Reviewerly/exHarmony huggingface dataset:
Multi-GAT: A Graphical Attention-Based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition

Recognizing human activities is one of the crucial capabilities that a robot needs to have to be useful around people. Although modern robots are equipped with various types of sensors, human activity recognition (HAR) still remains a challenging problem, particularly in the presence of noisy sensor data. In this work, we introduce a multimodal graphical attention-based HAR approach, called Multi-GAT, which hierarchically learns complementary multimodal features. We develop a multimodal mixture-of-experts model to disentangle and extract salient modality-specific features that enable feature interactions. Additionally, we introduce a novel message-passing based graphical attention approach to capture cross-modal relation for extracting complementary multimodal features. The experimental results on two multimodal human activity datasets suggest that Multi-GAT outperformed state-of-the-art HAR algorithms across all datasets and metrics tested. Finally, the experimental results with noisy sensor data indicate that Multi-GAT consistently outperforms all the evaluated baselines. The robust performance suggests that Multi-GAT can enable seamless human-robot collaboration in noisy human environments.
Quantum control of bosonic modes with superconducting circuits

Bosonic modes have wide applications in various quantum technologies, such as optical photons for quantum communication, magnons in spin ensembles for quantum information storage and mechanical modes for reversible microwave-to-optical quantum transduction. There is emerging interest in utilizing bosonic modes for quantum information processing, with circuit quantum electrodynamics (circuit QED) as one of the leading architectures. Quantum information can be encoded into subspaces of a bosonic superconducting cavity mode with long coherence time. However, standard Gaussian operations (e.g., beam splitting and two-mode squeezing) are insufficient for universal quantum computing. The major challenge is to introduce additional nonlinear control beyond Gaussian operations without adding significant bosonic loss or decoherence. Here we review recent advances in universal control of a single bosonic code with superconducting circuits, including unitary control, quantum feedback control, driven-dissipative control and holonomic dissipative control. Various approaches to entangling different bosonic modes are also discussed.
Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities

Abstract Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats of data breaches. Modern encryption techniques ensure security and are considered as the best option to protect stored data and data in transit from an unauthorized third-party. However, a decryption process is necessary when the data must be processed or analyzed, falling into the initial problem of data vulnerability. Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography. It allows a non-trustworthy third-party resource to process encrypted information without disclosing confidential data. In this paper, we analyze the fundamental concepts of FHE, practical implementations, state-of-the-art approaches, limitations, advantages, disadvantages, potential applications, and development tools focusing on neural networks. In recent years, FHE development demonstrates remarkable progress. However, current literature in the homomorphic neural networks is almost exclusively addressed by practitioners looking for suitable implementations. It still lacks comprehensive and more thorough reviews. We focus on the privacy-preserving homomorphic encryption cryptosystems targeted at neural networks identifying current solutions, open issues, challenges, opportunities, and potential research directions.
The Effects of Subjective Norm on the Intention to Use Social Media Networks: An Exploratory Study Using PLS-SEM and Machine Learning Approach

Several research has been conducted on social media application's acceptance, but factors that impact educational purposes are completely ignored in this research. Therefore, the research has been conducted with the purpose of developing a conceptual model, which is derived from the Technology Acceptance Model (TAM). The subjective norm of the study is to find out social media's acceptance in education by students. To find out the exact conclusion, the research follows the questionnaire survey method in which 310 questionnaires were distributed to the students of the United Arab Emirates' well-reputed university. In this questionnaire survey, two famous approaches were used to examine the collected data that is the partial least squares-structural equation modeling (PLS-SEM) and Machine Learning approach (ML). From the above-stated study, it has been observed that perceived usefulness, subjective norms, and perceived ease of use are proven to be significant measures of student's intention that motivates them to use social media networks for their educational purpose.
TSLRLN: Tensor subspace low-rank learning with non-local prior for hyperspectral image mixed denoising

