Reviewerly/exHarmony huggingface dataset:
Training or Battling a Monster of a Location-Based Augmented-Reality Game While Descending Stairs: An Observational Study of Inattentional Blindness and Deafness and Risk-Taking Inclinations

Several emerging smartphone augmented-reality games such as "The Walking Dead: Our World", "Jurassic World Alive", and "Pokémon Go" require three primary tasks: training or battling a monster, capturing a monster, and searching for a monster, which involve different levels of perceptual load. Using Pokémon Go as a single case study, this study examined inattentional blindness and deafness and risk-taking inclinations among participants concurrently descending stairs and engaging in these three tasks. Participants descending stairs in Taipei Medical University were observed through recordings obtained from Wi-Fi cameras to determine whether they engaged in risk-taking behaviours such as hopping, not using the handrail, and stopping suddenly. After the participants descended the stairs, they were interviewed to obtain additional information regarding demographics, Pokémon tasks (training or battling a Pokémon, capturing a Pokémon, or searching for a Pokémon), data plan, and screen size. Inattentional blindness and deafness were investigated by determining whether participants saw something unusual, a police ascending the stairs, and heard the national anthem played by the police, respectively. In total, 1036 participants descended the stairs and underwent the interview between August 2016 and July 2018. Logistic regression models revealed that training or battling a Pokémon was most associated with inattentional blindness, deafness, not using the handrail, and stopping suddenly, whereas hopping behaviour was the commonest among those capturing a Pokémon. Other contributory factors include a large smartphone screen (≥5 in), unlimited mobile data, being an undergraduate student, and an increase in the daily gaming hours. Development of smartphone apps towards detection of stair locomotion may be beneficial for curbing phone use in general and AR game playing in particular.
"FoRent

Car rental agencies primarily serve people who require temporary vehicles. However, many car rental damage scam cases involving car renter fail to present any evidence and being unfairly charged, for the damage that did not happen during rental. Concerning the current car rental systems that only allow customers to inspect the physical condition of the car, the objective of this project is to develop a mobile application for car rental system, FoRent, that implements the car diagnostic features. Through this system, the car owner and car renter will be able to record the physical condition and retrieve forensic data of the car using ELM327 device, before signing the rental agreement. In order to reduce security risk, this project introduces a security protocol to provide the integrity and availability of the data in FoRent. Enhancing the existing paper-and-pen car rental system, FoRent is remarkable as it introduces these special features that meet the requirement of the car rental company and customers.
Digitizing physical documents using optical character recognition

The need to convert printed text into a computer documented form which can be edited has increased rapidly in recent years which is fulfilled by using Optical Character Recognition (OCR). The challenge is to develop a character recognition mechanism which can convert these scanned images to an electronic mode which will provide the feature to reuse this text, access to every line and word of the document. This paper analyzes the architecture and method used for text recognition in OCR performed by Tesseract and extend this to an application which can transform sources of large number of paper printed documents like magazines, books, newspapers, etc. to an editable electronic format. This paper hence provides an application system that can make digitization of the physical documents faster and better with more accuracy.
Application-based authentication on an inter-VM traffic in a Cloud environment

Cloud Computing (CC) is an innovative computing model in which resources are provided as a service over the Internet, on an as-needed basis. It is a large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Since cloud is often enabled by virtualization and share a common attribute, that is, the allocation of resources, applications, and even OSs, adequate safeguards and security measures are essential. In fact, Virtualization creates new targets for intrusion due to the complexity of access and difficulty in monitoring all interconnection points between systems, applications, and data sets. This raises many questions about the appropriate infrastructure, processes, and strategy for enacting detection and response to intrusion in a Cloud environment. Hence, without strict controls put in place within the Cloud, guests could violate and bypass security policies, intercept unauthorized client data, and initiate or become the target of security attacks. This article shines the light on the issues of security within Cloud Computing, especially inter-VM traffic visibility. In addition, the paper lays the proposition of an Application Based Security (ABS) approach in order to enforce an application-based authentication between VMs, through various security mechanisms, filtering, structures, and policies.
Security analysis and economic feasibility for virtualization usage in University datacenters

In this paper, authors have analyzed economic andsecurity aspects of server virtualization. The experimentalanalysis includes: the business value of virtualization and impactof virtual environments on computing and security parameters,with applying modern cryptographic systems for data protection.The experiment was designed simultaneously on the traditionalserver and virtual environment. The obtained results show thesignificant advantages of virtual environment in the form ofoptimal allocation and utilization of physical computing resources.Also, the results indicate a significant improvement of parametersin the information security, introducing of the virtual networkadapter which presents virtualized communication channelsconcept between the application server and database server.
SIGCSE technical symposium 2019 report

SIGCSE celebrated its 50th Technical Symposium in Minneapolis, Minnesota, USA from February 27 to March 2, 2019. We were delighted to have served as Symposium and Program co-chairs of the symposium. In spite of the historic 30.4 inches of record snowfall during the month of February, the Golden Anniversary of the Technical Symposium broke another record with 1809 attendees!
BLOCKCHAIN AS A KEY CONPONENT IN THE MACHINE TOOL SECTOR UNDER INDUSTRY 4.0

Blockchain is a new technology that is disrupting every sector or process in where value is involved. It allows the collaboration of multiple parties in a safe and secure way, even if they don’t trust each other, without the need of a central entity. The problems that this technology addresses are fully aligned with the objectives of the Industry 4.0 in its different levels. The distributed ledger technology it uses can make the concept of machine collaboration real. In this environment, the machine tool manufacturers could play an important role using its advantages for designing smart, autonomous and adaptive collaborative machines. They can also leverage this technology at a business level, creating new opportunities and increasing their capability of offering a wider range of products and services. Keywords: Blockchain, Industry 4.0, distributed ledger, machine tool industry, SME.
Design of a Multiword Expressions Database

Abstract The paper proposes design of a generic database for multiword expressions (MWE), based on the requirements for implementation of the lexicon of Czech MWEs. The lexicon is aimed at different goals concerning lexicography, teaching Czech as a foreign language, and theoretical issues of MWEs as entities standing between lexicon and grammar, as well as for NLP tasks such as tagging and parsing, identification and search of MWEs, or word sense and semantic disambiguation. The database is designed to account for flexibility in morphology and word order, syntactic and lexical variants and even creatively used fragments. Current state of implementation is presented together with some emerging issues, problems and solutions.
A Half-Circular Distribution on a Circle

Up to now, circular distributions are defined in [0,2 π), except for axial distributions on a semicircle.However, some circular data lie within just half of this range and thus may be better fitted by a half-circular distribution, which we propose and develop in this paper using the inverse stereographic projection technique on a gamma distributed variable.The basic properties of the distribution are derived while its parameters are estimated using the maximum likelihood estimation method.We show the practical value of the distribution by applying it to an eye data set obtained from a glaucoma clinic at the University of Malaya Medical Centre, Malaysia.
The relationship between organisational trust and organisational identification and its effect on organisational loyalty

Most of management literature in general and organisational behaviour in particular agree on the importance of organisational trust and organisational identification in the success and sustainability of organisations, these variables significantly affect important indicators of success in the organisation such as organisational citizenship, organisational loyalty, employee satisfaction, etc. The purpose of this study is to examine the relationship between organisational trust, organisational identification and organisational loyalty in the private Jordanian hospitals, providing a framework of four scenarios for the relationship between these variables and working on determining the optimal relationship between the three variables. The study framework included the direct relationships of organisational trust and organisational identification and its impact on organisational loyalty, the indirect effect of organisational trust on organisational loyalty with the existence of organisational identification as a mediator variable, and organisational identification on organisational loyalty with the existence of organisational trust in organisational loyalty as a mediator variable. The study showed that all relationships in the study framework had a significant effect on organisational loyalty. However, the optimal relationship was the effect of organisational identification in organisational loyalty with the existence of organisational trust as a mediator variable.
A dynamic strategy-proof algorithm for allocation and pricing of cloud services

In this paper, we present a dynamic strategy-proof algorithm for allocation and pricing. The algorithm relies on market history to forecast a benchmark price to ensure truthful valuation from the market participants. The algorithm also ensures that the utility is evenly distributed between buyer and seller. The utility is the difference between the buyer's maximum offer and the seller's minimum acceptable price. The results of the experimental studies carried out shows that: 1) the budget limits of the buyers with successful allocations were not exceeded; 2) only market participants with truthful offers and bids were allocated services; 3) the utility is higher when there is no strategy-proof for preventing overbid and that there is at least a 50% increase in price per unit when there is no strategy-proof to prevent overbid; 4) in terms of computational efficiency, the algorithm converges in polynomial time with a worst-case running time of O(n2).
Deep Generative and Discriminative Domain Adaptation

