capacity and transmission power of IIoT devices. To solve it, we propose a DRL algorithm based on deep deterministic policy gradient. Simulation results show proposed algorithm outperforms others.
Decentralized Multi-Agent Planning Using Model Predictive Control and Time-Aware Safe Corridors

Multi-agent planning has numerous real-world applications, such as autonomous agriculture, search and rescue, and infrastructure inspection. It is thus of tremendous benefit to improve on the state-of-the-art in multiple metrics to make autonomous multi-agent systems more efficient and a more viable real-world solution. In this letter, we propose a new framework for multi-agent planning in a static environment that improves upon the existing state-of-the-art in multiple areas such as computation time, trajectory smoothness, and feasibility. The proposed method first generates a global path that only avoids static obstacles and then generates a Safe Corridor around it. It then extends the notion of Safe Corridors and makes them time-aware in order to account for the future positions of other agents. The time-aware Safe Corridor, along with a local reference trajectory generated from the global path, are used in an Mixed-Integer Quadratic Program/Model Predictive Control formulation that generates a collision-free optimal trajectory. The proposed framework is real-time, decentralized, and synchronous. It is compared to two recent state-of-the-art methods in simulations. It outperforms both methods in robustness as well as feasibility and computation time.
<i>Eiffel</i>: Efficient and Fair Scheduling in Adaptive Federated Learning

Emerging machine learning (ML) technologies, in combination with the increasing computational power of mobile devices, lead to the extensive adoption of ML-based applications. Different from conventional model training that needs to collect all the user data in centralized cloud servers, federated learning (FL) has recently drawn increasing research attention as it enables privacy-preserving model training. With FL, decentralized edge devices in participation, train their model copies locally over their siloed datasets, and periodically synchronize the model parameters. However, model training is computationally extensive which easily drains the battery of mobile devices. In addition, due to the uneven distribution of siloed datasets, the shared model may become biased. To address the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficiency</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fairness</i> concerns in a resource-constrained federated learning setting, in this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eiffel</i> to judiciously select mobile devices to participate in the global model aggregation, and adaptively adjust the frequency of local and global model updates. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eiffel</i> aims to make scheduling and coordination for the federated learning towards both resource efficiency and model fairness. We have conducted theoretical analysis of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eiffel</i> from the perspectives of fairness and convergence. Extensive experiments with a wide variety of real-world datasets and models, both on a networked prototype system and in a larger-scale simulated environment, have demonstrated that while maintaining similar accuracy performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eiffel</i> outperforms existing baselines with respect to reducing communication overhead by up to 6× for higher efficiency and improving the fairness metric by up to 57% compared to the state-of-the-art algorithms.
Composite fault diagnosis of analog circuit system using chaotic game optimization-assisted deep ELM-AE

When multiple electronic components in an analog circuit fail simultaneously, since its high complexity, it is arduous to establish an accurate fault model and diagnose the composite faults precisely. Traditional approach for composite fault diagnosis is severely constrained since it excessively relies on domain expertise and signal processing technology. The rapid development of deep learning provides a new idea for it. In this study, an improved strategy based on deep extreme learning machine with autoencoder (DELM-AE) is proposed, which possesses more prominent capability on feature extraction and representation. The proposed method first utilizes multiple extreme learning machine with autoencoder (ELM-AE) to adaptively extract the hidden features from original collected signals layer by layer, which does not need to design representative features manually. Subsequently, efficient classification will be conducted based on extreme learning machine algorithm. All parameter values of the proposed network are determined at one time, eliminating the iterative updating process. To further enhance the performance of DELM-AE on feature extraction and classification, a novel chaos game optimization algorithm (CGO) is introduced to optimize the initial network parameters. The experimental results on two typical test circuits demonstrate that the proposed method, CGO-DELM-AE, can effectively diagnose the composite faults of analog circuit.
The use of digital tools by secondary school teachers for the implementation of distance learning in the context of digital transformation in Ukraine

In this paper, we raise the discussion on the use of digital learning tools by teachers of Ukrainian secondary schools for the organization of distance learning. In order to collect data we have used the survey instruments (Google questionnaire) and involved 1463 respondents who revealed the state of the use of digital learning tools for the organization of distance learning in the quarantine period caused by COVID-19 pandemic. The study was conducted in all regions of Ukraine. The results show that teachers have a need to master their knowledge and practical skills on digital instruments for the organization of distance learning with students. On the other hand the paper presents the experience on how the problem of the mastering of teachers’ digital competencies can be organized and what are the steps to the organization of special distance learning courses for teachers in Ukraine. The experience of the creation of Web portal of Ukrainian Open University of Postgraduate Education is presented. We propose on how the needs and requirements of teachers regarding the implementation of the distance learning in schools and the development of their digital competencies should be resolved.
Elderly Fall Detection Based on Improved YOLOv5s Network

