Reviewerly/exHarmony huggingface dataset:
Privacy Preservation in Resource-Constrained IoT Devices Using Blockchain—A Survey

With opportunities brought by Internet of Things (IoT), it is quite a challenge to assure privacy preservation when a huge number of resource-constrained distributed devices is involved. Blockchain has become popular for its benefits, including decentralization, persistence, immutability, auditability and consensus. With the implementation of blockchain in IoT, the benefits provided by blockchain can be derived in order to make IoT more efficient and maintain trust. In this paper, we discuss some applications of IoT in different fields and privacy-related issues faced by IoT in resource-constrained devices. We discuss some applications of blockchain in vast majority of areas, and the opportunities it brings to resolve IoT privacy limitations. We, then, survey different researches based on the implementation of blockchain in IoT. The goal of this paper is to survey recent researches based on the implementation of blockchain in IoT for privacy preservation. After analyzing the recent solutions, we see that the blockchain is an optimal way for preventing identity disclosure, monitoring, and providing tracking in IoT.
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification

Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignment problem between images. At the cost of incorporating a pose estimator, many works introduce pose information to alleviate the misalignment in both training and testing. To achieve high accuracy while preserving low inference complexity, we propose a network named Pose-Guided Feature Learning with Knowledge Distillation (PGFL-KD), where the pose information is exploited to regularize the learning of semantics aligned features but is discarded in testing. PGFL-KD consists of a main branch (MB), and two pose-guided branches, e.g., a foreground-enhanced branch (FEB), and a body part semantics aligned branch (SAB). The FEB intends to emphasise the features of visible body parts while excluding the interference of obstructions and background (e.g., foreground feature alignment). The SAB encourages different channel groups to focus on different body parts to have body part semantics aligned representation. To get rid of the dependency on pose information when testing, we regularize the MB to learn the merits of the FEB and SAB through knowledge distillation and interaction-based training. Extensive experiments on occluded, partial, and holistic ReID tasks show the effectiveness of our proposed network.
A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.
Prospect of Artificial Intelligence Based on Electronic Medical Records

With the advent of the big data era, the interest of the international community is focusing on increasing the utilization of medical big data. Many hospitals are attempting to increase the efficiency of their operations and patient management by adopting artificial intelligence (AI) technology that enables the use of electronic medical record (EMR) data. EMR includes information about a patient's health history, such as diagnoses, medicines, tests, allergies, immunizations, treatment plans, personalized medical care, and improvement of medical quality and safety. EMR data can also be used for AI-based new drug development. In particular, it is effective to develop AI that can predict the occurrence of specific diseases or provide individualized customized treatments by classifying the individualized characteristics of patients. In order to improve performance of artificial intelligence research using EMR data, standardization and refinement of data are essential. In addition, since EMR data deal with sensitive personal information of patients, it is also vital to protect the patient's privacy. There are already various supports for the use of EMR data in the Korean government, and researchers are encouraged to be proactive.
EVA-GCN: Head Pose Estimation Based on Graph Convolutional Networks

Head pose estimation is an important task in many real-world applications. Since the facial landmarks usually serve as the common input that is shared by multiple downstream tasks, utilizing landmarks to acquire high-precision head pose estimation is of practical value for many real-world applications. However, existing landmark-based methods have a major drawback in model expressive power, making them hard to achieve comparable performance to the landmark-free methods. In this paper, we propose a strong baseline method which views the head pose estimation as a graph regression problem. We construct a landmark-connection graph, and propose to leverage the Graph Convolutional Networks (GCN) to model the complex nonlinear mappings between the graph typologies and the head pose angles. Specifically, we design a novel GCN architecture which utilizes joint Edge-Vertex Attention (EVA) mechanism to overcome the unstable landmark detection. Moreover, we introduce the Adaptive Channel Attention (ACA) and the Densely-Connected Architecture (DCA) to boost the performance further. We evaluate the proposed method on three challenging benchmark datasets. Experiment results demonstrate that our method achieves better performance in comparison with the state-of-the-art landmark-based and landmark-free methods.
DepFiN: A 12nm, 3.8TOPs depth-first CNN processor for high res. image processing

