Reviewerly/exHarmony huggingface dataset:
Re-thinking and Re-defining the Learning Process? Students’ Feedback on Online Distance Instruction

The paper deals with the re-thinking and re-defining the process of online distance instruction. The main objective of the presented research is to detect crucial features of the process of online distance instruction and propose didactic recommendations towards improving the quality. Data were collected via a questionnaire consisting of 18 items requiring both multiple-choice and open answers. The research sample included 272 respondents attending upper secondary and higher education institutions in the xxx Republic. Each respondent provided feedback on two courses taught within the winter semester (Sept 2020–Jan 2021). First, they reflected on the course they appreciated, liked the learning in it, and learned as much as possible. Second, they described the course which was designed and conducted in an inappropriate and unsatisfactory manner from their point of view, a course which did not suit their learning. Courses were assessed using four criteria: (1) First contact and communication, (2) Learning content acquisition, (3) Learning content delivery, (4) Student’s final feedback on online distance instruction, including teachers’ and learners’ effort devoted to teaching and learning, and the quality of learning outcomes. Based on the collected data, didactic recommendations were defined towards designing and conducting helpful online distance courses.
Adaptive and Hierarchical Large Message All-to-all Communication Algorithms for Large-scale Dense GPU Systems

In recent years, GPU-enhanced clusters have become more prevalent in High-Performance Computing (HPC), leading to a demand for more efficient multi-GPU communication. This makes it increasingly important to explore performance enhancements that can be attained through the communication middleware such as MPI, in order to fully take advantage of the GPUs available on these systems. In this paper, we propose locality-aware and adaptive schemes for hierarchical All-to-all collective communication on large-scale dense GPU systems. The proposed algorithms utilize the high bandwidth made available through the NVLink interconnect between GPUs in order to overlap communication latency. We focus on personalized and non-personalized all-to-all collective communication. These are components of modern scientific computing applications that utilize matrix transpose and three-dimensional Fast Fourier Transforms (FFT) and becoming more relevant for Deep Learning workloads with model and hybrid parallelisms. The performance evaluation with an application kernel performing three-dimensional FFT indicates that the proposed schemes for personalized all-to-all can lead to up to 15-25% lower execution time on 256 GPUs on the Lassen system. We demonstrate approximately 8% enhancement in training time for distributed K-FAC used in Deep Learning training on up to 128 GPUs. We also demonstrate approximately 22% and 30% improvement in the performance of non-personalized and personalized all-to-all benchmarks, respectively, compared to the state-of-the-art MPI libraries on the Summit and Lassen systems.
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We study a nonlocal version of the total variation-based model with L1 fidelity for image denoising, where the regularizing term is replaced with the fractional s-total variation. We discuss regularity of the level sets and uniqueness of solutions, both for high and low values of the fidelity parameter. We analyse in detail the case of binary data given by the characteristic functions of convex sets.
VeerEdge: Towards an Edge-Centric IoT Gateway

As the plethora of Internet of Things (IoT) devices gradually make their way into our lives, several Cloud Service Providers (CSPs) have developed IoT gateway platforms (SDKs) that solely connects IoT devices to their respective cloud. Such gateways have 1) cumbersome IoT device configuration; 2) inflexible IoT data managements; and 3) support no/little cross-vendor edge computation and cloud analytics. We term these commercial gateway SDKs as cloud-centric. In this paper, we study the state-of-the-art vendor-locked IoT Gateway solutions and approaches and propose an edge-centric paradigm through an evolutionary framework, dubbed VeerEdge for developing IoT gateways. We leverage computing and storage capabilities at the network edge for edge-based device & IoT service management and data processing. We exploit availability of multiple cloud services for "best" IoT data analytics. Evaluation results show that VeerEdge achieves this with negligible overhead in terms of latency, CPU and RAM usage when compared to state-of-the-art industrial IoT gateways.
Backwards-directed information flow analysis for concurrent programs

A number of approaches have been developed for analysing information flow in concurrent programs in a compositional manner, i.e., in terms of one thread at a time. Early approaches modelled the behaviour of a given thread's environment using simple read and write permissions on variables, or by associating specific behaviour with whether or not locks are held. Recent approaches allow more general representations of environmental behaviour, increasing applicability. This, however, comes at a cost. These approaches analyse the code in a forwards direction, from the start of the program to the end, constructing the program's entire state after each instruction. This process needs to take into account the environmental influence on all shared variables of the program. When environmental influence is modelled in a general way, this leads to increased complexity, hindering automation of the analysis. In this paper, we present a compositional information flow analysis for concurrent systems which is the first to support a general representation of environmental behaviour and be automated within a theorem prover. Our approach analyses the code in a backwards direction, from the end of the program to the start. Rather than constructing the entire state at each instruction, it generates only the security-related proof obligations. These are, in general, much simpler, referring to only a fraction of the program's shared variables and thus reducing the complexity introduced by environmental behaviour. For increased applicability, our approach analyses value-dependent information flow, where the security classification of a variable may depend on the current state. The resulting logic has been proved sound within the theorem prover Isabelle/HOL.
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [18] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.
Reservoir Computing in MEMS

