Reviewerly/exHarmony huggingface dataset:
Design and implementation of endogenous security container based on union file system

Abstract In order to solve the increasing attacks on container file system and the IO errors of containers in big data processing scenarios in cloud computing environment, a scheme based on the idea of heterogeneous redundancy in endogenous security and transformation of container union file system was proposed to improve the security and fault tolerance of containers. Based on the above scheme, experiments are carried out on Docker, the most popular container technology, and OverlayFS, the most representative union file system. The experimental results show that this scheme can improve the security and fault tolerance of containers on the premise of ensuring availability, and realize the endogenous security of containers.
A survey on privacy preservation in video big data

The explosive growth of video data application raises the risk of privacy leaks in the video big data environment. Compared with other data formats, video big data presents special characteristics including various representation, high dimension, complex content, and so on, making the privacy protection for video big data more difficult. This paper analyzes the life cycle and overall architecture of video big data, and then discusses the privacy preservation method recently developed for video applications. These methods are categorized into three types: privacy preservation method for video storage, for video analysis, and for video release. Via analyzing their application scenarios and technique concepts, we finally come to a conclusion about their advantages, disadvantages, and possible applications, and present several research issues for video big data privacy preservation.
Machine Learning Approaches to Personality Classification on Imbalanced MBTI Datasets

The MBTI (Myers-Briggs Type Indicator) is a widely known approach to personality classification. Datasets for the machine learning approach to personality classification using MBTI are highly imbalanced. Handling imbalanced data sets is a significant open problem with a considerable impact on machine learning methods. This paper presents the results of applying different techniques and suggests their best in mitigating the challenge of imbalanced MBTI datasets. Even though most techniques could be used and implemented to some other problems and areas, like images and sound processing, natural language processing has enough challenges to focus on natural language processing and the specific issue of the MBTI datasets.
Code Generator based on Voice Command for Multiple Programming Language

In this modern era, several new programming languages are introduced among the programmers and are the benediction for them but there are some challenging issues. The major problem of a programmer to memorize the syntaxes of different programming languages at the same time. It is very common among the programmers to mix up the syntaxes of different programming languages during coding. Moreover, the people who are suffering with Parkinson's disease cannot do programming because of their shaky hands. In additional, hand typing programming takes enough time to perform the coding. This study discusses how to reduce typing problems by hands, syntax memorization problems and time consuming problems during programming. However, the proposed system named VCL (Voice Command Language) is a very easy and time saving software. This software is capable of coding on multiple programming languages (such as Python, Java and C) by using same voice command against the syntax for all languages. Voice command is recognized by the Google Speech API and then software generate the code against the command. This software will help the programmers to focus more on logics rather than memorizing the syntaxes. The study will also help the programmers to speed up their programming performance and will create a new window for the hand injured people.
Quantitative analysis of bibliographic corpora

The subject of this chapter is bibliographic corpus analysis, with data from the Polish national bibliography from the period 1801–2019. The research allowed us to discover and compare quantitative characteristics of the bibliographic corpus and of the reference corpus of general language. It was shown that the two corpora differ significantly. In particular, differences in the share of particular parts of speech and of the frequency distribution of lexemes were demonstrated. The statistical distributions of word spectra were also studied. The best fit was obtained for generalized inverse Gauss-Poisson and Zipf-Mandelbrot distributions. The analysis of parameters of both distributions for bibliographic and reference corpora also revealed differences between them. The best perspective for future research on bibliographic corpora is, apart from quantitative linguistics, semantic analysis and text-mining.
Sign Language Action Recognition System Based on Deep Learning

Sign language is a special language that relies on body movements and visual communication. Aiming at the problem that the hearing-impaired can not communicate well with the outside world, a sign language recognition method based on skeleton extraction and residual network is proposed. Firstly, we build the RGB video data set of Chinese sign language, which contains commonly used Chinese sign language words and phrases. Next, we use OpenPose technology to extract human skeleton from Chinese sign language video and obtain human skeleton video. OpenPose can not only collect the whole body skeleton, but also collect the fine hand skeleton. Finally, a video sequence classification model based on ResNet and GRU is constructed. The constructed sign language sequence classification model is an end-to-end system, which inputs sign language skeleton video and outputs sign language classification results. The testing accuracy of this model is 98% in our small sign language database.
Deep Learning Approaches for Devanagari Handwriting Recognition

