Reviewerly/exHarmony huggingface dataset:
Comparison Study of Inertial Sensor Signal Combination for Human Activity Recognition based on Convolutional Neural Networks

Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The research community has developed various methodologies to detect human activity based on various input types. However, most of the research in the field has been focused on applications other than human-in-the-centre applications. This paper focused on optimising the input signals to maximise the HAR performance from wearable sensors. A model based on Convolutional Neural Networks (CNN) has been proposed and trained on different signal combinations of three Inertial Measurement Units (IMU) that exhibit the movements of the dominant hand, leg and chest of the subject. The results demonstrate k-fold cross-validation accuracy between 99.77 and 99.98% for signals with the modality of 12 or higher. The performance of lower dimension signals, except signals containing information from both chest and ankle, was far inferior, showing between 73 and 85% accuracy.
Multiscale Ship Detection Method in SAR Images Based on Information Compensation and Feature Enhancement

With the development of synthetic aperture radar (SAR)-based imaging technology, SAR ship detection has achieved notable breakthroughs. Due to the scale diversity and class imbalance of ships, ship detection in SAR images is still a substantial challenge. To solve these problems, this paper proposes a multiscale ship detection method for SAR images based on information compensation and feature enhancement. To improve the feature representations of multiscale ships, an information compensation module (ICM) and a feature enhancement module (FEM) are embedded into a feature pyramid network (FPN). Specifically, the ICM is constructed to extract and aggregate diverse spatial contextual information. At the same time, the FEM is introduced to solve the feature-level imbalance of the FPN by integrating multilevel ship features. In addition, a gradient density parameter is introduced to solve class imbalance problems. Experiments on a high-resolution SAR image dataset (HRSID) show that the proposed method achieves a comprehensive detection performance, i.e., 60.6% average precision (AP) and 67.1 frames per second (FPS) and outperforms other state-of-the-art methods.
Own the Unknown: An Anticipatory Approach to Prepare Society for the Quantum Age

Quantum technology promises to shift the boundaries of what machines can do. Advancing our capacities to acquire, process and transmit information, quantum technology has the potential to impact nearly all domains in society. Therefore, we should start anticipating the future role of quantum technology and the ethical, legal, social and policy implications that come with it. One way of informing ourselves about how to do this is by making use of historical analogies. The Netherlands Scientific Council for Government Policy (WRR) developed a framework for embedding so-called system technologies into society based on a historical analysis of how society dealt with such technologies in the past. In this article, the conceptual framework of system technologies is reinterpreted as an anticipatory strategy to prepare society for quantum technology and vice versa. The proposed strategy has five dimensions: (1) countering unrealistic perceptions (demystification), (2) investing in a facilitating socio-technical environment (contextualisation), (3) engaging stakeholders and civil society (engagement), (4) creating flexible frameworks (regulation) (5) and developing international 'quantum diplomacy' (positioning). By actively engaging in these processes, society enables itself to guide the development of quantum technology and its impact within society.
The Growing Need for Metaverse Regulation

The immersive technologies of virtual reality (VR) and augmented reality (AR) are reaching maturity levels that will soon enable widespread deployment in consumer markets. At the same time, major corporations have begun investing billions of dollars annually on immersive technologies and platforms. Combined, these conditions greatly increase prospects for broad adoption of virtual and augmented environments by large populations. Recently branded “the metaverse” by Meta and other platform providers, many major corporations have expressed plans for deploying consumer products within the next three to five years. With the prospect that the metaverse platforms gain broad adoption over the next decade, it’s important to consider the risks and plan for meaningful regulation as necessary.
Reducing deviant consumer behaviour with service robot guardians

