Reviewerly/exHarmony huggingface dataset:
Boosting UAV Tracking With Voxel-Based Trajectory-Aware Pre-Training

Siamese network-based object tracking has remarkably promoted the automatic capability for highly-maneuvered unmanned aerial vehicles (UAVs). However, the leading-edge tracking framework often depends on template matching, making it trapped when facing multiple views of object in consecutive frames. Moreover, the general image-level pretrained backbone can overfit to holistic representations, causing the misalignment to learn object-level properties in UAV tracking. To tackle these issues, this work presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRTrack</i> , a comprehensive framework to fully exploit the stereoscopic representation for UAV tracking. Specifically, a novel pre-training paradigm method is proposed. Through trajectory-aware reconstruction training (TRT), the capability of the backbone to extract stereoscopic structure feature is strengthened without any parameter increment. Accordingly, an innovative hierarchical self-attention Transformer is proposed to capture the local detail information and global structure knowledge. For optimizing the correlation map, we proposed a novel spatial correlation refinement (SCR) module, which promotes the capability of modeling the long-range spatial dependencies. Comprehensive experiments on three UAV challenging benchmarks demonstrate that the proposed TRTrack achieves superior UAV tracking performance in both precision and efficiency. Quantitative tests in real-world settings fully prove the effectiveness of our work.
FAEL: Fast Autonomous Exploration for Large-scale Environments With a Mobile Robot

Autonomous exploration in large-scale and complex environments is a challenging task. As the size of the environment increases, the significant overhead of exploration algorithms could overwhelm the computational capability of mobile platforms, prohibiting timely response to environmental changes. Meanwhile, the quality of exploration paths becomes increasingly important in larger scenes, as poorly selected paths greatly reduce efficiency. In this letter, a systematic framework is proposed to explore large-scale unknown environments. To enable high-frequency planning, a fast preprocessing of environmental information is presented, providing fundamental information to support high-frequency path planning. An path optimization formulation that comprehensively considers key factors about fast exploration is introduced. Further, an heuristic algorithm is devised to solve the NP-hard optimization problem, which empirically finds optimal solution in real time. Simulation results show the run time of our method is significantly shorter than existing ones. Our method completes exploration with the least time and shortest movement distance compared to current state-of-the-art methods.
A Prototype of Supply Chain Traceability using Solana as blockchain and IoT

The main objective of this article is to present a prototype that integrates blockchain and Internet-of-Things (IoT) devices to digitize information across a generic Supply Chain (SC). Blockchain bring many benefits to the operation of improving provenance and reliability, such as, digitizing the interaction among SC actors to allow implementation of smart contracts to support a secure and authenticated chain. In technical terms, blockchain is a decentralized database where information recording is secure and blockchain-based traceability in the SC can address shortcomings that exist in centralized solutions. Provenance could be achieved by implementing SC business logic within smart contracts using blockchains. This article presents an architecture of software that combines blockchain with IoT devices that allows traceability of a generic product from source to destination, through multi-echelon suppliers, logistics, manufacturers to the end-customer. The presented software architecture uses the Solana blockchain for the implementation of SC processes and business logic. This blockchain was selected from a review, reported in this article, of several widely used blockchain networks. It was selected mainly due to its speed and cost of transactions. Within the blockchain we store SC related data and events communicated over the internet and mobile application channels, in a Solana blockchain using Solana's native blockchain libraries. the IoT devices used are Sigfox cloud gateway, and Sensit that uses LPWAN (Low power wide area network) wireless telecommunication for data transfer. In our IoT device, the blockchain stores temperature, humidity, light, location, tilting, door opening, vibration, and magnetic field. The goal is to use existing technologies to develop a software architecture for a medium term objective of an implementable generic blockchain for SCs.
Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space

Efficient Virtual Machine (VM) placement and task scheduling is considered a major challenge in cloud computing, given that the scheduling results directly affect user satisfaction and vendor benefits. This paper investigates the VM and task joint scheduling (VTJS) problem, and establishes a multi-objective mathematical model with the aim to minimize makespan, cost, and total tardiness. To solve this problem, a problem-specific three-layer encoding approach is designed, and a decomposition-based multi-objective evolutionary algorithm with pre-selection and dynamic resource allocation (MOEA/D-PD) is proposed, in which a customized two-stage guided local search method is also embedded. In MOEA/D-PD, a classifier model is built to filter the offspring solutions in decision space so that only promising solutions are evaluated, and computational resources are dynamically allocated to the subproblems on the bases of their contributions. The proposed algorithm is validated on a series of instances of different scales and compared with six state-of-the-art MOEAs. Experimental results show that the proposed algorithm outperforms the most known approaches from the literature.
Integrity and Privacy-Aware, Patient-Centric Health Record Access Control Framework Using a Blockchain

