Reviewerly/exHarmony huggingface dataset:
Efficient Revocable Anonymous Authentication Mechanism for Edge Intelligent Controllers

In the field of industrial control, edge computing puts forward higher requirements for the local processing ability of data. The traditional industrial programmable logic controller (PLC) cannot complete this task. The edge intelligent controller (EIC) is developed according to the demand for edge computing. As the core component of edge computing, the security and reliable operation of EIC has great significance and influence on the promotion and development of edge computing. For the access authentication of EIC, an efficient revocable anonymous authentication scheme for EICs by utilizing a group signature technique is proposed in this article, which can trace the identity information of the EIC and protect the privacy of the EIC, and apply an efficient revocation mechanism to revoke any illegal or abnormal EICs. Through security analysis, we prove that the authentication scheme is anonymity, integrity, traceability, forward secrecy, resistance to the replay attack, and efficient revocability. Furthermore, the performance analysis and comparisons show that the authentication scheme is efficient and feasible for EICs.
Distributed auditing protocol for untraceable transactions

Data integrity is a property that cloud service providers (CSPs) have focused on. The data integrity checking is assigned to third-party auditors (TPAs) to avoid invalid data storage in CSPs. Nevertheless, this type of mechanism is vulnerable to many threats, including insider/outsider adversaries. Moreover, several threats still exist with its presence, such as deleting, changing, or destroying the recorded data. The study presents a blockchain-based distributed auditing (BB-DA) protocol for auditing confidential/untraceable transactions in a distributed manner. The BB-DA protocol provides message/transaction confidentiality, user privacy, and security against privileged insider adversaries, who want to create disorder in the auditing process by refusing to sign/audit, and those who aim to do so in the BB-DA protocol will fail even if they cooperate. To provide message confidentiality and security against malicious insiders, the ElGamal cryptosystem is used in the BB-DA protocol, and to avoid destroying the submitted data (transactions) by CSPs, the blockchain is used as a distributed and immutable database in the BB-DA protocol. The Zerocoin is applied in the BB-DA protocol for providing user untraceability and anonymity. The security analysis of the BB-DA protocol proves that the offered protocol is secure against the key-only, known message, and chosen message attacks. Additionally, the analysis indicates that it provides security against malicious insiders who want to learn any knowledge about corresponded transactions or break users’ anonymity. The paper’s comparison and evaluation show that the BB-DA offers more features and is more efficient than other auditing protocols.
A Super-resolution-based Approach for the Detection of Covid—19 Infection From Chest X-ray Images

X-ray is the most accessible imaging modality for detecting Covid-19 infection. However, X-ray image resolution depends on the amount of radiation dose. The Lesser the dosage, the lower the resolution, the higher the noise and patient safety. Detecting Covid-19 infection would be more precise with high-resolution chest X-ray images. The current article explores an edge-preserving single-scale residual learning-based super-resolution method to enhance low-resolution chest X-ray images. We used unsharp masking to preserve small, medium, and high-scale details while super-resolving the given image. The method produces a clear view of the pulmonary opacities in chest X-ray images after super-resolution reconstruction. Statistical feature metrics of first and second-order showed superior quality reconstruction by the proposed method for the given Covid-19 chest X-ray images. Further, to measure the effectiveness of super-resolution, we used an Inception v3 based deep learning model to classify chest X-ray images of Covid-19, pneumonia, and normal class. The performance of the classification model with super-resolved chest X-ray images was tested against 400 images belonging to two different classes at a time. We obtained increased precision of 94% and 96% accuracy in detecting Covid-19 infection in chest X-ray images after super-resolution compared to 64% precision and 68% accuracy before super-resolution.
On-screen keyboard controlled by gaze for Vietnamese people with amyotrophic lateral sclerosis

BACKGROUND: On-screen keyboard controlled by gaze is the most useful approach to help people with Amyotrophic Lateral Sclerosis (ALS) communicate. However, the design of the traditional on-screen keyboard hinders gaze typing for languages other than English. OBJECTIVE: The purpose of this paper is developing a specialized on-screen keyboard controlled by gaze for Vietnamese people with ALS. METHODS: Designed on-screen keyboard layouts and selection mechanism based on the requirements of eye typing and Vietnamese language characteristics from the statistics results on the Vietnamese Wikipedia Corpus dataset. During the experiment, we also absorbed and modified some design features according to the users’ suggestions. RESULTS: The authors proposed two new specialized on-screen keyboard layouts controlled by eye gaze for the Vietnamese language which both speed up typing and reduce typos. The results of the experiment are an average typing speed of respectively 23.1 and 26.1 characters per minute, and an average error rate of 1.0% and 0.5% for the two layouts. All participants were satisfied when using the system at their house. CONCLUSIONS: Although the effectiveness of the proposed method depends on the time of use and the familiarity of users with the new keyboard, the potential of the proposed method is great.
Similarity attack: An adversarial attack game for image classification based on deep learning

