Reviewerly/exHarmony huggingface dataset:
A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method

Unmanned Aerial Vehicle (UAV) swarms are attracting more and more research attention due to their low cost and high efficiency. Task allocation is a highly important process for a UAV swarm, currently suffering from several constraints such as large scale and real-time requirements, with a possible solution being quite challenging. Hence, this paper models the behavioral characteristics of a wolf pack in its natural environment and exploits it to solve a UAV swarm dynamic task allocation problem in complex scenes. Specifically, the proposed method considers the non-balanced task assignment problem and utilizes the task scheduling algorithm to make the assignment results meet the task performance constraints. Moreover, a path planning algorithm and a coverage search algorithm appropriate for UAV swarms in complex scenes are proposed. Simulation experiments involving different target numbers and random target positions demonstrate that the suggested method achieves high task completion and balances the UAVs’ load affording a suitable solution for complex scenes.
Digital Teaching Competence: A Systematic Review

This systematic literature review aimed to discover how the concept of digital teaching competence (DTC) has been developed, how its dimensions have been defined, and how educational development models and models that evaluate teachers’ digital teaching competence have been constructed. Concurrently, this review aimed to draw conclusions on the implementation processes of digital teaching competence in order to uncover its strengths and limitations, and to propose future lines of research to develop it further in initial teacher training programmes. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was proposed in order to achieve these aims. We chose the time frame of 2015 to the end of 2021 in an attempt to cover the appearance of the first scientific articles dealing with the subject, up until the present day. Thus, the inclusion criteria covered scientific research articles from the Web of Science (WoS) and Scopus databases, in English or Spanish, that focused on samples of teachers in primary education, secondary education, baccalaureates, and initial teacher training. The database searches, which will be detailed in depth later, initially provided a corpus of 127 articles, which was reduced to 26 articles after screening for duplicity and applying the inclusion criteria.
Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation

Characterization of complex subsurface structures is challenging due to the demand to preserve geological realism of the training images in earth and environmental sciences. In this work, we propose a novel method to reconstruct complex hydrological structures by using deep convolutional generative adversarial networks (DCGAN) in the Monte-Carlo simulation process, named MC-GAN. Network architectures for reconstructing both two-dimensional (2D) and three-dimensional (3D) complex spatial structures are provided in this method. We first exploit the robust DCGAN to reproduce abundant and various spatial pattern blocks. Then, we combine the various heterogeneous patterns to reconstruct a complex hydrological structure by using the Monte-Carlo stochastic simulation process. The method is able to represent multiple-scale spatial structures under the premise of using the same generative adversarial network architecture. It not only ensures the simulation efficiency, but also makes the heterogeneous patterns in the realizations more diverse. Three sets of training images were used to test the capability of the proposed method. The experiment results demonstrate that our method can accurately characterize complex heterogeneous spatial structures. At the same time, the trained deep learning model can be reused effectively to generate multiple-scale spatial structures.
The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems

Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy, fairness, and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our proposed approach is interpretable, allowing to explain to the user why she has been recommended specific POIs, based on the learned context weights from user past check-ins; for example, if you are in Rome and our method recommends you a historical place like ‘Colosseum’, it can also provide an explanation why this item is recommended to you based on your personal preference on context (e.g., you were recommended to visit ‘Colosseum’ because in the past your visited historical places). Third, by analyzing the fairness of recommendation with respect to users (based on their activity levels) and items (based on the popularity of items), we found that a model which is recommend fairly on one dataset can recommend unfair on another dataset.
Fitness Apps's purchase behaviour: Amalgamation of Stimulus-Organism-Behaviour-Consequence framework (S–O–B–C) and the innovation resistance theory (IRT)

With the rise in popularity and use of fitness apps, more research is needed to learn more about how people use the apps so that they keep using them. Stimulus-Organism-Behaviour-Consequence framework (S–O–B–C) and the Innovation Resistance Theory (IRT) together in this study to learn more about how people make decisions about how to use apps for fitness. Eight hundred and fifty-eight users of various fitness apps have responded to the questionnaire. Results show that people who are open to changing their fitness systems from traditional to app-based services enjoy new experiences and new product behaviours. For them, the perceived functional barriers to service adoption don't hold them back. At the same time the users of fitness apps try to get health and fitness information from as many sources as possible, like friends, peer groups, the newspaper and social networking apps. Users with a high level of health consciousness are more likely to be active in their health and fitness. People who use apps make decisions based on new information from the integrated model. App providers should think about how to get and keep users interested in their apps, personalise their experience, and design features that make users invested in the apps.
An Image Encryption Scheme Synchronizing Optimized Chaotic Systems Implemented on Raspberry Pis

