Reviewerly/exHarmony huggingface dataset:
Impact of VR and Desktop Gaming on Electroencephalogram (EEG) Ratings

User experience (UX) studies in video games have become increasingly prevalent in the literature in recent years due to an increasing global focus in UX, multiple new video game platforms and the growing utilisation of virtual reality technology. To have a video game perform at an acceptable level for the user, research scientists must investigate which parts of a video game are effective in terms of overall game enjoyment and playability. There are various factors that affect the overall user experience in a video game. In this study, we take a closer look at the differences in brain waves using an electroencephalogram (EEG) and UX satisfaction metrics in both desktop and VR gaming conditions utilising a commercial EEG device capable of measuring a range of brain activity.
AI Approaches for Breast Cancer Diagnosis: A Comprehensive Study

According to the report of the World Health Organization (WHO), the most common and dangerous disease among women is known to be breast cancer. Breast cancer is a life-threatening disease and may cause death of women. Early detection of breast cancer can decrease mortality rate and improve survival rate in women. Various artificial intelligence (AI) approaches have been used by the research community to build computer-aided diagnosis (CAD) systems for early detection of breast cancer. This study presents various breast imaging modalities used for the cancer diagnosis, related work in this domain, various pre-processing techniques to improve the quality of breast images and applications of machine learning (ML) for breast imaging. The study also presents various deep learning (DL) approaches to build a system for automated breast cancer diagnosis. Various pre-trained deep learning models are also presented in the study. Due to an imbalanced and inconsistent dataset, AI models may not perform well. We have also discussed various techniques to improve the performance of the model. Prediction, segmentation, and classification deep neural network models along with the various imaging modalities are presented which are beneficial for the diagnosis process of breast cancer.
Study of the visualization and Interaction of data : Take the Historical Data of the Winter Olympics as an Example

Traditional data is displayed in forms such as tables, which are difficult to quickly perceive. To better understand the data, we need more innovative forms to Make Data Visible and Interactive. This article explores interactive data perception design based on Processing programming language, gesture recognition sensor and Arduino hardware development tools. Taking the historical data of the Winter Olympics as an exmple, a digital media design practice has been conducted.
Robustness of Adversarial Images Against Filters

This article addresses the robustness issue of adversarial images against filters. Given an image $$\mathcal {A}$$ , that a convolutional neural network and a human both classify as belonging to a category $$c_\mathcal{{A}}$$ , one considers an adversarial image $$\mathcal {D}$$ that the neural network classifies in a category $$c_t \ne c_\mathcal{{A}}$$ , although a human would not notice any difference between $$\mathcal {D}$$ and $$\mathcal {A}$$ . Would the application of a filter F (such as the Gaussian blur filter) to $$\mathcal {D}$$ still lead to an adversarial image $$F(\mathcal {{D}})$$ that fools the neural network? To address this issue, we perform a study on VGG-16 trained on CIFAR-10, with adversarial images obtained thanks to an evolutionary algorithm run on a specific image $$\mathcal {A}$$ taken in one category of CIFAR-10. Exposed to 4 individual filters, we show that the outputted filtered adversarial images essentially do remain adversarial in some sense. We also show that combining filters may render our EA attack less effective. We therefore design a new evolutionary algorithm, whose aim is to create adversarial images that do pass the filter test, do fool VGG-16 and do remain close enough to $$\mathcal {A}$$ that a human would not notice any difference. We show that this is indeed the case by running this new algorithm on the same image $$\mathcal {A}$$ .
Inclusive Higher Vocational Education for Special Needs Students in the Scandinavian Countries and in the Russian Federation: Comparative Analysis

The paper aims to reveal similarities and differences in organization of inclusive higher education in the Scandinavian countries and in the Russian Federation. The article analyzes interpretation of the term “competence” in the western pedagogics, identifies professional and vital (personal and social) competences, describes experience of the Scandinavian countries in organizing group-based training focused on forming special needs students’ social and interpersonal competence; examines approaches to inclusive education in the Scandinavian countries and in the Russian Federation. The authors describe current European trends in higher education, reveal specificity of special needs students’ training in Denmark, Norway and Sweden, and herein lies scientific originality of the study. The research findings are as follows: the authors justify efficiency of group-based training focused on forming special needs students’ social, interpersonal, intercultural competence; analyze implementation of special education trajectories in the Scandinavian countries.
Simulated Annealing based approach for Virtual Machine Live Migration

Virtual machine migration technique is used in cloud computing to increase the reliability and scalability of the cloud computing systems. It helps the service providers to achieve resource efficiency and quality of service. At the time of live migration, the underlying virtual machine continuously works until the entire or part of data is migrated from source to destination. Live migration of Virtual Machine (VM) acts as an important technique that allows the management of resources, maintenance of server and load balancing in cloud data centers, but there is a degradation of performance at the source and destination physical machines. Different live migrations techniques have been proposed, each showing different properties like completion time, amount of data transferred, down time of VM and degradation of performance of VM. In this paper, a live migration algorithm, Live Migration Annealing (LMA) Virtual Machine Migration that makes use of an evaluation function to perform analysis on the time series data collected over the iterations made during the live migration period is proposed. It embodies the concept of exploration and exploitation of knowledge and space. It also takes ideas from the Iterative depth first Search to perform the iterations and Simulated Annealing to find the pages eligible for the live Migration. All the eligible pages undergo a phase called Selection phase before being finally sent to the destination virtual machine which incorporates the idea of Second Chance. Live Migration is only performed if it isn’t happening at the expense of the downtime. An overall decrease in the number of iterations and downtime with least possible live migration time is achieved through the proposed algorithm.
A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration

Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform such a deployment on System-on-Chips. Based on a high-level Python interface that mimics the leading Deep Learning software frameworks, it offers an easy way to implement a hardware-accelerated DNN on an FPGA. To do this, our design methodology covers the three main phases: (a) customization: where the user specifies the optimizations needed on each DNN layer, (b) generation: the framework generates on the Cloud the necessary binaries for both FPGA and software parts, and (c) deployment: the SoC on the Edge receives the resulting files serving to program the FPGA and related Python libraries for user applications. Among the study cases, an optimized DNN for the MNIST database can speed up more than 60× a software version on the ZYNQ 7020 SoC and still consume less than 0.43W. A comparison with the state-of-the-art frameworks demonstrates that our methodology offers the best trade-off between throughput, power consumption, and system cost.
Hotel Management Platform Design Based on Virtual Reality

The previous two-dimensional visual display mode cannot meet the needs of information acquisition in hotel management, so this paper introduces VR technology into the hotel industry and proposes a hotel management platform based on B/S mode. When building a 3D Hotel, it is combined with GIS technology. The administrator can complete the basic hotel design and management functions through 3D graphical user interface, realize the realistic display of complex network information, and set and present it to users through the secondary development function of VRGIS platform. At the same time, it introduces the implementation process of key technologies such as data preprocessing, scene fusion, Java structure and database connection, which provides hotel managers with an easy planning and entity operation interface. Finally, the running test results of the platform prove the feasibility and effectiveness of the scheme, which can support the management innovation practice of virtual reality in the hotel industry.
Computer-Aided Translation Reconstructing the Development Concept of English Education in Higher Vocational Colleges

In the era of information technology, Internet engineering technology has made unprecedented development. Computer network has important advantages in data communication, resource sharing and centralized processing. The integration of computer technology in English education in higher vocational colleges should conform to the development trend of the times and be an important guarantee to improve the teaching level and stimulate students' autonomous learning. This paper first analyzes the development background and application of computer aided translation. On this basis, in order to accelerate the application of computer technology in teaching practice, this paper reconstructs the English professional education model of higher vocational colleges from the aspects of subject education concept, student assessment system, teacher and student identity role. This research will provide reference for the reform and development of computer-aided English education.
<scp>Multiscale</scp> decomposition and fusion‐based color contrast restoration for various water‐colored environments

Abstract Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.
An Evaluation of Machine Learning Frameworks

Artificial Intelligence (AI) and Machine Learning (ML) have become increasingly important for any organization that wants to stay competitive and speed up its processes. However, while organizations can choose from a variety of machine and deep learning (DL) frameworks, it is important to remember that these frameworks serve very different purposes. Therefore, the choice of a framework adapted to your needs is a decision of the utmost importance. In this article, we present an evaluation of some of the most popular machine and deep learning frameworks developed, based on an image recognition task.
Evaluate the Contribution of Multiple Participants in Federated Learning

To address the challenge of distributed data source, Federated Learning meets with great demand of algorithmic predictions and decisions without taking a risk of privacy leakage, leaving data valuation a consequent task. Establishing an effective profit distribution model enables multiple participants to get involved in a fair incentive. Shapley Value serves as an excellent measure for calculating the contribution of the model since it provides a fair dividend of payoffs. However, it fails under the existence of data replication or dataset partition. In this work, we design a function to recalculate Shapley Value overcoming the failure mentioned before. The testing experiments have proved that new calculation improves the SV performance for about 50% compared with the original index such as the model accuracy or total loss.
A Vector Field Histogram Based Fuzzy Planner for Robot Navigation in Dense Crowds

Real-time planning is an essential requirement for mobile robot navigation in dense crowds. This paper proposes a fuzzy planner using a vector field in polar coordinates for robot navigation in a human-robot coexisting environment. The proposed planner constructs a vector field histogram in polar coordinates based on the observed obstacles. An objective function is proposed to get an optimal sector among the vector field histogram. Finally, a fuzzy inference system is proposed to calculate the linear and angular velocities of the robot based on the value of the selected sector and the current robot heading. The performance of the proposed planner is evaluated in simulations and real-world experiments using a SUMMIT mobile robot with a Microsoft Kinect RGB-D camera and a HOKUYO-10LX laser sensor. In both simulations and real-world experiments, the results show that the proposed method enables the robot to reach its destination with safe and efficient motion. The computing time of the proposed planner in each planning period is only 1.4e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> seconds, which is suitable for mobile robots with a low computational cost.
Comparative Study of Normative Document Modeling and Monitoring