Purpose The purpose of this research is to understand whether service robots can safeguard servicescapes from deviant consumer behaviour. Using routine activity theory, this research examines whether increasing the perceived humanness of service robots reduces customer intentions to commit deviant consumer behaviour and whether this negative relationship is mediated by perceived empathy and perceived risk of being caught. Design/methodology/approach Five hundred and fifty-three US residents responded to a hypothetical scenario that manipulated the humanness of a service agent (from self-service technology, to robot, to human employee) across seven conditions and measured the likelihood of deviant consumer behaviour, empathy towards the service robot, perceived risk of being caught and punished and negative attitudes towards robots. Findings The results indicate that replacing human service agents with different types of service robots does inadvertently reduce customer perceptions of capable guardianship (i.e. the human element that deters potential offenders from committing crimes) in the servicescape and creates conditions that allow customers to perpetrate more deviant consumer behaviour. Practical implications When investing in technology such as service robots, service providers need to consider the unintended cost of customer misbehaviour (specifically deviant consumer behaviour) in their return-on-investment assessments to optimise their asset investment decisions. Originality/value Moving beyond research on customer adoption and use, this research examines the unintended consequences that might arise when deploying service robots in a technology-infused service environment. Humanised service robots offer more guardianship than self-service technology but do not replace human employees in preventing deviant consumer behaviour, as they remain more capable of deterring customer misbehaviour.
Application of Wavelet Characteristics and GMDH Neural Networks for Precise Estimation of Oil Product Types and Volume Fractions

Given that one of the most critical operations in the oil and gas industry is to instantly determine the volume and type of product passing through the pipelines, in this research, a detection system for monitoring oil pipelines is proposed. The proposed system works in such a way that the radiation from the dual-energy source which symmetrically emits radiation, was received by the NaI detector after passing through the shield window and test pipeline. In the test pipe, four petroleum products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated in pairs in different volume fractions. A total of 118 simulations were performed, and their signals were categorized. Then, feature extraction operations were started to reduce the volume of data, increase accuracy, increase the learning speed of the neural network, and better interpret the data. Wavelet features were extracted from the recorded signal and used as GMDH neural network input. The signals of each test were divided into details and approximation sections and characteristics with the names STD of A3, D3, D2 and were extracted. This described structure is modelled in the Monte Carlo N Particle code (MCNP). In fact, precise estimation of oil product types and volume fractions were done using a combination of symmetrical source and asymmetrical neural network. Four GMDH neural networks were trained to estimate the volumetric ratio of each product, and the maximum RMSE was 0.63. In addition to this high accuracy, the low implementation and computational cost compared to previous detection methods are among the advantages of present investigation, which increases its application in the oil industry.
Privacy-Preserving Semantic Segmentation Using Vision Transformer

In this paper, we propose a privacy-preserving semantic segmentation method that uses encrypted images and models with the vision transformer (ViT), called the segmentation transformer (SETR). The combined use of encrypted images and SETR allows us not only to apply images without sensitive visual information to SETR as query images but to also maintain the same accuracy as that of using plain images. Previously, privacy-preserving methods with encrypted images for deep neural networks have focused on image classification tasks. In addition, the conventional methods result in a lower accuracy than models trained with plain images due to the influence of image encryption. To overcome these issues, a novel method for privacy-preserving semantic segmentation is proposed by using an embedding that the ViT structure has for the first time. In experiments, the proposed privacy-preserving semantic segmentation was demonstrated to have the same accuracy as that of using plain images under the use of encrypted images.
Emotional Practices and How We Can Trace Them: Diplomats, Emojis, and Multilateral Negotiations at the UNHRC

ABSTRACT This article suggests a new approach for looking at emotions. In the framework that is developed, emotions are practices that are performed in context and not only felt or had. On the theoretical side, three concepts inspired by Bourdieu's work are introduced: hexis, emotional sense, and emotional performance. On the methodological side, this framework is used to make sense of emojis in digital exchanges. Emojis are the literal display of an emotion “on paper”—or rather, on screen—and constitute a simplified way to read the emotional communication between individuals. They are not epiphenomenal. Given the widespread use of instant messaging applications, they are an accessible and effective means for individuals to perform emotions. In turn, this framework opens up the possibility to analyze better how and why mundane emotions matter in international politics. How diplomats use emojis on WhatsApp during negotiations at the UN Human Rights Council in Geneva serves as an illustration. Often perceived as guided by rational calculations, diplomats also master informal and interpersonal skills to persuade, negotiate, and build connections. This fundamental social dimension of diplomatic work puts their (online) emotional practices at the center of their performances.
Hidden Issuer Anonymous Credential