The problem of aging population in our country is becoming more and more serious, falling on the road accidently has been the first murder for people over 65 years of age. In this article, a real-time detection method for elderly fall behavior based on improved YOLOv5s is proposed to detect whether the elderly fall in real time, so that they can receive timely and effective treatment. First, the asymmetric convolution blocks (AC.B) convolution module is used in the Backbone network to replace the existing basic convolution to improve the feature extraction capability. Then, the spatial attention mechanism module is added to the residual structure of the Backbone network to extract more feature location information. Finally, the feature layer structure is improved to remove the feature layer for small targets so that the network can pay more attention to the semantic level information, and at the same time, the classifier is set. The proposed algorithm is trained on the URFD public dataset, and the test set is used for verification. The experimental results show that the average accuracy of all categories of the algorithm reaches 97.2%, which is increased by 3.5% compared to YOLOv5s.Thus the proposed algorithm can accurately detect the fall behavior of the elderly.
A post-quantum lattice based lightweight authentication and code-based hybrid encryption scheme for IoT devices

The Internet of Things (IoT) introduces an active connection between smart devices for revolutionizing our modern lives in this world. But, IoT devices often exhibit several security issues, so transmission between the nodes should be protected using cryptographic approaches. However, the complexity of conventional cryptographic approaches is very high and is vulnerable to quantum attacks. This paper presents a robust and lightweight post-quantum lattice-based authentication and code-based hybrid encryption scheme for resource-constrained IoT devices. The proposed Ring-Learning with Errors (Ring-LWE) based authentication scheme introduces Bernstein reconstruction in polynomial multiplication to achieve minimal computation cost; hence, resource-limited IoT devices are viable to use the reliable authentication mutually. This approach offers indefinite identity privacy and location privacy. Hence, the proposed signature generation and verification process are highly efficient compared to the existing ring signature systems. Also, the proposed post-quantum hybrid code-based encryption scheme follows Diagonal Structure Based QC‑LDPC Codes with column loop optimization and Simplified Log Domain Sum-Product Algorithm (SLDSPA) to provide the function of light weight encryption with minimum hardware requirements. The total authentication delay of the proposed authentication scheme is 23% less than the authentication scheme that is considered conventional polynomial multiplication. Also, the optimized design of the proposed code based HE uses only 64 slices and 640 slices on Xilinx Virtex-6 FPGA for encoding and decoding processes, respectively. These simulation results prove the effectiveness of the proposed cryptographic scheme against other competitive systems in terms of its functionality and hardware complexities.
Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features

To address the problems that most convolutional neural network-based image defogging algorithm models capture incomplete global feature information and incomplete defogging, this paper proposes an end-to-end convolutional neural network and vision transformer hybrid image defogging algorithm. First, the shallow features of the haze image were extracted by a preprocessing module. Then, a symmetric network structure including a convolutional neural network (CNN) branch and a vision transformer branch was used to capture the local features and global features of the haze image, respectively. The mixed features were fused using convolutional layers to cover the global representation while retaining the local features. Finally, the features obtained by the encoder and decoder were fused to obtain richer feature information. The experimental results show that the proposed defogging algorithm achieved better defogging results in both the uniform and non-uniform haze datasets, solves the problems of dark and distorted colors after image defogging, and the recovered images are more natural for detail processing.
ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots

Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots.One stumbling block is the lack of a domain-specific discourse corpus for training these systems.Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates.When designing a dialogue system for social robots, the user experience is prioritized above industrial robots.We provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ) to overcome these challenges, a fully-labeled discourse dataset covering four robotics domains.It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings.Small talk concepts and human-to-human conversation strategies are provided to support humanlike answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement.Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset.ToD4IR's performance in a real manufacturing context was validated through a series of trials.Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset.Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IRfor reproducible research.
Two Decades of Bengali Handwritten Digit Recognition: A Survey

Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis of the challenges, the underlying recognition process, and possible future directions. In this paper, the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of state-of-the-art datasets and approaches towards offline BHDR have been analyzed. Furthermore, several real-life application-specific studies, which involve BHDR, have also been discussed in detail. This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas.
LSNet: Real-time attention semantic segmentation network with linear complexity

From high-resolution images to get context information remains a challenge in real-time semantic segmentation. Despite recent advances in context information acquisition based on Self-Attention,the Self-Attention module will cause the calculation degree to increase exponentially when facing the semantic segmentation task. In this article, we propose a real-time semantic segmentation network (LSNet) which strikes a balance between segmentation accuracy and inference speed. Firstly, we propose a linear attention module to reduce the computational complexity of Self-Attention from O(n2) to O(n). Secondly, we propose a context information fusion module by using a cross-attention mechanism to fill in a lot of spatial details for high-level semantic information. Finally, we design a real-time semantic segmentation network LSNet to greatly improve the real-time inference speed and ensure the accuracy of network segmentation. To verify the effectiveness of the LSNet network, we conducted a lot of comparative experiments on CamVid, Cityscapes, COCO-Stuff datasets, and the experimental results show that LSNet achieves an excellent trade-off between accuracy and speed on both Cityscapes, CamVid and COCO-Stuff datasets.