Blockchains are gaining traction as secure and reliable platforms for data sharing in fields such as banking, supply chain management, food production, energy, the Internet, and medical services. Furthermore, when decentralized, a blockchain can be regarded as an immutable ledger storing data entries. Moreover, this modern technology was designed to disrupt various data-driven industries, including the healthcare industry. While electronic healthcare services have enabled more straightforward and accessible treatment, patient privacy has become vulnerable to external and internal attacks by healthcare personnel. Therefore, we aimed to design a framework to control patient health records that ensures the patient can provide the necessary permissions to those who access his/her health records. This framework will record all activities via blockchain and usage control. Through this framework, we aim to create a user-centric and privacy-aware experience. A literature review and experiments have been performed to select an optimized and placable blockchain operating system. In addition, performance analysis showed that the OS and smart contracts work at an acceptable speed.
A Study of Speech Recognition for Kazakh Based on Unsupervised Pre-Training

Building a good speech recognition system usually requires a lot of pairing data, which poses a big challenge for low-resource languages, such as Kazakh. In recent years, unsupervised pre-training has achieved good performance in low-resource speech recognition, but it is rarely used in Kazakh and other Central and West Asian languages. In this paper, wav2vec2.0 is improved by integrating a Factorized TDNN layer to better preserve the relationship between the voice and the time step before and after the quantization, which is called wav2vec-F. The unsupervised pre-training strategy was used to learn the potential speech representation from a large number of unlabeled audio data and was applied to the cross-language ASR task, which was optimized using the noise contrast binary classification task. At the same time, speech synthesis is used to promote the performance of speech recognition. The experiment shows that wav2vec-F can effectively utilize the unlabeled data from non-target languages, and the multi-language pre-training is obviously better than the single-language pre-training. The data enhancement method using speech synthesis can bring huge benefits. Compared with the baseline model, Librispeech's test-clean dataset has an average reduction of 1.9% in the word error rate. On the Kazakh KSC test set, the pre-training using only Kazakh reduced the word error rate by 3.8%. The pre-training of a small amount of Kazakh speech data synthesized by multi-language combined with TTS achieved a word error rate of 8.6% on the KSC test set when the labeled data were only 10 h, which was comparable to the results of the previous end-to-end model when the labeled data were 30 times less.
ModularFed: Leveraging modularity in federated learning frameworks

Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols covering three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework’s components’ design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients’ deficiency, data distribution, and network latency, which entails a fair comparison of techniques outlying FL technologies. In our evaluation, we examine the applicability of our framework addressing major FL domains, including statistical distribution and modular-based resource monitoring tools and client selection. Moreover, our comparison analysis indicates that our architecture has an inconsiderable impact on performance compared to other approaches.
A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keypoints achieved from the model are considered as predicted joint locations. True keypoints, as the ground truth body joint for individual yoga poses, are identified manually using the open source image annotation tool named Makesense AI. A detailed analysis of the body joint detection accuracy is proposed in the form of percentage of corrected keypoints (PCK) and percentage of detected joints (PDJ) for individual body parts and individual body joints, respectively. An algorithm is designed to measure PCK and PDJ in which the distance between the predicted joint location and true joint location is calculated. The experiment evaluation suggests that the adopted model obtained 93.9% PCK for the goddess pose. The maximum PCK achieved for the goddess pose, i.e., 93.9%, PDJ evaluation was carried out in the staggering mode where maximum PDJ is obtained as 90% to 100% for almost all the body joints.
Implementing ICT in classroom practice: what else matters besides the ICT infrastructure?

The large-scale International Computer and Information Literacy Study (2018) has an interesting finding concerning Luxembourg teachers. Luxembourg has one of the highest reported level of technology-related resources for teaching and learning, but a relatively lower reported use of ICT in classroom practice.ICT innovation requires a high initial level of financial investment in technology, and Luxembourg has achieved this since 2015. Once the necessary financial investment in ICT technology has been made, the key question is what else matters to increase the use of ICT in teaching. To identify the relevant factors, we used the "Four in Balance" model, aimed explicitly at monitoring the implementation of ICT in schools.Using data for 420 teachers in Luxembourg, we identify that within such a technology-driven approach to digitalization, teachers' vision of ICT use in teaching, level of expertise, and the use of digital learning materials in class are significant support factors. Leadership and collaboration, in the form of an explicit vision of setting ICT as a priority for teaching in the school, also prove to be important.Through these findings, we show that the initial investment in school infrastructure for ICT needs to be associated in its implementation with teachers' ICT-related beliefs, attitudes, and ICT expertise.The online version contains supplementary material available at 10.1186/s40536-022-00144-6.
Mask Detection Method Based on YOLO-GBC Network

