Reviewerly/exHarmony huggingface dataset:
Facilitating Bodily Insights Using Electromyography-Based Biofeedback during Physical Activity

Physical exercises can benefit our health, but avoiding improper form and overexertion is essential. Facilitating bodily insights can encourage learning about exercise form, allowing users to gain a deeper understanding of their physiology. To investigate this, we conducted a lab experiment where amateur users performed bicep curls, and interviews with sports coaches. Participants were provided with FitBack—a system that monitors muscle activity during exercises via electromyography (EMG) and offers real-time biofeedback. Amateurs reported that they were successful in improving their exercise form and could acquire deeper bodily insights. Coaches reflected on how understanding muscle activity through EMG could be effectively used for increasing body awareness during coaching, highlighting that EMG-based biofeedback is beneficial for a diverse set of users. Our work contributes insights into using bodily sensing to help users understand their bodies. We contribute guidelines for designing systems that use EMG biofeedback effectively in physical activity.
Cross-sentence Neural Language Models for Conversational Speech Recognition

An important research direction in automatic speech recognition (ASR) has centered around the development of effective methods to rerank the output hypotheses of an ASR system with more sophisticated language models (LMs) for further gains. A current mainstream school of thoughts for ASR N-best hypothesis reranking is to employ a recurrent neural network (RNN)-based LM or its variants, with performance superiority over the conventional n-gram LMs across a range of ASR tasks. In real scenarios such as a long conversation, a sequence of consecutive sentences may jointly contain ample cues of conversation-level information such as topical coherence, lexical entrainment and adjacency pairs, which however remains to be underexplored. In view of this, we first formulate ASR N-best reranking as a prediction problem, putting forward an effective cross-sentence neural LM approach that reranks the ASR N-best hypotheses of an upcoming sentence by taking into consideration the word usage in its precedent sentences. Furthermore, we also explore to extract task-specific global topical information of the cross-sentence history in an unsupervised manner for better ASR performance. Extensive experiments conducted on the AMI conversational benchmark corpus indicate the effectiveness and feasibility of our methods in comparison to several state-of-the-art reranking methods.
Digital Ethnography in Cybercrime Research: Some Notes from the Virtual Field

Ethnographers must now confront the multi-sited, digital and mobile nature of social, cultural and economic life. As a result, the use of digital ethnography, traditional ethnographic methods modified to interact with online communities and environments, has steadily increased in anthropology and the social sciences. Criminologists are beginning to make use of the approach in response to the increasing need to account for the complex digital features of contemporary forms of criminality, victimization, policing and punishment. In this chapter, we outline some selected details of our experiences as ethnographers conducting criminological research in virtual worlds. We cover key issues that range from practical challenges and ethical quandaries through analytical capabilities to epistemological issues in the hope our reflections go some way in helping budding digital ethnographers in criminology.
On Mode Collapse in Generative Adversarial Networks

Generative adversarial networks (GANs) have shown extraordinary performance in generating high quality samples in domains including image, video, and text. GANs therefore have great potential in learning complex probability distributions in high dimensional spaces. However, current methods often miss capturing some of the modes in the examples, known as the mode collapse problem. The reason for this issue can be traced to that the initial generated manifold fails to cover the whole data manifold, while the training process is hard to recover from this failure. In this paper, we propose GANs with supervision signal (SSGAN), which introduces a supervision signal to alleviate this issue. The supervision signal tells the generator an approximate output corresponding to the input noise, which ensures the generated manifold to be close to the data manifold. Therefore, the generator could be able to better capture the whole data distribution. We have conducted experiments on MNIST, CIFAR 10 and CelebA datasets. The results show that our method outperforms several SoTA approaches measured by the inception score, mode score, and the newly proposed matching score.
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.
Intelligent Network Slicing With Edge Computing for Internet of Vehicles

In this paper, we present an application-specific Multi-Access Edge Computing (MEC) network architecture by leveraging the Control and User Plane Separation (CUPS) in mobile core networks to offload data processing from central servers to edge servers to reduce the transmitted traffic volume and also the response latency of connected vehicle mobility service. We first apply deep learning to classify packets of different applications to different Radio Access Networks (RAN) slices for application-specific spectrum scheduling. Then, we slice Evolved Packet Core (EPC) and deploy EPC data plane slices on-demand for each application and route packets from RAN slices to edge servers. By applying network slicing, multiple RAN, EPC and MEC slices that support different categories of services with different quality of service (QoS) requirements can be deployed in the same physical infrastructure. We prototype the proposed application-specific CUPS architecture using modified open source software OpenAirInterface on our deeply programmable platform. The preliminary experimental results show the feasibility and efficiency of proposed application-specific CUPS architecture, which can achieve a significant decrease in transmission data volume and latency.
Autonomic Provisioning in Software Development Life Cycle Process