In order to allow users to incorrectly identify images by manipulating them using deep neural networks, this paper analyses the shortcomings of deep learning for image classification. It also develops a game that uses this technique. In the game, players can select one of their preferred product categories, causing the model to classify other product categories incorrectly as the one they selected. The goal of this game is to demonstrate to players the limitations of AI. We evaluate these programs based on their overall effectiveness, user satisfaction, and achievement of their objectives. The results show that this program is a successful method for arousing curiosity and stimulating thought. They can learn to appreciate the limitations of AI and the need to prioritize AI security in their daily activities.
A fitting algorithm based on multi-touch gesture for rapid generation of railway line

Human-computer interaction (HCI) technology plays a critically essential role in the computer-aided design of railway line locations. However, the traditional interactive design with a mouse+keyboard cannot well meet the rapid generation requirements of the railway line during scheme discussion. This research presents a fitting algorithm for the rapid generation of railway lines by using a multi-touch gesture algorithm. The fitting method from free hand-drawing lines to satisfied railway lines is proposed. Then the interactive operation hand gestures are defined and implemented into the railway line location design. The hand-drawing lines generated by defined gestures are automatically fitted with the target horizontal line by using the inflection detection algorithm based on Euclidean Distance (ED). Meanwhile, the vertical line can be fitted by a similar algorithm with an extreme point-to-point (EPP) and chord-to-point distance accumulation (CPDA). Moreover, a real-world example verification is carried out. The multi-touch gesture algorithm is applied for the automatic fitting of the railway line. Compared with the traditional interactive methods, the consumption time of railway line generation by using the multi-touch interactive mode is decreased by about 15%. This research provides fundamental support for rapid scheme discussion of railway line generation based on natural HCI, which is well-matched with modern handheld devices, and the requirements of rapid selection as well as the quick comparison of railway line schemes in the early stage of design.
An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

A recommender system may recommend certain items that the users would not prefer. This can be caused by either the imperfection of the recommender system or the change of user preferences. When those failed recommendations appear often in the system, the users may consider that the recommender system is not able to capture the user preference. This can result in abandoning to further use the recommender system. However, given the possible failed recommendations, most recommender systems will ignore the non-preferred recommendations. Therefore, this paper proposes failure recovery solution for recommender systems with an adaptive filter. On the one hand, the proposed solution can deal with the failed recommendations while keeping the user engagement. Additionally, it allows the recommender system to dynamically fine tune the preferred items and become a long-term application. Also, the adaptive filter can avoid the cost of constantly updating the recommender learning model.
Colour image encryption based on hybrid bit-level scrambling, ciphering, and public key cryptography

This paper proposes an image encryption technique using three stages algorithms based on hyper-chaotic maps. In the first scenario, bit-level scrambling (BLS) using a 2D coupled chaotic map (2D-CCM) is used to encrypt the bits of the basic colour image. In the second strategy, the scrambled bit level is XORed with pseudo random bit generator (PRBG). The PRBG is designed using a combination of chaotic maps, including, logistic map (LM), sine map (SM), 5D chaotic map (5D-CM), enhanced quadratic map (EQM), and 2D henon SM (2D-HSM). The pubic key based on the Chebyshev polynomial chaotic map is used as the final phase of the encryption algorithms. The performance analysis of the proposed image encryption technique is validated through various criteria such as fundamental space analysis, correlation coefficient, entropy, the number of pixels changes rate (NPCR), and unified average-changing intensity (UACI). Also, the obtained results are compared with other recent studies. The simulation results demonstrated that the proposed technique has robust security and it provides the image with high protection against various attacks.
Reflections on light: Developing new methods for producing anamorphic sculpture

Abstract This paper presents novel catoptric anamorphic sculptures made possible by the development of bespoke software. The authors detail the production of a catoptric anamorphic sculpture involving a concave mirror and examine the audience’s experience. The reflections the mirror creates are described as being holographic. This effect is known as a “real image” and only occurs using a concave mirror. The authors present the digital tools they have developed to facilitate anamorphic artistic production and extend the limits of what has been achieved in the past. They end with an outline of future work, including glass lenses, and propose using video projection mapping.
gOd, mOther and sOldier: A Story of Oppression, Told Through the Lens of AI