Low-rank methods have earned high regard for solving problems of mixed denoising in hyperspectral images (HSI). However, for low-rank matrix/tensor-based denoising methods, high computational complexity and high tuning difficulty often accompany good results. To address this challenge, in this paper, we propose a tensor subspace low-rank learning method with a non-local prior to exploit the low-rankness of both spatial and spectral modes of an HSI tensor. Technically, the original noisy HSI tensor was first projected to a low-dimensional subspace. Then, an orthogonal tensor basis of subspace and a tensor coefficient were alternatively learned. The parameter-free non-local prior was enforced in the tensor subspace instead of in the original HSI tensor. Eventually, the t-linear representation of basis and coefficient tensors achieved the restoration of the latent clean low-rank tensor. The proposed method realizes complete tensor operations for subspace low-rank learning and avoids the correlation loss bought about by tensor flattening. Through comparing with the latest denoising methods by using several quantitative and qualitative indexes, extensive experiments conducted on two simulated and two real datasets have proved that the proposed method not only realizes the high accuracy of mixed denoising, but also remarkably improves the computational efficiency and usability in real applications.
Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Two datasets (private and public datasets) were obtained and used for developing and validating CAD. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. These images were automatically classified using machine learning and two types of image feature extraction: conventional texture analysis (TA) and homology-based image processing (HI). Multiscale analysis was used in the image feature extraction, after which automatic classification was performed using the image features and eight machine learning algorithms. The multicategory accuracy of our CAD system was evaluated in the two datasets. In both the public and private datasets, the CAD system with HI was better than that with TA. It was possible to build an accurate CAD system for lung tissues. HI was more useful for the CAD systems than TA.
Ultrafast Ising Machines using spin torque nano-oscillators

Combinatorial optimization problems are known for being particularly hard to solve on traditional von Neumann architectures. This has led to the development of Ising Machines (IMs) based on quantum annealers and optical and electronic oscillators, demonstrating speed-ups compared to central processing unit (CPU) and graphics processing unit (GPU) algorithms. Spin torque nano-oscillators (STNOs) have shown GHz operating frequency, nanoscale size, and nanosecond turn-on time, which would allow their use in ultrafast oscillator-based IMs. Here, we show using numerical simulations based on STNO auto-oscillator theory that STNOs exhibit fundamental characteristics needed to realize IMs, including in-phase/out-of-phase synchronization and second harmonic injection locking phase binarization. Furthermore, we demonstrate numerically that large STNO network IMs can solve Max-Cut problems on nanosecond timescales.
On the suitability of blockchain platforms for IoT applications: Architectures, security, privacy, and performance

Blockchain and distributed ledger technologies have received significant interest in various areas beyond the financial sector, with profound applications in the Internet of Things (IoT), providing the means for creating truly trustless and secure solutions for IoT applications. Taking into account the weak security defences that the majority of IoT devices have, it is critical that a blockchain-based solution targeting the IoT is not only capable of addressing the many challenges IoT is facing, but also does not introduce other defects, in terms of performance, making its adoption hard to achieve. This paper aims at addressing the above needs by providing a comprehensive and coherent review of the available blockchain solutions to determine their ability to meet the requirements and tackle the challenges of the IoT, using the smart home as the reference domain. Key architectural aspects of blockchain solutions are examined in terms of their ability to withstand various types of common IoT and blockchain attacks, deliver enhanced privacy features, and assure adequate performance levels while processing large amounts of transactions being generated in an IoT environment. The analysis carried out identified that the defences currently provided by blockchain platforms are not sufficient to thwart all the prominent attacks against blockchains, with blockchain 1.0 and 2.0 platforms being susceptible to the majority of them. On the other side, privacy related mechanisms are being supported, to varying degrees, by all platforms investigated; however, each of the them tackles specific only privacy aspects, thus rendering the overall privacy evaluation a challenging task which needs to be considered in an ad-hoc basis. If the underlying consensus protocols performance and fault tolerance is also considered, then only a small number of platforms meet the requirements of our reference IoT domain.
PyHessian: Neural Networks Through the Lens of the Hessian