High resolution pixel processing (PP) tasks like demosaicing, denoising, and super-resolution strongly benefit from Convo-lutional Neural Network (CNN) approaches, yet give rise to different architectural challenges compared to typical classification CNNs, preventing real-time execution on existing SotA CNN processors. The 12nm DepFiN processor is the first processor optimized for a wide range of pixel processing CNNs, innovating 1.) at system level, by shifting the memory/IO trade-off through line-based extreme layer fusion (depth-first CNN computation); 2.) at architecture level, by achieving near 100% utilization @3.8TOPs, even on traditionally challenging depthwise (DW)-pointwise (PW) and ShiftNet layers, through an optimized dataflow for high resolution PP; 3.) at gate level, by drastically reducing switching activity through improved register file (REGF) and Multiply-Accumulate (MAC) interfacing.DepFiN brings high-resolution pixel processing to mobile platforms by achieving up to 3.8 TOPs and 20 TOPs/W, e.g. enabling 2x super-resolution to with the FSRCNN network while limiting off-chip IO to 3.2M features/inference.
Sentiment analysis using novel and interpretable architectures of Hidden Markov Models

Sentiment analysis aims to formulate automated methods to recognize sentiments, opinions and emotions in text. Many methods and approaches have been utilized but most of them do not disclose the way that decisions are made and operate as black boxes. Hidden Markov Models (HMMs) constitute a quite suitable and potent approach for sentiment analysis, since they can utilize the sequential nature of the text, a piece of information that machine learning methods cannot properly utilize. However, little attention has been paid to formulating and applying sophisticated HMM-based methods and advanced training approaches for accomplishing sentiment analysis tasks. In this article, we introduce novel, interpretable HMM-based methods for recognizing sentiments in text and we examine their performance under various architectures, training methods, orders and ensembles. The introduced models possess interpretability, they can indicate the sentimental parts of a sentence and illustrate the way that the overall sentiment evolves from the start to the end of it. A concrete experimental study is conducted and the results show that the introduced HMMs methods and the training approaches are quite competitive with machine learning methods and that they outperform traditional HMMs. Furthermore, the designed HMMs methods possess great interpretability and can be an efficient approach for sentiment analysis.
Real-Time Federated Evolutionary Neural Architecture Search

Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication resources, since a large number of model parameters must be transmitted between the server and clients. The other challenge is that training large machine learning models such as deep neural networks in federated learning requires a large amount of computational resources, which may be unrealistic for edge devices such as mobile phones. The problem becomes worse when deep neural architecture search (NAS) is to be carried out in federated learning. To address the above challenges, we propose an evolutionary approach to real-time federated NAS that not only optimizes the model performance but also reduces the local payload. During the search, a double-sampling technique is introduced, in which for each individual, only a randomly sampled submodel is transmitted to a number of randomly sampled clients for training. This way, we effectively reduce computational and communication costs required for evolutionary optimization, making the proposed framework well suitable for real-time federated NAS.
Artificial bee colony algorithm based on multiple neighborhood topologies

In recent years, as an effective global optimization technique, artificial bee colony (ABC) algorithm has attracted increasing attention for its good performance yet easy implementation. However, the solution search equation of ABC emphasizes exploration over exploitation, which greatly affects the convergence speed and accuracy of ABC. To solve this problem, many ABC variants have been developed to enhance exploitation by using the superior individuals. However, the concept of neighborhood topology has rarely been considered, which has significant effect on the dissemination of search information among individuals. Hence, in this work, we propose a new ABC variant based on multiple neighborhood topologies (named ABC-MNT). In ABC-MNT, three kinds of neighborhood topologies are used for different individuals to perform diverse abilities of disseminating search information, contributing to a better balance between exploration and exploitation. Considering the characteristics of different neighborhood topologies, three modified solution search equations are assigned to the three neighborhood topologies, respectively, to generate offspring. Furthermore, to preserve search experience of the scout bee phase, the global neighborhood search and opposition-based learning techniques are used. Extensive experiments are carried out on two widely used test suites, and eight well-established ABC variants and five other evolutionary algorithms are included in the performance comparison. The comparison results verify that ABC-MNT can obtain better or at least comparable performance on most of the benchmark functions.
A Decomposition Model for Stereo Matching

