Reviewerly/exHarmony huggingface dataset:
CapsGaNet: Deep Neural Network Based on Capsule and GRU for Human Activity Recognition

The advances in deep learning with the ability to automatically extract advanced features have achieved a bright prospect for human activity recognition (HAR). However, the traditional HAR methods still have the deficiencies of incomplete feature extraction, which may lead to incorrect recognition results. To resolve the above problem, a novel framework for spatiotemporal multi-feature extraction on HAR called CapsGaNet is propounded, which is based on capsule and gated recurrent units (GRU) with attention mechanisms. The proposed framework involves a spatial feature extraction layer consisting of capsule blocks, a temporal feature extraction layer consisting of GRU with attention mechanisms, and an output layer. At the same time, considering the actual demands for recognizing aggressive activities in some specific scenarios like smart prison, we constructed a daily and aggressive activity dataset (DAAD). Moreover, based on the acceleration characteristics of aggressive activity, a threshold-based approach for aggressive activity detection is propounded to meet the needs of high real-time and low computational complexity in prison scenarios. The experiments are performed on the wireless sensor data mining (WISDM) dataset and the DAAD dataset, and the results verify that the propounded CapsGaNet could effectually improve the recognition accuracy. The proposed threshold-based approach for aggressive activity detection provides a more effective HAR way by using smart sensor devices in smart prison scenarios.
Formation control with lane preference for connected and automated vehicles in multi-lane scenarios

Multi-lane roads are typical scenarios in traffic systems. Vehicles usually have preference on lanes according to their routes and destinations. Few of the existing studies looks into the problem of controlling vehicles to drive on their desired lanes. This paper proposes a formation control method that considers vehicles’ preference on different lanes. A bi-level formation control framework is utilized to plan collision-free motion for vehicles, where relative target assignment and path planning are performed in the upper level, and trajectory planning and tracking are performed in the lower level. The collision-free multi-vehicle path planning problem considering lane preference is decoupled into two sub problems: calculating assignment list with non-decreasing cost and planning collision-free paths according to given assignment result. The Conflict-based Searching (CBS) method is utilized to plan collision-free paths for vehicles based on given assignment results. Case study is conducted and simulations are carried out in a three-lane road scenario. The results indicate that the proposed formation control method significantly reduces congestion and improves traffic efficiency at high traffic volumes, compared to the rule-based method.
Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images

Multi-scale approaches have been widely studied in pathology image analysis. These offer an ability to characterize tissues in an image at various scales, in which the tissues may appear differently. Many of such methods have focused on extracting multi-scale hand-crafted features and applied them to various tasks in pathology image analysis. Even, several deep learning methods explicitly adopt the multi-scale approaches. However, most of these methods simply merge the multi-scale features together or adopt the coarse-to-fine/fine-to-coarse strategy, which uses the features one at a time in a sequential manner. Utilizing the multi-scale features in a cooperative and discriminative fashion, the learning capabilities could be further improved. Herein, we propose a multi-scale approach that can identify and leverage the patterns of the multiple scales within a deep neural network and provide the superior capability of cancer classification. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, which can be easily embedded in the current framework of the deep neural networks. To evaluate the proposed method, multiple sets of pathology images are employed. Under the various experimental settings, the proposed method is systematically assessed and shows an improved classification performance in comparison to other competing methods.
Structural target-aware model for thermal infrared tracking

Thermal InfraRed (TIR) target trackers are easy to be interfered by similar objects, while susceptible to the influence of the target occlusion. To solve these problems, we propose a structural target-aware model (STAMT) for the thermal infrared target tracking tasks. Specifically, the proposed STAMT tracker can learn a target-aware model, which can add more attention to the target area to accurately identify the target from similar objects. In addition, considering the situation that the target is partially occluded in the tracking process, a structural weight model is proposed to locate the target through the unoccluded reliable target part. Ablation studies show the effectiveness of each component in the proposed tracker. Without bells and whistles, the experimental results demonstrate that our STAMT tracker performs favorably against state-of-the-art trackers on PTB-TIR and LSOTB-TIR datasets.
MTRBNet: Multi-Branch Topology Residual Block-Based Network for Low-Light Enhancement

The learning-based low-light image enhancement methods have remarkable performance due to the robust feature learning and mapping capabilities. This paper proposes a multi-branch topology residual block (MTRB)-based network (MTRBNet), which can alleviate training difficulties and more efficiently use the parameters between neurons. Compared with the previous residual block, the proposed MTRB increases the width of the network and simultaneously transmits information along with the depth and width directions, which can effectively select network nodes to promote the network learning capacity. Meanwhile, the feature information of neighbor nodes is transferred to each other, thereby maximizing the information flow of the convolution unit. The proposed information connection and feedback mechanism can improve the network’s ability to capture the global and local features. We analyze the pros and cons of two multi-feature fusion strategies (i.e., addition and concatenation) and three normalization methods on the quantitative results. In addition, we embed our MTRB into traditional Encoder-Decoder structure to improve the image enhancement results under different low-light imaging conditions. Experiments on the LOL image dataset have demonstrated that our MTRBNet achieves superior performance compared with several state-of-the-art methods.
Design and Evaluation of an Augmented Reality Head-mounted Display Interface for Human Robot Teams Collaborating in Physically Shared Manufacturing Tasks

We provide an experimental evaluation of a wearable augmented reality (AR) system we have developed for human-robot teams working on tasks requiring collaboration in shared physical workspace. Recent advances in AR technology have facilitated the development of more intuitive user interfaces for many human-robot interaction applications. While it has been anticipated that AR can provide a more intuitive interface to robot assistants helping human workers in various manufacturing scenarios, existing studies in robotics have been largely limited to teleoperation and programming. Industry 5.0 envisions cooperation between human and robot working in teams. Indeed, there exist many industrial tasks that can benefit from human-robot collaboration. A prime example is high-value composite manufacturing. Working with our industry partner towards this example application, we evaluated our AR interface design for shared physical workspace collaboration in human-robot teams. We conducted a multi-dimensional analysis of our interface using established metrics. Results from our user study (n = 26) show that, subjectively, the AR interface feels more novel and a standard joystick interface feels more dependable to users. However, the AR interface was found to reduce physical demand and task completion time, while increasing robot utilization. Furthermore, user’s freedom of choice to collaborate with the robot may also affect the perceived usability of the system.
Blockchain application in P2P energy markets: social and legal aspects

Flexible and distributed energy markets are a reality that is progressively reaching many regions. Despite their clear benefits, they should be accepted by the prosumers. Additionally, blockchain technology and smart contracts have been characterised as a technological enabler for the energy sector and P2P Energy Markets (PEM). However, little research has been done to explore blockchain's user-centred perspective. Therefore, this paper analyses the reluctance and/or concerns of prosumers regarding smart contracts, and investigates their perception on blockchain within PEMs. The authors present the results of a survey conducted across several European countries addressing the implementation of automated trading systems and analysing the adoption of smart contracts. Considering that the main survey outcomes are related to the regulation and legislation uncertainty around blockchain usage, this paper explores also the fit of smart contracts from a legal perspective. Additionally, a set of recommendations to be used as the basis for the design and development of PEMs is delivered, aiming to adopt blockchain and smart contracts. As a key take-away, the authors confirm the crucial role that blockchain will play in the deployment of fair, secure, flexible and distributed energy markets by ensuring transparency in the exchange of information between prosumers and energy stakeholders.
Automated Parts-Based Model for Recognizing Human–Object Interactions from Aerial Imagery with Fully Convolutional Network