Identity Management Systems (IMS) allow users to prove characteristics about themselves to multiple service providers. IMS evolved from impractical, site-by-site authentication, to versatile, privacyenhancing Self Sovereign Identity (SSI) Frameworks. SSI frameworks often use Anonymous Credential schemes to provide user privacy, and more precisely unlinkability between uses of these credentials. However, these schemes imply the disclosure of the identity of the Issuer of a given credential to any service provider. This can lead to information leaks. We deal with this problem by introducing a new Anonymous Credential scheme that allows a user to hide the Issuer of a credential, while being able to convince the service providers that they can trust the credential, in the absence of a trusted setup. We prove this new scheme secure under the Computational Diffie Hellman assumption, and Decisional Diffie Hellman assumption, in the Random Oracle Model. We show that this scheme is efficient enough to be used with laptops, and to be integrated into SSI frameworks or any other IMS.
Assessing digital competence and its relationship with the socioeconomic level of Chilean university students

Abstract Digital competence (DC) is one of the key aspects in citizen development in the digital age. The DC is particularly important in forming university students and future teachers. This article presents the main results of a study to evaluate DC and its relationship with the socioeconomic level of first-year students of pedagogy in three Chilean public universities, located in the north, center, and south of the country. A quantitative research methodology was used, with a sample of 817 students, the data were collected through the DIGCOMP-PED evaluation instrument, which evaluates DC development using the DIGCOMP framework. The results were analyzed at the general and socioeconomic level on the variables of the educational establishment where they attended high school and the territorial area of the university they attended. The main results indicate that the level of DC achievement is intermediate, the areas with the highest levels of achievement were “network security” and “online communication and collaboration.” On the other hand, the lowest levels of achievement were reached in the areas “information and digital literacy,” “digital content creation,” and “problem solving.” The level of DC is higher among students of private establishments and those who attend universities located in the central area.
Perceptually-Aware Per-Title Encoding for Adaptive Video Streaming

In live streaming applications, a fixed set of bitrate-resolution pairs (known as bitrate ladder) is used for simplicity and efficiency to avoid the additional encoding run-time required to find optimum resolution-bitrate pairs for every video content. However, an optimized bitrate ladder may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience (QoE). This paper introduces a perceptually-aware per-title encoding (PPTE) scheme for video streaming applications. In this scheme, optimized bitrate-resolution pairs are predicted online based on Just Noticeable Difference (JND) in quality perception to avoid adding perceptually similar representations in the bitrate ladder. To this end, Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for each video segment are used. Experimental results show that, on average, PPTE yields bitrate savings of 16.47% and 27.02% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming accompanied by a 30.69% cumulative decrease in storage space for various representations.
Design and application of Nagar-Bardini structure-based interval type-2 fuzzy logic systems optimized with the combination of backpropagation algorithms and recursive least square algorithms

This paper designs a kind of Nagar-Bardini structure based on interval type-2 (IT2) fuzzy logic systems for permanent magnetic drive uncertain parameter forecasting. For each fuzzy rule, the consequent, antecedent and input measurement of IT2 membership functions (MFs) are selected as the Gaussian type-2 MFs with uncertain standard deviations. Backpropagation algorithms and recursive least square algorithms are used to optimize the antecedent, input measurement and consequent parameters of fuzzy logic system (FLS) forecasters, respectively. Compared with the corresponding singleton and non-singleton type-1 (T1) FLSs, two Monte Carlo simulation instances on the basis of data of PMD process show the effectiveness and superiority of non-singleton IT2 FLSs according to the convergence analysis.
Regulatory Framework for eHealth Data Policies in Zimbabwe: Measuring FAIR Equivalency

