Reviewerly/exHarmony huggingface dataset:
Virtual and Real Body Representation in Mixed Reality: An Analysis of Self-presence and Immersive Environments

Over the last decade, most of studies were focused on assessments of presence, degrees of immersion and user involvement with virtual reality, few works, however, addressed the mixed reality environments. In virtual reality, mostly through an egocentric point of view, the user is usually represented by a virtual body. In this work, we present an approach where the user can see himself instead of a virtual character. Different than augmented reality approaches, we only detach the user’s body and his/her proxies interfaces. We conducted four experiments to measure and evaluate quantitatively and qualitatively the feeling of presence. We measure and compare the degree of presence in different modes and techniques of representation of the user’s body through virtually integrated questionnaires. We also analyzed what factors influence to a greater or lesser feeling of presence. For this study, we adapted a real bike with an haptic interface. Results show that there is not necessarily a better or worse type of avatar, but their appearance must match the scenarios and others elements in the virtual environment to greatly increase the participant sense of presence.
Application of random elements in ISS

The -threshold image secret sharing (ISS) encodes a secret image into n shares. When k or more shares are obtained, the secret image can be decoded; however, less than k shares could decode none of the secret image. ISS primarily includes polynomial-based ISS and visual secret sharing (VSS). In this study, the authors find that the random elements in ISS can be used not only to hide information but also to obtain more features such as multiple decryptions and comprehensible share. They have established an application model of random elements that is suitable for both polynomial-based ISS and VSS. On the basis of the model, they have extended three algorithms to achieve information hiding, multiple decryptions and comprehensible share. Experiments indicate the effectiveness of these algorithms.
Simple and Coverage Path Planning for Robots: A Survey

Path planning plays a significant role in most of the applications in the field of robotics. Path planning ensures that the robot follows a planned and efficient path in the environment. This paper discusses the types of path planning, i.e., simple and coverage path planning. It explores the advancements made in the research and development of various algorithms and approaches under the two types. It also discusses the application of these path planning strategies in real-life scenarios. The final section describes the application of path planning in search and rescue operations in a disaster environment. Based on the study, some algorithms and approaches are suggested for the most efficient use of the robot in this scenario.
Athena: A Mobile Based Application for Women's Safety with GPS Tracking and Police Notification for Rizal Province

The study aims to develop a Mobile Based Application for Woman's Safety to help authorities track criminals through the use of the said application by providing real-time SMS Notification, GPS Tracking, and direct emergency call to nearby police station inside the province. Rizal province was chosen as the beneficiary of this study during this phase. The system will also help authorities prevent crimes before it happens depending on the user response. The study is best suited for individuals commuting to uncomfortable destinations. The study requires users to have an active mobile network load to maximize the potential of the system. It involves host server which accept and process complaints initiated by the users. The development of the “Athena: A Mobile Based Application for Woman's Safety with GPS Tracking and Police Notification for Rizal Province” utilizes Agile methodology in Android platform provide the following functions: sends notification via SMS, video and image capturing, and direct calling the police and GPS tracking in time of distress. The developed system also showed its effectivity in processing the different features stated in the scope and delimitations of the study.
PV Cell Orientation Angles Optimization for a Base Station Equipped With Several PV Cells

We jointly optimize the orientation angles of N photovoltaic (PV) cells powering one base station (BS). There are more degrees of freedom for matching the energy generation profile of the PV cells to the energy consumption profile of the BS by deploying the PV cells with potentially different orientation angles. We derive analytically the energy generation profile of any randomly inclined and oriented PV cell from the irradiance values received at a horizontally mounted PV cell at the same location. The energy drawn per day from the main grid by the BS given its energy consumption profile is used as the performance metric to determine the optimal orientation angles. Our results show that deploying one PV cell (or several PV cells) with the (same) optimized orientation angle is recommended for BSs with an energy consumption profile that has one significant local maximum between sunrise and sunset. Deploying two PV cells (or two equal-sized groups of PV cells) where the two orientation angles (of the two groups) are jointly optimized is recommended for BSs with an energy consumption profile that has significant local maxima in the morning as well as in the afternoon or with a constant energy consumption profile.
Lessons from Large-Scale Software as a Service at Databricks

The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software, which has not been heavily studied in research. I will explain some of these challenges based on my experience at Databricks, a startup that provides a data analytics platform as a service on AWS and Azure. Databricks manages millions of VMs per day to run data engineering and machine learning workloads using Apache Spark, TensorFlow, Python and other software for thousands of customers. Two main challenges arise in this context: (1) building a reliable, scalable control plane that can manage thousands of customers at once and (2) adapting the data processing software itself (e.g. Apache Spark) for an elastic cloud environment (for instance, autoscaling instead of assuming static clusters). These challenges are especially significant for data analytics workloads whose users constantly push boundaries in terms of scale (e.g. number of VMs used, data size, metadata size, number of concurrent users, etc). I'll describe some of the common challenges that our new services face and some of the main ways that Databricks has extended and modified open source analytics software for the cloud environment (e.g., designing an autoscaling engine for Apache Spark and creating a transactional storage layer on top of S3 in the Delta Lake open source product).
Communication and security trade-offs for wearable medical sensor systems in hospitals

