Reviewerly/exHarmony huggingface dataset:
ElectronicsAR: Design and Evaluation of a Mobile and Tangible High-Fidelity Augmented Electronics Toolkit

Exploring and interacting with electronics is challenging as the internal processes of components are not visible. Further barriers to engagement with electronics include fear of injury and hardware damage. In response, Augmented Reality (AR) applications address those challenges to make internal processes and the functionality of circuits visible. However, current apps are either limited to abstract low-fidelity applications or entirely virtual environments. We present ElectronicsAR, a tangible high-fidelity AR electronics kit with scaled hardware components representing the shape of real electronics. Our evaluation with 24 participants showed that users were more efficient and more effective at naming components, as well as building and debugging circuits. We discuss our findings in the context of ElectronicsAR's unique characteristics that we contrast with related work. Based on this, we discuss opportunities for future research to design functional mobile AR applications that meet the needs of beginners and experts.
Design and Evaluation of a Silent Speech-Based Selection Method for Eye-Gaze Pointing

We investigate silent speech as a hands-free selection method in eye-gaze pointing. We first propose a stripped-down image-based model that can recognize a small number of silent commands almost as fast as state-of-the-art speech recognition models. We then compare it with other hands-free selection methods (dwell, speech) in a Fitts' law study. Results revealed that speech and silent speech are comparable in throughput and selection time, but the latter is significantly more accurate than the other methods. A follow-up study revealed that target selection around the center of a display is significantly faster and more accurate, while around the top corners and the bottom are slower and error prone. We then present a method for selecting menu items with eye-gaze and silent speech. A study revealed that it significantly reduces task completion time and error rate.
AngleCAD: Surface-Based 3D Modelling Techniques on Foldable Touchscreens

3D modelling and printing are becoming increasingly popular. However, beginners often face high barriers of entry when trying to use existing 3D modelling tools, even for creating simple objects. This is further complicated on mobile devices by the lack of direct manipulation in the Z dimension. In this paper, we explore the possibility of using foldable mobile devices for modelling simple objects by constructing a 2.5D display and interaction space with folded touch screens. We present a set of novel interaction techniques - AngleCAD, which allows users to view and navigate a 3D space through folded screens, and to modify the 3D object using the physical support of touchscreens and folding angles. The design of these techniques was inspired by woodworking practices to support surface-based operations that allow users to cut, snap and taper objects directly with the touch screen, and extrude and drill them according to the physical fold angle. A preliminary study identified the benefits of this approach and the key design factors that affect the user experience.
Ethereum Blockchain Based Logistics Application for Smart Supply Chain Management

In recent years, compared to traditional supply chain systems Blockchain technology provides countless opportunities and potential to improve and develop the framework in a supply chain management model. We developed a client-side application which acts as a supply chain management platform built using Blockchain technology and Web3-net that provides access to cutting-edge services which include inventory tracking, order placement and product attribute information log. Cryptocurrency transactions are stored on a shared, digital ledger called a blockchain. This decentralized technology is spread across many computers and records every transaction. The platform provides secure and authentic information to its users at all stages using a decentralized app or DAPP which we have developed to ensure a seamless experience for verifying and maintaining data regarding all inventory parts and orders. This paper gives a model demonstration of the implementation of Blockchain Technology at the industry level. However, additional functionalities can be added in the future with enhanced compatibility.
An optical-cryptographic encryption scheme using a keystream synthesizer based on chaotic speckle nature

