Reviewerly/exHarmony huggingface dataset:
Visualizing Deep Learning-Based Radio Modulation Classifier

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learning-based radio modulation classifiers are lacking interpretability, and there is little explanation or visibility into what kinds of radio features are extracted and chosen for classification. In this article, we visualize different deep learning-based radio modulation classifiers by introducing a class activation vector. Specifically, both convolutional neural networks (CNN) based classifier and long short-term memory (LSTM) based classifier are separately studied, and their extracted radio features are visualized. We explore different hyperparameter settings via extensive numerical evaluations and show both the CNN-based classifier and LSTM-based classifiers extract similar radio features relating to modulation reference points. In particular, for the LSTM-based classifier, its obtained radio features are similar to the knowledge of human experts. Our numerical results indicate the radio features extracted by deep learning-based classifiers greatly depend on the contents carried by radio signals, and a short radio sample may lead to misclassification.
An explainable XGBoost–based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus

Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD-related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human understanding on the reasoning process, maximize transparency and embellish trust towards the models' adoption in clinical practice. The aim of the present study is to develop and evaluate an explainable personalized risk prediction model for the fatal or non-fatal CVD incidence in T2DM individuals. An explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the Tree SHAP (SHapley Additive exPlanations) method is deployed for the calculation of the 5-year CVD risk and the generation of individual explanations on the model's decisions. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The obtained results (AUC=71.13%) indicate the potential of the proposed approach to handle the unbalanced nature of the used dataset, while providing clinically meaningful insights about the model's decision process.
Test-retest reliability of the virtual reality sickness evaluation using electroencephalography (EEG)

No reliable quantitative and objective measurement method for virtual reality (VR) sickness has been firmly established to date. Electroencephalography (EEG) may be a strong candidate to evaluate VR sickness objectively. However, no test-retest evaluation has been made for VR sickness using EEG. To recruit VR sickness-sensitive participants, we tested 858 participants (age = 20′s–50′s) using the Motion Sickness Susceptibility Questionnaire (MSSQ). Among them, we recruited 21 males (average age = 25.0) who obtained the 75th percentile of scores on the MSSQ (32.9 ± 5.7). VR sickness was evaluated twice (one week apart) using EEG with VR video content designed to cause VR sickness. A Simulation Sickness Questionnaire (SSQ) was also used to evaluate VR sickness. In terms of the reliability of EEG, ICC and Cronbach's alpha analyses showed that three waves (delta, theta, and alpha) were consistent in two areas (frontal and central). A significant difference in EEG was also found repeatedly between the baseline and VR sickness (delta, theta, and alpha) in two areas (frontal and central). We evaluated EEG for its reliability and found specific waves and areas that showed good consistency and significant changes associated with VR sickness. These findings may support further research of VR sickness evaluation.
VulDetector: Detecting Vulnerabilities Using Weighted Feature Graph Comparison

Code similarity is one promising approach to detect vulnerabilities hidden in software programs. However, due to the complexity and diversity of source code, current methods suffer low accuracy, high false negative and poor performance, especially in analyzing a large program. In this paper, we propose to tackle these problems by presenting VulDetector, a static-analysis tool to detect C/C++ vulnerabilities based on graph comparison at the granularity of function. At the key of VulDetector is a weighted feature graph (WFG) model which characterizes function with a small yet semantically rich graph. It first pinpoints vulnerability-sensitive keywords to slice the control flow graph of a function, thereby reducing the graph size without compromising security-related semantics. Then, each sliced subgraph is characterized using WFG, which provides both syntactic and semantic features in varying degrees of security. As for graph comparison, we take full usage of vulnerability graph and patch graph to improve accuracy. In addition, we propose two optimization methods based on analysis of vulnerabilities. We have implemented VulDetector to automatically detect vulnerabilities in software programs with known vulnerabilities. The experimental results prove the effectiveness and efficiency of VulDetector.
Fright, attention, and joy while killing zombies in Virtual Reality: A psychophysiological analysis of VR user experience

