Reviewerly/exHarmony huggingface dataset:
Cross dataset non-binary fall detection using a ConvLSTM-attention network

Fall detection is an important consideration to ambient assisted living research. Falls can result in severe injury and death in the case of the elderly and therefore fall detection systems are pivotal towards the provision of timely care to people experiencing a fall. This paper presents a ConvLSTM (Convolutional Long Short Term Memory)-attention network for the detection of falls considering fall direction and severity. Using data from two publicly available datasets, experiments have been conducted in a non-binary fall versus activity of daily living (ADL) scenario and also a combined ADL recognition and non-binary fall detection scenario. The results for both datasets demonstrate the effectiveness of the proposed method in terms of achieving an average recall of more than 90% in all cases. This work will serve to contribute to the growing utilization of attention mechanisms for time series based classification problems.
Communication-Efficient Federated Bayesian Learning via Client Selection

Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server. Since communication resources are limited, selecting the clients with most informative local learning updates can improve the model convergence and communication efficiency. In this paper, we propose a client selection scheme for DSVGD based on Hilbert Inner Product (HIP). We derive the upper bound on the decrease of the global free energy per iteration, which is then minimized to speed up the model convergence. We evaluate and compare our scheme with conventional schemes in terms of model accuracy, convergence speed, and stability using various learning tasks and datasets.
Secure and Energy-Efficient Proximity-Based Pairing for IoT Devices

Internet of Things (IoT) devices are largely resource-constrained embedded devices with limited user interface and battery capacity. As a consequence, bootstrapping a secure connection between an IoT device and a wireless network (e.g., WiFi network) becomes a challenging problem since the traditional Pre-Shared Key (PSK) based authentication cannot be directly applied. Proximity-based device authentication is a promising mechanism to enable secure pairing of an IoT device to a wireless network. However, existing solutions do not deliberately consider energy-efficiency nor the tradeoff between energy consumption and security strength in the pairing process. This paper fills this gap by enhancing the energy-efficiency and studying the tradeoff between energy consumption and security strength of an existing proximity-based IoT device authentication protocol, called Move2Auth. An optimization problem is formulated to minimize the energy consumption incurred by the pairing protocol while satisfying the desired security performance. Experimental results based on Raspberry Pi devices show the energy-efficiency advantage of the proposed scheme over the existing one.
On Potentials of Few-Shot Learning for AI-Enabled Internet of Medical Things

With the world heading towards big data, insurmountable amounts of data are being generated from Internet of Things devices around the world. Within the healthcare paradigm, Internet of Medical Things has been receiving increased attention as a key enabler technology for connected healthcare. Furthermore, there has been a growing interest in the potential of moving artificial intelligence to the EDGE. Within this context, this paper discusses the potentials and challenges of adopting few-shot learning on the EDGE within medical applications. Few-shot learning has been receiving renowned interest lately for its ability to tackle challenges related to learning from constrained resources. A systematic overview of the presence of few-shot learning in the medical domain is presented. Furthermore, we discuss some of the state-of-art promising research attempts of moving few-shot learning to the embedded computing paradigm.
Pedestrian Target Tracking Algorithm on Fusion Detection

Aiming at the problems of serious occlusion, deformation and rapid scale change in pedestrian tracking of mobile robot with vision, a pedestrian tracking algorithm with detection is proposed based on the effective convolution operators handcraft(ECO-HC), which solves the problems of target loss and inaccurate positioning caused by occlusion and background interference in the tracking process. Occlusion standard and model update threshold are set according to the peak value of confidence response. Furtherly, the position and scale of the target are corrected by using YOLO detection algorithm. The algorithm is verified on the pedestrian subset of OTB100 dataset. Experimental results show that the improved algorithm is optimal compared with other algorithms, and the overall accuracy and success rate are 93.50% and 91.80% respectively.
Transactive Green Energy Exchange Market Blockchain-based

There has been a conflict between producers and consumers eliminated through using PV applications in residential and commercial properties. Blockchain technology provides prosumers, consumers, and utilities with a new, cost-effective, and more secure trading platform through automating direct energy transactions using consensus-based verification and cryptographic hashing. In Qatar’s Education City Community Housing, Agent-Based Modeling (ABM) was used to simulate the environment to develop a blockchain-based energy trading solution. A Geographical Information System was also included in the simulation. Day-day energy activities data were collected and analyzed to simulate realistic trading characteristics on a small trading market to help stakeholders to understand the volatility of a real energy trading market towards the development of a blockchain-based market. Readjusting parameters enable the assessment and customization of transactional characteristics performed in the local community housing market.
Dynamics and Numerics of Reciprocal-Kind Zhang Neural Network Solving Time-Dependent Linear Matrix-Vector Equation System

We propose a novel neural-dynamic approach called reciprocal-kind Zhang neural network (RKZNN) for solving time-dependent linear matrix-vector equation system (TD-LMVES). The model of the approach is depicted in an explicit dynamic equation, which can be simulated by numerical algorithms for ordinary derivative equations. Computer simulations are performed to verify the effectiveness of the proposed approach. The results illustrate that the numerical solutions computed via RKZNN converge to the theoretical ones, and the convergence rate can be adjusted by changing the value of convergence parameter in the dynamic equation.
Sampled-data control of fuzzy systems

This paper studies the stability and stabilization of fuzzy systems based on sampled-data control. By making fully use of sampling information, a Lyapunov-Krasovskii functional (LKF) is given. Combing with tighter inequalities and several processing techniques, some results are built to guarantee the fuzzy systems to be stable. Finally, the sampled-data controller is designed.
SS8: Source Data-free Domain Adaptation via Deep Clustering with Weighted Self-labelling

As a new topic of transfer learning, source data-free domain adaptation (SFDA) is currently receiving a lot of attention due to the increasing demands on the safety and privacy. Due to that the dependence on the labelled source data is cut off by mining auxiliary information to regulate the self-training, the self-supervised learning becomes a promising SFDA solution. Among these proposed self-supervisions, the pseudo-label is a widely used and fundamental means to provide the supervision signal of category. However, the existing methods do not have a special strategy to mitigate the noise in the pseudo-labels well. This paper propose a deep clustering with weighted self-labelling (DC-WSL) approach to address the SFDA problem. Specifically, we first develop a low-entropy k-means method to generate more robust and credible clustering centers. And then, the pseudo-labels are assigned to all target data based on the distance from these centers, along with adaptive confidence scores as the weighted parameters. After that, based on these pseudo-labels with credible evaluation, we perform a self-training on the target domain under the regulation of deep clustering. The experimental results on two domain adaptation benchmarks confirm the effectiveness of the proposed method.
SemanticAnchors: Sequential Fusion using Lidar Point Cloud and Anchors with Semantic Annotations for 3D Object Detection

3D object detection is an important task in autonomous driving scenario, which is the basis of perception and understanding of 3D scenes. LiDAR and camera are two commonly used sensors in 3D object detection tasks. However, using a single sensor to collect data will make some objects difficult to detect because both sensors have insurmountable shortcomings. Unexpectedly, LiDAR-only detection methods tend to show better performance than the multi-sensor methods in public benchmarks. This shows that people need to further explore the methods of combining the data of the two sensors. Recently, PointPainting has been presented to combine the data of LiDAR and camera more efficiently by attaching the result of image semantic segmentation to point cloud data as new channels. In this paper, we propose an error anchor punishment mechanism based on image semantic segmentation results. After the semantic augmentation of the point cloud data, we judge whether the semantic result of each point is correct by traversing the groundtruth boxes. Further, we assign different weights to each anchor according to the error points contained in each anchor. Experimental results on the KITTI valid set show that SemanticAnchors achieves better performance in both 3D and birds eyes view benchmarks. In particular, our method adds little extra computation and achieves performance improvement in all categories.
Object Detection Based On Multi-Process