We present PYHESSIAN, a new scalable framework that enables fast computation of Hessian (i.e., second-order derivative) information for deep neural networks. PYHESSIAN enables fast computations of the top Hessian eigenvalues, the Hessian trace, and the full Hessian eigenvalue/spectral density; it supports distributed-memory execution on cloud/supercomputer systems; and it is available as open source [1]. This general framework can be used to analyze neural network models, including the topology of the loss landscape (i.e., curvature information) to gain insight into the behavior of different models/optimizers. As an example, we analyze the effect of residual connections and Batch Normalization layers on the trainability of neural networks. One recent claim, based on simpler first-order analysis, is that residual connections and Batch Normalization make the loss landscape "smoother," thus making it easier for Stochastic Gradient Descent to converge to a good solution. Our second-order analysis, easily enabled by PYHESSIAN, shows new finer-scale insights, demonstrating that while conventional wisdom is sometimes validated, in other cases it is simply incorrect. In particular, we find that Batch Normalization does not necessarily make the loss landscape smoother, especially for shallow networks.
Factors Impacts Organization Digital Transformation and Organization Decision Making During Covid19 Pandemic

The Digital transformation concept defined as how firms and organizations utilize the power and possibility of technology like cloud computing, artificial intelligence, and machine learning IoT technologies to enhance the business operation and customers' experience or create new business lines and methods. In this study will qualitative research will conduct focusing on the impact of internal and external factors of operational decision making approaches under the digital transformation umbrella during the COVID-19 pandemic. The main results indicated that senior management support and technology infrastructure readiness has a positive impact factor in digital transformation and organization performance as a result, where data breaching threats had a negative impact on organization adoption of digital transformation.
Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.
The Impact of Social Media Marketing Communications on Consumer Response During the COVID-19: Does the Brand Equity of a University Matter?

The main objective of the current study is to investigate the impact of social media marketing communication on the consumer response to University in UAE during COVID-19. In addition to that, the study has also examined the mediating role of brand equity of university in the relationship between social media marketing communication and consumer response during the COVID-19. The schedule for the academic session of an academic year, their starting and ending dates, schedule for vacations all vary from country to country; as a result, the conditions were not similar and varied due to the same reason. However, few countries suspended their classic room lectures from March or April 2020 up to further notification. We employed a combination of inferential and descriptive analyses to carry out the data analysis. For this purpose, the PLS-SEM approach was integrated, which is a second-generation technique for structural equation modeling. PLS-SEM is a relatively new approach and provides reliable results when coupled with SEM models. The response rate is above 50%. Customer-Based Brand Equity illustrated the significant indirect impacts of these significant indirect effects on the association among Customer-Based Brand Equity (CBBE) and social media marketing communications. As a result, this research further added the perception of Customer-Based Brand Equity (CBBE) in the perception of UNIVERSITY perception through endorsing the Customer-Based Brand Equity (CBBE), which estimates the brand sustainability, explicit consumers associations, functional brand image, perception, hedonic brand image and experiences with UNIVERSITY brands by brand awareness. This research work also positioned the two levels of social media communication, such as UGC and FCC, into marketing communication through endorsing the degrees of social media advertising, social media promotions, and social media interactive marketing. Hence, the research was capable of discussing the various roles of these social media marketing communications on the consumer response and the growth of Customer-Based Brand Equity (CBBE). Moreover, Customer-Based Brand Equity was illustrated to have significant influences on consumer responses.
Vox2Vox: 3D-GAN for Brain Tumour Segmentation

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core. Although brain tumours can easily be detected using multi-modal MRI, accurate tumor segmentation is a challenging task. Hence, using the data provided by the BraTS Challenge 2020, we propose a 3D volume-to-volume Generative Adversarial Network for segmentation of brain tumours. The model, called Vox2Vox, generates realistic segmentation outputs from multi-channel 3D MR images, segmenting the whole, core and enhancing tumor with mean values of 87.20%, 81.14%, and 78.67% as dice scores and 6.44mm, 24.36 mm, and 18.95 mm for Hausdorff distance 95 percentile for the BraTS testing set after ensembling 10 Vox2Vox models obtained with a 10-fold cross-validation. The code is available at https://github.com/mdciri/Vox2Vox .
Few-shot Network Anomaly Detection via Cross-network Meta-learning