Advancement in software technologies has helped IT developers to work efficiently and enhance quality of the products but simultaneously; it has also presented the problem of increasing complexity in terms of software architectures, programming code and software management task. These issues raise chances of vulnerabilities in the software system. Run-time solution to the faults and failures of software need to be done. One such solution for handling run-time management tasks is Autonomic Computing. Autonomic computing technique, automate the management task to prevent the occurrence of vulnerability based issues. Further, to reduce the occurrence of system failures, an idea to provide an autonomic advisor to the Software Development Life Cycle process has been proposed in this paper. The autonomic advisor gives autonomic features based required suggestions to the developers during the software development process. It will also help to perform risk analysis during development. This will lead to the development of quality and efficient software systems. This paper also provides a review of the existing work of the autonomic computing including its challenges and their effects on management process of the system.
On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition

Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph. Most of the recently proposed GCN-based methods improve the performance by learning the graph structure at each layer of the network using spatial attention applied on a predefined graph Adjacency matrix that is optimized jointly with model's parameters in an end-to-end manner. In this paper, we analyze the spatial attention used in spatio-temporal GCN layers and propose a symmetric spatial attention for better reflecting the symmetric property of the relative positions of the human body joints when executing actions. We also highlight the connection of spatio-temporal GCN layers employing additive spatial attention to bilinear layers, and we propose the spatiotemporal bilinear network (ST-BLN) which does not require the use of predefined Adjacency matrices and allows for more flexible design of the model. Experimental results show that the three models lead to effectively the same performance. Moreover, by exploiting the flexibility provided by the proposed ST-BLN, one can increase the efficiency of the model.
Fuzzy Logic on Quantum Annealers

Quantum computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement, and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as Big Data, where computational efficiency represents a nonnegligible constraint to be taken into account. In order to pave the way toward this innovative scenario, this article introduces a novel representation of fuzzy sets and operators based on quadratic unconstrained binary optimization problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.
Organizational Data Breach: Building Conscious Care Behavior in Incident Response

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses. This research study draws upon the literature in the areas of information security, incident response, theory of planned behaviour, and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation. The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore's history to-date, the 2018 SingHealth data breach. The single in-depth case study observed information security awareness, policy, experience, attitude, subjective norms, perceived behavioral control, threat appraisal and self-efficacy as emerging prominently in the framework's applicability in incident handling. The data analysis did not support threat severity relationship with conscious care behaviour. The findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.
A Systematic Review of Research on High-Immersion Virtual Reality for Language Learning

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Non-fungible tokens and libraries

Purpose This paper aims to help information professionals understand the foundational concepts of this technology and how these are related to libraries so that they can evolve services alongside it. Design/methodology/approach This column will define what a non-fungible tokens (NFT) is, explore the relevant trends impacting its development and examine how it intersects with the traditional roles of the library. Findings NFTs represent a new and growing technology that intersections with many of the same concepts that are core to librarianship. Libraries are community institutions that engender widespread trust, whereas NFTs are built atop cryptocurrency that seeks to enable anonymous peer-to-peer interactions. Originality/value Summary.
An analytical method for the optical analysis of Linear Fresnel Reflectors with a flat receiver

The optical analysis of Linear Fresnel Reflectors solar concentrators deals with the calculation of the absorbed flux at the receiver. Due to concentrator discrete and complex geometry ray tracing numerical simulations is the main used method, in parallel with some analytical approaches that have been developed for specific cases. This paper presents an analytical method for Linear Fresnel Reflectors with a flat receiver. It is based on Zhu's vector-based method and Rabl's concepts of acceptance and effective source, although new shading (including receiver shading), blocking, and cosine losses analyzes are presented, with the inclusion of end-losses effect. As shown, the problem is better described by the concept of intercept factor, the effective aperture area used to collect the incident sunlight. Comparison tests with ray tracing simulations performed in SolTrace for three different effective sources were carried out to validate the analytical model for both factorized and biaxial models of the intercept factor, including energetic evaluations for Évora, Portugal. In general, analytical results do agree very well with ray tracing, better for the factorized model than the biaxial. Errors in the analytical estimative of intercept factor can be high up to 132% for the biaxial model at high longitudinal incidence angles; on the other hand, errors in the amount of annual absorbed energy were high up to only 3%.
SeMo-YOLO: A Multiscale Object Detection Network in Satellite Remote Sensing Images

