Reviewerly/exHarmony huggingface dataset:
National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people’s lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero–one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.
A randomized controlled pilot study examining immediate effects of embodying a virtual reality superhero in people with chronic low back pain

Purpose: In virtual reality, avatar embodiment can spur perceptions and behaviours related the avatars' characteristics.We tested whether embodying superhero-like avatars can change self-perceptions in people with chronic low back pain (CLBP).Design: A non-blinded pilot randomized controlled trial.Methods: Participants were randomly allocated to embody a superhero (VR-SH, n ¼ 20) or a neutral, nonsuperhero (VR-Play, n ¼ 10) avatar.Primary outcomes related to body image (e.g., self-perceived vulnerability) and pain intensity were assessed at baseline, during exposure, immediately after and at one-week follow-up.Fear of movement, strength and patient impression of change were also recorded. Results:The VR-SH group reported gains in body image during (p < .001,g 2 p ¼0.71) and immediately after (p < .001,g 2 p ¼0.66) VR, whereas the VR-Play group reported small gains during VR (p ¼ .021,g 2 p ¼0.46) but not immediately after (p ¼ .076,g 2 p ¼0.31).Pain, strength and fear of movement did not change and there were no sustained effects at follow-up (all ps > .05).A greater proportion of people in the VR-SH group reported at least minimal improvement at follow-up (7/20 vs. 1/10). Conclusion:A VR-SH session produced temporary positive effects on body image.Future research may consider whether larger and sustained effects can be obtained with multisession exposures or explore combined interventions. � IMPLICATIONS FOR REHABILITATION� Physical confidence and bodily trust can be significantly enhanced in people with chronic back pain using virtual reality.� These positive body image effects may have implications for rehabilitation, such as in enhancing confidence with movement � Whether a multisession intervention might produce more robust effects and changes in pain is yet to be determined
A network attack link detection method based on the knowledge graph

Existing network security detection technologies often detect various network security problems separately. There are limitations in these technologies because of the correlation between entity attack detection and attack capital information. To resolve the problem of poor detection performance of existing network attack link detection methods, this paper proposes a network attack link detection method based on the knowledge graph. Specifically, the link knowledge graph is constructed, the embedded calculation of the knowledge graph of the link attack node is established, a knowledge graph audit model of link attack security detection is constructed, and the detection rules of the link attack knowledge graph are established to identify the association between the link network attack entity and the attack asset attribute, and improve the identification and detection of the attack source by associating the knowledge graph. Experimental results show that the proposed method has better link attack association awareness than other methods reported before.
A Temporal Type-2 Fuzzy System for Time-Dependent Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and decisions, which are easy to understand, analyze, and augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can provide an explainable framework, while also modeling uncertainties present in real-world environments, which renders it suitable for applications where explainability is a requirement. However, most real-life processes are not characterized by high levels of uncertainties alone; they are inherently time-dependent as well, i.e., the processes change with time. In this work, we present novel Temporal Type-2 FLS Based Approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a measurement's occurrence in the time domain using (the measurement's) frequency of occurrence. In Temporal Type-2 Fuzzy Sets (TT2FSs), a four-dimensional (4D) time-dependent membership function is developed where relations are used to construct the inter-relations between the elements of the universe of discourse and its frequency of occurrence. The TXAI system manifested better classification prowess, with 10-fold test datasets, with a mean recall of 95.40\% than a standard XAI system (based on non-temporal general type-2 (GT2) fuzzy sets) that had a mean recall of 87.04\%. TXAI also performed significantly better than most non-explainable AI systems between 3.95\%, to 19.04\% improvement gain in mean recall. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency of occurrence values embedded in the TXAI model; viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioural or biological processes.
NASA

Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications. To tackle this limitation, pioneering works have developed handcrafted multiplication-free DNNs, which require expert knowledge and time-consuming manual iteration, calling for fast development tools. To this end, we propose a Neural Architecture Search and Acceleration framework dubbed NASA, which enables automated multiplication-reduced DNN development and integrates a dedicated multiplication-reduced accelerator for boosting DNNs' achievable efficiency. Specifically, NASA adopts neural architecture search (NAS) spaces that augment the state-of-the-art one with hardware inspired multiplication-free operators, such as shift and adder, armed with a novel progressive pretrain strategy (PGP) together with customized training recipes to automatically search for optimal multiplication-reduced DNNs; On top of that, NASA further develops a dedicated accelerator, which advocates a chunk-based template and auto-mapper dedicated for NASA-NAS resulting DNNs to better leverage their algorithmic properties for boosting hardware efficiency. Experimental results and ablation studies consistently validate the advantages of NASA's algorithm-hardware co-design framework in terms of achievable accuracy and efficiency tradeoffs. Codes are available at https://github.com/shihuihong214/NASA.
Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning

This paper presents an adaptive trajectory planning approach for nonlinear dynamical systems based on deep reinforcement learning (DRL). This methodology is applied to the authors’ recently published optimization-based trajectory planning approach named nonlinear model predictive horizon (NMPH). The resulting design, which we call ‘adaptive NMPH’, generates optimal trajectories for an autonomous vehicle based on the system’s states and its environment. This is done by tuning the NMPH’s parameters online using two different actor-critic DRL-based algorithms, deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). Both adaptive NMPH variants are trained and evaluated on an aerial drone inside a high-fidelity simulation environment. The results demonstrate the learning curves, sample complexity, and stability of the DRL-based adaptation scheme and show the superior performance of adaptive NMPH relative to our earlier designs.
Crypto-Based Secure Outbound Supply Chain Authentication for Industry 4.0

Industry 4.0 is regarded as one of the most promising evolutionary techniques for improving manufacturing productivity and conditions. When the manufacturing process becomes more complicated, the entire process must be supplied under the administrative IoT, in which devices from various environments are intended to operate on the same job, ensuring greater privacy and security among the devices throughout the communication process. Some common authentication methods provide third-party security while also dealing with key management difficulties. As a result, due to the fact that key management is yet unsolvable, a greater emphasis on privacy and security is required. A unique and effective security technique called outbound supply chain theorem authentication helped by blockchain is expected to create confidence among many domains in this research. The authentication procedure is used to ensure security. The outbound supply chain authentication solution ensures device privacy while allowing authentication across linked devices. The key distribution among the linked parties, on the other hand, must be arranged through an appropriate communication channel. The simulation is carried out in a MATLAB environment, and the results reveal that the authentication procedure is more successful.
Flexible Photorealistic VR Training System for Electrical Operators

Virtual reality can be a very effective tool for professional training, especially when training performed in reality would pose a high risk for the trainees. However, efficient use of VR in practical industrial training requires high-quality VR training environments - 3D scenes and interactive scenarios. Because creating such a VR environment is a complex and time-consuming task, existing VR training systems generally do not offer the flexibility that would allow trainers to adjust the environments to changing training contexts and requirements. In many cases, in industrial training, however, one can benefit from the componentization and repeatability of the training content to provide such flexibility. In this paper, we propose a method of creating photorealistic virtual reality training scenes with the use of a library of training objects. Designing training objects is a relatively complex process, but once a library of objects is available, a designer can assemble new training scenes from the objects available in the library. Training scenarios can be added using an easy-to-use graphical tool.
Bibliographic Activities of Libraries: Theoretical Analysis