The ability to adapt to and learn from different domains and environments is crucial for agents to generalize. In this paper we propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discriminative models correspond to specific choices of the prior over parameters. By maximizing both the marginal and the conditional log-likelihoods, our models can use both labeled instances from the source domain as well as unlabeled instances from both source and target domains. We show that the popular reconstruction loss of autoencoder corresponds to an upper bound of the negative marginal log-likelihoods of unlabeled instances, and give a generalization bound that explicitly incorporates it into the analysis. We instantiate our framework using neural networks, and build a concrete model, DAuto.
A Scalable System for Executing and Scoring K-Means Clustering Techniques and Its Impact on Applications in Agriculture

We present Centaurus - a scalable, open source, clustering service for K-means clustering of correlated, multidimensional data. Centaurus provides users with automatic deployment via public or private cloud resources, model selection (using Bayesian information criterion), and data visualisation. We apply Centaurus to a real-world, agricultural analytics application and compare its results to the industry standard clustering approach. The application uses soil electrical conductivity (EC) measurements, GPS coordinates, and elevation data from a field to produce a 'map' of differing soil zones (so that management can be specialised for each). We use Centaurus and these datasets to empirically evaluate the impact of considering multiple K-means variants and large numbers of experiments. We show that Centaurus yields more consistent and useful clusterings than the competitive approach for use in zone-based soil decision-support applications where a 'hard' decision is required.
A Fully Rational Argumentation System for Preordered Defeasible Rules

Structured argumentation is a family of formal approaches for the handling of defeasible, potentially inconsistent information. Many models for structured argumentation distinguish between strict and defeasible inference rules. Defeasible rules often come with varying degrees of strength which is formally represented by a preorder over the defeasible rules. Various lifting principles have been presented in the literature to determine the relative strength of an argument by considering the strength of the defeasible rules used in its construction. The strength of arguments then comes into play when determining whether an attack (a purely syntactic relationship between arguments) results in a defeat (i.e. a successful attack). In \citecaminada2007evaluation,Wu2012, several rationality postulates were proposed that serve as a measure to assess the normative rationality of structured argumentation formalisms. In \citeheyninck2017revisiting, the first formalism satisfying all rationality postulates for structured argumentation when taking into account totally ordered defeasible rules was proposed. In many settings, assuming a total order greatly limits the realistic modelling capabilities of a formal system, e.g. when agents do not know the actual preferences of each rule or since different agents have different preferences over defeasible rules. Our paper shows that in the more general setting of preorders, violations of several rationality postulates can occur. We show how for a wide class of lifting principles, these violations can be avoided, resulting in the first Dung-based system that satisfies all four rationality postulates for preordered defeasible rule bases.
GKB: A Predictive Analytics Framework to Generate Online Product Recommendations

Recommender Systems are essential to many of the largest internet companies’ core products. Online users today expect sites that offer a large assortment of products to also serve recommendations. These recommendations are based on various pieces of data including user ratings of products and product features. In this paper, we explore the use case of applying foundational recommender system techniques and algorithms to provide book recommendations. First, we introduce what recommender systems are and the different types. Then, we will describe the Greenquist-Kilitcioglu-Bari (GKB) framework, an end to end process of building out a fully functional and live recommendation system that can be hosted on the internet, which has an RMSE of 0.842. Steps of the process that will be highlighted are data collection and preprocessing, model selection and evaluation, combining different models to create a hybrid model, and hosting the models on a live website that can serve recommendations in real time to many users. We also use the trained model to serve recommendations to a new user that was not part of the training process. This approach creates promises beyond book recommendations and can be applied to marketing, finance, politics, e-commerce, and any data matching applications.
Extension of Coordinate Systems and Outdoor Autonomous Swarming Flight using 130 Mini Quadrotors

