Reviewerly/exHarmony huggingface dataset:
Performance, Simulator Sickness, and Immersion of a Ball-Sorting Task in Virtual and Augmented Realities

Virtual Reality (VR) and Augmented Reality (AR) can be defined by the amount of virtual elements displayed to a human’s senses: VR is completely synthetic and AR is partially synthetic. This paper compares VR and AR systems for variations of three ball-sorting task scenarios, and evaluates both user performance and reaction (i.e., simulator sickness and immersion). The VR system scored higher, with statistical significance, than the AR system in terms of effectiveness per each scenario and completion rate of all scenarios. The VR system also scored significantly lower than the AR system in terms of percentage error and total false positives. The VR group scored significantly lower than the AR group in efficiency performance: the VR group had less time spent in each scenario, less total time duration, and higher overall relative efficiency. Although post-scenario simulator sickness did not differ significantly between VR and AR, the VR condition had an increase in disorientation from pre-to-post scenarios. Significant correlations between performance effectiveness and post-scenario simulator sickness were not found. Finally, the AR system scored significantly higher on the immersion measure item for the level of challenge the scenarios provided. AR interface issues are discussed as a potential factor in performance decrement, and AR interface solutions are given. AR may be preferred over VR if disorientation is a concern. Study limits include causality ambiguity and experimental control. Next steps include testing VR or AR systems exclusively, and testing whether the increased challenge from the AR immersion is beneficial to educational applications.
Natural scene text detection based on multiscale connectionist text proposal network

The technique of recognising text in natural scene pictures is widely used in social production. For the existing identification methods, it is difficult to accurately identify in complex environments. The accuracy of the detection determines the efficiency of the identification. A text detection method based on Multiscale Connectionist Text Proposal Network is proposed. The Multiscale-Region Proposal Network regresses and classifies the extracted region to obtain the final candidate region. Taking a large number of commodity image samples as a dataset, the multi-scale joint text proposal network is used to detect and locate the text content area in the image. The experimental results show that the proposed algorithm improves the detection accuracy in complex environments.
The Algorithm of Spiking Neural Network And Application in Poker Games

With the development of artificial intelligence, the mainstream neural networks currently have many insurmountable problems, such as a large amount of calculation, high power consumption, and low intelligence. In order to solve the above problems, the spiking neural network simulates "creating the brain" according to the characteristics of the human brain, and realizes intelligence with a neural network close to that of a living being. This article uses biological causality to creatively propose a multi-layered spiking neural network structure with universality, and a learning algorithm for spiking neural network, and apply it to poker games, so that the poker robot can learn a person's card ability The ultimate degree of personification was 85%, which verified the feasibility of the structure and algorithm of impulsive neural network.
Red and Blue Channels Correction Based on Green Channel and Median-Based Dual-Intensity Images Fusion for Turbid Underwater Image Quality Enhancement

One of the main problems encountered in processing the turbid underwater images is the effect of greenish color cast that overshadows the actual color of an object. This paper introduces a new technique which focuses on the enhancement of turbid underwater images. The proposed method integrates two major steps. The first step is specially designed to reduce the greenish color cast problem. The blue and red channels are improved according to the difference between these channels and the reference channel in terms of the total pixel values. Then, the median-based dual-intensity images fusion approach is applied to all color channels to improve the image contrast. Qualitative and quantitative evaluation is used to test the effectiveness of the proposed method. The results show that the proposed method is very effective in improving the visibility of the turbid underwater images.
Optimization of Convolutional Neural Networks for Face Recognition Problems Aimed to Resources Constrained Platforms

In this paper is proposed a method to obtain high recognition rates of face recognition problems using low complexity convolutional neural networks, tailored for resource-constrained computing platforms. Different variants of CNN architectures were considered and thoroughly evaluated on datasets such as YouTube Faces Database and Aligned Face Dataset for Face Recognition. We experiment by training our architectures on various computing platforms including CPU, GPU and evaluate performances such as recognition rates, prediction estimation, throughput and latency. In optimizing the specific parameters, a trade-off between a good accuracy and a low complexity was considered.
Evaluating Hands-On and Hands-Free Input Methods for a Simple Game