Guaranteeing security in information exchange is a challenge in public networks, such as in the highly popular application layer Message Queue Telemetry Transport (MQTT) protocol. On the one hand, chaos generators have shown their usefulness in masking data that can be recovered while having the appropriate binary string. Privacy can then be accomplished by implementing synchronization techniques to connect the transmitter and receiver, among millions of users, to encrypt and decrypt data having the correct public key. On the other hand, chaotic binary sequences can be generated on Rapsberry Pis that can be connected over MQTT. To provide privacy and security, the transmitter and receiver (among millions of devices) can be synchronized to have the same chaotic public key to encrypt and decrypt data. In this manner, this paper shows the implementation of optimized chaos generators on Raspberry Pis that are wirelessly connected via MQTT for the IoT protocol. The publisher encrypts data that are public to millions of interconnected devices, but the data are decrypted by the subscribers having the correct chaotic binary sequence. The image encryption system is tested by performing NIST, TestU01, NPCR, UACI and other statistical analyses.
MF2-Net: A multipath feature fusion network for medical image segmentation

In this paper, we propose a multipath feature fusion convolutional neural network (MF2-Net) with novel and efficient spatial group convolution (SGC) modules with a multipath feature fusion network for the automated segmentation of medical images. The proposed MF2-Net was designed with multiple encoder paths to extract layer-specific multiscale information. Each encoder path employs SGC modules composed of stacked asymmetric kernels of different sizes (k×1 and 1×k). In the SGC modules, the context details of high-level features are encoded at varying scales, and neighbor feature information is incorporated with higher precision. In addition, the encoded features fused at the bottleneck layer capture abundant semantic features from input images. Furthermore, the guided block mechanism is used to refine the segmentation boundaries at the decoder stage by integrating the skip connection from the encoder stage. We verified that the SGC modules in a multipath feature fusion network improve the segmentation accuracy with fewer learnable parameters. Experimental results demonstrated that the proposed model outperformed existing medical image segmentation methods by an average score of 0.97 on publicly available datasets.
Deep Reinforcement Learning for Robot Collision Avoidance With Self-State-Attention and Sensor Fusion

3D LiDAR sensors can provide 3D point clouds of the environment, and are widely used in automobile navigation; while 2D LiDAR sensors can only provide point cloud in a 2D sweeping plane, and then are only used for navigating robots of small height, e.g., floor mopping robots. In this letter, we propose a simple yet effective deep reinforcement learning (DRL) method with our self-state-attention unit and give a solution that can use low-cost devices (i.e., a 2D LiDAR sensor and a monocular camera) to navigate a tall mobile robot of one meter height. The overrall pipeline is that we (1) infer the dense depth information of RGB images with the aid of the 2D LiDAR sensor data (i.e., point clouds in a plane with fixed height), (2) further filter the dense depth map into a 2D minimal depth data and fuse with 2D LiDAR data, and (3) make use of DRL module with our self-state-attention unit to a partially observable sequential decision making problem that can deal with partially accurate data. We present a novel DRL training scheme for robot navigation, proposing a concise and effective self-state-attention unit and proving that applying this unit can replace multi-stage training, achieve better results and generalization capability. Experiments on both simulated data and a real robot show that our method can perform efficient collision avoidance only using low-cost 2D LiDAR sensor and monocular camera.
Challenges and opportunities of blockchain energy applications: Interrelatedness among technological, economic, social, environmental, and institutional dimensions