Abstract We propose a comprehensive and efficient optical-cryptographic encryption scheme to improve the security of vulnerable optical encryption systems. Based on cryptography theory, we increase data protection using diffusion-confusion processes acting in the input and output of the optical encryption scheme. The new enhanced ciphertext has attributes of a one-way function that is easy to achieve but almost impossible to revert without the correct random phase masks used for diffusion-confusion operations. Despite the straightforward design, combining optical, cryptographic, and chaos methods increases the number of encryption–decryption keys raising two significant concerns. First, the management and transmission of several keys carry a high storage footprint, and second, it impairs the optical high-speed information processing capability. We overcome these two drawbacks by applying a new optical keystream synthesizer based on the stochastic speckle nature and a maximum entropy constraint in a forward–backward propagation algorithm. This new approach quickly replicates the necessary keystream in the encryption–decryption stations offering higher security and efficiency than other reported systems. We validate the feasibility of this proposal by synthesizing an experimental keystream and performing 24-bit data encryption–decryption. Then, we verify the security improvement of two vulnerable cryptosystems and their robustness against cryptanalysis attacks. Finally, we perform experimental encryption–decryption.
Cybersecurity Framework Prioritization for Healthcare Organizations Using a Novel Interval-Valued Pythagorean Fuzzy CRITIC

Cybersecurity is the discipline of defending systems, networks, and programs against digital assaults intended to gain access to, alter, or delete sensitive data, or disrupt regular business activities. With the transformation of digitalization, information on the internet becomes vulnerable to cyber attacks, and healthcare organizations have a critical importance in this regard. Digital healthcare technology is widely used across the world, and the security of healthcare data and equipment is a growing problem since medical equipment has been exposed to new cybersecurity risks as its access to current computer networks has increased. However, the cybersecurity frameworks offered provide a generic framework for all organizations, and prioritizing the categories within the framework for the healthcare organization is critical in terms of developing an effective security policy. In this study, an internationally accepted cybersecurity framework is evaluated by health experts, and the framework is prioritized for the use by healthcare organizations. Since such a task is carried out on linguistic expressions and experts may be uncertain about some of the categories, there is a need for a model that both converts linguistic expressions into numerical measurable form while comprehensively addressing the vagueness. For this purpose, a novel interval-valued Pythagorean fuzzy CRiteria Importance Through Intercriteria Correlation (CRITIC) method has been developed for ranking the categories within each domain of the National Institute for Standards and Technology (NIST) cybersecurity framework for the use of healthcare organizations. A sensitivity analysis, theoretical and practical consequences, and future research recommendations are also provided within the study.
Digital Transformation and Prosumers Activities in the Energy Sector

Changes are taking place in the energy sector that can be divided into technological change and social change. The technological field includes digitization, which allows for the creation of new business models based on blockchain, distributed energy, and remote measurements. In addition, it influences the creation of a network of interconnected generating units while maintaining independence and a centralized structure. The social changes include, firstly: consumer activity in the energy market, which is possible thanks to technological changes, and secondly: moving towards reducing the negative impact on the environment. Both trends of these changes interpenetrate and allow for the emergence of energy-friendly for humans and the environment. Technological innovations such as remote meters, devices for remote control and monitoring, and digital twins, used in the energy sector allow for increasing the efficiency of the sector and its faster development towards zero emissions.
Trace Concealment Histogram-Shifting-Based Reversible Data Hiding with Improved Skipping Embedding and High-Precision Edge Predictor (ChinaMFS 2022)

Reversible data hiding (RDH) is a special class of steganography, in which the cover image can be perfectly recovered upon the extraction of the secret data. However, most image-based RDH schemes focus on improving capacity–distortion performance. In this paper, we propose a novel RDH scheme which not only effectively conceals the traces left by HS but also improves capacity–distortion performance. First, high-precision edge predictor LS-ET (Least Square predictor with Edge Type) is proposed, and the predictor divides pixels into five types, i.e., weak edge, horizontal edge, vertical edge, positive diagonal edge, and negative diagonal edge. Different types of target pixels utilize different training pixels with stronger local consistency to improve accuracy. Then, a novel prediction-based histogram-shifting (HS) framework is designed to conceal embedding traces in the stego images. Finally, we improve both the data-coding method and the skipping embedding strategy to improve the image quality. Experimental results demonstrate that the capacity–distortion performance of the proposed scheme outperforms the other trace concealment schemes and is comparable to the state-of-the-art schemes utilizing sorting technique, multiple histogram modification, and excellent LS-based predictors. Moreover, it can conceal the embedding traces left by the traditional HS schemes to a certain extent, reducing the risk of being steganalyzed.
Design and Implementation of an Asynchronous Finite State Controller for Wheeled Mobile Robots

