Reviewerly/exHarmony huggingface dataset:
A comparative analysis of simulators for the Cloud to Fog continuum

The Cloud to Fog continuum is a very dense and complex scenario. At the core level (Cloud) resources are vast, whilst they become scarce at the Edge (Fog). This complexity leads to the need of simulation tools in order to evaluate the performance of novel mechanisms that hardly can be tested in real scenarios. Thus, simulation represents a solution for early stage evaluation before moving to real-world (and more expensive and complex) testbeds. However, selecting the appropriate simulation tool can be complex in itself. This paper presents a conceptual review on six Cloud/Fog Simulation tools, describing their main characteristics and what they allow to experiment. A practical overview of the most representative Cloud/Fog simulators is presented, reporting about their resource consumption and execution time. The aim of this survey is to enlighten other researchers in the selection of the appropriate Cloud/Fog simulation tool for their goals and to know what they can expect from said tools.
A novel facial emotion recognition scheme based on graph mining

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.
Global and Local Knowledge-Aware Attention Network for Action Recognition

Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essential parts of video frames that are relevant to the action. In this article, we propose a novel global and local knowledge-aware attention network to address this challenge for action recognition. The proposed network incorporates two types of attention mechanism called statistic-based attention (SA) and learning-based attention (LA) to attach higher importance to the crucial elements in each video frame. As global pooling (GP) models capture global information, while attention models focus on the significant details to make full use of their implicit complementary advantages, our network adopts a three-stream architecture, including two attention streams and a GP stream. Each attention stream employs a fusion layer to combine global and local information and produces composite features. Furthermore, global-attention (GA) regularization is proposed to guide two attention streams to better model dynamics of composite features with the reference to the global information. Fusion at the softmax layer is adopted to make better use of the implicit complementary advantages between SA, LA, and GP streams and get the final comprehensive predictions. The proposed network is trained in an end-to-end fashion and learns efficient video-level features both spatially and temporally. Extensive experiments are conducted on three challenging benchmarks, Kinetics, HMDB51, and UCF101, and experimental results demonstrate that the proposed network outperforms most state-of-the-art methods.
A systematic design method of adaptive augmented reality work instruction for complex industrial operations

One of the barriers that prevent augmented reality (AR) from being widely adopted in diverse and complex industrial operations is the lack of adaptive and scalable AR work instruction (ARWI). This paper suggests a systematic method to solve the problem. The proposed method presents an adaptive representation structure that expresses and controls those interrelated aspects of ARWI adaptiveness: authoring, environment, guiding and process control. The proposed method also provides a detailed approach to achieve the adaptiveness of ARWI in its authoring and running process. The ARWI design workflow suggested can incorporate industrial AR elements via on-site operators’ inputs without programming instead of developers or engineers. At the same time, the adaptive ARWI can adapt to different people, environment objects, and processes of complex industrial operations at runtime. This advantage brings ARWI design closer to real applications and makes the technology more adoptable. Three user studies, using the disassembly operations of an aircraft engine’s hydraulic actuator and other two practical cases, are used to assess the proposed method and support our claims.
Quantum fast Poisson solver: the algorithm and complete and modular circuit design

The Poisson equation has applications across many areas of physics and engineering, such as the dynamic process simulation of ocean current. Here we present a quantum Fast Poisson Solver, including the algorithm and the complete and modular circuit design. The algorithm takes the HHL algorithm as the template. The controlled rotation is performed based on the arc cotangent function which is evaluated by the Plouffe's binary expansion method. And the same method is used to compute the cosine function for the eigenvalue approximation in phase estimation. Quantum algorithms for solving square root and reciprocal functions are developed based on the non-restoring digit-recurrence method. These advances make the algorithm's complexity lower and the circuit-design more modular. The number of the qubits and operations used by the circuit are O(dlog2({\epsilon}-1)) and O(dlog3({\epsilon}-1)), respectively. We demonstrate our circuits on a quantum virtual computing system installed on the Sunway TaihuLight supercomputer. This is an important step toward practical applications of quantum Fast Poisson Solver in the near-term hybrid classical/quantum devices.
Dynamics-Aware Unsupervised Discovery of Skills

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is difficult, and even then, the model might not generalize well outside the distribution of states on which it was trained. In this work, we combine model-based learning with model-free learning of primitives that make model-based planning easy. To that end, we aim to answer the question: how can we discover skills whose outcomes are easy to predict? We propose an unsupervised learning algorithm, Dynamics-Aware Discovery of Skills (DADS), which simultaneously discovers predictable behaviors and learns their dynamics. Our method can leverage continuous skill spaces, theoretically, allowing us to learn infinitely many behaviors even for high-dimensional state-spaces. We demonstrate that zero-shot planning in the learned latent space significantly outperforms standard MBRL and model-free goal-conditioned RL, can handle sparse-reward tasks, and substantially improves over prior hierarchical RL methods for unsupervised skill discovery.
Virtual Reality Design for Stroke Rehabilitation