We conducted an experiment to test user input via facial tracking in a simple computerized snake game. We compared two hands-on methods with a hands-free facial-tracking method to evaluate the potential of a hands-free point-select tool: CameraMouse. In addition to the experiment, we conducted a case study with a participant with mild cerebral palsy. For the experiment we observed mean game scores of 5.89, 3.34, and 1.94 for the keyboard, touchpad, and CameraMouse input methods, respectively. The mean game score using CameraMouse was 0.55 during the case study. We also generated trace files for the path of the snake and the cursor for the touchpad and CameraMouse pointing methods. In a qualitative assessment, all participants provided valuable feedback on their choice of input method and level of fatigue. Unsurprisingly, the keyboard was the most popular input method. However, positive comments were received for CameraMouse. Participants also referred to a few other simple games, such as Temple Rush and Subway Surfer, as potential candidates for interaction with the CameraMouse.
WORKS BY F. NIETZSCHE IN THE LIBRARIES OF SIBERIA DURING THE PRE-REVOLUTIONARY PERIOD

Reading range modernization entails changes in social, political, economic and moral views of the society, and therefore studying this problem is an actual direction of modern research. In the pre-revolutionary period the predominating role of religion and church, characteristic for the traditional society in the whole, clearly revealed itself in the range of reading, which basic segment was religious and spiritual moral literature. Emergence of philosophic works, including those by F. Nietzsche with his critical attitude towards religion and church in the whole and Christianity in particular, should be stated as one of indications of the Siberians’ range of reading modernization in the period under study. It has become the basic argument for to choose studying namely his works presence in Siberian libraries as the most obvious case of remote provinces inhabitants’ reading modernization at that time. The aim of the article is to identify modernization processes in the Siberians’ reading on the example of works by F. Nietzsche presence in it. Information from 26 catalogues of Siberian pre-revolutionary libraries is given. They reflect collections of 19 libraries, located in provincial, regional and county centers. City public libraries as well as local commercial and community assembly ones, chosen because of their most openness to users, predominate. The results of the study show that during the period under review many Siberian libraries had works by F. Nietzsche. Their presence in the libraries of Siberia can be considered the evidence of reading and the whole book culture modernization in the region. The identified data allow to clarify and supplement the regional picture of library collections and the potential repertoire of the Siberians’ reading, as well as to add to the argument in favor of modernization processes presence in their reading in the late 19th – early 20th century.
Information Fusion-Based Digital Forensics Framework in Cloud Environment

People can access their files for using cloud computing technology conveniently and deploy commonly used applications. In the meantime, cloud services also can be utilized by criminals to commit malicious activities, and the criminal acts is hard to track because the crime detail information would be easily lost at the data deluge of cloud environment. To perform investigation in cloud efficiently, many digital forensics frameworks are used to cloud environment. In this paper we present a cloud forensics framework that based on data mining technique, and it is divided into four layers: abstract layer, information collection layer, data storage layer and information fusion layer. In order to analysis and segregate the relevance evidence for proving crime, we propose employ fusion clustering algorithm to analyze data information in information fusion layer.
Cloud data acquisition and processing model based on blockchain

Data outsourcing has gradually become a mainstream solution, but once data is outsourced, data owners will without the control of the data hardware, there is a possibility that the integrity of the data will be destroyed objectively. Many current studies have achieved low network overhead cloud data set verification by designing algorithmic structures (e.g., hashing, Merkel verification trees); however, cloud service providers may not recognize the incompleteness of cloud data to avoid liability or business factors fact. There is a need to build a secure, reliable, non-tamperable, and non-forgeable verification system for accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. This paper uses the Hadoop framework to implement data collection and storage of the HBase system based on big data architecture. In summary, based on the research of blockchain cloud data collection and storage technology, based on the existing big data storage middleware, a large flow, high concurrency and high availability data collection and processing system has been realized.
Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection

Abstract Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving the highest accuracy (97.44%) compared to other classifiers.
Information Visualization-Based Study on Interactive Design of Elderly Health Management Application

In the world, the growth of the elderly population is an inevitable trend of social development. The problem of aging affects all aspects of society. In the interface design, designers focus on how to meet the cognitive and behavioral needs of the elderly group, and match the developing design concept with the elderly industry. Based on the concept of information visualization, this paper introduces the operation mechanism of information visualization and then analyzes the relationship between information visualization and the interaction design of elderly health management application. And this article summarizes the functional requirements and design principles of elderly health management application. It is designed to help the elder to get information, process information and take action when using the information interactive interface, so as to obtain a better interactive experience.
Key Generation for Electrical Smart Meters using Hash Functions

The electrical power grid has been undergoing improvements to turn it into a more efficient system known as the Smart Grid. A key element in this upgrade is the Smart Meter. A compromise on the cyber-security of the smart meter would have far-reaching effects affecting millions of users. Effective cryptography is needed to protect it. This work looks at how secret keys for use in encryption can be generated through the use of hash functions. An analysis is carried out on 100 generated keys to see if they have an obvious relationship. The average difference in the bits of the keys were found to be 49.578% indicating strong independence between the keys. This method would thus be a good approach of producing 128 bit keys for use in encryption.
Revisiting Visualization Task Taxonomies: Specifying Functions for the Data Transformations Stage

Several visualization task taxonomies have been defined in the literature, taking into account several factors, such as the user’s goals when analyzing a visual representation of data, the data characteristics, how they are mapped onto the visualization, to name a few. Some studies also use task taxonomies as a tool to evaluate the effectiveness of visual representations. Because each task taxonomy may have been created for a different purpose, we find that they often overlap, and the task definitions are often implicit or ambiguous. We have analyzed several visualization taxonomies, and realized they crosscut different stages of the visualization process. In this work, we focus on the data transformations stage and define a set of data functions related to the tasks in the studied taxonomies. We specify these functions to bring clarity and consistency, as well as to enable them to be used in different scenarios. This work is a first step toward a more comprehensive visualization ontology.
A Platform for Free Weight Exercise Monitoring Using Passive Tags

Free weight exercise is a balanced exercise which depends on repetition and proper movement of the body parts. Proper monitoring and guidance are required to avoid accidental injury during repetitions of the exercise. The main objective of the work is to design a device which can detect the exercise and detect the exercise details like the number of rotations and motion of the hand. The developed device in this work tracks the number of times the exercise has been repeated and motion of the hand to guide the person. FEXT is a free weight exercise tracking device which uses passive tags and sensors to track the exercise. This device tracks the exercise in three parts: detection of the exercise, detection of the number of repetition, and detection of the motion of the hand. For the detection of the exercise, RFID tags and RFID reader are used, and for the detection of the number of repetition, the Hall effect sensor is used. The RFID reader is displaying the ID number of the RFID tag with the help of microcontroller. Each ID number can be assigned a type of exercise. For detection of the number of repetitions detect gets printed as soon as the magnet is brought near the Hall effect sensor. For the detection of hand movement, MATLAB and C# software are used. The developed device is capable of tracking the exercise type, and the number of times the exercise is being repeated. The motion of the hand is also tracked. The device can replace a personal coach and since low-cost sensors are used the device which is cost-effective.
LA QUALITY ASSURANCE IN THE CONTEXT OF EUROPEAN INTEGRATION