One of the priority areas of the research activity of the Russian State Library (RSL), in particular its scientific bibliographic activity, is currently the study “Information and bibliographic activity of Russian libraries (theoretical and practical aspects)”. Relevance of the research is caused by the fact that information and bibliographic activity refers to the main activities of libraries of various types and kinds. Libraries create bibliographic information for wide distribution, provide bibliographic services to their readers (subscribers). Bibliographic information is also used in various particular library processes (acquisition, library and information services, organization of mass events, etc.). The article considers the results of the analysis of the R&amp;D object — bibliographic (information and bibliographic) activity of libraries as a system object (essence, boundaries, objects and subjects of activity, internal structure, etc.). The analysis was carried out on the basis of theoretical concepts developed by the domestic bibliography science, as well as the modern terminological system of the library and information sphere, fixed in national standards. The author identified and analysed the main directions of bibliographic activity in the library (library bibliography): bibliographic processing of library collections (library bibliographic cataloguing); formation of reference and bibliographic tool; creation of bibliographic resources and products of “non-catalogue” type (indexes, lists, reviews, etc.); bibliographic service; bibliographic education; scientific and methodological activities in the field of bibliography.
The Current Stage of Development of the Schedules of Library Bibliographic Classification

Library bibliographic classification (LBC) is the National classification system of the Russian Federation. The article highlights the history of creation of the Medium, Abridged and specialized versions of the LBC schedules. Specialized version of the LBC schedules — Schedules for children’s and school libraries — is designed to organize collections and catalogues in children’s and school libraries of all systems and departments, including libraries serving both children and adults. The author carried out the analysis of different versions of schedules for compliance with the unity of structure and scientific content. That task was performed for the Medium and Abridged schedules of the LBC as a whole, as well as in the sections of social, humanities and applied sciences Schedules for childrenʼs and school libraries. The article considers the current preparation for the release of the sixth edition of Schedules for childrenʼs and school libraries based on the second edition of the Abridged schedules of the LBC. It will contain the new class “1 Interdisciplinary knowledge”, the expanded and modified class “9 Universal content literature”, the new edition of the Schedules of standard divisions, as well as the revised sections of natural sciences. All other sections of the sixth edition will be edited: there will be made clarifications, corrections, additions to the content and methodological apparatus of the schedules. The article presents the structure and content of the sections “15 Cybernetics”, “16 Computer Science and Information Technology”, as well as the revised section “22 Physical and Mathematical Sciences”. The release of the sixth edition of the Schedules for childrenʼs and school libraries will complete the formation of the modernized National classification system of the Russian Federation, observing the unity of structure and scientific content of all versions of the schedules.
A New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique

In this article, a new reversible data hiding method using most significant bits and center shifting technique in dual images is proposed. The proposed reversible data hiding method aims to securely hide high-capacity secret data. Instead of directly embedding the secret data, the method calculates new values with (n + 1) bits secret data and the n most significant bits of the cover image pixel values. Thus, it is impossible to extract secret data without obtaining the original cover image. Also, the center shifting process is performed to minimize the mean square error. After this process, the secret data to be hidden in the dual images are in the range [−3×2(n−1)+1, 3×2(n−1)−1]. The pixel values of stego images are obtained by using the secret data in this range value and cover image pixel values. As a result of experimental studies, when the payload is 2.5 bits per pixel (bpp), the peak signal-to-noise ratio value (PSNR), which expresses the visual quality, is above 34 (dB). In addition, the proposed method has proven secure against RS (regular and singular) analysis attacks.
Efficient Object Detection in SAR Images Based on Computation-Aware Neural Architecture Search