For the problems of inaccurate recognition and the high missed detection rate of existing mask detection algorithms in actual scenes, a novel mask detection algorithm based on the YOLO-GBC network is proposed. Specifically, in the backbone network part, the global attention mechanism (GAM) is integrated to improve the ability to extract key information through cross-latitude information interaction. The cross-layer cascade method is adopted to improve the feature pyramid structure to achieve effective bidirectional cross-scale connection and weighted feature fusion. The sampling method of content-aware reassembly of features (CARAFE) is integrated into the feature pyramid network to fully retain the semantic information and global features of the feature map. NMS is replaced with Soft-NMS to improve model prediction frame accuracy by confidence decay method. The experimental results show that the average accuracy (mAP) of the YOLO-GBC reached 91.2% in the mask detection data set, which is 2.3% higher than the baseline YOLOv5, and the detection speed reached 64FPS. The accuracy and recall have also been improved to varying degrees, increasing the detection task of correctly wearing masks.
TaxonPrompt: Taxonomy-aware curriculum prompt learning for few-shot event classification

Event classification (EC) aims to assign the event labels to unlabeled sentences and tends to struggle in real-world applications when only a few annotated samples are available. Previous studies have mainly focused on using meta-learning to overcome the low-resource problem where label data from other tasks are still required for model learning and selection. Accordingly, prompt learning-based approaches are proposed to address the low-resource issue. However, such approaches generally ignore task-specific information and adopt demonstration learning for fine-tuning, which fails to leverage the most informative examples for training and hurts performance. Thus, we propose a taxonomy-aware prompt learning framework TaxonPrompt that trains the language model with samples from easy to hard by imitating the human curricula, which effectively alleviates the classification bottleneck caused by insufficient data. We first design an event prompt generation (EPG) for automatically generating task-specific templates using sentences, labels, and trigger words. Then, we propose a Fisher information-based demonstration filtering (FDF) to dynamically select the most informative support examples for each query to train the model. We have conducted extensive experiments on two EC datasets: FewEvent and RAMS. The experimental results demonstrate the superiority of the proposed model over state-of-the-art baselines. In particular, our approach works well in the scenario of an extremely small number of available task resources and therefore constitutes a solution for few-shot event classification.
Self-attention learning network for face super-resolution

Existing face super-resolution methods depend on deep convolutional networks (DCN) to recover high-quality reconstructed images. They either acquire information in a single space by designing complex models for direct reconstruction, or employ additional networks to extract multiple prior information to enhance the representation of features. However, existing methods are still challenging to perform well due to the inability to learn complete and uniform representations. To this end, we propose a self-attention learning network (SLNet) for three-stage face super-resolution, which fully explores the interdependence of low- and high-level spaces to achieve compensation of the information used for reconstruction. Firstly, SLNet uses a hierarchical feature learning framework to obtain shallow information in the low-level space. Then, the shallow information with cumulative errors due to DCN is improved under high-resolution (HR) supervision, while bringing an intermediate reconstruction result and a powerful intermediate benchmark. Finally, the improved feature representation is further enhanced in high-level space by a multi-scale context-aware encoder-decoder for facial reconstruction. The features in both spaces are explored progressively from coarse to fine reconstruction information. The experimental results show that SLNet has a competitive performance compared to the state-of-the-art methods.
A Framework for User-Focused Electronic Health Record System Leveraging Hyperledger Fabric

This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare industry applications. The focus of this research explores methods of using blockchain technology that prioritise users. The investigation of several concepts used in developing web applications has been determined. The study identified that an open-source project, known as Hyperledger Fabric, can be utilised to construct a novel method of storing EMRs. The framework provides a test network that can be customised to satisfy the need of several projects, including storing medical records. This research additionally outlines the difficulties encountered and problems that need to be resolved before Hyperledger Fabric can be successfully implemented in healthcare systems. Considering all types of blockchains available, the needs are met by Hyperledger Fabric, which offers a distributed and secure environment for healthcare systems. Blockchain has the potential to transform healthcare by putting the patient at the centre of the system and enhancing health data protection and interoperability. Also, by using grant and revoke access mechanisms, patients have complete control over their medical information as well as authorized doctors who are allowed to view records. This functionality is made possible by the chaincode defined in the blockchain platform. The research study has both practitioner and research implications for the development of secure blockchain-based EMRs.
An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform

Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of 0.0625 bits per pixel, outperforming many related RDH schemes with complete reversibility.
Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication

Deep neural networks have been recently applied in automatic modulation classification task and achieved remarkable success. However, Existing neural networks mainly focus on the purely data-driven architecture design, and fail to explore the hand-crafted feature mechanisms which are particularly significant for radio signal presentation in wireless communication. Inspired by digital signal processing theories, we propose frequency learning attention networks (FLANs) to analyze the radio spectral bias from frequency perspective, based on a multi-spectral attention mechanism for learning-based frequency components selection. FLANs are the general case of classical global average pooling and leverage identical structures of the popular neural networks. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset.