Reviewerly/exHarmony huggingface dataset:
Recognition Punches in Karate Using Acceleration Sensors and Convolution Neural Networks

Coaches and athletes need to understand the kinematics and dynamics of karate kicks to improve the training process and results.The research was aimed at studying the automatic recognition of punches in karate using only linear acceleration sensors.Accelerometers were part of the Inertial Measurement Units (IMUs), which were attached to the left and right wrist of the athlete.To develop a model of punches, highly qualified athletes with 3-7 years of karate experience participated in the research.We analyzed the acceleration fields of various karate punches: Yun Tsuki, Mawashi Tsuki, Age of Tsuki, Uraken.A simpler approach to extracting features without calculating their statistical characteristics is proposed.To solve the classification problem, various architectures of convolutional neural networks are used: multilayer perceptron, 1-and 2-dimension Convolution Networks.Since the recognition of punches was carried out in the conditions of a shadow fight, in addition to the recognition of punches, another output parameter was introducedmovement without punches.Studies have shown a high level of punch recognition based on the developed models.The multi-class accuracy value is 0.96, and the average F1 value is 0.97 for five different punch classes.Thus, the proposed approach is more suitable for practical implementation in automatic learning systems.
URNet: A U-Shaped Residual Network for Lightweight Image Super-Resolution

It is extremely important and necessary for low computing power or portable devices to design more lightweight algorithms for image super-resolution (SR). Recently, most SR methods have achieved outstanding performance by sacrificing computational cost and memory storage, or vice versa. To address this problem, we introduce a lightweight U-shaped residual network (URNet) for fast and accurate image SR. Specifically, we propose a more effective feature distillation pyramid residual group (FDPRG) to extract features from low-resolution images. The FDPRG can effectively reuse the learned features with dense shortcuts and capture multi-scale information with a cascaded feature pyramid block. Based on the U-shaped structure, we utilize a step-by-step fusion strategy to improve the performance of feature fusion of different blocks. This strategy is different from the general SR methods which only use a single Concat operation to fuse the features of all basic blocks. Moreover, a lightweight asymmetric residual non-local block is proposed to model the global context information and further improve the performance of SR. Finally, a high-frequency loss function is designed to alleviate smoothing image details caused by pixel-wise loss. Simultaneously, the proposed modules and high-frequency loss function can be easily plugged into multiple mature architectures to improve the performance of SR. Extensive experiments on multiple natural image datasets and remote sensing image datasets show the URNet achieves a better trade-off between image SR performance and model complexity against other state-of-the-art SR methods.
Interactive fuzzy Bayesian search algorithm: A new reinforced swarm intelligence tested on engineering and mathematical optimization problems

The current study deals with introducing a new probabilistic, self-adaptive, and gradient-free search algorithm. In the proposed method new Bayesian and Fuzzy auxiliary mechanisms are defined and simultaneously employed to extremely adjust the trade-off between exploration and exploitation search behaviors of the swarm-based technique so-called Interactive Search Algorithm (ISA). In this regard, a nine-rule fuzzy decision-making strategy and a hierarchical forecasting Bayesian formulation are developed. The integrated fuzzy and Bayesian mechanisms permanently monitor the search process, and try to dynamically tune the search behavior of each agent based on the governing conditions of the current problem and cause the proposed method to work as a self-adaptive search algorithm. This new search technique is named Interactive Fuzzy Bayesian Search Algorithm (IFBSA) and its performance is tested on a suit of unconstrained mathematical functions and constrained structural and mechanical optimization problems with different properties. Acquired outcomes demonstrate that the proposed IFBSA, thanks to its dual supplementary module, provides promising and superior results in the terms of accuracy, stability, and convergence rate.
A comprehensive study on DNA based Security scheme Using Deep Learning in Healthcare

The internet is widening day by day followed by security issues related to it. There are many approaches to achieve a secure cyber environment but still, there always exists room for the optimal choice to attain security goals. Artificial intelligence (AI) has given a new vision to the technology by implementing its remarkable applications for common users by providing ease in human life with its unique inventions. Deep learning (DL) has emerged from machine learning that relates to the branch of AI itself. In DL, the machine adopts the behavior of the human mind through its various hidden layers to generate the output similarly as the human mind should do. Research is on DNA-based security using DL that works similarly to cryptography and ensures secure data transfer among cyber users. This combination is studied by recent researchers and found its successfulness in enhancing the security of data. Moreover, the DNA scheme has empowered security systems by safeguarding against major cyberattacks. Although, it is being used in multiple real-world domains, we concise our research on the health sector where it is used to handle various forms of patient's health data records. The study found that the combination of DNA sequence along with DL techniques enhances data confidentiality to be shared with concerned legitimate users and hence ensuring data confidentiality, integrity, authorization, and authentication and this is what we require in the medical field too. So, DNA security mechanisms undoubtedly strengthen the security of data by implementing DL methods and techniques in the health section.
EduCOR: An Educational and Career-Oriented Recommendation Ontology

