Reviewerly/exHarmony huggingface dataset:
Efficient Federated Meta-Learning Over Multi-Access Wireless Networks

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today’s edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. In addition, since the available radio spectrum and IoT devices’ energy capacity are usually insufficient, it is crucial to control the resource allocation and energy consumption when deploying FML in practical wireless networks. To overcome the challenges, in this paper, we rigorously analyze the contribution of each device to the global loss reduction in each round and develop an FML algorithm (called NUFM) with a non-uniform device selection scheme to accelerate the convergence. After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost. By deconstructing the original problem step by step, we devise a joint device selection and resource allocation strategy to solve the problem with theoretical guarantees. Further, we show that the computational complexity of NUFM can be reduced from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(d^{2})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(d)$ </tex-math></inline-formula> (with the model dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> ) via combining two first-order approximation techniques. Extensive simulation results demonstrate the effectiveness and superiority of the proposed methods in comparison with existing baselines.
Special issue on feature engineering editorial

In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XGBoost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called “featurization”) from different sources (e.g., transactional data, network data, time series data, text data, etc.).
Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells

• Extreme learning machine is employed to predict data and filter noised data. • Data shortage and noised data obstacles for parameter extraction are solved. • Eight meta -heuristic algorithms are employed to two solid oxide fuel cell models. • Practical performance of the proposed method is validated in the 5 kW cell stack. • Two constructive recommendations are proposed for future development. A precise, fast, and robust parameter extraction technique of solid oxide fuel cell models is extremely crucial for optimal control and behavior analysis. In this paper, a novel extreme learning machine based method is proposed to extract unknown parameters of solid oxide fuel cell models including electrochemical model and simple electrochemical model. At first, extreme learning machine is applied to overcome two thorny obstacles (e.g., data shortage and noised data) via predicting additional data and updating noised data. Then, both original data collected from a 5 kW solid oxide fuel cell stack and processed data are transferred to effectively guide eight prominent meta-heuristic algorithms for effective parameter extraction. The performance of extreme learning machine is thoroughly investigated in two typical operation conditions through a comprehensive comparison based on various training data. Simulation results validate that the proposed approach can effectively contribute to searching efficient model parameters along with high accuracy, prominent stability, high speed, and great robustness. Particularly, the accuracy of parameter extraction for electrochemical model and simple electrochemical model can be improved by 49.3% and 65.6% at most, respectively.
A survey of COVID-19 contact-tracing apps

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies.
Differentiable Bi-Sparse Multi-View Co-Clustering

Deep multi-view clustering utilizes neural networks to extract the potential peculiarities of complementarity and consistency information among multi-view features. This can obtain a consistent representation that improves clustering performance. Although a multitude of deep multi-view clustering approaches have been proposed, most lack theoretic interpretability while maintaining the advantages of good performance. In this paper, we propose an effective differentiable network with alternating iterative optimization for multi-view co-clustering termed differentiable bi-sparse multi-view co-clustering (DBMC) and an extension named elevated DBMC (EDBMC). The proposed methods are transformed into equivalent deep networks based on the constructed objective loss functions. They have the advantages of strong interpretability of the classical machine learning methods and the superior performance of deep networks. Moreover, DBMC and EDBMC can learn a joint and consistent collaborative representation from multi-source features and guarantee sparsity between multi-view feature space and single-view sample space. Meanwhile, they can be converted into deep differentiable network frameworks with block-wise iterative training. Correspondingly, we design two three-step iterative differentiable networks to resolve resultant optimization problems with theoretically guaranteed convergence. Extensive experiments on six multi-view benchmark datasets demonstrate that the proposed frameworks outperform other state-of-the-art multi-view clustering methods.
A black-box adversarial attack strategy with adjustable sparsity and generalizability for deep image classifiers

Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback has hitherto been the norm for such adversarial attacks. However, black-box attacks are much more practical for real-world applications. Universal perturbations applicable across multiple images are gaining popularity due to their innate generalizability. There have also been efforts to restrict the perturbations to a few pixels in the image. This helps to retain visual similarity with the original images making such attacks hard to detect. This paper marks an important step that combines all these directions of research. We propose the DEceit algorithm for constructing effective universal pixel-restricted perturbations using only black-box feedback from the target network. We conduct empirical investigations using the ImageNet validation set on the state-of-the-art deep neural classifiers by varying the number of pixels to be perturbed from a meager 10 pixels to as high as all pixels in the image. We find that perturbing only about 10% of the pixels in an image using DEceit achieves a commendable and highly transferable Fooling Rate while retaining the visual quality. We further demonstrate that DEceit can be successfully applied to image-dependent attacks as well. In both sets of experiments, we outperform several state-of-the-art methods.
Immersive Virtual Reality for Older Adults