Advanced aerial images have led to the development of improved human–object interaction recognition (HOI) methods for usage in surveillance, security, and public monitoring systems. Despite the ever-increasing rate of research being conducted in the field of HOI, the existing challenges of occlusion, scale variation, fast motion, and illumination variation continue to attract more researchers. In particular, accurate identification of human body parts, the involved objects, and robust features is the key to effective HOI recognition systems. However, identifying different human body parts and extracting their features is a tedious and rather ineffective task. Based on the assumption that only a few body parts are usually involved in a particular interaction, this article proposes a novel parts-based model for recognizing complex human–object interactions in videos and images captured using ground and aerial cameras. Gamma correction and non-local means denoising techniques have been used for pre-processing the video frames and Felzenszwalb’s algorithm has been utilized for image segmentation. After segmentation, twelve human body parts have been detected and five of them have been shortlisted based on their involvement in the interactions. Four kinds of features have been extracted and concatenated into a large feature vector, which has been optimized using the t-distributed stochastic neighbor embedding (t-SNE) technique. Finally, the interactions have been classified using a fully convolutional network (FCN). The proposed system has been validated on the ground and aerial videos of the VIRAT Video, YouTube Aerial, and SYSU 3D HOI datasets, achieving average accuracies of 82.55%, 86.63%, and 91.68% on these datasets, respectively.
You shall not (by)pass!

Memory Protection Keys for Userspace (PKU) is a recent hardware feature that allows programs to assign virtual memory pages to protection domains, and to change domain access permissions using inexpensive, unprivileged instructions. Several in-process memory isolation approaches leverage this feature to prevent untrusted code from accessing sensitive program state and data. Typically, PKU-based isolation schemes need to be used in conjunction with mitigations such as CFI because untrusted code, when compromised, can otherwise bypass the PKU access permissions using unprivileged instructions or operating system APIs.
An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system

Solar energy's probabilistic and changeable nature raises serious challenges about ensuring dependable, cheap, and secure control of power energy networks through the usage of solar energy globally for a green and efficient society. The most recent advancements in renewable energy have provided new insights on how to overcome the limits of various power sources. It is critical to correctly estimate statistics in order to ensure that energy PV systems be used to their full potential. The ability to predict changes in sun irradiation with greater accuracy could help to improve service quality. This combination of solar electricity and precision forecasting can help with distribution and planning. Emerging technologies such as artificial intelligence and machine learning, which are designed primarily to cope with difficulties related to renewable source intermittency and ambiguity, represent a possibility to address these issues. This paper analyses the integration of artificial intelligence in many sectors of renewable power systems, such as the forecasting of the realistic model employed by AI.To forecast solar energy via Artificial Neural Networks (ANN), SVM, and Random Forest we offer three distinct types of solar prediction algorithms (RF). The results showed that our approach obtained MAE = 0.9558 (Training phase), 1.7853 (Testing phase) and MFE = 0.4456 (Training phase), 0.5621 (Testing phase) outstanding performance for artificial neural network algorithm compared to SVM and RF algorithm.
FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data

Federated Learning (FL) can combine multiple clients for training and keep client data local, which is a good way to protect data privacy. There are many excellent FL algorithms. However, most of these can only process data with regular structures, such as images and videos. They cannot process non-Euclidean spatial data, that is, irregular data. To address this problem, we propose a Federated Learning-Based Graph Convolutional Network (FedGCN). First, we propose a Graph Convolutional Network (GCN) as a local model of FL. Based on the classical graph convolutional neural network, TopK pooling layers and full connection layers are added to this model to improve the feature extraction ability. Furthermore, to prevent pooling layers from losing information, cross-layer fusion is used in the GCN, giving FL an excellent ability to process non-Euclidean spatial data. Second, in this paper, a federated aggregation algorithm based on an online adjustable attention mechanism is proposed. The trainable parameter ρ is introduced into the attention mechanism. The aggregation method assigns the corresponding attention coefficient to each local model, which reduces the damage caused by the inefficient local model parameters to the global model and improves the fault tolerance and accuracy of the FL algorithm. Finally, we conduct experiments on six non-Euclidean spatial datasets to verify that the proposed algorithm not only has good accuracy but also has a certain degree of generality. The proposed algorithm can also perform well in different graph neural networks.
Implementation of a Wild Animal Intrusion Detection Model Based on Internet of Things

Wild animal invasion is a huge issue that results in the loss of vast amounts of resources and puts human lives in jeopardy. As a result, people lose their crops, possessions, and perhaps even their lives. To address this problem, this study offers a system that uses the Internet of Things to aid in the detection of wild animal intrusions on agricultural farms by monitoring the field. Ultrasonic sensors are adopted at the field’s corners, first detecting the intrusion, then capturing the image of the invader with a camera mounted on the E-vehicle embedded with a Node MCU Microcontroller that is monitoring the field. An alert message is sent to the farmer via IoT application. The performance of the proposed system has been analyzed with respective to the captured images of the intruder and notification alert. Using this proposed model, anyone can detect any type of intrusion around the field effectively.
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