Abstract The authors present gOd, mOther and sOldier—Nowhere in Somewhere Series 2022, a work that was conceptualized and created by artist Jinjoon Lee and his TX Creative Media Lab at KAIST, realized through the remote cooperation of eight local collaborators across Southeast Asia. The authors used artificial intelligence–based object detectors and sonification techniques in a work of media art to symbolize the voicelessness of those at the margins of society in Southeast Asia. These algorithms and concepts, and the work as a whole, artistically demonstrate how marginalized people are misrepresented and misunderstood when interpreted out of context.
Roles of Technology in the Science Classroom

The ability to use technology within the context of science is important for science students; however, the integration of technology within science is not implemented consistently across the curriculum, nor across grade and age levels. More insight is needed as to how science and technology are integrated in the classroom. Technology usage takes many forms in the classroom, including educational and instructional technology, programming and computational thinking, mathematics modeling, science-specific tools, and dataset usage. This chapter consists of three main sections: a meta study of forms of technology used in science education, a series of vignettes of actual use of technology in the classroom, and guidance on thoughtful adoption of technology.
Fast and Accurate Framework for Ontology Matching in Web of Things

The Web of Things (WoT) can help with knowledge discovery and interoperability issues in many Internet of Things (IoT) applications. This article focuses on semantic modeling of WoT and proposes a new approach called Decomposition for Ontology Matching (DOM) to discover relevant knowledge by exploring correlations between WoT data using decomposition strategies. The DOM technique adopts several decomposition techniques to order highly linked ontologies of WoT data into similar groups. The main idea is to decompose the instances of each ontology into similar groups and then match instances of similar groups instead of entire instances of two ontologies. Three main algorithms for decomposition have been developed. The first algorithm is based on radar scanning, which determines the distribution of distances between each instance and all other instances to determine the cluster centroid. The second algorithm is based on adaptive grid clustering, where it focuses on distribution information and the construction of spanning trees. The third algorithm is based on split index clustering, where instances are divided into groups of cells from which noise is removed during the merging process. Several studies were conducted with different ontology databases to illustrate the use of the DOM technique. The results show that DOM outperforms state-of-the-art ontology matching models in terms of computational cost while maintaining the quality of the matching. Moreover, these results demonstrate that DOM is capable of handling various large datasets in WoT contexts.
Information and Communication Technologies (ICTs) implementation

The availability and per cent frequency usage of mobiles among the sampled farmers was found to be high in the study area. High the use of ICTs boosts agricultural production and the farmers with new technologies are fared better due to more access and sharing of information. The relationship between education, socio-economic status, extension contact, mass media exposure, scientific orientation, economic motivation and risk orientation was significantly positive with availability/accessibility, number of ICTs tool usage and usage pattern of ICTs by the farmers, whereas relationship of age was found to be negative. Similarly, the regression coefficient showed the impact of age, land holding and irrigation facilities with availability/ accessibility, number of ICTs tool usage and usage pattern of ICTs by the farmers is negative whereas education, socio-economic status, extension contacts, mass media exposure, scientific orientation, economic motivation and risk orientation positively affect availability/accessibility, number of ICTs tool usage and ICTs’ usage pattern by the farmers in the study area. All other personal variables were significantly and positively related with availability and accessibility of ICTs tools, number of ICTs tools usage by farmers and usages pattern of ICTs in farming system. Regression coefficient of education, socio-economic status, extension contact, mass media exposure, scientific orientation, economic motivation, and risk orientation revealed that one unit increase in these variables would increase the number of ICTs tools usage by the farmers respectively whereas age land holding and irrigation facilities has significantly negative regression coeffieicint.
Directions of digital transformation of scientific and educational activities

The article discusses the current directions of digital transformation of scientific and educational activities. In 2021, the Ministry of Science and Higher Education developed a Strategy for the Digital Transformation of Science and Higher Education, which is aimed at achieving digital maturity as the basis for strategic development and digital transformation of scientific organizations and educational institutions of higher education. Five main directions of digital transformation of science and higher education are presented: architecture of digital transformation; development of digital services; data management; infrastructure modernization; human resources management. Additionally, seven more subprojects have been identified: datahab; data management system for science and higher education; digital transformation architecture; digital university; unified science service platform; software and hardware marketplace; digital education; service hub. The Strategy of Digital Transformation of the branch of science and higher education reflects the focus on digital interaction of information systems, their interconnectedness, subordination and unity of work in relation to scientific and educational organizations of higher education. One of the vectors of digital transformation at present is a certain shift in its focus, which consists not only in digitalization of various processes and types of professional activity, but also in changing and transforming digital interaction systems. The key aspects of the digital transformation of science and education are new, in their content, directions and models of activity, which are based on the synthesis of constantly updated digital solutions and services, opportunities for continuing professional education and development for specialists, the availability of conditions for organizing digital changes, support and coordination of effective communication between participants in digital transformation projects within the framework of professional interaction.
ELMShip: An Efficient Ship Classifier Using Extreme Learning Machines