In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A refinement network is also applied to improving the fusion result. Compared with high-performance methods like PSMNet and GANet, our method achieves 10−100× speed increase while obtaining comparable disparity estimation results.
No-Reference Quality Assessment for Screen Content Images Using Visual Edge Model and AdaBoosting Neural Network

In this paper, a competitive no-reference metric is proposed to assess the perceptive quality of screen content images (SCIs), which uses the human visual edge model and AdaBoosting neural network. Inspired by the existing theory that the edge information which reflects the visual quality of SCI is effectively captured by the human visual difference of the Gaussian (DOG) model, we compute two types of multi-scale edge maps via the DOG operator firstly. Specifically, two types of edge maps contain contour and edge information respectively. Then after locally normalizing edge maps, L-moments distribution estimation is utilized to fit their DOG coefficients, and the fitted L-moments parameters can be regarded as edge features. Finally, to obtain the final perceptive quality score, we use an AdaBoosting back-propagation neural network (ABPNN) to map the quality-aware features to the perceptual quality score of SCIs. The reason why the ABPNN is regarded as the appropriate approach for the visual quality assessment of SCIs is that we abandon the regression network with a shallow structure, try a regression network with a deep architecture, and achieve a good generalization ability. The proposed method delivers highly competitive performance and shows high consistency with the human visual system (HVS) on the public SCI-oriented databases.
GLMNet: Graph learning-matching convolutional networks for feature matching

Recently, graph convolutional networks (GCNs) have been employed for graph matching problem. It can integrate graph node feature embedding, node-wise affinity learning and matching optimization together in a unified end-to-end model. However, first, the matching graphs feeding to existing graph matching networks are generally fixed and independent of graph matching task, which thus are not guaranteed to be optimal for the graph matching task. Second, existing methods generally employ smoothing-based graph convolution to generate graph node embeddings, in which extensive smoothing convolution operation may dilute the desired discriminatory information of graph nodes. To overcome these issues, we propose a novel Graph Learning-Matching Network (GLMNet) for graph matching problem. GLMNet has three main aspects. (1) It integrates graph learning into graph matching which thus adaptively learns a pair of optimal graphs for graph matching task. (2) It further employs a Laplacian sharpening graph convolution to generate more discriminative node embeddings for graph matching. (3) A new constraint regularized loss is designed for GLMNet training which can encode the desired one-to-one matching constraints in matching optimization. Experiments demonstrate the effectiveness of GLMNet.
An Anchor-Free Siamese Target Tracking Network for Hyperspectral Video

Hyperspectal target tracking is aimed at taking advantage of the spectral and spatial information in the target tracking. However, due to the limited training samples, the existing hyperspectral target trackers cannot exploit semantic information of hyperspectral image. In this paper, in order to solve this problem, we propose an anchor-free Siamese network for hyperspectral video target tracking (HA-Net). A spectral classification branch is introduced to the anchor-free Siamese network to increase the network's ability to identify objects. This branch exploits all the bands of the hyperspectral video for end-to-end training, to obtain more discriminative features. By fusing the classification response map of the spectral classification branch with the classification response map of the anchor-free Siamese network, the ability of the network to distinguish foreground and background can be enhanced. At the same time, the anchor-free tracking network can reduce the calculation time of the network. The experiments conducted on hyperspectral video showed that HA-Net can effectively exploit the spectral features and significantly improve the performance of the tracking network.
Improved YOLOv4 for Aerial Object Detection

