Reviewerly/exHarmony huggingface dataset:
Designing Gaze-Based Interactions for Teleoperation: Eye Stick and Eye Click

Perspective-taking and attentional switching are some of the ergonomic challenges that existing teleoperation human-machine interface designs need to address. This study developed two gaze interaction methods, the Eye Stick and the Eye Click, which were based on the joystick metaphor and the navigation metaphor, respectively, to be used in exocentric perspective teleoperation scenarios. We conducted two user studies to test the task performance and the subjective experience of the gaze interaction methods in a virtual ground vehicle teleoperation task. The results showed that compared with a traditional joystick design, the Eye Stick led to a shorter driving distance and the Eye Click led to less task time, and the gaze interaction methods had performance advantages in more difficult mazes. After multiple task sessions, the participants reported that the gaze interaction methods and the traditional joystick were similar in terms of task workload, perceived learnability, and satisfaction; however, the perceived usability of the Eye Stick was not as good as the Eye Click and the traditional joystick. In conclusion, both the Eye Stick and the Eye Click are feasible and promising gaze interaction methods for teleoperation applications with task performance advantages; however, more research is needed to optimize their user experience design.
The Justice and Ontology of Gastrospaces

In this paper, we establish gastrospaces as a subject of philosophical inquiry and an item for policy agendas. We first explain their political value, as key sites where members of liberal democratic societies can develop the capacity for a sense of justice and the capacity to form, revise, and pursue a conception of the good. Integrating political philosophy with analytic ontology, we then unfold a theoretical framework for gastrospaces: first, we show the limits of the concept of "third place;" second, we lay out the foundations for an ontological model of gastrospaces; third, we introduce five features of gastrospaces that connect their ontology with their political value and with the realization of justice goals. We conclude by briefly illustrating three potential levels of intervention concerning the design, use, and modification of gastrospaces: institutions, keepers, and users.
Applications of data analysis on scholarly long documents

Theses and dissertations record the work of graduate students and are typically a requirement at the culmination of the graduate degree. Thus, they contain important information that reflects a graduate student’s exploration of their research topic. Although print submission was commonplace early on, most universities now require students to submit an electronic version. The electronic document referred to as an ETD henceforth has become the primary way of submitting, storing, and distributing graduate work. Millions of such documents have been created in the past two decades. They are maintained and stored by university libraries, digital repositories, and other academic publishing companies. These online repositories have increased access to such documents. Nonetheless, these documents fail to meet the needs of researchers, who find it challenging to find and access knowledge from such long documents. The worldwide ETD collection has increased in volume to become what is known as ‘scholarly big data’. Apart from the text body, these documents contain a myriad of other pieces of knowledge like tables, figures, definitions, literature reviews, and references. There is a growing demand amongst researchers across various domains to make this collection of scholarly documents more computationally driven. We use ideas from natural language processing, information retrieval, and machine learning to excavate knowledge from this rich information source. In this paper, we examine some of the challenges we face, identify some key areas of exploration, and discuss our methods to mitigate the challenges.
Multivariate Gaussian processes: definitions, examples and applications

Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. With the fast development of Gaussian process applications, it is necessary to consolidate the fundamentals of vector-valued stochastic processes, in particular multivariate Gaussian processes, which is the essential theory for many applied problems with multiple correlated responses. In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof. In addition, several fundamental properties of multivariate Gaussian processes, such as strict stationarity and independence, are introduced. We further derive multivariate Brownian motion including It\^o lemma as a special case of a multivariate Gaussian process, and present a brief introduction to multivariate Gaussian process regression as a useful statistical learning method for multi-output prediction problems.
A large dataset of scientific text reuse in Open-Access publications

We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publications and include a variety of reuse types, ranging from boilerplate text to verbatim copying to quotations and paraphrases. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. The Webis-STEREO-21 does not indicate if a reuse case is legitimate or not, as its focus is on the general study of text reuse in science, which is legitimate in the vast majority of cases. It allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.
Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images.
Improving Out of Network Earthquake Locations Using Prior Seismicity for Use in Earthquake Early Warning

ABSTRACT Timely alerts sent through earthquake early warning (EEW) programs allow those alerted to take protective actions to mitigate their risk from potentially damaging shaking. Over the past few years, ShakeAlert, the EEW program focused on the west coast of the contiguous United States, has grown, alerting communities within California, Oregon, and Washington about earthquakes where damaging shaking is expected. ShakeAlert uses a set of algorithms including the point-source algorithm, earthquake point-source integrated code (EPIC), to determine the location, magnitude, and origin time of potential earthquakes. Although EPIC produces low-latency and low error solutions for many events originating within the seismic network on land, numerous recent small earthquakes rupturing offshore of northern California have EPIC location solutions with high error (&amp;gt;50 km compared to USGS locations). Because most events are occurring offshore, there is a limited number of stations that can trigger and contribute information in a timely manner for use in EEW. To better constrain location solutions in this region, we propose to include information about contemporary past seismicity into EPIC’s grid-search algorithm through a Bayesian framework. This prior information layer downweights high error locations where EPIC’s proposed event location coincides with an area of low prior seismicity in preference for locations with a similar level of data fit that also have higher past seismicity. This addition to EPIC lowers the mean location error offshore northern California from 58 to 14 km.
Cross-validation

The model validation is assessing how the results of a statistical analysis will generalize to an independent data set. There are several techniques for model validation. One familiar and famous method for model validation is cross-validation which is called via other names such as out-of-sample testing and rotation estimation that is discussed in this chapter. This technique is mainly applied in settings where the output is predicted, and determining the accuracy of the predictive model in practice. There are two main subsets of cross-validation in machine learning: exhaustive and nonexhaustive. The exhaustive cross-validation method learns and tests all possible ways to divide the original sample into a training and a validation set which is included Leave-P-Out- and Leave-one-out Cross-Validation. Nonexhaustive is included on Hold-out and K-fold. The cross-validation branch is too extensive, we just deep into the most familiar one, K-fold cross-validation. Finally, we solved two extensive problems about K-fold cross-validation by using the deep learning technique.
Exploration on the Operation Status and Optimization Strategy of Networked Teaching of Physical Education Curriculum Based on AI Algorithm

Artificial Intelligence (AI) and networked teaching have made great progress in recent years, especially the application of AI algorithms in teaching, which has been relatively mature. The online teaching of physical education (PE) courses is an important practice of networked teaching, but many colleges and universities ignore the importance of AI when carrying out such teaching work. Many colleges and universities have not played a good role in AI in teaching, which leads to inefficient PE teaching and low learning enthusiasm of students.
VCNet: A Self-explaining Model for Realistic Counterfactual Generation

Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet – Variational Counter Net – a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem. Our contribution is the generation of counterfactuals that are close to the distribution of the predicted class. This is done by learning a variational autoencoder conditionally to the output of the predictor in a join-training fashion. We present an empirical evaluation on tabular datasets and across several interpretability metrics. The results are competitive with the state-of-the-art method.
A FAIR-Decide framework for pharmaceutical R&amp;D: FAIR data cost–benefit assessment

The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific research data. Adherence to these principles in managing data assets in pharmaceutical research and development (R&D) offers pharmaceutical companies the potential to maximise the value of such assets, but the endeavour is costly and challenging. We describe the 'FAIR-Decide' framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits. This framework supports decision-making on FAIRification in the pharmaceutical R&D industry and can be integrated into a company's data management strategy.
A survey of quantum secure group signature schemes: Lattice-based approach

The group signatures allow any member to sign the message on behalf of the group. While anyone who does not need to be a group member can verify the signature, they do not know who the signer is. The first examples are constructed using classical techniques such as the discrete logarithm and the integer factorization problem. The fact that the cryptosystems using classical techniques will be broken in polynomial time with quantum computing power increases the interest in post-quantum cryptography. Lattice-based cryptography is a good alternative for the post-quantum world due to its simple structures, efficient implementations, and worst-case hardness. Therefore, the lattice-based group signature schemes have been proposed for quantum era. In this paper, our aim is to review group signature schemes over lattices. We classify the schemes into four groups according to the fact that they allow adding and revoking members: static, group signature schemes with revocation mechanism, partially dynamic and fully dynamic. Firstly, we define the algorithms needed to construct the schemes. We give the development process and requirements for constructing group signatures before we review all group signature schemes over lattices. In addition, we compare all proposed lattice-based group signature schemes in view of underlying lattice problems, security requirements, the sizes of group public and secret signing key, signature size and security model. The main contribution of this study is that it is the first study examining lattice-based group signature schemes in detail.
Investigation of visual and game-playing performance under different genders, display and operation interfaces

Technological progress has led to 3D displays becoming a trend in the market, yet few studies have evaluated 3D techniques in head-mounted displays (HMD) under different topics simultaneously, such as gender and operation interfaces. This study investigated these research issues to examine the effect of gender (male, female), display interface (2D TV, 3D HMD), and operation interface (joystick, keyboard) on usability, accuracy, change of critical flicker frequency (change of CFF), Simulator Sickness Questionnaire (SSQ), and iGroup Presence Questionnaire (IPQ). Thirty participants with no NBA2K15 basketball game-playing experience were recruited in the experiment. The results showed significant effects of gender on accuracy, usability, and change of CFF, where males demonstrated higher accuracy, lower usability, and lower visual fatigue. In addition, display interface had significant effects on usability, SSQ and IPQ. The 2D TV was associated with higher usability and lower SSQ and IPQ. Furthermore, the operation interface had significant effects on accuracy, usability, change of CFF, and IPQ. The joystick led to a higher usability and IPQ but a lower change of CFF. The results provide suggestions to game players that choosing 2D TVs with joysticks generates greater usability. In addition, although they achieved a higher presence, players also had greater visual fatigue when wearing HMDs. This study concluded that males have better performance in accuracy and visual fatigue while females have better usability when playing basketball games.
Insecurity Refactoring: Automated Injection of Vulnerabilities in Source Code