Remote sensing techniques are becoming more sophisticated as radar imaging techniques mature. Synthetic aperture radar (SAR) can now provide high-resolution images for day-and-night earth observation. Detecting objects in SAR images is increasingly playing a significant role in a series of applications. In this paper, we address an edge detection problem that applies to scenarios with ship-like objects, where the detection accuracy and efficiency must be considered together. The key to ship detection lies in feature extraction. To efficiently extract features, many existing studies have proposed lightweight neural networks by pruning well-known models in the computer vision field. We found that although different baseline models have been tailored, a large amount of computation is still required. In order to achieve a lighter neural network-based ship detector, we propose Darts_Tiny, a novel differentiable neural architecture search model, to design dedicated convolutional neural networks automatically. Darts_Tiny is customized from Darts. It prunes superfluous operations to simplify the search model and adopts a computation-aware search process to enhance the detection efficiency. The computation-aware search process not only integrates a scheme cutting down the number of channels on purpose but also adopts a synthetic loss function combining the cross-entropy loss and the amount of computation. Comprehensive experiments are conducted to evaluate Darts_Tiny on two open datasets, HRSID and SSDD. Experimental results demonstrate that our neural networks win by at least an order of magnitude in terms of model complexity compared with SOTA lightweight models. A representative model obtained from Darts_Tiny (158 KB model volume, 28 K parameters and 0.58 G computations) yields a faster detection speed such that more than 750 frames per second (800×800 SAR images) could be achieved when testing on a platform equipped with an Nvidia Tesla V100 and an Intel Xeon Platinum 8260. The lightweight neural networks generated by Darts_Tiny are still competitive in detection accuracy: the F1 score can still reach more than 83 and 90, respectively, on HRSID and SSDD.
A multitask comparison of word- and character-frequency effects in Chinese reading.

In this study, we examined the effects of word and character frequency across three commonly used word-identification tasks (lexical decision, naming, and sentence reading) using the same set of two-character target words (N = 60) and participants (N = 82). Facilitatory effects of word frequency were observed across all three tasks. The character-frequency effects, however, were facilitatory for naming but inhibitory for both lexical decision and reading. Further correlational analyses indicated that participants' performance (as measured using overall response latencies and the sizes of the frequency effects) was not consistent across tasks but was relatively reliable within the lexical-decision and reading tasks. These findings are discussed in relation to what is known about the reading of Chinese versus alphabetic scripts, word-identification tasks, and models of word identification. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Theoretical Basis for Creating a Virtual-Educational Museum

The theoretical issues of creating a virtual educational museum are widely discussed in the article. The focus of the article is directed towards the issues of the importance of developing innovative didactic models in developing digital and software-technological competencies among students. The article displays the factors for mastering the native language and enriching the mental lexicon for the development of critical thinking processes in young students, the connection of these factors with perception, imagination, thinking, controlling brain activity in the information age, and the special role of virtual educational tools in the development of algorithmic thinking. Virtual-educational museums are interpreted as an illustration of presenting historical-cultural heritage in various ways using information and communication technologies. Didactic value of virtual educational museums is highlighted. The requirements for the content and design of the virtual educational museum are described. The procedure of creating a virtual-educational museum on the science of “Informatics and Information Technologies” and the analytical results of education based on the virtual-museum are shown. Moreover, this article highlights the role of virtual educational museums in mastering science, developing thinking, and understanding the national values.
Weakly Supervised Point Clouds Transformer for 3D Object Detection

The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of a point clouds transformer that is used for 3D object detection. The aim is to decrease the required amount of supervision needed for training, as a result of the high cost of annotating a 3D datasets. We propose an Unsupervised Voting Proposal Module, which learns randomly preset anchor points and uses voting network to select prepared anchor points of high quality. Then it distills information into student and teacher network. In terms of student network, we apply ResNet network to efficiently extract local characteristics. However, it also can lose much global information. To provide the input which incorporates the global and local information as the input of student networks, we adopt the self-attention mechanism of transformer to extract global features, and the ResNet layers to extract region proposals. The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet. On the challenging KITTI datasets, the experimental results have achieved the highest level of average precision compared with the most recent weakly supervised 3D object detectors.
Application of Deep Neural Network for Real-Time Voice Command Recognition

The article proposes a method for voice command recognition in real time mode using a deep neural network. which is built with a combination of different artificial neural networks architectures. Pre-processing of the sound signal is used for further recognition. The evaluation of the recognition efficiency is carried out by means of a comparative analysis. The results of experimental studies (voice recognition accuracy of 85%) demonstrate the features of the proposed method.