Reviewerly/exHarmony huggingface dataset:
CAST: A Cross-Article Structure Theory for Multi-Article Summarization

Over the last decade, discourse relations, also referred to as rhetorical or coherence relations, have been used to improve a range of natural language processing applications. Researchers have devised several theories, including rhetorical structure theory and cross-document structure theory, to examine relations between generic text units in single and multiple documents, respectively. In this paper, we propose a cross-article structure theory (CAST), that extends the benefit of discourse relations to multi-scientific article applications. It is based on the rhetorical structure theory (RST) and the cross-document structure theory (CST). The insight that underpins CAST is to consider both intra-section and cross-section relations. At the outset, these relations are classified based on the structural features of the article (that is, their appearance within each section type) and then the relations between text portions across multiple articles are classified. The practicality of the theory is showcased by solving a problem that consists to identify the types of relations which exist between each pair of sentences in related sections of different articles. A CAST bank was created and the k-nearest neighbors algorithm was used to develop two classifiers based on CAST and CST, respectively. The performance results obtained markedly demonstrate the role of the specific relations to scientific articles in CAST. Other applications of CAST could address the redundancy and readability problems, which represent main issues for different tasks, such as the summarization of multiple articles.
Constant weight strings in constant time

Code based cryptosystems often need to encode either a message or a random bitstring into one of fixed length and fixed (Hamming) weight. The lack of an efficient and reliable bijective map presents a problem in building constructions around the said cryptosystems to attain security against active attackers. We present an efficiently computable, bijective function which yields the desired mapping. Furthermore, we delineate how the said function can be computed in constant time. We experimentally validate the effectiveness and efficiency of our approach, comparing it against the current state of the art solutions, achieving three to four orders of magnitude improvements in computation time, and validate its constant runtime.
Path Planning of Robot in Coal Mine Using Genetic Membrane Algorithms

Safety, intelligence and precision are the new trend of coal mining. To solve the influence of complex underground environment on the accuracy and reliability of robot path planning, a model of membrane computing system is constructed. Based on the efficient parallelism of the model, genetic membrane algorithm is designed to realize path planning for underground robots. The algorithm improves the efficiency of global search by applying the idea of solving the traveling salesman problem to the robot motion planning. Experiments proof the genetic membrane algorithm that can reduce the system error and has good convergence. Through stability analysis, it shows that the model and algorithm have theoretical feasibility and practical application value in path planning for the robot of coal mine.
Child-Computer Interaction SIG: Looking Forward After 18 Years

This SIG will provide child-computer interaction researchers and practitioners an opportunity to discuss future directions for the field after 18 years of Interaction Design and Children conferences. Topics for discussion include interdisciplinarity, theory and rigor, impact, emerging areas of research, and ethics.
Augmented reality for SCADA

Abstract Questions of construction of the Augmented Reality for application in SCADA-systems are considered. It is marked, that last years researchers from Russia actively started studying this branch. The author considers the augmented reality in a section of a reality-virtuality continuum taking into account restrictions of memory of the operator and social aspect of application augmented reality. Recommendations on environmental analysis and reading of data from display devices are given. The greatest efficiency of application augmented reality is seen in joint activity and carrying out of works which demand coordinated work. The necessity of the further researches for developing of network architecture, safety, confidentiality and protection against extraneous intervention is noted. Advantage of augmented reality is reuse of software, databases and knowledge. At the replacement of maintenance staff and users of SCADA-system there is no need to teach them all the knowledge and skills at once. They will receive them directly in the process of application.
Participatory Design Tools: Exploring the Value of Design through Materiality

As participatory design (PD) approaches become a normative part of contemporary business practice, it becomes increasingly important to ensure that PD tools and methods are accessible and understandable to a larger audience, to fully mobilise and harness their value. This research seeks to explore the materiality of different PD tools and methods as a means to articulate the value of design to broader participant groups, explore what tools can be brought in from everyday use artefacts to reduce the barrier of participation, and what implications their use has on understanding the value of design. A series of experiments are conducted in a variety of distinct, specific contexts to explore the materiality as part of a broader program of research. The contribution aims to develop a suite of strong concepts that can be used to inform design practice and subsequent development of PD tools.
Negative Fragment Answers

Abstract This chapter discusses the interaction of negation with fragment answers. The ability to use negative concord items as fragment answers has been taken as evidence of their having an inherent negative force; this chapter considers positions for and against this view, and what kind of assumptions (for the licensing of NCIs and/or for the interpretation of elliptical structure in fragments) would be required on each view, as well as considering the implications of double-negation readings for NCI fragments, and the availability of NPI fragments. The chapter also investigates the cooccurrence of a negator with a fragment answer (as in Who ate the cake?—Not John, anyway), exploring what ramifications such structures have for the syntax of fragments, and in particular for the choice between sententialist (elliptical) and non-sententialist analyses of fragments.
Evaluation of Natural User Interfaces in the Creative Industries

The case study presented in this paper is concerned with the applicability of natural user interfaces (NUI) in the context of previsualization (previs). For this purpose, we have developed a virtual reality (VR) based tool that includes NUIs as a novel way to perform previs-related tasks. For the application domains of animation, film, and theater, we conducted a quantitative and qualitative assessment of the prototype by realising projects that resembled real life productions in the creative industries. In collaboration with industry experts with different creative backgrounds, we conducted a large-scale evaluation and examined the potential of NUIs in a professional work context. Our results indicate that NUIs can offer a usable alternative to standard 3D design software, requiring only a short familiarization phase instead of extensive training to achieve the intended outcome.
Effects of Virtual Gait Visualization in Walk-in-Place on Body Ownership and Presence

We investigate the effects of different ways of visualizing the virtual gait of the avatar in the context of Walk-in-Place (WIP) based navigation in a virtual environment (VE). In our study, participants navigated through a VE using the WIP method while inhabiting an avatar. We varied the leg motions of the avatar while performing the WIP gesture: (1) Fixed Body the legs stood still; (2) Prerecorded Animation the legs moved in a fixed predetermined pace (plausible but not in accordance to that of the user in general); (3) Synchronized Motion the legs moved according (synchronized) to the those of the users. Our results indicate that the sense of presence improved significantly by visualizing the leg movement, synchronized or not. This in turn further enhanced the sense of body ownership especially when the leg motion was synchronized to that of the user (Synchronized Motion). However, a significant level of simulation sickness was reported when the virtual leg motion did not match the user's (Fixed Body and Prerecorded Animation). We discuss the implications for representing the avatar locomotion in immersive virtual environments.
Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods

Biomedical classification problems are of great interest to both medical practitioners and computer scientists.Due to the harmful consequences of a wrong decision in this ambit, computational methods must be carefully designed to provide a reliable tool for helping physicians to obtain accurate predictions on unseen cases.Computational Intelligence (CI) provides robust models to perform optimization, classification and regression tasks.These models have been previously designed, mainly based on the expertise of computer scientists, to solve a vast number of biomedical problems.As the number of both CI algorithms and biomedical problems continues to grow, selecting the right method to solve a given problem becomes more challenging.To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required.In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergic framework to automatically design and optimize pattern classifiers.Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems.Experimental results on benchmark datasets allow us to conclude that the automatically designed classifiers reach higher or equal performance than those designed by computer specialists.
Reviewing of Applied Research with an Industry 4.0 Perspective

This note provides a perspective on questions to be used in reviewing applied research using analytics and statistical analysis. The paper emphasizes applied research related to Industry 4.0. It expands on a paper providing similar guidelines with a general focus on information quality (Kenett and Shmueli, 2016a). The note is methodological. It covers aspects of study design, algorithmic and inferential methods in frequentism analysis, Bayesian methods in Bayesian analysis, selective inference aspects, severe testing properties and presentation of findings. Information quality is based on good responses to questions about a specific report such as what is the goal of the analysis, is the data resolution adequate for the stated or implicit goal, how is data from different sources integrated, has a generalization claim been made, on what basis?In reviewing studies done in Industry 4.0 topics, one finds data collected actively or passively, models developed with empirical methods, first principles or as hybrid models. Industry, as opposed to science, is less concerned with reproducibility of results, but it should. The industrial cycle provides short term opportunities to try out new products of new process set ups and, based on the results, determine follow up actions. Deriving misleading conclusions can be however very costly and time consuming.
Design and Implementation of Elasticsearch for Media Data