Offline Handwriting Recognition, allows to recognize and convert physical files and images to a digital form. Handwriting recognition is a vast area of research and a lot of contribution has been witnessed in past decades. Devanagari Script is one of the scripts for which many sophisticated handwriting recognition systems are not available. Deep Learning techniques, however, have been able to generate excellent results and enhanced performance. This paper thoroughly examines the recent advancements in deep learning architectures to recognize Devanagari Handwritten characters from varying datasets. The paper has been focused on Devanagari Handwritten Recognition Systems background, challenges, opportunities and future research trends. Several publicly available datasets have also been mentioned along with their data size, type of characters. A comparative study has been done to present the results and insights of the state-of-the-art approaches.
On the Scalability of Compositions of Service-Oriented Applications

One of the current challenges in the context of service-oriented applications is the generation of composition plans for applications that optimize their QoS attributes by taking advantage of the resources offered by different providers, and generate them as efficiently as possible. Scalability is indeed a major issue, and the problem becomes a real challenge for applications with big numbers of services. In this work, we propose a divide-and-conquer algorithm that exploits the architecture of applications, to reduce the size of the search space. With it, we are able to recompose the solution for the global problem, with a significant gain in execution time. A variant of the algorithm—in which, when a sub-problem cannot be further divided without loosing information, a solver is used to find the optimal solution for it—allows us to trade execution time and precision. We report on the extensive experimentation carried out, where applications with up to \(2\,000\) services are considered, and which includes a comparison with the results delivered by GA solvers.
Leksykalna analiza zrozumiałości tekstów medycznych na podstawie opisów badań obrazowych i wypisów ze szpitala

This article presents a lexical analysis of two types of medical texts written in Polish: hospital discharge summaries and image reports. The aim of the analysis is to assess the readability of the medical texts under investigation and its importance in the process of translation. It was assumed that the language of discharge summaries should be relatively comprehensible for a layman (patient), so that he or she could understand the description of the diagnosis and follow the instructions provided by doctors regarding post-hospital measures (such as adherence to medication regime and recommended further medical examinations). Image reports are written by experts for experts, so it can be expected that the language used in image reports is highly specialized and barely comprehensible for a patient. The pilot study has demonstrated that the discharge summaries, contrary to our assumptions, turned out to be slightly less readable for an average reader (patient) than the image reports.
Islamic Ethic and Information Security Management on Islamic Higher Education: An Emperical Study to Measure Organization Performance

Complexity of information security has become a major issue for organizations due to incessant threats to information assets. Islamic higher education is particularly concerned with security owing to the inherent vulnerability of sensitive information assets in education as Islamic value really related to the principle of Confidentiality, Integrity, and availability (CIA). While the elements and factors have been at the centre of information security activities for many years, often only the technical management components (firewall, anti-virus, hoax etc.) have been the main area of interest for many practitioners. The problem addressed in this quantitative study is the dearth of non-technical security management perspectives in information security management in higher education. Two prominent research bodies, the theory of reasoned action and general deterrents theory will be integrated to formulate the study framework. Moreover, Islamic value and dimension in information security are employed. The survey instrument will be sent to IT Muslim practitioners in 53 Islamic public higher education. Survey data will be analyzed using correlation and regression analyses
A SOAF model extension for incorporating user feedback and preference to improve social service discovery

The rapid development of service-oriented computing has increased the number of services published on the Web including those available on social networks. Therefore, discovering relevant services in response to a user query has been addressed from different perspectives. To discover services that fulfill user-specific needs, discovery mechanisms require information about the user, such as quality preferences, feedback, etc. Nevertheless, proposed discovery models have limitations in the user profile construction process and the majority of user profiles in such systems are keyword-based. To tackle this issue, we consider the problem of exploiting user feedback and preference to enhance the quality of service discovery. We propose to build a user profile based on their interactions with social services. For this purpose, we propose to augment the SOAF network structure with user feedback and quality preference. The results show that our model provides high-quality results when enriching users’ profiles with such information.
Visual Navigation Using Sparse Optical Flow and Time-to-Transit