This chapter explores the use of the Duffing nonlinearity and fast dynamics found in microelectromechanical beam oscillators for reservoir computing applications. General properties of MEMS are discussed, and the Duffing microscale beam characteristics are analyzed through analytical models and simulations. The reservoir computer is then constructed around a single such nonlinear oscillator through temporal multiplexing of the input and self-coupling via delayed feedback. The parameters of the resulting physical system are finally adjusted for optimal performance on computing the parity of a binary input stream, as well as on a spoken digit recognition task.
Edge computing with optical neural networks via WDM weight broadcasting

We introduce an optical neural-network architecture for edge computing that takes advantage of wavelength multiplexing, high-bandwidth modulation, and integration detection. Our protocol consists of a server and a client, which divide the task of neural-network inference into two steps: (1) a difficult step of optical weight distribution, performed at the server and (2) an easy step of modulation and integration detection, performed at the edge device. This arrangement allows for large-scale neural networks to be run on low-power edge devices accessible by an optical link. We perform simulations to estimate the speed and energy limits of this scheme.
Segmentation of Lymph Nodes in Ultrasound Images Using U-Net Convolutional Neural Networks and Gabor-Based Anisotropic Diffusion

The automated segmentation of lymph nodes (LNs) in ultrasound images is challenging, largely because of speckle noise and echogenic hila. This paper proposes a fully automatic and accurate method for LN segmentation in ultrasound that overcomes these issues. The proposed segmentation method integrates diffusion-based despeckling, U-Net convolutional neural networks and morphological operations. First, the speckle noise is suppressed and the lymph node edges are enhanced using Gabor-based anisotropic diffusion (GAD). Then, a modified U-Net model is used to segment the LNs excluding any echogenic hila. Finally, morphological operations are undertaken to segment the entire LNs by filling in any regions occupied by echogenic hila. A total of 531 lymph nodes from 526 patients were segmented using the proposed method. Its segmentation performance was evaluated in terms of its accuracy, sensitivity, specificity, Jaccard similarity and Dice coefficient, for which it achieved values of 0.934, 0.939, 0.937, 0.763 and 0.865, respectively. The proposed method automatically and accurately segments LNs in ultrasound images, enhancing the prospects of being able to undertake artificial intelligence (AI)-based diagnosis of lymph node diseases.
Towards a Blockchain Weather Derivative Financial Instrument for Hedging Volumetric Risks of Solar Power Producers

The weather-dependent nature of solar power makes Solar Power Producers susceptible to the unpredictability of sunshine. Temperature-based weather derivatives have recently emerged as an effective volumetric risk cross-hedge for Solar Power Producers, given that temperature positively correlates with solar radiation. Temperature-based put and call options, mostly traded on organized exchanges, require users to pay premiums that are costly and difficult to price. Swap contracts can serve as an economical alternative since they do not require premiums. However, they are only traded over-the-counter and are thus illiquid and liable to counterparty credit risks. For these reasons, a novel blockchain temperature-based weather derivative swap marketplace is proposed that mitigates the risks inherent in traditional swap contracts. The payoff structure and governing mechanisms of the smart contract that underpins this instrument are also developed. A preliminary investigation of this new financial instrument shows its efficacy in hedging volumetric risks of Solar Power Producers.
A semantic rule based digital fraud detection

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.
RSAMSR: A deep neural network based on residual self-encoding and attention mechanism for image super-resolution

This paper proposes an improved residual self-encoding and attention mechanism super-resolution (RSAMSR) network. Firstly, we construct a new structure through the multi-path convolution and design an attention mechanism module. Then the input data are divided into high and low-frequency components sent to the residual network with different depths for processing based on the spatial scaling theory. Finally, we introduce a self-encoding network to remove image noise. The model uses the L1 loss function for data training on the DIV2K data set and is compared with some state-of-the-art SR networks in four different public datasets of Set5, Set14, B100, and Urban100 under the magnification factor ×2, ×3, and ×4. Detailed experimental results show that the proposed model has fewer model parameters, the best objective criteria PSNR and SSIM, and the best subjective visual effect.
Mixture factor analysis with distance metric constraint for dimensionality reduction