In this paper, we model normative document repositories and normative documents of two countries – the Republic of Latvia and the United Kingdom and suggest a framework for tracking normative documents. The framework is used for the retrieval of normative documents, their analysis, and their linking to the normative documents already present in the storage module of the framework. We propose splitting normative documents into parts to allow separate tracking of changes in sections and paragraphs for the purpose of normative document monitoring. Information Retrieval, thus, can be done at an article, a paragraph, or a document level.
End-to-End Chained Pedestrian Multi-Object Tracking Based on Multi-Feature Fusion

An end-to-end chained network with multi-feature fusion is proposed for the trade-off of tracking speed and accuracy, which integrates target detection, feature extraction and data association into a framework. It chains paired bounding boxes estimated from overlapping nodes by IOU (Intersection Over Union) matching, whose each node covers two adjacent frames. Besides, the bidirectional feature pyramid that includes two aggregation paths is presented for multi-feature fusion, in which deformable convolution V2 is applied. Decreasing sample imbalance and gradient contribution difference, focal loss and BalancedL1 Loss form multi-task learning loss. The results on MOT17 dataset indicate that the model achieve superior tracking speed (21.6FPS) and accuracy (69.6MOTA, 81.0MOTP).
Robust Machine Learning via Privacy/ Rate-Distortion Theory

Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between optimal robust learning and the privacy-utility tradeoff problem, which is a generalization of the rate-distortion problem. The saddle point of the game between a robust classifier and an adversarial perturbation can be found via the solution of a maximum conditional entropy problem. This information-theoretic perspective sheds light on the fundamental tradeoff between robustness and clean data performance, which ultimately arises from the geometric structure of the underlying data distribution and perturbation constraints.
A Meta-interactive Compositional Approach that Fosters Musical Emergence through Ludic Expressivity

The concept of gamified interactive models and its novel extensions, such as playification, has been widely approached in order to engage users in many fields. In fields such as HCI and AI, however, these approaches were not yet employed for supporting users to create different forms of artworks, like a musical corpus. While allowing novel forms of interactivity with partially-autonomous systems, these techniques could also foster the emergence of artworks not limited to experts. Hence, in this paper we introduce the concept of meta-interactivity for compositional interfaces, which extends an individual's capabilities by the translation of an effort into a proficiency. We present how this approach can be effective through a novel system that enables non-experts to compose coherent musical pieces through the use of imagetic elements in a virtual environment. We conduct experiments with a population of musical experts and non-experts, showing that non-experts were able to learn and create high-quality musical productions through our interactive approach.
IoT-Based Human Fall Detection Solution Using Morlet Wavelet

Human fall detection is a problem that needs to be addressed to decrease the significant number of elderly people being affected, disabled, or even killed by falls. While the prevention of falls is a goal harder, or impossible, to be achieved, the fast detection and aid of people are two aspects that technological solutions can help with. With the support of internet of things devices, a fall detection solution for building deployment is proposed in this paper. The classification of fall is done using the Morlet wavelet in the fog-computing layer, enabling the detection of falls near the person and near the people who can provide first aid. The proposed solution of this paper was tested using a new dataset created using a human-body model. The results are promising, proving the efficiency of the proposed solution.
Accelerated failure time modeling via nonparametric mixtures

An accelerated failure time (AFT) model assuming a log-linear relationship between failure time and a set of covariates can be either parametric or semiparametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data. The semiparametric AFT model is more flexible and robust to departures from the distributional assumption than its parametric counterpart. However, the semiparametric AFT model is subject to producing biased results for estimating any quantities involving an intercept. Estimating an intercept requires a separate procedure. Moreover, a consistent estimation of the intercept requires stringent conditions. Thus, essential quantities such as mean failure times might not be reliably estimated using semiparametric AFT models, which can be naturally done in the framework of parametric AFT models. Meanwhile, parametric AFT models can be severely impaired by misspecifications. To overcome this, we propose a new type of the AFT model using a nonparametric Gaussian-scale mixture distribution. We also provide feasible algorithms to estimate the parameters and mixing distribution. The finite sample properties of the proposed estimators are investigated via an extensive stimulation study. The proposed estimators are illustrated using a real dataset.
Automatic evaluation of complex alignments: An instance-based approach

Ontology matching is the task of generating a set of correspondences (i.e., an alignment) between the entities of different ontologies. While most efforts on alignment evaluation have been dedicated to the evaluation of simple alignments (i.e., those linking one single entity of a source ontology to one single entity of a target ontology), the emergence of matchers providing complex alignments (i.e., those composed of correspondences involving logical constructors or transformation functions) requires new strategies for addressing the problem of automatically evaluating complex alignments. This paper proposes (i) a benchmark for complex alignment evaluation composed of an automatic evaluation system that relies on queries and instances, and (ii) a dataset about conference organisation. This dataset is composed of populated ontologies and a set of competency questions for alignment as SPARQL queries. State-of-the-art alignments are evaluated and a discussion on the difficulties of the evaluation task is provided.
Structure Perception in 3D Point Clouds

Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projections of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rotation, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under rotation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns perceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.