The article provides information on problems of the education internationalization, the basic principles of integration of higher education and programmes aimed at ensuring the quality of training in European and Eurasian educational spaces. Particular attention is paid to existing approaches to guarantee the quality of higher education and the formation of quality assessment systems at the international, national and regional levels to create the conditions for relevant quality management of education. The construction of an effective system for ensuring and assessing the quality of education in many countries of the world, including Russia, is one of the priorities of the national educational policy. This article is aimed at expanding knowledge about the integration processes in the field of education by using new teaching technologies, research and debates on topics related to the history, politics, economics and legislation of the European Union, as well as EU relations with other regions of the world.
Analyzing the User Experience of Virtual Reality Storytelling with Visual and Aural Stimuli

Storytelling is an integral part of narratives relating to our daily events, news, personal experiences, and fantasies. While humans have long narrated their stories, the mediums they have used to do so have evolved over time through the effects of technological developments: initially, storytelling was solely oral, then written forms were added, and now, with the effects of new media, such narratives have also begun to employ photography and video. These new media tools are also undergoing their own processes of expansion and development. Today one of the most attention-getting are those using Virtual Reality (VR) technologies, a means that allows users to experience being-in-the-virtual-environments, with possibilities of becoming entirely immersed in a virtual environment. The ability to experience an environment with three-dimensional features enhances the experience in sensorial ways, with simultaneous stimulation of both the user's visual and auditory sensorial systems. The aim of this study is to gain a better understanding of what exactly the user experiences through VR storytelling. To this end we have conducted an experimental research based on an examination of the immersive experience in VR, which constructs the presence feeling. The experiment has been designed to study the effects on forty users. These participants used the HTC Vive head-mounted display to experience the contents of a story called "Allumette" (designed by Penrose Studios). User behaviors were recorded and observed by the tools used to collect data from both the physical world and the virtual environment. Users' physical movements were documented as coordinate data, while the behavioral reflections in the virtual environment were recorded as a video. Following this virtual experimentation, users were asked to answer a questionnaire that measured their responses to their VR storytelling experience. User experience was finally measured by analyzing both the behavioral outputs of the subjects and the questionnaire. "Cinemetrics" methodology was implemented to analyze the camera movements, which were considered as the user behavioral reflections in VR. The results of this study based on analyzing the behaviors and the reactions to visual and aural stimuli in the VR environment both lead to a clearer understanding of VR storytelling and uses these results to propose a design guide for VR storytelling.
Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time

We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a function that maps position and orientation of the transducer to ultrasound images. This is a first step towards realistic patient-specific simulations that will enable improved training and retrospective examination of complex cases. Our models can simulate a 2D image in under 4 ms (well within real-time constraints), and produce simulated images that preserve the content (anatomical structures and artefacts) of real ultrasound images.
Usable Security by Design: A Pattern Approach

Security and usability are often in conflict. There is a recognition that security cannot be achieved in real sense unless it incorporates the human factor (usability elements). Despite this recognition, the state of the art identifies many challenges and reasons for conflicts between security and usability. This paper discusses some of these challenges while proposing the use of design patterns to handle those challenges. While justifying the use of patterns as one of the effective ways of handling the problem (conflicts), the paper presents a proposal for participatory usable security design patterns workshop. The workshop provides a forum for discussing a variety of issues concerning the usability and security conflicts while documenting the instances of conflicts and suitable tradeoffs as design patterns for use by other designers and developers. A catalog of usable security design patterns can assist the system designers and developers by positively influencing their decision-making abilities when it comes to conflicts.
Prediction-Based Uncertainty Estimation for Adaptive Crowd Navigation

Fast, collision-free motion through human environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion and other agents is often severely limited. By contrast, biological systems routinely make decisions by taking into consideration what might exist in the future based on prior experience. In this paper, we present an approach that provides robotic systems the ability to make future predictions of the environment. We evaluate several deep network architectures, including purely generative and adversarial models for map prediction. We further extend this approach to predict future pedestrian motion. We show that prediction plays a key role in enabling an adaptive, risk-sensitive control policy. Our algorithms are able to generate future maps with a structural similarity index metric up to 0.899 compared to the ground truth map. Further, our adaptive crowd navigation algorithm is able to reduce the number of collisions by 43% in the presence of novel pedestrian motion not seen during training.