Abstract To date, research on the user experience of Virtual Reality (VR) games is sparse even though VR and immersive gaming experiences are emerging as a substantial consumer based within the videogame industry. Using facial electromyography and skin conductance response measurement, the current research presents the first psychophysiological evidence to show that: (1) VR games may heighten affective responses such as fear and arousal; (2) different levels of immersion in various gameplay modes may evoke higher affective responses; and (3) these heightened affective responses significantly correlate with users’ experience of enjoyment at the precise onset of the affective response. Due to the second‐by‐second nature of psychophysiological measurement, the study's findings also pinpoint a specific sequence (i.e., entrapment in a dark room) within the gameplay that significantly evoked nearly 2.5‐times higher fear with arousal as well as enjoyment. These findings contribute to the ongoing study of VR in the fields of media psychology, media studies, human‐computer interaction studies, and marketing by demonstrating the strong link between immersion, affective responses, and positive user experience toward a horror VR game and their marketing implications.
Gated Path Selection Network for Semantic Segmentation

Semantic segmentation is a challenging task that needs to handle large scale variations, deformations and different viewpoints. In this paper, we develop a novel network named Gated Path Selection Network (GPSNet), which aims to learn adaptive receptive fields. In GPSNet, we first design a two-dimensional multi-scale network - SuperNet, which densely incorporates features from growing receptive fields. To dynamically select desirable semantic context, a gate prediction module is further introduced. In contrast to previous works that focus on optimizing sample positions on the regular grids, GPSNet can adaptively capture free form dense semantic contexts. The derived adaptive receptive fields are data-dependent, and are flexible that can model different object geometric transformations. On two representative semantic segmentation datasets, i.e., Cityscapes, and ADE20K, we show that the proposed approach consistently outperforms previous methods and achieves competitive performance without bells and whistles.
Virtual reality sports auxiliary training system based on embedded system and computer technology

Virtual Reality technology, the complex environment can be a simulated, traditional sports courses at the stadium, equipment, it is due to limitations such as safety, to provide scientific and accurate teaching and learning's, a complex by visualizing the theoretical knowledge in the abstract sport, you can get more technical knowledge. Compared to the traditional physical education activities, simulate sports scene only by generating VR panoramas that cannot be virtual reality technology. Based on this, you can improve the enthusiasm and athletic ability of students effectively. Because of immaturity and high-priced equipment of VR technology, at present, people that education is using unpopular making, carefully virtual reality technology. In this context, we can apply virtual reality technology to sports training, which can obtain more effective training effect. In the first part of this paper, we talk about the research of virtual reality panorama, the second part about the research of virtual reality video, the third part about the application of virtual reality in physical education teaching, and the fourth part is the conclusion and investigation process. In the study, VR, as a new technology, found that you have the prospect of a wide range of applications. As long as it has been used in scientific and rational, you can VR is to promote the level of improvement and sports greatly sports scene. Users can increase users' interest; it can be immersed in a variety of preset virtual locations. Therefore, in the VR device, which allows the user to improve the experience of the VR device, you can interact well in a virtual scene.
On robust asymmetric Lagrangian <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e9749" altimg="si228.svg"><mml:mi>ν</mml:mi></mml:math>-twin support vector regression using pinball loss function

The main objective of twin support vector regression (TSVR) is to find the optimum regression function based on the ε-insensitive up- and down-bound with equal influences on the regression function where all the data points have a different location above the up-bound points and below the down-bound points. However, the effects of all data points must be distinct based on their distribution in the regression function. Recently, asymmetric ν-twin support vector regression (Asy-ν-TSVR) is encouraged on the same subject but still, the present matrices in the mathematical formulation have faced the problem of semi-definite. In order to handle this problem effectively, a new regressor model named as robust asymmetric Lagrangian ν-twin support vector regression using pinball loss function (URALTSVR) proposes as a pair of the unconstrained minimization problem to handle not only the noise sensitivity and instability of re-sampling but also consist positive definite matrices. Here, we suggest the proposed model URALTSVR in such a way where the pinball loss function is playing a vital role to control the fitting error inside the asymmetric tube. One of the advantages is that unlike TSVR and Asy-ν-TSVR, it considers the concept of structural risk minimization principle through the inclusion of regularization term as well as change the one-norm of the vector of the slack variable by two-norm, which yields the dual problem to be strongly convex, stable and well-posed. Aforementioned, the proposed formulation has a continuous and piecewise quadratic problem that is solved by their gradients based iterative approaches. Specifically, we analyze the three implementations of URALTSVR with the baselines approaches support vector regression (SVR), TSVR and Asy-ν-TSVR, which discard the dependencies to solve a pair of quadratic programming problem (QPP) for obtaining the unique global solution. Overall, SRALTSVR1 based on smooth approximation function performs outstanding for artificial and real-world datasets.
Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers

This article proposes a primary and backup resource allocation model that provides a probabilistic protection guarantee for virtual machines against multiple failures of physical machines in a cloud provider to minimize the required total capacity. A physical machine allocates both primary and backup computing resources for virtual machines. When any failure occurs, the survived physical machines with preplanned backup resources recover the virtual machines on the failed physical machines and take over the workloads. The probability that the protection provided by a physical machine does not succeed is guaranteed within a given number. Providing the probabilistic protection can reduce the required backup capacity by allowing backup resource sharing, but it leads to a nonlinear programing problem in a general-capacity case against multiple failures. We apply robust optimization with extensive mathematical operations to formulate the primary and backup resource allocation problem as a mixed integer linear programming problem, where capacity fragmentation is suppressed. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The results reveal that the proposed model saves about one-third of the total capacity in our examined cases; it outperforms the conventional models in terms of both blocking probability and resource utilization.
An enhanced differential evolution algorithm with a new oppositional-mutual learning strategy

Global optimization has been a hot research topic in various engineering applications, where differential evolution (DE) is one of the most popular approaches. Actually, it is inevitable for DE to trap into local optima when dealing with complex optimization problems. Dynamic opposite learning (DOL), which is a new variant of opposition-based learning (OBL), has the potential for enhancing DE, due to its strong exploration capability contributed by the asymmetric and dynamic search space. To balance exploration and exploitation, an adjustable weight parameter of the search space is adopted, yet adjusting the value needs a lot of tests and expert experience. Instead of relying on the additional weight parameter, a mutual learning (ML) strategy, which leads individuals to learn from each other deterministically, is combined with DOL for raising exploitation. The trade-off between exploration and exploitation is guaranteed by randomly switching DOL and ML in the population initialization process and the generation jumping process. A hybrid strategy, named oppositional-mutual learning (OML), is thereby generated, and it is applied for the performance improvement of DE. Benchmarks from CEC 2014, including unimodal, multi-modal, hybrid and composition functions, were adopted to evaluate the performance of the oppositional-mutual learning DE (OMLDE). Numerical results with the comparisons to the state-of-the-art counterparts show that OMLDE has significant advantages of converging to the global optimum on most functions, which also validates the superiority of the novel OML strategy.
How to Solve Millionaires’ Problem with Two Kinds of Cards

Abstract Card-based cryptography, introduced by den Boer aims to realize multiparty computation (MPC) by using physical cards. We propose several efficient card-based protocols for the millionaires’ problem by introducing a new operation called Private Permutation (PP) instead of the shuffle used in most of existing card-based cryptography. Shuffle is a useful randomization technique by exploiting the property of card shuffling, but it requires a strong assumption from the viewpoint of arithmetic MPC because shuffle assumes that public randomization is possible. On the other hand, private randomness can be used in PPs, which enables us to design card-based protocols taking ideas of arithmetic MPCs into account. Actually, we show that Yao’s millionaires’ protocol can be easily transformed into a card-based protocol by using PPs, which is not straightforward by using shuffles because Yao’s protocol uses private randomness. Furthermore, we propose entirely novel and efficient card-based millionaire protocols based on PPs by securely updating bitwise comparisons between two numbers, which unveil a power of PPs. As another interest of these protocols, we point out they have a deep connection to the well-known logical puzzle known as “The fork in the road.”
Italian protocol for massive solar integration: From solar imbalance regulation to firm 24/365 solar generation