Drawing inspiration from biology, we describe the way in which visual sensing with a monocular camera can provide a reliable signal for navigation of mobile robots. The work takes inspiration from the classic paper [3] which described a behavioral strategy pursued by diving sea birds based on a visual cue called time-to-contact. A closely related concept of time-to-transit, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> , is defined, and it is shown that steering laws based on monocular camera perceptions of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> can reliably steer a mobile vehicle. The contribution of the paper is two-fold. It provides a simple theory of robust vision-based steering control. It goes on to show how the theory guides the implementation of robust visual navigation using ROS-Gazebo simulations as well as deployment and experiments with a camera-equipped Jackal robot. As will be noted, there is an extensive literature on how animals use optical flow to guide their movements. The novelty of the work below is the introduction of the concepts of Eulerian optical flow and time-to-transit, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> and the demonstration that control laws based on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> -values associated with an aggregated set of features in the field of view can be used to reliably steer a laboratory robot.
POSTER: How Dangerous is My Click? Boosting Website Fingerprinting By Considering Sequences of Webpages

Website fingerprinting (WFP) is a special case of traffic analysis, where a passive attacker infers information about the content of encrypted and anonymized connections by observing patterns of data flows. Although modern WFP attacks pose a serious threat to online privacy of users, including Tor users, they usually aim to detect single pages only. By ignoring the browsing behavior of users, the attacker excludes valuable information: users visit multiple pages of a single website consecutively, e.g., by following links. In this paper, we propose two novel methods that can take advantage of the consecutive visits of multiple pages to detect websites. We show that two up to three clicks within a site allow attackers to boost the accuracy by more than 20% and to dramatically increase the threat to users' privacy. We argue that WFP defenses have to consider this new dimension of the attack surface.
Older and younger adults’ revision of health misconceptions

Although ample younger adult research has detailed effective strategies for revising misconceptions, research with older adults is less extensive. Older adults may be less able to correct errors in knowledge due to age-related changes in cognition, but it is also possible that older adults' revision of misconceptions has been limited by methodologies which do not provide adequate support for correction. In two experiments, we examined how older and younger adults revise health-related misconceptions when provided with cognitive support in the form of explicit detailed feedback and an immediate test. Older and younger adults in Experiment 1 answered true/false health statements, received feedback with a detailed explanation of the correct response, took an additional test on the same statements immediately following the initial test, and completed a final test 1-week later. Older and younger adults corrected a similar proportion of misconceptions immediately and maintained most of those revisions across a 1-week delay. In Experiment 2, older adults corrected the same proportion of misconceptions on the final test regardless of whether or not they received a test immediately following feedback. Overall, older adults revised health misconceptions as effectively as did younger adults but variables influencing correction (e.g., belief in feedback) may differ.
ML Tools for the Web: A Way for Rapid Prototyping and HCI Research

Machine learning (ML) has become a powerful tool with the potential to enable new interactions and user experiences. Although the use of ML in HCI research is growing, the process of prototyping and deploying ML remains challenging. We claim that ML tools designed to be used on the Web are suitable for fast prototyping and HCI research. In this chapter, we review literature, current technologies, and use cases of ML tools for the Web. We also provide a case study, using TensorFlow.js—a major Web ML library, to demonstrate how to prototype with Web ML tools in different prototyping scenarios. At the end, we discuss challenges and future directions of designing tools for fast prototyping and research.
A new neural network approach to density calculation on ceramic materials

The materials’ consolidation, especially ceramics, is very important in advanced research development and industrial technologies. Science of sintering with all incoming novelties is the base of all these processes. A very important question in all of this is how to get the more precise structure parameters within the morphology of different ceramic materials. In that sense, the advanced procedure in collecting precise data in submicro-processes is also in direction of advanced miniaturization. Our research, based on different electrophysical parameters, like relative capacitance, breakdown voltage, and [Formula: see text], has been used in neural networks and graph theory successful applications. We extended furthermore our neural network back propagation (BP) on sintering parameters’ data. Prognosed mapping we can succeed if we use the coefficients, implemented by the training procedure. In this paper, we continue to apply the novelty from the previous research, where the error is calculated as a difference between the designed and actual network output. So, the weight coefficients contribute in error generation. We used the experimental data of sintered materials’ density, measured and calculated in the bulk, and developed possibility to calculate the materials’ density inside of consolidated structures. The BP procedure here is like a tool to come down between the layers, with much more precise materials’ density, in the points on morphology, which are interesting for different microstructure developments and applications. We practically replaced the errors’ network by density values, from ceramic consolidation. Our neural networks’ application novelty is successfully applied within the experimental ceramic material density [Formula: see text] [kg/m 3 ], confirming the direction way to implement this procedure in other density cases. There are many different mathematical tools or tools from the field of artificial intelligence that can be used in such or similar applications. We choose to use artificial neural networks because of their simplicity and their self-improvement process, through BP error control. All of this contributes to the great improvement in the whole research and science of sintering technology, which is important for collecting more efficient and faster results.
Salient Target Detection in RGB-T Image based on Multi-level Semantic Information