This article presents the extension of existing coordinate systems for autonomous swarming flight and subsequent experimental results using 130 mini quadrotors. The flight performance of mini quadrotors has been continuously improved in our laboratory, the Unmanned System Research Group (USRG). Mini quadrotors can fly autonomously using a GNSS-based navigation algorithm. We also extended the horizontal and equatorial coordinate systems to more easily generate a three-dimensional flight path, and these coordinate systems represent a powerful tool for the generation of flight paths and the improvement in flight quality. To confirm the effectiveness of the proposed coordinate systems, we assessed autonomous swarming flight using 130 mini quadrotors.
Multiagent Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a best-first search which is efficient in large search spaces and is effective at balancing exploration versus exploitation. In this work, we introduce a novel extension for MCTS, called Multiagent Monte Carlo Tree Search (MAMCTS), which pairs MCTS with difference evaluations. We demonstrate the performance of MAMCTS in a cooperative, multiagent path-planning domain called Multiagent Gridworld. We show that MAMCTS using difference evaluations outperforms MAMCTS using local rewards by up to 31.4% and MAMCTS using the global reward by up to 88.9% for a system with 1,000 agents.
An Augmented Reality Platform for Preoperative Surgical Planning

Researching in new technologies for diagnosis, planning and medical treatment have allowed the development of computer tools that provide new ways of representing data obtained from patient’s medical images such as computed tomography (CT) and magnetic resonance imaging (MRI). In this sense, augmented reality (AR) technologies provide a new form of data representation by combining the common analysis using images and the ability to superimpose virtual 3D representations of the organs of the human body in the real environment. In this paper we describe the development of a generic computer platform based on augmented reality technology for surgical preoperative planning. In particular, the surgeon can navigate in the 3D models of the patient’s organs in order to have the possibility to perfectly understand the anatomy and plan in the best way the surgical procedure. In addition, a touchless interaction with the virtual organs is available thanks to the use of an armband provided of electromyographic muscle sensors. To validate the system, we focused in two cases of study: navigation through aorta artery for mitral valve repair surgery and navigation through vascular structures in the brain for the treatment of cerebral aneurysms.
Neural Stochastic Differential Equations.

Deep neural networks whose parameters are distributed according to typicalinitialization schemes exhibit undesirable properties that can emerge as thenumber of layers increases. These issues include a vanishing dependency on theinput and a concentration on restrictive families of functions includingconstant functions. We address these problems by considering the limit ofinfinite total depth and examine the conditions under which we achieveconvergence to well-behaved continuous-time processes. Doing so we establishthe connection between infinitely deep residual networks and solutions tostochastic differential equations, i.e. diffusion processes. We show that deepneural networks satisfying such connection don't suffer from the mentionedpathologies and analyze the SDE limits to shed light on their behavior.
Compositional Convolutional Networks For Robust Object Classification under Occlusion.

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize well to partially occluded objects and to mask attacks. In contrast to DCNNs, compositional models are robust to partial occlusion, however, they are not as discriminative as deep models. In this work, we combine DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks. Our model is learned in two steps. First, a standard DCNN is trained for image classification. Subsequently, we cluster the DCNN features into dictionaries. We show that the dictionary components resemble object part detectors and learn the spatial distribution of parts for each object class. We propose mixtures of compositional models to account for large changes in the spatial activation patterns (e.g. due to changes in the 3D pose of an object). At runtime, an image is first classified by the DCNN in a feedforward manner. The prediction uncertainty is used to detect partially occluded objects, which in turn are classified by the compositional model. Our experimental results demonstrate that combining compositional models and DCNNs resolves a fundamental problem of current deep learning approaches to computer vision: The combined model recognizes occluded objects, even when it has not been exposed to occluded objects during training, while at the same time maintaining high discriminative performance for non-occluded objects.
Mutual Information-Based Word Embedding for Unsupervised Cross-Domain Sentiment Classification

Unsupervised cross-domain sentiment classification is a challenging task, which trains a sentiment classifier using labeled source domain data and could be applied to unlabeled target domain. This problem can be solved by training crossdomain word embedding. This paper proposes a method of mutual information-based word embedding. Firstly, source domain word embedding is trained based on source domain data. Then the pivots with strong sentimental polarities are selected based on mutual information (MI). According to the MI value of pivots, the transfer coefficient from the source domain to the target domain is calculated. Finally, the transfer coefficient is used as a constraint of the objective of the skipgram model to train the word embedding of the target domain. Experiments show that the proposed method is superior to the cross-domain word embedding based on word frequency.