Wheeled mobile robots (WMRs) can navigate in uncontrolled environments with the assistance of electronic or physical devices. Several works have been conducted on the control and management of the path-tracking of a vehicle in different road scenarios. This paper aims to create an asynchronous finite state control law for a WMR. The control law is based on a proportional–integral–derivative controller, and the performance of the proposed model is evaluated in virtual and real environments in two different scenarios. In the first one, the WMR must perform a zig-zag maneuver between obstacles, while the second one involves a double left lane change. In the proposed scenarios, the WMR drives along a path until an obstacle is detected at less than 50 cm, causing the WMR to check whether the first lane is free to go and move on. These scenarios and the related required engineering approaches seem particularly suitable for system engineering in a student’s laboratory for the design and implementation of automated guidance system modeling.
A Cultural-Centered Model Based on User Experience and Learning Preferences of Online Teaching Platforms for Chinese National University Students: Taking Teaching Platforms of WeCom, VooV Meeting, and DingTalk as Examples

This study aims to explore online teaching from the perspective of the cultural-centered model (CCM) of user experience (UX) design and learning preferences (LP) for by investigating in Chinese national universities. A mixed-method approach was used based on the results of a questionnaire survey; results showed that questions mostly reported by students mainly concerned the need for test preparation, such as playback and optimized interactive functions. The need for playback feedback also indicates that teachers are reluctant to turn on the playback function when teaching online, and there are certain pressures and concerns. In terms of interface functions and user interface (UI) design, more students tend to prefer simple and easy-to-operate designs, while neutral colors are the main color choices. In general, under the test-oriented education model, the above phenomenon reflects that the students at national universities in China do not have a significant demand for communication and discussion in online courses, which is related to the overall characteristics of being shy to express their opinions and more concerned and anxious about the pressure of course examinations. For the above phenomenon, the paper discusses the possibility of a teaching platform and proposes four main platform interactions under the curriculum to better improve the UX of online teaching and meet the needs of students. Finally, this study presents that cultural differences and educational background significantly impact online learning preferences. The UX and UI of the teaching platform system should be based on generality, and more attention should be paid to the cultural characteristics and realistic needs of user groups.
Fully Attentional Network for Low-Resource Academic Machine Translation and Post Editing

English is accepted as an academic language in the world. This necessitates the use of English in their academic studies for speakers of other languages. Even when these researchers are competent in the use of the English language, some mistakes may occur while writing an academic article. To solve this problem, academicians tend to use automatic translation programs or get assistance from people with an advanced level of English. This study offers an expert system to enable assistance to the researchers throughout their academic article writing process. In this study, Turkish which is considered among low-resource languages is used as the source language. The proposed model combines the transformer encoder-decoder architecture model with the pre-trained Sci-BERT language model via the shallow fusion method. The model uses a Fully Attentional Network Layer instead of a Feed-Forward Network Layer in the known shallow fusion method. In this way, a higher success rate could be achieved by increasing the attention at the word level. Different metrics were used to evaluate the created model. The model created as a result of the experiments reached 45.1 BLEU and 73.2 METEOR scores. In addition, the proposed model achieved 20.12 and 20.56 scores, respectively, with the zero-shot translation method in the World Machine Translation (2017–2018) test datasets. The proposed method could inspire other low-resource languages to include the language model in the translation system. In this study, a corpus composed entirely of academic sentences is also introduced to be used in the translation system. The corpus consists of 1.2 million parallel sentences. The proposed model and corpus are made available to researchers on our GitHub page.
Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.
Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

Abstract For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross‐covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time‐varying and steady‐state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real‐time applications. A simulation applied to the two‐mass spring damper mechanical system shows their effectiveness.
Intelligent contracts: Making smart contracts smart for blockchain intelligence