Abstract The FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable (FAIR)—aim to improve the management of digital data assets for improved decision making. FAIR comprises 15 elements (called facets) that explain how data should be able to be reused by researchers and policymakers. For this research, eight policy documents were reviewed from Zimbabwe's Ministry of Health and Ministry of Information and Communication Technology (ICT) from 1999 to 2020. These were scrutinised to determine the mention of the FAIR Guidelines or FAIR Equivalent principles. The vision, mission statement and objectives of these documents were analysed relative to the 15 facets of FAIR. The research found that none of the policy documents in health/eHealth or ICT in Zimbabwe explicitly mention the FAIR Guidelines, but all contain some FAIR Equivalent principles. Hence, the regulatory framework for health/eHealth data management in Zimbabwe is aligned with the FAIR Guidelines and, therefore, a policy window is open for the adoption of FAIR Guidelines in relation to health/eHealth data management.
FAIR Equivalency, Regulatory Framework and Adoption Potential of FAIR Guidelines in Health in Kenya

Abstract This study explored the regulatory framework in Kenya that may facilitate the implementation of the FAIR Guidelines in health research, as well as the possibility of adopting the FAIR Guidelines at the national level. Fourteen key documents pivotal to the emerging digital health sector in Kenya were identified and analysed using a comprehensive coding and labelling approach based on a binary system for whether or not they mention the FAIR Guidelines or terms and vocabulary related to the FAIR Guidelines. The analysis revealed gaps in data stewardship that could be filled by the implementation of the FAIR Guidelines and, although the documents analysed do not explicitly mention the FAIR Guidelines, FAIR Equivalent terminology and practices are mentioned in varying detail. However, our analysis shows that there are still no provisions for the introduction and implementation of the FAIR Guidelines in health research in Kenya. Therefore, we recommend that the leadership be provided with a comprehensive introduction to the FAIR Guidelines, success stories about the FAIRification of data and research infrastructure in other parts of the world, and a demonstration of the steps needed for the FAIRification of health data in Kenya.
Fusion-based underwater image enhancement with category-specific color correction and dehazing

Underwater imaging is usually affected by water scattering and absorption, resulting in image blur and color distortion. In order to achieve color correction and dehazing for different underwater scenes, in this paper we report a fusion-based underwater image enhancement technique. First, statistics of the hue channel of underwater images are used to divide the underwater images into two categories: color-distorted images and non-distorted images. Then, category-specific combinations of color compensation and color constancy algorithms are used to remove the color shift. Second, a ground-dehazing algorithm using haze-line prior is employed to remove the haze in the underwater image. Finally, a channel-wise fusion method based on the CIE L* a* b* color space is used to fuse the color-corrected image and dehazed image. For experimental validation, we built a setup to acquire underwater images. The experimental results validate that the category-specific color correction strategy is robust to different categories of underwater images and the fusion strategy simultaneously removes haze and corrects color casts. The quantitative metrics on the UIEBD and EUVP datasets validate its state-of-the-art performance.
HWMWOA: A Hybrid WMA–WOA Algorithm with Adaptive Cauchy Mutation for Global Optimization and Data Classification

Combinatorial metaheuristic optimization algorithms have newly become a remarkable domain for handling real-world and engineering design optimization problems. In this paper, the Whale Optimization Algorithm (WOA) and the Woodpecker Mating Algorithm (WMA) are combined as HWMWOA. WOA is an effective algorithm with the advantage of global searching ability, where the control parameters are very less. But WOA is more probable to get trapped in the local optimum points and miss diversity of population, therefore suffering from premature convergence. The fundamental goal of the HWMWOA algorithm is to overcome the drawbacks of WOA. This betterment includes three basic mechanisms. First, a modified position update equation of WMA by efficient exploration ability is embedded into HWMWOA. Second, a new self-regulation Cauchy mutation operator is allocated to the proposed hybrid method. Finally, an arithmetic spiral movement with a novel search guide pattern is used in the suggested HWMWOA algorithm. The efficiency of the suggested algorithm is appraised over 48 test functions, and the optimal outcomes are compared with 15 most popular and newest metaheuristic optimization algorithms. Moreover, the HWMWOA algorithm is applied for simultaneously optimizing the parameters of SVM (Support Vector Machine) and feature weighting to handle the data classification problem on several real-world datasets from the UCI database. The outcomes prove the superiority of the suggested hybrid algorithm compared to both WOA and WMA. In addition, the results represent that the HWMWOA algorithm outperforms other efficient techniques impressively.