Dimensionality reduction (DR) is a key preprocessing stage in high-dimensional data classification. Traditional linear DR algorithms, e.g., Linear Discriminant Analysis, transform the original data into a low-dimensional subspace with a linear transformation matrix. However, these methods cannot handle complex nonlinearly separable data. Although some nonlinear DR methods, e.g., Locally Linear Embedding, are proposed to solve this problem, most of them are unsupervised, which only focus on the data structure hidden in the original high-dimensional space, rather than maximizing the inter-class separability of the transformed data, thus reducing the classification accuracy. To tackle this challenge, a novel supervised nonlinear DR algorithm, distance metric restricted mixture factor analysis (DMR-MFA), is proposed for high-dimensional data classification. In DMR-MFA, the original data is divided into several clusters, and the generation of original data in each cluster is described via a factor analysis model. Meanwhile, the distance metric constraint (DMC) is used for maximizing the separability of transformed low-dimensional data from different classes. Moreover, the optimal model parameters are learned via the joint optimization of log-likelihood function and DMC loss function, which makes the DMR-MFA possible to obtain the more separable low-dimensional embeddings while accurately describing the original data. Experimental results on synthetic data, benchmark datasets and high-resolution range profile data demonstrate that our method can handle nonlinearly separable data and improves the classification accuracy of data with high dimensionality.
Malicious Encryption Traffic Detection Based on NLP

The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.
Dynamic Adherent Raindrop Simulator for Automotive Vision Systems

The automotive domain is highly regulated, with many safety-critical aspects to consider. This means that a great deal of testing is required to validate the performance of automotive systems, under all possible environmental conditions. For vision-based systems, camera images are among the most important input sources of information, and using high-quality images is integral to the system’s performance. Rain, as a type of adverse weather condition, degrades the image quality which reflects negatively on the vision-based algorithms. Collecting representative sets of data under different rain conditions is required for system testing and performance evaluation. This usually is both costly and time-consuming. Augmenting the sets of real rained images in system testing is an attractive, feasible alternative. In this paper, we present an adherent rain simulator system, that adds simulated rain to clear image frames captured in real drive cycles. We test the quality of simulated rained images against real rained ones, using common image similarity metrics. We also compare the performance of deep learning-based object detectors, using our simulated rained images vs. real rained images. The results show that object detectors show similar performance using simulated and real rained images. A comparative analysis shows that our model produces more realistic raindrops, compared to a ray-tracing-based raindrop simulator.
Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.
The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Scatterplots can encode a third dimension by using additional channels like size or color (e.g. bubble charts). We explore a potential misinterpretation of trivariate scatterplots, which we call the weighted average illusion, where locations of larger and darker points are given more weight toward x- and y-mean estimates. This systematic bias is sensitive to a designer's choice of size or lightness ranges mapped onto the data. In this paper, we quantify this bias against varying size/lightness ranges and data correlations. We discuss possible explanations for its cause by measuring attention given to individual data points using a vision science technique called the centroid method. Our work illustrates how ensemble processing mechanisms and mental shortcuts can significantly distort visual summaries of data, and can lead to misjudgments like the demonstrated weighted average illusion.
Electrically-switchable foundry-processed phase change photonic devices

Optical phase change materials (PCMs) are a unique class of materials which exhibit extraordinarily large optical property change (e.g. refractive index change &gt; 1) when undergoing a solid-state phase transition, and they have witnessed increasing adoption in active integrated photonics and metasurface devices in recent years. Here we report integration of chalcogenide phase change materials in the Lincoln Laboratory 8-inch Si foundry process and the demonstration of electrothermally switched phase-change photonic devices building on a wafer-scale silicon-on-insulator heater platform.
Resolving cold start and sparse data challenge in recommender systems using multi-level singular value decomposition

Recommender systems estimate users’ tendency and recommend a list of suitable items. Two fundamental challenges in recommender systems include cold start and sparse data. In order to overcome these challenges, employing contextual similarity measures, utilizing the features of the users and items, and applying machine learning methods have been presented. However, a method called the context feature singular value decomposition is presented as the first step. In this method, the user-context feature matrix, the item-context feature matrix, and the context similarity matrix are created to mitigate cold start. In the second step, matrices obtained in the previous step are applied as components of a multi-level singular value decomposition matrix and momentum stochastic gradient descent feature to reduce sparse data. The results obtained from the presented method are compared to those of existing approaches, indicating that the proposed method improves the accuracy of suggested entities in recommender systems
3D-DWT cross-band statistics and features for No-Reference Video Quality Assessment (NR-VQA)

This paper presents a robust, novel, and computationally efficient noise estimation-based NR-VQA model. It uses four novel sub-band features; namely cross-band statistics, sub-band kurtosis, sub-band energy ratios, and natural video statistics in three-dimensional discrete wavelet transform (3D-DWT) domain. As the HVS shows high sensitivity towards the structural and textural information, a three-dimensional gray level co-occurrence matrix (3D-GLCM) is used to select top 25% video blocks having maximal textural information. We validate the performance of the proposed method on five publicly available and widely used video quality databases. Extensive experiments show that the proposed model yields considerably better and robust performance than other contemporary state-of-the-art NR-VQA methods at lower computational costs.