Object detection technology has been widely used. Traditionally, it usually uses a single process. However, a single process model can no longer meet the needs. With the complexity of object detection application scenarios, the entire system is divided into sub-modules, and data transmission between sub-modules is efficient. The multi-process architecture is adopted, which increases the cohesion of software modules and reduces coupling. Moreover, we need to adopt an efficient process communication and synchronization method, process communication to solve data transmission, and process synchronization to solve the problem of read and write conflicts. This article has made an innovation that introduces the concept of the shared memory frame buffer that uses multiple processes for object detection. It uses shared memory technology and process synchronization technology to complete inter-process communication and synchronization. Different processes use shared memory for efficient data transmission and use mutual exclusion locks to complete inter-process synchronization and control message communication. The server process is responsible for loading images and some general image processing algorithms. The client process only needs to implement a specific object detection algorithm, mount the shared memory and perform object detection on the image, and then send the detection results to the server. On the one hand, this solution reduces the time complexity. It only needs the main process to load the image into the shared memory, and other processes jointly use it to improve execution efficiency. If it is a very large image such as a GB level, the effect is more significant. On the other hand, sub-modules perform different functions respectively, which reduces code coupling and improves cohesion.
Online Sparse Streaming Feature Selection with Uncertainty

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncer-tain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FSU) algorithm. OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before conducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experi-ments, OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FSU outperforms its competitors when missing data are en-countered in OSFS.
Oceanic Object Detection Based on An Improved SSD Algorithm

In this paper, an optical image object detection method, which combines Single Shot MultiBox Detector (SSD) and attention mechanism, is proposed. Firstly, a typical oceanic object dataset, which contains more than 17,000 images of ships, islands, rail stockades and underwater cables, is constructed. Then, we analyze the advantages of SSD network in multi-scale prediction ability and the reasons for its poor prediction effect on small objects. A detection method, which introduces an attention mechanism after output feature maps of SSD, is proposed. To be specific, this scheme adds CBAM after six output feature maps of SSD. Finally, on the basis of real-time detection, comparative experiments are conducted on oceanic object dataset to select the scheme that can significantly improve the detection accuracy. The results show that the mean average precision (mAP) value under the proposed scheme is 81.90%, and the detection speed is 33.1 frames per second (FPS). Compared to the original SSD, we achieve 3.69% relative improvement on the object detection.
UAV Recognition and Tracking Method Based on YOLOv5

In recent years, civilian Unmanned Aerial Vehicles(UAV) have been widely used in various fields, which bringing convenience to people while also having many negative effects. Illegal flying UAVs have affected people's daily life, and there is an urgent need to establish an efficient UAV countermeasure mechanism for critical places such as government, hospitals, universities, and research institutes. To this end, this paper proposes a UAV recognition and tracking method for complex environments, firstly pre-identify UAV targets by YOLOv5, then combine the motion trajectory of targets in continuous images to discard the error outputs, finally use Kernel Correlation Filter to improve the continuous tracking capability. Experiments show that the algorithm in this paper can effectively improve 9.13% precision and the 11.54% recall compared with YOLOv5s network, and the recognition speed can reach to 27.64fps. Tested on the UAV interception platform developed by our team, the experimental results show that the platform deployed with our algorithm can continuously track the targets and intercept the intruding UAVs efficiently.
A Multi-modal Virtual-Real Fusion System for Multi-task Human-Computer Interaction

Due to the complexity of the task and the diversification of the scene, the traditional interactive control method can no longer meet the requirements of users. To solve this problem, a multi-modal virtual-real fusion system for multi-task human-computer interaction is proposed in this paper, which integrates eye movement, gesture and voice. Frist, aiming at the phenomenon of multi-task and multi-modal, a task-modal matching model is established. Then, the task-modal matching model is abstracted into a multi-objective optimization problem, and a method for solving this problem is designed and a matching scheme is successfully obtained. Meanwhile, the construction of the system is completed for the virtual-real fusion environment, and the control of unmanned car and virtual car is realized. The system can carry out multi-modal interaction and complete multiple tasks in real scene, virtual scene, parallel system and virtual-real fusion scene. Finally, the experiment proves the stability and reliability of the system.
EGSA: Enhanced and Global Semantic Activation for Weakly Supervised Object Localization

Weakly supervised object localization(WSOL) is a task that only uses image-level supervision information to locate objects. Traditional CNN-based methods always locate the most discriminative regions of objects and cannot well balance the accuracy of classification and localization. To solve this problem, we propose an enhanced and global semantic activation(EGSA) method based on the vision transformer model. We first use an attention reassign module to get a comprehensive attention map that contains the correlation between each image patch and the global dependency of the class token. Then a mask selection module that generates a mask map by comparing with mask threshold is proposed to obtain the token feature map of the non-discriminative object region. By coupling the above two maps and combining it with a semantic aware map contains the information of class token, the final localization map with enhanced and global semantic activation can be built. And experiments on two common benchmark datasets CUB-200-2011 and ILSVRC demonstrate the efficiency of our method.
Hybrid Enhanced Echo State Network for Nonlinear Prediction of Multivariate Chaotic Time Series