With the deep integration of blockchain and Artificial Intelligence (AI), more and more blockchain-based AI tasks are accomplished using Smart Contracts (SCs) and create win-win solutions. That is, blockchain provides a trustworthy and decentralized data infrastructure for AI, and AI helps blockchain perform tasks requiring intelligence. Since these special SCs designed for blockchain-based AI tasks have different characteristics from the widely studied SCs designed for business logic, we name them Intelligent Contracts (ICs) for a focused study. In this paper, we systematically analyze ICs and propose a constructive framework for their construction and application. Specifically, we first formulate two construction modes of current ICs, including encoding AI models and scheduling AI collaboration. Then, we compare the characteristics of these two modes theoretically and experimentally as a reference for future mode selection. Finally, to extend the application of ICs and encourage AI-driven blockchain intelligence, we propose a technical route that helps blockchain autonomously respond to AI tasks through the dynamic and optimal configuration of ICs. Using typical AI tasks of classifying IRIS, MNIST, and ImageNet data sets as examples, we implement and thoroughly evaluate two modes of ICs on Ethereum. Based on the constructed ICs, we illustrate their optimal configuration and automatic response process. Experimental results demonstrate the effectiveness and feasibility of the proposed framework.
Diabetes Twitter Classification Using Hybrid GSA

In today’s world, it is important to understand individuals’ health related opinions through sentiment analysis. Recently, many deep learning methods such as deep convolution neural network (CNN), Gated Recurrent Unit (GRU) and LSTM (Long short-term memory) are used to classify sentiments. This chapter focuses on diabetes tweet classification using the proposed Hybrid GSA, which is a combination of a Capsule Network (Deep Learning technique) and a Gravitational Search Algorithm. This approach is applied to perform sentiment classification such as positive, strong positive, negative, strong negative, and neutral using tweets on Twitter. It is observed from the results that the proposed approach produced better classification results compared to the existing approach. This work proved to be very effective in handling health tweets and accurate in classification.
Preserving the Healthcare Data by Using PPDM Technique

The recent advancement in technology catalyse the processing of vast amount of data. It leads to the purpose of extracting and storing those data in an electronic form. Data mining contributes a significant part in fulfilling this process. In the realm of data mining, privacy and security are two important problems. To resolve the above problems, the need of privacy preserving in data mining (PPDM) has been arisen. Under healthcare domain, the data mining will process the sensitive information which includes name, age, health records etc., Hence, PPDM plays a key role in securing that sensitive information and protecting those data from the intruders. Individual data is not only protected by PPDM, but it is also protected from leakage. In this paper, the PPDM technique such as anonymization technique and cryptographic technique has been analysed deeply with the view of its execution time. It is also focused that based on comparison, which method would be more efficient for protecting data from unauthorized access in the healthcare domain.
CFOG-like image registration algorithm based on 3D-structural feature descriptor for suburban optical and SAR

Owing to nonlinear radiation differences between optical and synthetic aperture radar (SAR) images, and the inevitable SAR speckle noise, the registration of optical and SAR images is challenging work. This paper proposes a novel pixel-wise 3D-structural feature descriptor based on channel-features-of-oriented-gradients (CFOG), thus, it inherits the fast and robust advantages of CFOG. First, the Block-Harris and grid points feature extraction method is used to obtain sufficient uniformly distributed keypoints from the reference images. Then, the new feature descriptor based on the combination of first-order oriented gradients and second-order gradients (FOSG) is proposed. Specifically, two different operators, the Sobel and the ratio of exponentially weighted averages are utilized to calculate the gradient for the optical and SAR images, respectively. On this basis, the first-order oriented gradients and second-order gradients are calculated by a 3D-Gaussian filter and then normalized along the Z-direction to form the FOSG by superposition. Finally, template matching based on the sum of squared differences similarity metric is implemented in the frequency domain. Our method is experimentally compared with five state-of-the-art methods using six pairs of suburban optical and SAR images. The results show that the proposed algorithm is robust to nonlinear radiation differences and SAR speckle noise, and achieves high accuracy.
On the importance of metadata when sharing and opening data

While data sharing increases, most open data are difficult to re-use or to identify due to the lack of related metada. In this editorial, I discussed about the importance of those metadata in the context of genomic, and why they are mandatory to ensure the success of data sharing.