Drones equipped with cameras are being used for surveillance purposes. These surveillance systems need vision-based object detection of ground objects which look very small because of the altitude of drones. We propose an improved YOLOv4 model targeted for vision-based small object detection. We investigated the performance of state of the art YOLOv4 object detector on the VisDrone dataset. We enhanced the features of small objects by connecting Upsampling layers and concatenating the upsampled features with the original features to obtain more refined and grained features for small objects. Experiments showed that the modified YOLOv4 achieved 2 percent better mAP results as compared to the original YOLOv4 at different image resolutions on the VisDrone dataset while running at the same speed as the original YOLOv4.
An Improved Dyna-<i>Q</i> Algorithm for Mobile Robot Path Planning in Unknown Dynamic Environment

This article deals with the problem of mobile robot path planning in an unknown environment that contains both static and dynamic obstacles, utilizing a reinforcement learning approach. We propose an improved Dyna- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> algorithm, which incorporates heuristic search strategies, simulated annealing mechanism, and reactive navigation principle into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning based on the Dyna architecture. A novel action-selection strategy combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -greedy policy with the cooling schedule control is presented, which, together with the heuristic reward function and heuristic actions, can tackle the exploration-exploitation dilemma and enhance the performance of global searching, convergence property, and learning efficiency for path planning. The proposed method is superior to the classical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning and Dyna- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> algorithms in an unknown static environment, and it is successfully applied to an uncertain environment with multiple dynamic obstacles in simulations. Further, practical experiments are conducted by integrating MATLAB and robot operating system (ROS) on a physical robot platform, and the mobile robot manages to find a collision-free path, thus fulfilling autonomous navigation tasks in the real world.
LED<sup>2</sup>-Net: Monocular 360° Layout Estimation via Differentiable Depth Rendering

Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360° layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. Our method achieves state-of-the-art performance on numerous 360° layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.
Faculty experiences on emergency remote teaching during COVID-19: a multicentre qualitative analysis

Purpose During COVID 19 pandemic emergency remote teaching (ERT) in higher education emerged and faculty members had to go through a transformation in teaching-learning without preparedness. The purpose of the study is to understand the instructional delivery experiences of faculty members, explore the challenges and how they overcame these challenges during the transition from traditional classroom teaching to ERT. Design/methodology/approach A qualitative research approach using phenomenology is adapted for the study. The study is conducted in selected renowned government and private universities offering professional education in India, Malaysia, Oman and the United Arab Emirates. Data analysis is using NVivo, data management software, based on Ricouer’s theory of interpretation. Findings The findings identify unique challenges and opportunities in faculty experiences during the implementation of ERT and universities require more preparedness in implementing a revised pedagogy. Addressing these unique challenges is, therefore, essential in effective change management and ensuring the effectiveness of instructional delivery. Research limitations/implications The study comprises faculty experiences from only selected countries (the United Arab Emirates, Oman, India and Malaysia) and disciplines such as business studies, design and architecture, engineering, hospitality and tourism management, medicine and nursing. The research contributes towards change management and adaptability strategies during emergency transitions. Practical implications The study has implications in the field of education, administration, research and society at large. This is an era of change that has witnessed tremendous possibilities of digital technology in enhancing remote teaching and learning at all levels of education worldwide. The study enumerates the factors influencing the paradigm shift in the pedagogy for present and future higher education. The present study also highlights how challenging this transformation was to the lives of professional academics and emphasized how effectively the faculty need to be mentored for the future by the administration. Future research can envisage effective tools and techniques for strengthening professional education at universities. The social context and human experiences in ERT and their impact on the process of learning are also addressed in the study. Social implications The study aims to understand the social context and human experiences in the process of ERT and their impact on the process of learning. Originality/value The findings of the study would throw light into the factors influencing the paradigm shift in the pedagogy for present and future higher education.