As a kind of special nonlinear phenomenon, chaos has obtained much attention due to its interesting characteristics, such as randomness, sensibility, and complexity. How to predict chaos effectively and accurately is a significant issue in the nonlinear area. In this paper, a hybrid enhanced echo state network (HEESN) is proposed for the nonlinear prediction of multivariate chaotic time series. The HEESN scheme is contributed by three interactional aspects: output weight regularization, initial parameter optimization, and chaotic signal reconstruction. First, to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient, and sparsity degree) are learned by a linear-weighted particle swarm optimization (LW-PSO) to further improve prediction accuracy and reliability. Third, recommendations of key settings in the signal reconstruction stage (i.e., embedding dimension and time delay) are studied and given according to the temporal complexity and signal-to-noise ratio of the predicted time series. Extensive experiments about computational complexity and three evaluating metrics are carried out on one chaotic benchmark. The analyzed results indicate that the proposed HEESN performs promisingly on multivariate chaotic time series prediction.
Distributed-Particle-Swarm-Optimization-Incorporated Second-order Latent Factor Model

Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Building an LF model is a large-scale non-convex problem. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a distributed adaptive SLF (DASLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that DASLF model has a competitive advantage over state-of-the-art models in data representation ability.
Improving Multi-model Hybrid Chinese Long-text Classification through BERT Optimisation

Text classification is an almost unavoidable process in natural language processing and has a wide range of application scenarios in industry. Although many existing methods can achieve superior classification results, raising the effect of text classification not only poses a great challenge, but also provides a longitudinal study of technological improvement. Based on the pre-trained bidirectional encoder representations from transformer (BERT) model and in-depth research on deep learning, we propose a multi-model, mixed-Chinese classification model (MCCM) based on BERT (MCCM-BERT) to process Chinese text-classification tasks. The experimental results show that the proposed MCCM BERT model outperforms BERT in text classification tasks, especially in Chinese long text classification, with an accuracy improvement of up to 2.28%.
An On-Chain Smart Contract Protocol for Tangle

Chain-structured blockchains (e.g., Bitcoin and Ethereum) are often criticized for resource waste, low scalability, and high transaction fees. Tangle has been proposed to overcome these drawbacks by adopting a directed acyclic graph structure, new consensus mechanisms, etc. Particularly, Tangle defines a transaction-processing rule, which requires that new incoming transactions should approve several existing transactions before being attached to Tangle, to exclude miners and transaction fees. However, this rule makes it difficult to support smart contract (SC) in Tangle, an essential component of numerous decentralized applications, because the execution and verification of SC usually require transaction fees as incentive awards. In this work, we propose an “on-Tangle” SC protocol called equivalent-exchange-based smart contract (EESC), which runs on the Tangle core, to address this challenge. EESC extends the transaction-processing rule to SC and hence maintains Tangle's advantages of no fees and no mining. In EESC, a user should verify other users' SCs before submitting its SC. The workload of verifying these existing SCs is greater than that of verifying the newly submitted one. Extensive simulations verify that EESC is fast and efficient and can well achieve our goal.
Multi-scale feature fusion filtering module in convolutional neural networks

Recent research has demonstrated that the multiscale representation of CNNs is considerably improved by the attention mechanism. However, the majority of current multiscale feature representation techniques only employ a small number of attention blocks in the attention mechanism, ignoring multiscale-level contextual data. This research suggests a multiscale fused feature filtering module as a solution to this issue (MFFFM). It enables branch-specific feature-selective learning of multiscale contextual data. These branches employ various sizes of null convolutions, and they further employ spatial injection correlation and channel correlation to provide channel feature responses that are adaptive. Tiny ImageNet, CIFAR-100, and MS COCO dataset experimental findings demonstrate that MFFFM provides extremely competitive outcomes in comparison to earlier baseline models.