With the development of science and technology, a lot of information and data are generated rapidly in the process of using computers, and the amount of data also presents an explosive growth. Traditional relational databases such as mysql are gradually unable to meet the needs of users for quick retrieval. But Elasticsearch makes up for this slow retrieval by providing users with a quick way to do it. Therefore, in this paper, we use the Flask framework in python to write a system for rapid retrieval and visualization of media data. Firstly, a nonrelational database like MongoDB was used to store the raw media data we crawled from the network. Then import the data into the cluster set up by Elasticsearch, create a map of the data, add a Chinese word segmentation parser, and set up an inverted index, so that the data can be used to accurately retrieve information in the future. At the same time, Kibana can be used to visually display and present the data.
Comparison of three dwell-time-based gaze text entry methods

Gaze text entry (GTE) with use of visual keyboards displayed on computer screen is an important topic for both the scientists dealing with the gaze interaction and potential users i.e. people with physical disabilities and their families. The most commonly used technique for GTE is based on dwell-time regions, at which the user needs to look longer to activate the associated action, in our case - entering the letter. In the article, we present the results of tests of three GTE systems (gaze keyboards) on a sample of 29 participants. We compare the objective measures of usability, namely the text entry rate and the number of errors, as well as subjective ones, obtained using SUS questionnaire. Additionally, two similar keyboards based on the ‘Qwerty’ buttons layout were compared in terms of time to the first fixation and its duration in the areas of interest (AOI) corresponding to the visual buttons. One of these gaze keyboards, the so called ’Molecular’ one, contains dynamic elements that have been designed and implemented in our laboratory, and which aim is to support the search for buttons by increasing the size of buttons with suggested letters, without significant change of their positions.
A Steganography Algorithm Based on GM Model of optimized Parameters

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.
Structuring the Evaluation of Location-Related Mobile Device Evidence

Location-related mobile device evidence is increasingly used to address forensic questions in criminal investigations. Evaluating this form of evidence, and expressing evaluative conclusions in this forensic discipline, are challenging because of the broad range of technological subtleties that can interact with circumstantial features of cases in complex ways. These challenges make this type of digital evidence prone to misinterpretations by both forensic practitioners and legal decision-makers. To mitigate the risk of misleading digital forensic findings, it is crucial to follow a structured approach to evaluation of location-related mobile device evidence. This work presents an evaluation framework widely used in forensic science that employs scientific reasoning within a logical Bayesian framework to clearly distinguish between, on the one hand, what has been observed (i.e., what data are available) and, on the other hand, how those data shed light on uncertain target propositions. This paper provides case examples to illustrate the advantages and difficulties of applying this approach to location-based mobile device evidence. This work helps digital forensic practitioners follow the principles of balanced evaluation and convey location-related mobile device evidence in a way that allows decision-makers to properly understand the relative strength of, and limitations in, digital forensic results.
The Concept of ‘Bringing Your Own Device’ in Scaffolded and Augmented Education

Location-based games have enabled new opportunities for augmenting the traditional learning space. In a time when most students have their own smartphones, the concept of bringing your own device (BYOD) also seems promising in educational settings. However, playing at random in augmented reality environments will not automatically bring curriculum-aligned learning outcomes. This chapter analyses and discusses how the combination of BYOD and augmented reality gaming might be scaffolded to support collaborative curriculum-aligned learning. The overall research strategy was a case-study approach with Affordance theory, Social Constructivism and BYOD as theoretical assumptions for deductive analysis. The case units were two outdoor sessions for middle school students with curriculum-aligned assignments in Mathematics and Social Science solved by playing the augmented reality game Pokémon GO. Data have been collected by video recordings of the outdoor sessions with spy glasses and a handheld camera. Results indicate that the augmented reality environment stimulates active learning, but that there like in traditional learning, is a need for scaffolding to achieve the stated learning outcomes.
A New Approach to Locate Software Vulnerabilities Using Code Metrics