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user's learning goals, academic and psychological parameters, and the labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use-case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.
Application of the unscented Kalman filter in position estimation a case study on a robot for precise positioning

High-precision measurement is a common task in many engineering applications. In these cases, sensor fusion algorithms represented by Kalman filter and its variants are effective and practical. It is introduced in the article, how to use this sensor fusion algorithm in a high-precision tracking task, where only one sensor (tacheometer) is used in the project. It is also discussed in the article how to build estimation models, including measurement model and motion model, for specific robot positioning problems. A variety of related models are compared in this article. The best model that utilize the prior knowledge of robot motion is proposed, which can not only meet the high precision requirements, but also have robustness to the robot operating environment.
Remote sensing image watermarking based on motion blur degeneration and restoration model

To protect the copyright of satellite remote sensing images, this paper applies image degradation and restoration methods to the embedding and extraction of watermarks, based on the characteristics of image degradation caused by satellite imaging, and designs a watermarking algorithm based on motion blur degradation/restoration, assuming that the degradation parameters of the imaging equipment are known. The digital watermarking signal is generated using a chaotic sequence, embedded based on the motion blur degradation model, and extracted by a combination of inverse and Wiener filtering. The algorithm is verified by experimental simulations, which show that the proposed watermarking scheme has excellent imperceptibility and robustness.
FIFL: A Fair Incentive Mechanism for Federated Learning

Federated learning is a novel machine learning framework that enables multiple devices to collaboratively train high-performance models while preserving data privacy. Federated learning is a kind of crowdsourcing computing, where a task publisher shares profit with workers to utilize their data and computing resources. Intuitively, devices have no interest to participate in training without rewards that match their expended resources. In addition, guarding against malicious workers is also essential because they may upload meaningless updates to get undeserving rewards or damage the global model. In order to effectively solve these problems, we propose FIFL, a fair incentive mechanism for federated learning. FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real-time worker assessment mechanism. We evaluate the effectiveness of FIFL through theoretical analysis and comprehensive experiments. The evaluation results show that FIFL fairly distributes rewards according to workers’ behaviour and quality. FIFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. In the unreliable scenario containing attackers which destroy the model’s performance, the system revenue of FIFL outperforms the baselines by more than 46.7%.
BPT: A Blockchain-Based Privacy Information Preserving System for Trust Data Collection Over Distributed Mobile-Edge Network

Contemporarily, the fast development of computing, communication, and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such a process also raises two challenging but critical issues: 1) trustworthy and 2) privacy issue for data collectors. Therefore, this article proposes a novel system, which is designed over the distributed mobile-edge network to sufficiently exploit advantages of blockchain and differential privacy (DP) to collect trustworthy data and protect privacy for data collectors. First, to improve trustworthiness of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Second, with the assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthiness for data collectors. Third, we enforce DP on the data stored in a global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experimental results prove that the proposed system comprehensively improves performance of data collections in distributed network without adding any additional cost for the cloud server, compared to other schemes.
A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset.
Long-term variable Q transform: A novel time-frequency transform algorithm for synthetic speech detection

The advanced speech synthetic techniques pose great threats to current speech-based biometric systems, which causes urgent demand for efficient countermeasures. However, existing detection features cannot generalize well for synthetic speeches created by unknown methods. Based on the analysis of different synthetic techniques, it is found that temporal long-term relation and high-frequency information are useful to capture more generalized and robust artifacts of synthetic speeches. To leverage the aforementioned clues, a novel time-frequency transform algorithm known as long-term variable Q transform (L-VQT) is proposed through time-frequency analysis. The core idea of L-VQT is that the variation of discrete frequencies is designed to follow the power function, which aims to perform long temporal windows at low frequencies through small frequency bandwidths. Besides, sufficient high-frequency information can be obtained by properly varying the exponent of the power function. Then, the log power spectrum feature based on L-VQT is sent to a modified Densely Connected Convolutional Network (DenseNet), referred to as Light DenseNet, which is used as a single system to obtain the detection result. Extensive experiments are conducted on ASVspoof 2019 logical access corpus. The proposed detection scheme achieves better generalization capability when compared with other state-of-the-art methods. It also retains a more robust performance in noisy conditions.
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification

Most existing few-shot classification methods only consider generalization on one dataset (i.e., single-domain), failing to transfer across various seen and unseen domains. In this paper, we consider the more realistic multi-domain few-shot classification problem to investigate the cross-domain generalization. Two challenges exist in this new setting: (1) how to efficiently generate multi-domain feature representation, and (2) how to explore domain correlations for better cross-domain generalization. We propose a parameter-efficient multi-mode modulator to address both challenges. First, the modulator is designed to maintain multiple modulation parameters (one for each domain) in a single network, thus achieving single-network multi-domain representation. Given a particular domain, domain-aware features can be efficiently generated with the well-devised separative selection module and cooperative query module. Second, we further divide the modulation parameters into the domain-specific set and the domain-cooperative set to explore the intra-domain information and inter-domain correlations, respectively. The intra-domain information describes each domain independently to prevent negative interference. The inter-domain correlations guide information sharing among relevant domains to enrich their own representation. Moreover, unseen domains can utilize the correlations to obtain an adaptive combination of seen domains for extrapolation. We demonstrate that the proposed multi-mode modulator achieves state-of-the-art results on the challenging META-DATASET benchmark, especially for unseen test domains.
SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large-margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>F</mi><mn>1</mn></math> score of 93.45%, outperforming the baseline models, which used RGB data alone.
Towards Visual Explainable Active Learning for Zero-Shot Classification

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attributes. Designing a suitable class-attribute matrix is the key to the subsequent procedure, but this design process is tedious and trial-and-error with no guidance. This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems. This approach promotes human-AI teaming with four actions (ask, explain, recommend, respond) in each interaction loop. The machine asks contrastive questions to guide humans in the thinking process of attributes. A novel visualization called semantic map explains the current status of the machine. Therefore analysts can better understand why the machine misclassifies objects. Moreover, the machine recommends the labels of classes for each attribute to ease the labeling burden. Finally, humans can steer the model by modifying the labels interactively, and the machine adjusts its recommendations. The visual explainable active learning approach improves humans' efficiency of building zero-shot classification models interactively, compared with the method without guidance. We justify our results with user studies using the standard benchmarks for zero-shot classification.
Ecologies of Public Trust: The NHS COVID-19 Contact Tracing App

In April 2020, close to the start of the first U.K. COVID-19 lockdown, the U.K. government announced the development of a COVID-19 contact tracing app, which was later trialled on the U.K. island, the Isle of Wight, in May/June 2020. United Kingdom surveys found general support for the development of such an app, which seemed strongly influenced by public trust. Institutions developing the app were called upon to fulfil the commitment to public trust by acting with trustworthiness. Such calls presuppose that public trust associated with the app can emerge if the conditions for trustworthiness are met and that public trust is simplistic, i.e., linearly the sum of each member of the publics' individual - U.K. government trust relationship. Drawing on a synthesis of the trust literature and fifteen interviews with members of the public trialling the app on the Isle of Wight, this paper aims to explore what trust mechanisms and relationships are at play when thinking about public trust in the context of the U.K. COVID-19 app. We argue that public trust is a complex social phenomenon and not linearly correlated with institutional trustworthiness. As such, attention needs to widen from calls for trustworthy infrastructures as a way to build public trust, to a deeper understanding of those doing the trusting; in particular, what or whom do people place their trust in (or not) when considering whether using the app and why. An understanding of this will help when trying to secure public trust during the implementation of necessary public health measures.
One-Shot Learning-Based Animal Video Segmentation

Deep learning-based video segmentation methods can offer a good performance after being trained on the large-scale pixel labeled datasets. However, a pixel-wise manual labeling of animal images is challenging and time consuming due to irregular contours and motion blur. To achieve desirable tradeoffs between the accuracy and speed, a novel one-shot learning-based approach is proposed in this article to segment animal video with only one labeled frame. The proposed approach consists of the following three main modules: guidance frame selection utilizes “BubbleNet” to choose one frame for manual labeling, which can leverage the fine-tuning effects of the only labeled frame; Xception-based fully convolutional network localizes dense prediction using depthwise separable convolutions based on one single labeled frame; and postprocessing is used to remove outliers and sharpen object contours, which consists of two submodules—test time augmentation and conditional random field. Extensive experiments have been conducted on the DAVIS 2016 animal dataset. Our proposed video segmentation approach achieved mean intersection-over-union score of 89.5% on the DAVIS 2016 animal dataset with less run time, and outperformed the state-of-art methods (OSVOS and OSMN). The proposed one-shot learning-based approach achieves real-time and automatic segmentation of animals with only one labeled video frame. This can be potentially used further as a baseline for intelligent perception-based monitoring of animals and other domain-specific applications. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> The source code, datasets, and pre-trained weights for this work are publicly [Online]. Available: <uri>https://github.com/tengfeixue-victor/One-Shot-Animal-Video-Segmentation</uri>. </fn>
Detection of malignant melanoma in H&amp;E-stained images using deep learning techniques

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.