Despite the proliferation of research on immersive virtual reality (IVR) technologies for older adults, comprehensive guidelines on designing immersive and engaging VR for older adults remain sparse. Therefore, we first compounded 67 guidelines based on published literature. Next, to empirically ground these design recommendations, we provided 37 older adults of diverse ages, education levels, and cognitive abilities with a first VR experience. Analyzing interviews with the 37 older adults via the Laddering method, we found that they generally reported positive experiences with their first VR exposure. With these deepened insights, we reflect on, nuance, and contextualize existing design guidelines, and formulate points to bear in mind when designing accessible and engaging VR experiences for older persons.
A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Recently, 360 • or omnidirectional videos have become increasingly popular for both personal and enterprize use-cases.However, 360 • video streaming has very high bandwidth and processing requirements.State-of-the-art viewport-based streaming solutions lower these requirements by performing selective streaming based on long-term Field-of-View (FoV) prediction mechanisms.However, sometimes user movement is extremely unpredictable during some parts of the video, and applying these solutions adversely affects the overall quality of experience (QoE).This paper proposes a novel Combined Field-of-View tile-based adaptive streaming solution (CFOV) that improves end-user QoE for 360 • video streaming.CFOV performs interactive tile selection based on more accurate dynamical viewing area identification by combining the results of two FoV prediction mechanisms.It also employs an innovative priority-based bitrate adaptation approach that ensures improved bitrate budget distribution between different tiles.We evaluate the proposed solution with a comprehensive set of experiments involving four immersive videos, diverse tiling patterns (i.e., 4x3, 6x4, and 8x6), different segment lengths (i.e., 1s, 2s, and 3s), and 48 empirical head movement traces under different bandwidth settings.The evaluation employs a newly defined QoE metric specifically introduced to assess the streaming performance of 360 • videos objectively.The experimental findings show that, compared to alternative approaches, our proposed solution can achieve a higher viewport match and can significantly improve the user QoE for different watching behaviors and content characteristics.
Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

Maintaining large and complex software is a significant task in IT industry. One reason for that is the development of code smells which are design flaws that lead to future bugs and errors. Code smells can be treated with regular refactoring, and their detection is the first step in the software maintenance process. Detecting code smells with machine learning algorithms eliminate the need of extensive knowledge required regarding properties of code smell and threshold values. Ensemble machine learning algorithms use a combination of several same or different classifiers to further aid the performance and reduces the variance. In our study, three hybrid feature selection techniques with ensemble machine learning algorithms are employed to improve the performance in detecting code smells. Seven machine learning classifiers with different kernel variations, along with three boosting designs, two stacking methods, and bagging were implemented. For feature selection, combination of filter-wrapper, filter-embedded, and wrapper-embedded methods have been executed. Performance measures for detecting four code smells are evaluated and are compared with the performance when feature selection is not employed. It is found out that performance measure after application of hybrid feature selection increased, accuracy by 21.43%, AUC value by 53.24%, and f-measure by 76.06%. Univariate ROC with Lasso is the best hybrid feature selection technique with 90.48% accuracy and 94.5% ROC AUC value. Random Forest and Logistic regression are the best performing machine learning classifiers. Data class is most detectable code smell. Stacking always gave better results when compared with individual classifiers.
Factors affecting the adoption and use of AVR technology in higher and tertiary education

The research study assesses the factors that determine the adoption of Augmented and Virtual Reality (AVR) technology in Higher and Tertiary Education. In spite of the well-established potential benefits of AVR technology in education, it appears that most Higher and Tertiary Institutions have not holistically adopted it as a method of teaching and learning. Thus, it is important to investigate the reasons why AVR has not been adopted. Inductive qualitative research was conducted virtually with the aid of a questionnaire instrument. The Technology Acceptance Model (TAM) was used to establish the determinants for the adoption of AVR Technology. The research study found out that the main reasons for the lack of adoption of AVR technology were due to the lack of coordinating policies; lack of enabling infrastructures; perceived complexity of the use of AVR technology and a lack of a framework that guides the holistic as well as homogenous adoption of AVR technology in Higher and Tertiary Education. The research concluded that an AVR technology was necessary and should factor in the barriers to adoption. The study recommends inter-professional working of key players such as the Ministry of Higher Education (in endorsing an AVR technology model), the lecturers (using and encouraging AVR technology), and the students (active participation and use of AVR technology to learn). Prior to this study, the aspect of inter-professional working as a solution to the adoption and use of AVR in Higher and Tertiary Education had not been explored. However, more still needs to be done within Virtual Training and the development of an AVR model for teaching and learning.
Blockchain for Securing AI Applications and Open Innovations

Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI).This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate.However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level.The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions.Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications.These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain.Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense.However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness.This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems.Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review.The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution.However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.
A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques

This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work.
Impact of avatar facial anthropomorphism on body ownership, attractiveness and social presence in collaborative tasks in immersive virtual environments

Effective collaboration in immersive virtual environments requires to be able to communicate flawlessly using both verbal and non-verbal communication. We present two experiments investigating the impact of facial anthropomorphism on the sense of body ownership, avatar attractiveness, social presence and performance in two collaborative tasks. In the first experiment participants have to solve a construction game according to their partner’s instructions using three avatars presenting different facial properties. Results reveal no significant difference in terms of body ownership and social presence, but demonstrate significant differences in terms of attractiveness and completion duration of the collaborative task. Unexpectedly, correlation analyses also reveal a link between attractiveness and performance. The more attractive the avatar, the shorter the completion duration of the game. Our second experiment was designed to investigate further the potential impact of the task carried out on the sense of social presence using the same avatars. While we observed a very high sense of social presence in both tasks (asymmetric collaboration and negotiation) with every avatar, our results did not reveal significant difference between the three conditions. However, we observed statistically significant differences between the two task types. The scores of the co-presence and of the perceived message understanding dimensions of social presence were higher during the negotiation task. The sense of social presence appears to be task sensitive, especially when non-verbal communication becomes more important during face-to-face interaction in immersive collaborative virtual environments.
Understanding international perceptions of the severity of harmful content online

Online social media platforms constantly struggle with harmful content such as misinformation and violence, but how to effectively moderate and prioritize such content for billions of global users with different backgrounds and values presents a challenge. Through an international survey with 1,696 internet users across 8 different countries across the world, this empirical study examines how international users perceive harmful content online and the similarities and differences in their perceptions. We found that across countries, the perceived severity consistently followed an exponential growth as the harmful content became more severe, but what harmful content were perceived as more or less severe varied significantly. Our results challenge platform content moderation's status quo of using a one-size-fits-all approach to govern international users, and provide guidance on how platforms may wish to prioritize and customize their moderation of harmful content.
Simulating nonstationary non-Gaussian vector process based on continuous wavelet transform

In the present study, we propose a new iterative algorithm based on discretized continuous wavelet transform (CWT) to simulate nonstationary non-Gaussian vector process for prescribed marginal probability distribution functions and the time-scale power spectral density function matrix. The algorithm is designed for a CWT pair within the wavelet analysis framework. It can be used with analytical wavelets satisfying the admissibility condition and can cope with time-dependent and time-independent coherence. The proposed algorithm is applied to generate nonstationary downburst winds and seismic ground motions at multiple sites within the wavelet analysis framework. The numerical validation analysis confirms that the proposed algorithm can lead to the simulated records matching the prescribed marginal probability distribution, marginal time-scale power spectral density function and coherence function.
Polarization descattering imaging through turbid water without prior knowledge

The traditional polarization imaging model needs to obtain the polarization information of backscattered light in advance, and is also not suitable for the recovery of the target with high polarization characteristics. In this work, the aforementioned shortcomings are overcame by adding the necessary constraints. Then, we propose a new method that can obtain the degree of linear polarization of the target and scattering medium simultaneously, and conduct a polarization descattering imaging experiment through turbid water. The images recovered by our method present the high contrast and similarity to the targets without scattering. Further, compared with other polarization underwater imaging strategies, this method is more suitable for the targets with different polarization characteristics and does not require any prior knowledge of target light or backscattered light.
Do cryptocurrency exchanges fake trading volumes? An empirical analysis of wash trading based on data mining

Cryptocurrency exchanges, which act as a platform for cryptocurrency trading, play a vital role in the ever-growing cryptocurrency market. However, with the rapid development of this emerging market, some unethical phenomena including faking trading volume have also appeared in cryptocurrency exchanges. To this end, this paper proposes a data mining-based method based on off-chain data and on-chain transaction data to detect the exchanges that fake trading volume. In particular, we first collect off-chain data from the websites of five exchanges and the on-chain data provided by a blockchain browser, and then analyze them from two perspectives, including transaction number and transaction amount. The empirical results suggest that Huobi exchange fakes trading volume most obviously, while Binance trading is relatively the most honest. In addition, different exchanges adopt distinct counterfeiting strategies when creating wash trading.