A new algorithm for ship classification named ELMShip is presented. The proposed methodology uses an extreme learning machine algorithm trained via features extracted via a pre-trained deep neural network. Here, we used an open available labelled dataset of 6252 images and five different classes. The original data was augmented via the extraction of subimages using squared sliding windows of different sizes and overlapped ratios to improve our calculations. Upon fine-tuning, we found that our best model outperforms the previously published results for all classes in all evaluated metrics.
Single underwater image enhancement based on adaptive correction of channel differential and fusion

Clear underwater images are necessary in many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. In order to overcome the limitations of the available color correction and deblurring algorithms, this paper proposed a fusion-based image enhancement method for various water areas. We proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval. Subsequently, using these two methods, we took two images from a single underwater image as inputs of the fusion framework. Finally, we obtained a satisfactory underwater image. To validate the effectiveness of the experiment, we tested our method using public datasets. The results showed that the proposed method can adaptively correct color casts and significantly enhance the details and quality of attenuated underwater images.
Pretrained E-commerce Knowledge Graph Model for Product Classification

Product classification in E-commerce platforms aim to classify a product into correct category, in order to obtain a better guiding service for customer to purchase. Language model is usually used to encode product title into product embedding, and then fed into a classifier for multi-class classification. However, the product title is usually ambiguous and noisy, leading to poor prediction performance. To address this issue, we propose a novel pretrained E-commerce knowledge graph (PEKG) model to learn the representation of product from EKG, and then used it for fine-tuning. We formulate the pretraining on EKG as a multi-view learning problem, where the EKG is divided into four views. From PEKG, user and product representation is proposed to learn via aggregation of its neighbor information, and the semantic meaning from the EKG is learned via translation-based method. We experimentally prove that our proposal significantly outperforms baseline,showing that the PEKG can learn useful representation of product.
Integrated, Coordinated, Open, and Networked (ICON) Scientific and Societal Relevance

Abstract This article is composed of three independent commentaries about the state and future of I ntegrated, C oordinated, O pen, and N etworked (ICON) principles in the Science and Society section of the American Geophysical Union (Goldman et al., 2022, https://doi.org/10.1029/2021EA002099 ). The ICON approach pioneers a new way of doing science that I ntegrates physical, chemical, biological, or social attributes across scales, C oordinates with consistent protocols and methods to enable transferability, is O pen through the research lifecycle with findable, accessible, interoperable, and reusable principles, and ensures mutual benefit to N etwork across a broad range of stakeholders. Scientific discoveries, rapid scientific and technological advancements, and solutions improve our ability to face challenges and benefit society. Without innovative approaches and integrity, we cannot solve the challenges of the 21st century, such as educational, agricultural, and health equity as well as climate change and environmental sustainability. Many societal challenges require evolved frameworks and measures to address complex problems. Here, we present three independent commentaries that discuss the connection of science to three broad areas of society and present challenges and untapped opportunities in these areas. The broad areas are Citizen Science, Collaboration across Natural Sciences and Social Sciences, and Education Policy which embody the ICON processes that can create a better understanding of the role of science in society and sustainable development. The ICON processes described in these broad areas offer universal lessons and can be adapted to other areas of impact in society.
Gesture and Texture in the Electroacoustic Improvised Music of Jin Sangtae, Hong Chulki and Tetuzi Akiyama

This article will examine the music of two improvisation scenes – Seoul’s ‘Dotolim’ and Tokyo’s onkyô – with a particular focus on their use of gesture and texture. The article centres on an analysis of a trio performance by Jin Sangtae, Hong Chulki and Tetuzi Akiyama, which effectively brings these two musical communities together in a collaborative performance. This analysis demonstrates that focusing on gesture and texture can help in perceiving essential aspects of an improvised performance, including understanding strategic modes of listening and interplay, musical structural developments (both micro and macro) as well as identifying applied roles of instrumental technique and approach.