Stroke is a leading cause of disability, and with the stroke survivor population rising in most countries it is increasingly difficult to provide optimal treatment to patients once they return home. Assistive technology solutions can potentially contribute to meeting demand, and also be cost effective. In this chapter, we consider the design and development of engaging serious virtual reality (VR) games for upper arm stroke rehabilitation. Fundamental design principles are summarised and related to our experience of creating game-based VR rehabilitation. The application of ideas from psychology, particularly behavioural change and flow theory are discussed, as well as related learning and gamification principles. We address how to manage differences between people through design, user profiling, and intelligent dynamic system behaviour, and we also explore how to account for variation in stroke survivor capability and personality. The idea of a hero's journey as a metaphor for stroke recovery is introduced and we discuss how this metaphor may guide system design, its relationship to game design principles, and how patient narratives and embedded stories might support engagement with treatment. An overview of our previous work is summarised and we discuss how our experience and increased knowledge and capability has informed improved approaches to development processes. Finally, our approach is illustrated with reference to a recent EU project.
The DeepFake Detection Challenge Dataset

Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code. To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition. Importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset. The DFDC dataset is by far the largest currently and publicly available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top submissions from the Kaggle contest. We show although Deepfake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real in-the-wild Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. Training, validation and testing corpuses can be downloaded from this https URL.
Electronic currency note sterilizer machine

Currency notes play a vital role in the world starting from micro business to daily needs of people across the world. As per the statistical reports given by Reserve bank of India, 21.1 trillion currency notes are in circulation among the people in 2019 financial year which is 17% higher than the previous years. The rapid spread of the corona virus infectivity has raised concerns over surface-to-humans transmissions, including through currency notes. This increases the panic level of spread of micro-organisms through the currency notes circulation which leads to the spread of diseases like COVID-19. The currency note may act as the carrier of these dreadful diseases which needs engineering solution to avoid this problem. Simulations are performed using TINKERCAD Arduino Simulator.
Analysis of hadoop MapReduce scheduling in heterogeneous environment

Over the last decade, several advancements have happened in distributed and parallel computing. A lot of data is generated daily from various sources, and this speedy data proliferation led to the development of many more frameworks that are efficient to handle such huge data e.g. - Microsoft Dryad, Apache Hadoop, etc. Apache Hadoop is an open-source application of Google MapReduce and is getting a lot of attention from various researchers. Proper scheduling of jobs needs to be done for better performance. Numerous efforts have been done in the development of existing MapReduce schedulers and in developing new optimized techniques or algorithms. This paper focuses on the Hadoop MapReduce framework, its shortcomings, various issues we face while scheduling jobs to nodes and algorithms proposed by various researchers. Furthermore, we then classify these algorithms on various quality measures that affect MapReduce performance.
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models

Effective methodologies for evaluating recommender systems are critical, so that different systems can be compared in a sound manner. A commonly overlooked aspect of evaluating recommender systems is the selection of the data splitting strategy. In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems during evaluation. In particular, we perform experiments comparing three common data splitting strategies, examining their impact over seven state-of-the-art recommendation models on two datasets. Our results demonstrate that the splitting strategy employed is an important confounding variable that can markedly alter the ranking of recommender systems, making much of the currently published literature non-comparable, even when the same datasets and metrics are used.
Hierarchical deep multi-modal network for medical visual question answering

Visual Question Answering in Medical domain (VQA-Med) plays an important role in providing medical assistance to the end-users. These users are expected to raise either a straightforward question with a Yes/No answer or a challenging question that requires a detailed and descriptive answer. The existing techniques in VQA-Med fail to distinguish between the different question types sometimes complicates the simpler problems, or over-simplifies the complicated ones. It is certainly true that for different question types, several distinct systems can lead to confusion and discomfort for the end-users. To address this issue, we propose a hierarchical deep multi-modal network that analyzes and classifies end-user questions/queries and then incorporates a query-specific approach for answer prediction. We refer our proposed approach as Hierarchical Question Segregation based Visual Question Answering, in short HQS-VQA. Our contributions are three-fold, viz. firstly, we propose a question segregation (QS) technique for VQA-Med; secondly, we integrate the QS model to the hierarchical deep multi-modal neural network to generate proper answers to the queries related to medical images; and thirdly, we study the impact of QS in Medical-VQA by comparing the performance of the proposed model with QS and a model without QS. We evaluate the performance of our proposed model on two benchmark datasets, viz. RAD and CLEF18. Experimental results show that our proposed HQS-VQA technique outperforms the baseline models with significant margins. We also conduct a detailed quantitative and qualitative analysis of the obtained results and discover potential causes of errors and their solutions.
Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems

Purpose This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of the proposed method in comparison to other optimization methods, two sets of test problems consisting of 13 constrained benchmark functions and 6 benchmark trusses are investigated numerically. The results indicate that the performance of the proposed method is competitive with other considered state-of-the-art optimization methods. Design/methodology/approach In this paper, a new physically-based metaheuristic algorithm called plasma generation optimization (PGO) algorithm is developed for solving constrained optimization problems. PGO is a population-based optimizer inspired by the process of plasma generation. In the proposed algorithm, each agent is considered as an electron. Movement of electrons and changing their energy levels are based on simulating excitation, de-excitation and ionization processes occurring through the plasma generation. In the proposed PGO, the global optimum is obtained when plasma is generated with the highest degree of ionization. Findings A new physically-based metaheuristic algorithm called the PGO algorithm is developed that is inspired from the process of plasma generation. Originality/value The results indicate that the performance of the proposed method is competitive with other state-of-the-art methods.
Swapping Autoencoder for Deep Image Manipulation

Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image. In particular, we encourage the components to represent structure and texture, by enforcing one component to encode co-occurrent patch statistics across different parts of an image. As our method is trained with an encoder, finding the latent codes for a new input image becomes trivial, rather than cumbersome. As a result, it can be used to manipulate real input images in various ways, including texture swapping, local and global editing, and latent code vector arithmetic. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.
Memory as a Computational Resource

Computer scientists have long recognized that naive implementations of algorithms often result in a paralyzing degree of redundant computation. More sophisticated implementations harness the power of memory by storing computational results and reusing them later. We review the application of these ideas to cognitive science, in four case studies (mental arithmetic, mental imagery, planning, and probabilistic inference). Despite their superficial differences, these cognitive processes share a common reliance on memory that enables efficient computation.
ImageNet Pre-trained CNNs for JPEG Steganalysis

In this paper, we investigate pre-trained computer-vision deep architectures, such as the EfficientNet, MixNet, and ResNet for steganalysis. These models pre-trained on ImageNet can be rather quickly refined for JPEG steganalysis while offering significantly better performance than CNNs designed purposely for steganalysis, such as the SRNet, trained from scratch. We show how different architectures compare on the ALASKA II dataset. We demonstrate that avoiding pooling/stride in the first layers enables better performance, as noticed by other top competitors, which aligns with the design choices of many CNNs designed for steganalysis. We also show how pre-trained computer-vision deep architectures perform on the ALASKA I dataset.
Deep Learning Inference Parallelization on Heterogeneous Processors With TensorRT

As deep learning (DL) inference applications are increasing, an embedded device tends to equip neural processing units (NPUs) in addition to a CPU and a GPU. For fast and efficient development of DL applications, TensorRT is provided as the software development kit for the NVIDIA hardware platform, including optimizer and runtime that delivers low latency and high throughput for DL inference. Like most DL frameworks, TensorRT assumes that the inference is executed on a single processing element, GPU, or NPU, not both. In this letter, we propose a parallelization methodology to maximize the throughput of a single DL application using both GPU and NPU by exploiting various types of parallelism on TensorRT. With six real-life benchmarks, we could achieve 81%–391% throughput improvement over the baseline inference using GPU only.
End-to-End Policy Learning for Active Visual Categorization

Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views at test time. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for this "active recognition" setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent's motions on its internal representation of the environment conditional on all past views. Results across three challenging datasets confirm both that our end-to-end system successfully learns meaningful policies for active category recognition, and that "learning to look ahead" further boosts recognition performance.
Unsupervised Feature Selection via Adaptive Multimeasure Fusion

Since multiple criteria can be adopted to estimate the similarity among the given data points, problem regarding diverse representations of pairwise relations is brought about. To address this issue, a novel self-adaptive multimeasure (SAMM) fusion problem is proposed, such that different measure functions can be adaptively merged into a unified similarity measure. Different from other approaches, we optimize similarity as a variable instead of presetting it as a priori, such that similarity can be adaptively evaluated based on integrating various measures. To further obtain the associated subspace representation, a graph-based dimensionality reduction problem is incorporated into the proposed SAMM problem, such that the related subspace can be achieved according to the unified similarity. In addition, sparsity-inducing ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,0</sub> regularization is introduced, such that a sparse projection is obtained for efficient feature selection (FS). Consequently, the SAMM-FS method can be summarized correspondingly.
Virtual environments and their role in developing perceptual-cognitive skills in sports

This chapter considers the design of training environments, and motivations for using virtual environments (VE). In the perceptual-cognitive skills area there are also systems that have some similar characteristics, use many of the same technologies, and are employed for sport training but do not meet the definition of a VE. The potential advantages of using a VE for training perceptual-cognitive skills in sports can be seen by looking at some of the evidence-based principles that have been proposed for practice design. When assessing the effectiveness of a sports training VE, it is essential to separate primary and secondary evaluation criteria. Psychological fidelity refers to the extent to which a VE recreates the perceptual-cognitive demands of the real task and leads to similar behaviours being observed from the user as in the real environment. Given the evidence for specificity of practice, it is likely that psychological fidelity is more strongly related to transfer of training than physical fidelity.