In recent studies, object detection of satellite remote sensing images by deep learning method has emerged as a major concern in the fields of environmental detection, military investigation and geography. The current object detection practice, such as Faster RCNN, SSD and YOLO (v1-v5), is using new CNN based deep learning method to replace conventional sliding window and handcraft detectors, and it has achieved impressive success for some specific fields in natural scene images. However, in the task of satellite remote sensing images, it's still a major challenge that the existing models cannot guarantee the multi-scale object detection and real-time monitoring with high accuracy. To address these challenges, in this paper, we proposed SeMo- YOLO, a novel one stage deep learning detector based on the combination of MobileNet, YOLOv3 and channel attention mechanism. Experiment result shows that SeMo-YOLO and it's tiny version can achieve significant improvement in the aspect of accuracy for multiscale remote sensing object detection. It has a mAP (@.5) of 92%, 94%, and 95% on RSOD, HRRSD2019 and SAR-SSDD datasets respectively, which has bring significant improvement in detection precision than the existing state-of-the-art Networks. In addition, the weight parameters of the proposed model are less than the existing YOLOv3 so as to it can achieve relatively higher detection speed near real time.
KAM-Net: Keypoint-Aware and Keypoint-Matching Network for Vehicle Detection From 2-D Point Cloud

Two-dimesional (2-D) LiDAR is an efficient alternative sensor for vehicle detection, which is one of the most critical tasks in autonomous driving. Compared to the fully developed 3-D LiDAR vehicle detection, 2-D LiDAR vehicle detection has much room to improve. Most existing state-of-the-art works represent 2-D point clouds as pseudo-images and then perform detection with traditional object detectors on 2-D images. However, they ignore the sparse representation and geometric information of vehicles in the 2-D cloud points. To address these issues, in this article, we present a novel <i>keypoint-aware and keypoint-matching network</i> termed as KAM-Net, which focuses on better detecting the vehicles by explicitly capturing and extracting the sparse information of L-shape in 2-D LiDAR point clouds. The whole framework consists of two stages&#x2014;namely, keypoint-aware stage and keypoint-matching stage. The keypoint-aware stage utilizes the heatmap and edge extraction module to simultaneously predict the position of L-shaped keypoints and inflection offset of L-shaped endpoints. The keypoint-matching stage is followed to group the keypoints and produce the oriented bounding boxes with axis by utilizing the endpoint-matching and L-shaped-matching methods. Further, we conduct extensive experiments on a recently released public dataset to evaluate the effectiveness of our approach. The results show that our KAM-Net achieves a new state-of-the-art performance. The source code is available at <uri>https://github.com/ispc-lab/KAM-Net</uri>.
Review on Applications of Blockchain for Electronic Health Records Systems

Electronic Health Record (EHR) is a digitized version of a person's health record which comes with a challenge of ensuring secure storage of such sensitive data, access only to necessary individuals, interoperability amongst healthcare providers and many other challenges. Recent trends have suggested blockchain as a technology in storing EHR based on its decentralized peer-to-peer network with an aim to ensure security, privacy and interoperability of health records. The study provides background information about blockchain and relevant theory for developing EHR systems and aims to look into different proposed blockchain approaches for storing EHRs.
Pencil shell matrix based image steganography with elevated embedding capacity

Image steganography is a mechanism that imperceptibly conceals information in a cover image while also enabling the concealed details to be reliably retrieved from that image. The three important criteria for calculating the effect and impact of any steganographic method are high embedding ability, better visual quality and privacy and security. In this paper, a double layered pencil shaped shell matrix based image steganography methodology with high security is proposed and with this extra layer an extra 2 bits can be hidden within every single pixel pair of the pencil shaped shell matrix. Dependent on the shell matrix made of pencil-shaped shells, every private digit is fitted in each cover pixel pair. Various experimental outcomes imply that the proposed methodology has achieved a higher capacity of 3.0 bpp with average 43.25 dB of PSNR and it outperforms other existing shell matrix based image steganographic schemes. Furthermore, the results of RS analysis, JPEG compression analysis, Q-index analysis, normalized absolute error (NAE) analysis etc. are clearly proves the robustness and security of the proposed methodology. Numerous sectors of various private and government organizations, including intellectual property rights, education, intelligent bureau, defence, etc. can be benefited immensely from this scheme.
Acoustic characterisation of string instruments by internal cavity measurements

The use of internal cavity sound measurements to characterise the acoustic properties of any hollow-bodied string instrument is described using the violin family as an example. Measurements are described using inexpensive sub-miniature electret microphones as sensors for the impact hammer, both internal and radiated sound, and bridge admittance. The excited vibrations of the body shell are shown to excite a rich spectrum of internal cavity resonances, which are strongly correlated with the radiated sound, particularly in the signature and transitional frequency ranges below around 1.5 kHz for the violin. Examples of how such measurements can be used as a working tool in the luthier's workshop and for research on the acoustics of string instruments of any kind or size are illustrated by measurements on the violin, viola, cello, and double bass.
Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10-5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.