The salient target detection aims to use models or algorithms to detect and segment the salient target regions in the image. As an image preprocessing step, The salient target detection has been widely researched and applied in computer vision fields such as image segmentation, image compression, object recognition, and image retrieval. In the face of complex environments, such as poor lighting conditions and cluttered backgrounds, saliency detection is still challenging. RGB-T complementary fusion for saliency detection has become a hot research direction to solve the above problems. Therefore, this paper makes full use of the advantages of RGB and thermal infrared images to complement each other, and proposes an end-to-end salient target detection network. To this end, the VGG16 backbone network is first used for rough feature extraction, and then the attention mechanism module is used to enhance and merge the high-level features of RGB and thermal infrared images to enrich semantic information. Then the DFPN module is designed to accumulate high-level features to obtain high-level semantic information, and at the same time, the high-level features integrate the semantic information of thermal infrared images. Then we designed the SIPM self-interactive pooling module to further refine the features so that it can adaptively handle multi-scale targets. Finally, we compared the proposed method with several other advanced methods on several public datasets, and the experimental results proved the effectiveness of our method.
WaC

The current encryption infrastructure is no match for an Artificial Superintelligence (ASI), the likely result of a possible intelligence explosion by a self-improving AI. That kind of ASI would likely be able to modify any software and thereby steal encryption keys instead of doing a brute-force attack. Under such circumstances, any private, public, or session key processed within a CPU must be considered compromised. Although an ASI with that kind of skill does not exist yet, it is important to be prepared - because that level of attack by an ASI is feasible. Reliable and unbreakable encryption and communication (Trustworthy Encryption and Communication) must be the bedrock technology for any ASI Safety solution that tries to keep ASI under control. No current solution can determine if the corresponding receiver or sender has dedicated crypto hardware or possibly compromised crypto soft-ware. The proposed solution is a hardware component with Key-Safe and an associated Encryption/Decryption Unit for processing data. This component will not allow any key, in particular not the public key to be in cleartext outside the Key-Safe. Keys are referred to via their hashcodes. If ASI was able to breach the hardware protection around the keys, then the solution will create evidence when stolen keys are being used outside the hardware component. Key-Safes and Hashcodes related to public/private keys can be integrated into a minimally extended version of TLS and PKI.
Enhancing the Customer Experience by Mixed Reality in the Retail Industry

research-article Share on Enhancing the Customer Experience by Mixed Reality in the Retail Industry Authors: Yirui Jiang Cranfield University, UK Cranfield University, UKView Profile , Trung Hieu Tran Cranfield University, UK Cranfield University, UKView Profile , Leon Williams Cranfield University, UK Cranfield University, UKView Profile , Jaime Palmer OrangeLV,Inc, USA OrangeLV,Inc, USAView Profile , Edgar Simson OrangeLV,Inc, USA OrangeLV,Inc, USAView Profile , Daniel Benson OrangeLV,Inc, USA OrangeLV,Inc, USAView Profile , Michael Christopher OrangeLV,Inc, USA OrangeLV,Inc, USAView Profile , Daila Christopher OrangeLV,Inc, USA OrangeLV,Inc, USAView Profile Authors Info & Claims AIVR 2021: 2021 5th International Conference on Artificial Intelligence and Virtual Reality (AIVR)July 2021 Pages 41–45https://doi.org/10.1145/3480433.3480438Online:08 November 2021Publication History 0citation50DownloadsMetricsTotal Citations0Total Downloads50Last 12 Months50Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access