Network anomaly detection, also known as graph anomaly detection, aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis. Due to the unbearable labeling cost, existing methods are predominately developed in an unsupervised manner. Nonetheless, the anomalies they identify may turn out to be data noises or uninteresting data instances due to the lack of prior knowledge on the anomalies of interest. Hence, it is critical to investigate and develop few-shot learning for network anomaly detection. In real-world scenarios, few labeled anomalies are also easy to be accessed on similar networks from the same domain as of the target network, while most of the existing works omit to leverage them and merely focus on a single network. Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks – Graph Deviation Networks (GDN) that can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Extensive evaluations demonstrate the efficacy of the proposed approach on few-shot or even one-shot network anomaly detection.
Features, barriers, and influencing factors of mobile learning in higher education: A systematic review

Mobile devices are not only a tool for communication, but also a powerful instrument for the economy, mass communication, and learning. Because the use of mobile devices and the Internet is growing rapidly, these tools are increasingly being utilized in learning and instruction. It is therefore important to investigate how mobile devices can be applied in learning. This article focuses on analyzing the features of mobile learning as well as the barriers and influencing factors of using mobile devices in learning. It pays particular attention to clarifying the factors, techniques, and strategies that enhance learners' experiences in using mobile devices. It reports a systematic review that examined studies published in 2006–2018 containing the keywords “mobile learning” “m-learning”, “undergraduate students” and/or “higher education.” The results of the review indicated mobile devices can be used as learning tools for tasks such as submitting homework, reflecting on immediate learning experiences, and sharing ideas. Instructors should consider three main components in mobile learning: learners' and instructors’ readiness, learning management and supporting systems.
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement

This paper presents a generic method for generating full facial 3D animation from speech. Existing approaches to audio-driven facial animation exhibit uncanny or static upper face animation, fail to produce accurate and plausible co-articulation or rely on person-specific models that limit their scalability. To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face. At the core of our approach is a categorical latent space for facial animation that disentangles audio-correlated and audio-uncorrelated information based on a novel cross-modality loss. Our approach ensures highly accurate lip motion, while also synthesizing plausible animation of the parts of the face that are uncorrelated to the audio signal, such as eye blinks and eye brow motion. We demonstrate that our approach outperforms several baselines and obtains state-of-the-art quality both qualitatively and quantitatively. A perceptual user study demonstrates that our approach is deemed more realistic than the current state-of-the-art in over 75% of cases. We recommend watching the supplemental video before reading the paper: https://github.com/facebookresearch/meshtalk
An efficient multilevel color image thresholding based on modified whale optimization algorithm

Color image segmentation is a vital preprocessing stage in various image processing applications. In threshold-based segmentation, the success of the image segmentation depends mainly on the optimal selection of thresholds. The selection of threshold values for multilevel thresholding is indeed a time-consuming process compared to bi-level thresholding. The issue of optimal threshold selection is formulated as an optimization problem in the case of color image segmentation using multilevel thresholding. To optimize the threshold selection for a multilevel color image thresholding, a modified whale optimization algorithm (MWOA) is proposed in this paper. The Otsu's and Kapur's functions have been used in the proposed strategy as a fitness function that can be maximized by MWOA. In the MWOA, the position of the whales is controlled by adapting the cosine function during optimization process. Further, the movements of search agents are regulated during the search process by introducing the correction factors in position updation. These changes incorporated in the MWOA provide a proper balance between the phases of exploration and exploitation and also avoid local optima problem. The performance of the MWOA is evaluated quantitatively and qualitatively based on the best fitness values in terms of PSNR, SSIM, and FSIM, further CPU computing time and Wilcoxon test. The experimental outcomes show that the proposed multilevel optimal color image thresholding using MWOA algorithm yields better performance results in terms of image quality, feature conservation, and convergence rate with less CPU computing time than other state-of-the-art algorithms.