Insecurity Refactoring is a change to the internal structure of software to inject a vulnerability without changing the observable behavior in a normal use case scenario. An implementation of Insecurity Refactoring is formally explained to inject vulnerabilities in source code projects by using static code analysis. It creates learning examples with source code patterns from known vulnerabilities. Insecurity Refactoring is achieved by creating an Adversary Controlled Input Dataflow tree based on a Code Property Graph. The tree is used to find possible injection paths. Transformation of the possible injection paths allows to inject vulnerabilities. Insertion of data flow patterns introduces different code patterns from related Common Vulnerabilities and Exposures (CVE) reports. The approach is evaluated on 307 open source projects. Additionally, insecurity-refactored projects are deployed in virtual machines to be used as learning examples. Different static code analysis tools, dynamic tools and manual inspections are used with modified projects to confirm the presence of vulnerabilities. The results show that in 8.1% of the open source projects it is possible to inject vulnerabilities. Different inspected code patterns from CVE reports can be inserted using corresponding data flow patterns. Furthermore the results reveal that the injected vulnerabilities are useful for a small sample size of attendees (n=16). Insecurity Refactoring is useful to automatically generate learning examples to improve software security training. It uses real projects as base whereas the injected vulnerabilities stem from real CVE reports. This makes the injected vulnerabilities unique and realistic.
AuthConFormer: Sensor-based Continuous Authentication of Smartphone Users Using A Convolutional Transformer

To prevent the leakage of personal information on smartphones, a method that can authenticate the smartphone user’s identity throughout the session is essential. Sensor-based continuous authentication is a promising potential approach that uses behavioral biometrics to authenticate smartphone user’s identity. Recent studies on continuous authentication have achieved encouraging progress. However, they suffer two limitations: 1) weak capability of capturing smartphone user’s behavioral patterns from biometric data sequences with time correlation because these methods don’t consider the long-range dependencies between the behavioral biometric data sequences; 2) poor performance under a high authentication frequency. To solve these issues, we present AuthConFormer, a novel continuous authentication system based on a proposed convolutional transformer, which is suitable for running on the mobile phone CPUs. The proposed convolutional transformer applies an inverted residual block instead of linear projection in the pure transformer to perform the query, key and value embeddings, respectively. Compared with the pure transformer, the proposed convolutional transformer requires less computation, and can make full use of the contextual information of 2D feature maps. We perform several experiments to evaluate the performance and effectiveness of the AuthConFormer on three public datasets. Experimental results show that the proposed AuthConFormer can achieve the mean values of 1.06% EER, 1.25% EER and 1.19% EER on three public datasets with a high authentication frequency (one authentication every 0.6s), respectively. Besides, the convolutional transformer model pre-trained on the HMOG dataset exhibits good cross-domain generalization capability on the BrainRun dataset.
Application of Blockchain Technology in agri-food supply chains: Opportunities and challenges

Blockchain is an emerging technology and has huge potential for application in the Agri food supply chain. The Agri food supply chain is very challenging because of complexities involved in it. This chapter is focused on exploring opportunities and challenges in applications of blockchain in the Agri food supply chain. Blockchain technology requires less storage capacity and helps in promoting sustainability in operations. Blockchain technology is a tampered proof digital ledger made up of blocks which are connected via a series of hash pointers. This technology is considered as a safe technology because it is difficult to alter, reverse, or make any change in the blocks containing the information. This chapter also discusses the application of blockchain technology by explaining the Walmart case study. The technology provides transparency, traceability, and better visibility in the Agri food supply chain. It helps in eliminating the middlemen and reducing the transaction costs in the fast-changing business environment. This chapter reviews the recent studies on the blockchain technology applications. It identifies the benefits such as reduction in economic losses; improvement in the supply chain trust and collaboration; sustainability; traceability and transparency; improvement of food quality, safety and security; simplifying the Agri food supply chain and streamline operations; better decision-making; minimizes the financial risks; and helps the producers to access financial services. This chapter further identifies challenges in implementing the blockchain technology. The identified challenges are lack of infrastructure, lack of government guidelines and regulations, interoperability and standardization, technical challenges, and high investments. Insights from this chapter will help professionals in implementing blockchain technology in Agri food supply chains in an effective manner to improve visibility across all operations.
Resource Allocation Modeling Framework to Refactor Software Design Smells

The domain to study design flaws in the software environment has created enough opportunity for the researchers. These design flaws i.e., code smells, were seen hindering the quality aspects of the software in many ways. Once detected, the segment of the software which was found to be infected with such a flaw has to be passed through some refactoring steps in order to remove it. To know about their working phenomenon in a better way, authors have innovatively talked about the smell detection mechanism using the NHPP modeling framework. Further the authors have also chosen to investigate about the amount of resources/efforts which should be allotted to various code smell categories. The authors have developed an optimization problem for the said purpose which is being validated on the real-life smell data set belonging to an open-source software system. The obtained results are in acceptable range and are justifying the applicability of the model.