As the popularity of Location-based Social Networks increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this article, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this article are as follows: (i) providing an extensive survey of context-aware location recommendation; (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, which can incorporate all the major contextual information into a single recommendation model; and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.
MantaRayWmark: An image adaptive multiple embedding strength optimization based watermarking using Manta Ray Foraging and bi-directional ELM

Digital watermarking schemes based on a single value of embedding strength do not take into account the local characteristics of the host signal for watermark embedding. Consequently, striking a balance between imperceptibility and the robustness of the watermarking scheme becomes a challenging task. The use of multiple values of the embedding strength provides a solution to this problem. Multiple embedding strength-based watermarking schemes often deploy meta-heuristic techniques to look for locally optimal values of the embedding strength. However, meta-heuristic techniques being inherently slow, such watermarking schemes cannot be applied in real-time applications. This paper proposes MantaRayWmark—a novel image adaptive watermarking scheme that uses Manta Ray Foraging Optimization (MRFO) to optimize the locally relevant multiple embedding strengths (MES) for balancing the imperceptibility and robustness. However, the time-intensive MRFO-based optimization is carried out only once to train a bi-directional ELM (B-ELM) for a set of images. Each training image is subjected to 4-level DTCWT to produce approximation coefficients. For B-ELM training, the approximation coefficients act as the input features and the corresponding MES values (obtained by way of MRFO using imperceptibility and robustness as the optimization criteria) act as the target outputs. Subsequently, given a new image, the trained B-ELM is used to predict the optimized multiple embedding strengths. For watermark embedding, an image is transformed using 4-level DTCWT and the resulting approximation sub-band matrix is decomposed using SVD to get the principal component (PC) matrix. To avoid the false-positive problem, a binary watermark is inserted into the PC matrix using the MES. To ensure the robustness of the proposed scheme against geometric attacks, we have deployed a novel convolution neural network (ConvNet) based geometric correction procedure. Further, image authentication is achieved by applying SVD-based hashing on the watermarked image. To ensure the security of watermark, the watermark is scrambled using transposition cipher. Experimental results reveal that MantaRayWmark achieves a high degree of watermark recoverability from the signed image in the presence of a number of common geometric and image processing attacks, thus proving its robustness. The results also demonstrate the superiority of the proposed MantaRayWmark when compared to the existing state-of-the-art schemes in terms of robustness and imperceptibility. The proposed scheme being time-efficient may find use its in various multimedia applications.
Unsupervised feature selection via adaptive graph and dependency score

Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. The representation methods include adaptive-graph-based methods and self-representation-based methods. The former methods have a longstanding and undiscovered problem about imbalanced neighbors, and the latter ones do not perform well when features are not linearly dependent. To deal with these problems, a novel unsupervised feature selection method is proposed to ensure k connectivity and eliminate more redundant features based on adaptive graph and dependency score (AGDS). Extensive experiments conducted on 13 benchmark datasets show the effectiveness of AGDS.
A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism

This paper designed a multipopulation cooperative coevolutionary framework with a two-stage orthogonal learning (OL) mechanism for the whale optimization algorithm (MCCWOA) to improve the performance of the whale optimization algorithm (WOA). In the framework, a prediction model of the neighborhood structure is established by discovering the guidance information of the following iteration process in the objective space at the first-stage OL. In the second-stage OL, an auxiliary vector pool with various features in the decision space is introduced to guide the candidates falling in the stagnant status to conduct more valuable exploration. According to the domain knowledge of the candidates, the population is divided into the elite population, the intermediate population, and the inferior population. The information of the subpopulations has interacted with the corresponding historical populations in the evolution processes to enhance the ability of cooperative coevolution among individuals. A standard set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed algorithm. The results of the statistical analysis, diversity analysis, and convergence analysis testified that the MCCWOA outperforms the 15 state-of-the-art algorithms regarding efficiency and significance.