This work describes a progressive strategy to achieve 100% penetration of intermittent renewables at minimal cost. The strategy works to optimally transform variable renewable energy (RE) into firm, effectively dispatchable generation. This functional dispatchability enables large-scale displacement of conventional generation at equal or lower production cost. By way of this strategy, we delineate a pathway for a full renewable energy transition of the Italian electric mix. In order to achieve 100% renewables in any grid, the fundamental imbalance between the supply of the resource and demand must be alleviated. We propose a transition that starts by addressing net load forecast imbalances resulting from renewables' prediction errors and ends with the transformation of intermittent renewables into firm, effectively dispatchable generation sources.
FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms

Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, therein included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions, or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a multivocal review and (ii) a technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. We also present a FaaS platform selection support system, called FaaStener, which can help researchers and practitioners to choose the FaaS platform most suited for their requirements.
A dynamic block image encryption using variable-length secret key and modified Henon map

In this paper, a dynamic block image encryption using variable-length key and modified Henon map is proposed. To surmount the defects of narrow chaotic range and frail chaotic behaviors when classical Henon map is implemented in practical situations, we design a simple modified Henon map with wide parameter range and high complexity. The Secure Hash Algorithm (SHA) is employed to obtain a 384-bit hash result of plain image considered as a raw key. A variable-length secret key (at least 128 bits), which is dynamically selective from the raw key, is regarded as a valid key to generate initial points of chaotic map. In confusion, we construct a chaotic but non-repetitive integer sequence to implement a substitution without repetition for disorganizing the high spatial relevance among pixels. This can sufficiently avert the problem of fixed points after a confusion, i.e., for any pixel within the confused image, its position will never overlap with the old one. In diffusion, we apply a multidirectional dynamic block diffusion mechanism to modify pixel values, which may not only spread the influences to whole image but reduce encryption time. The proposed scheme has been tested via various cryptographic analysis, and the security analysis demonstrates it has superior security and good efficiency.
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement

Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual description of faces can be much more complicated and detailed due to the variety of facial attributes and the parsing of high dimensional abstract natural language. In this paper, we propose a Text-to-Face model that not only produces images in high resolution (1024×1024) with text-to-image consistency, but also outputs multiple diverse faces to cover a wide range of unspecified facial features in a natural way. By fine-tuning the multi-label classifier and im age encoder, our model obtains the adjustment vectors and image embeddings which are used to transform the input noise vector sampled from the normal distribution. Afterwards, the transformed noise vector is fed into a pre-trained high-resolution image generator to produce a set of faces with the desired facial attributes. We refer to our model as TTF-HD. Experimental results show that TTF-HD generates high-quality synthesised faces from free-form text descriptions with state-of-the-art performance.
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults

Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy, they often have enormous compute and memory requirements. As a result of this, quantized neural networks (QNNs) are increasingly being adopted and deployed especially on embedded devices, thanks to their high accuracy, but also since they have significantly lower compute and memory requirements compared to their floating point equivalents. QNN deployment is also being evaluated for safety-critical applications, such as automotive, avionics, medical or industrial. These systems require functional safety, guaranteeing failure-free behaviour even in the presence of hardware faults. In general fault tolerance can be achieved by adding redundancy to the system, which further exacerbates the overall computational demands and makes it difficult to meet the power and performance requirements. In order to decrease the hardware cost for achieving functional safety, it is vital to explore domain-specific solutions which can exploit the inherent features of DNNs. In this work we present a novel methodology called fault-aware training (FAT), which includes error modeling during neural network (NN) training, to make QNNs resilient to specific fault models on the device. Our experiments show that by injecting faults in the convolutional layers during training, highly accurate convolutional neural networks (CNNs) can be trained which exhibits much better error tolerance compared to the original. Furthermore, we show that redundant systems which are built from QNNs trained with FAT achieve higher worse-case accuracy at lower hardware cost. This has been validated for numerous classification tasks including CIFAR10, GTSRB, SVHN and ImageNet.