The energy sector is undergoing a transition of decarbonization, decentralization, and digitalization. This paper explores the application of blockchain in this transition. Through qualitative content analysis, expert interviews and discussion rooted in recent literature, key insights on the practical challenges and opportunities of blockchain energy applications are investigated. This is done in technological, economic, social, environmental, and institutional dimensions in order to propose holistic insights. Key dentified issues include scalability, data privacy, costs of change, market dynamics, user experience, skills, multi-stakeholder governance, and regulatory change. Key identified opportunities also range across these dimensions, such as blockchain advancements, power market development, user research, and regulatory sandboxes. While most research on blockchain energy applications has been from the technological lens, this paper contributes with a multi-dimensional approach. The interrelatedness among these dimensions is one of the key messages of this paper. Both corporate strategy and public policy development need to integrate user behavior research, business model innovation and regulatory experimentation for a holistic approach in understanding and implementing blockchain, and other emerging technologies. It is suggested that the multi-dimensional challenges and opportunities identified in this study, and functioning of their interrelatedness, are of interest for further exploration both in academia and among practitioners.
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Answering SPJA queries under differential privacy (DP), including graph pattern counting under node-DP as an important special case, has received considerable attention in recent years. The dual challenge of foreign-key constraints and self-joins is particularly tricky to deal with, and no existing DP mechanisms can correctly handle both. For the special case of graph pattern counting under node-DP, the existing mechanisms are correct (i.e., satisfy DP), but they do not offer nontrivial utility guarantees or are very complicated and costly. In this paper, we propose the first DP mechanism for answering arbitrary SPJA queries in a database with foreign-key constraints. Meanwhile, it achieves a fairly strong notion of optimality, which can be considered as a small and natural relaxation of instance optimality. Finally, our mechanism is simple enough that it can be easily implemented on top of any RDBMS and an LP solver. Experimental results show that it offers order-of-magnitude improvements in terms of utility over existing techniques, even those specifically designed for graph pattern counting.
Cross-spectral coherence and dynamic connectedness among contactless digital payments and digital communities, enterprise collaboration, and virtual reality firms

This paper examines the dynamic connectedness and risk transmissions among firms in the Future Payments and Future Communication businesses. In doing so, the authors employ the daily data of key variables of interest from May 15, 2017 to April 16, 2021. The study employs two empirical methodologies namely, the quantiles coherency and the time-varying parameter-vector autoregressive (TVP-VAR) model. While the former helps to determine the interdependence between Future Payments and the components of the Future Communication firms at different market states and investment horizons, the latter suitably addresses the time-varying connectedness among the series. The results from the quantiles coherence approach reveal a heterogeneous pattern of interdependency between Future Payments and each of the Future Communication firms, as it varies across different market states and investment horizons. The net connectedness findings further mention that Future Payments are net transmitters of shocks to other firms in the system, indicating that the firms play a leading role in the general contactless digital technology industry. These findings have theoretical, managerial, and policy implications, especially in terms of the improved adoption of these new technologies to the firms' financial infrastructures and the maintenance of an optimal investment portfolio.
Impact analysis of keyword extraction using contextual word embedding

A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.
Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

Accurate damage degree prediction is important to ensure the safety, stability, and economic operation of the natural gas pipeline in service, especially those in long-term service. In order to maintain the efficiency and safety of natural gas pipeline transportation, the intelligent diagnosis and evaluation technology of pipeline is very important. In this paper, The opposition-based learning strategy and adaptive T-distribution mutation operator are introduced to optimize the sparrow search algorithm (SSA),improve the search ability,convergence speed and accuracy of SSA algorithm. 12 classical benchmark functions are used to evaluate the performance of the improved algorithm. Experimental results demonstrate the feasibility and validity of ISSA compared with the original Sparrow search algorithm. Based on its excellent performance, ISSA is used to optimize the input layer weight and bias parameters of deep extreme learning machine (DELM). Therefore, a comprehensive ISSA-DELM network model is constructed for intelligent quantitative evaluation of natural gas pipeline defects. The results show that the model can effectively overcome the problem that the DELM effect is affected by the random input weight and random bias of each ELM-AE, and improve the quantitative prediction performance. This will facilitate the assessment of the integrity and safety status of natural gas pipelines.
Secure Image Encryption Using Chaotic, Hybrid Chaotic and Block Cipher Approach

Secure image transmission is one of the most challenging problems in the age of communication technology. Millions of people use and transfer images for either personal or commercial purposes over the internet. One way of achieving secure image transmission over the network is encryption techniques that convert the original image into a non-understandable or scrambled form, called a cipher image, so that even if the attacker gets access to the cipher they would not be able to retrieve the original image. In this study, chaos-based image encryption and block cipher techniques are implemented and analyzed for image encryption. Arnold cat map in combination with a logistic map are used as native chaotic and hybrid chaotic approaches respectively whereas advanced encryption standard (AES) is used as a block cipher approach. The chaotic and AES methods are applied to encrypt images and are subjected to measures of different performance parameters such as peak signal to noise ratio (PSNR), number of pixels change rate (NPCR), unified average changing intensity (UACI), and histogram and computation time analysis to measure the strength of each algorithm. The results show that the hybrid chaotic map has better NPCR and UACI values which makes it more robust to differential attacks or chosen plain text attacks. The Arnold cat map is computationally efficient in comparison to the other two approaches. However, AES has a lower PSNR value (7.53 to 11.93) and has more variation between histograms of original and cipher images, thereby indicating that it is more resistant to statistical attacks than the other two approaches.
Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos

Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.