Automatic vulnerabilities prediction assists developers and minimizes resources allocated to fix software security issues. These costs can be minimized even more if the exact location of vulnerability is correctly indicated. In this study, the authors propose a new approach to using code metrics in vulnerability detection. The strength part of the proposed approach lies in using code metrics not to simply quantify characteristics of software components at a coarse granularity (package, file, class, function) such as complexity, coupling, etc., which is the approach commonly used in previous studies, but to quantify extracted pieces of code that hint presence of vulnerabilities at a fine granularity (few lines of code). Obtained results show that code metrics can be used with a machine learning technique not only to indicate vulnerable components wish was the aim of previous approaches but also to detect and locate vulnerabilities with very good accuracy.
Twice as Nice? A Preliminary Evaluation of Double Android Unlock Patterns

Android unlock patterns are a widely used form of graphical passwords, and like all password schemes, numerous studies have shown that users select a relatively guessable and non-diverse set of passwords. While proposals have been put forth for hardening patterns, such as increasing the number of or changing the location of contact points, none of these proposals has been implemented in the decade-plus since the interface's launch. We propose a new approach; instead of increasing the individual complexity, users select two sequential Android patterns, so called Double Patterns, that are visually super imposed on one another. This allows more complexity without dramatically changing the interface. We report on our preliminary findings of a large user study (n=634) of Double Patterns, finding strong evidence that the scheme is highly usable and increases the complexity of user choice.
New Mobilities: A Workshop on Mobility Beyond the Car

HCI research on mobility and transport has been dominated by a focus on the automobile. Yet urgent environmental concerns, along with new transport technologies, have created an opportunity for new ways of thinking about how we get from A to B. App-based services, innovations in electric motors, along with changing urban transport patterns, are transforming public transit. Technology is creating new collective transit services, as well as new ways for individuals to move, such as through rental, free-floating e-scooters, so called 'micro-mobility'. This workshop seeks to discuss and establish HCI perspectives on these new mobilities - engaging with and even inventing new modes of transport, fostering collaboration between scholars with varied topical interests around mobility. We seek to bring together a group of industry and academic collaborators, bringing new competences to HCI around the exciting opportunities of redesigning our contemporary mobilities.
Breast Lesion Segmentation in Ultrasound Images by CDeep3M

The proper segmentation of breast lesion based on mammography is crucial not only for traditional computer aided diagnosis but also for supervised learning by deep learning, which is significant for further quantitative analysis. To achieve a comprehensive diagnosis with versatile deep learning algorithms, a novel Docker based framework is proposed and designed by incorporating popular CNN based models CDeep3M. The validation is preceded under a unique amount of B-type ultrasound images. The results indicate that CDeep3M have a good capability for breast tumor prediction. Besides, the analysis shows that Docker framework is flexible and extensive for environmental construction for breast cancer real-time prediction, as well as other related applications.
The Techniques of Securing Imaging Data in Medical System

Over the years, the technology evolution has brought a huge change on the people daily life, however, it is undeniable that some vulnerabilities have also been arisen. The cyber-attacks and cyber threats are always the main challenge of cybersecurity. The unsecure network causes the data leakage, data theft and data interception that affect the data integrity. In the healthcare sector, the medical images are the important records for the organization to detect and diagnose the disease. Hence, the confidentiality of the medical images needs to be ensured to assure the integrity of the data. The techniques that are used to enhance the security and integrity of the medical images include cryptography, steganography and digital watermarking to prevent the medical images from being modified and the techniques can also be used for authentication in which the content of the medical images can be viewed only by the authorized person. This paper will describe the techniques used in medical image for security and integrity purposes, moreover, it will propose the suitable technique to use in medical images by hospitals.
Real-Time Long-Term Tracking with Adaptive Online Searching Model

Kernelized correlation filter (KCF) based trackers have drawn great attention for their superiority in terms of accuracy and speed in visual tracking problem. However, these methods are not robust under scale changes, rotation and occlusion due to their having a fixed size filter. In this work, we take advantage of the KCF tracker to propose a novel algorithm for long-term visual object tracking which handle scale variation using Log-Polar Transformation and Phase Correlation. To detect exactly loss tracker moment when object partly for fully occlusion, this paper propose an effective technique combining PRS ratio and histogram distance. We also learn an online SVM classifier on consecutive and reliable samples to redetect objects in case of tracking failure due to heavy occlusion or out of view movement. Experimental results in several challenging tracking datasets from camera UAV show that our tracker achieves remarkable speed in real-time application at 40FPS while handling scale changes and occlusion better than many state-of-the art tracking algorithms.