This paper describes an important first step in the development of a custom wearable health platform that allows the end-to-end secure monitoring of six vital parameters. We explore the impact of wireless network protocols and security schemes on the energy consumption of the wearable device. The results show that the energy efficiency is comparable to existing systems that support far less sensor data and that compromise on end-to-end security.
A Consortium Blockchain-Based Model for Data Sharing in Internet of Vehicles

Internet of Vehicles (IoV) provides a broad range of services of data exchange of traffic information to improve the effectiveness of smart vehicles. However, the security issues in Internet of Vehicles are multifaceted: data theft, message tampering and forgery, etc., which results in possibilities of incorrect data sharing. To address above issues, we proposed an efficient blockchain-based data sharing model. In this paper, we leverage the consortium blockchain and enhanced Diffie-Hellman algorithm to build a trust decentralized verifying mechanism, which is designed to secure the data sharing process in IoV. To improve the performance, we optimize the consensus mechanism on our blockchain-based system by decreasing the consensus delay without affecting the correctness of consensus verifying. The security analysis and simulation experiments show that our model meets security requirements and the overhead from our system is acceptable for IoV.
Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine

&lt;span&gt;Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. &lt;/span&gt;
TILM: Neural Language Models with Evolving Topical Influence

Content of text data are often influenced by contextual factors which often evolve over time (e.g., content of social media are often influenced by topics covered in the major news streams).Existing language models do not consider the influence of such related evolving topics, and thus are not optimal.In this paper, we propose to incorporate such topicalinfluence into a language model to both improve its accuracy and enable cross-stream analysis of topical influences.Specifically, we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream.Experimental results on six different text stream data comprised of conference paper titles show that the incorporation of evolving topical influence into a language model is beneficial and TILM outperforms multiple baselines in a challenging task of text forecasting.In addition to serving as a language model, TILM further enables interesting analysis of topical influence among multiple text streams.
Tourist’s Tour Prediction by Sequential Data Mining Approach

This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.
Identify of Spatial Similarity of Electroencephalography (EEG) during Working-Memory Maintenance

Working memory maintenance is one of the important procedures during working memory storage into long-term memory. This paper utilizes consensus clustering to analyze the spatial similarity amongst whole brain regions during working-memory maintenance processing with 128 channels of scalp Electroencephalography (EEG) records. This paper sets the methodology research to extract the similarity of spatial information processing on the larger brain system during working memory maintenance based on a data-driven method. Based on group analysis of 20 subjects, the EEG channels with similarities are extracted to illustrate the functional brain connectivity of material-specific memory maintenance. The power of alpha frequency band (8-12Hz) appears to provide the discriminative information of the material-specific memory maintenance.
Weighted support vector machine algorithm for efficient classification and prediction of binary response data

Abstract This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.
Project Aristo: Towards Machines that Capture and Reason with Science Knowledge

AI2's Project Aristo seeks to build a system that has a deep understanding of science, using knowledge captured mainly from large-scale text. Recently, Aristo achieved surprising success on the Grade 8 New York Regents Science Exams, scoring over 90% on the exam's non-diagram, multiple choice (NDMC) questions, where even 3 years ago the best systems scored less than 60%. In this talk, I will describe the journey of Aristo through various knowledge capture technologies that have helped it, including acquiring if/then rules, tables, knowledge graphs, and latent neural representations. I will also discuss the growing tension between capturing structured knowledge vs. capturing knowledge latently using neural models, the latter proving highly effective but hard to interpret. Finally I will speculate on the larger quest towards knowledgable machines that can reason, explain, and discuss, and how structured and latent knowledge can interact to help reach this goal.
Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification

The Optical Character Recognition (OCR) is software for text recognition that takes an image containing text, to transform it into strings, then save them into a format that make it able to use in text editing programs. The OCR plays a significant role in the transformation of printed materials into digital text files. These digital files can be very useful for children and adults who have awkward reading. This is because a digital text can be used with computer programs that allow people to read them in different ways. In this paper, we developed system for Turkish character recognition under visual studio (C#) development environment, where machine learning is used to accurately predict optical characters, the reason why it has a high precision and high recognition speed through deep learning, which is one of the machine learning methods for OCR when drawing letters by mouse on the screen, then recognize by using back propagation algorithm.
Hybrid Semantics-Aware Recommendations Exploiting Knowledge Graph Embeddings

Graph-based recommendation methods represent an established research line in the area of recommender systems. Basically, these approaches provide users with personalized suggestions by modeling a bipartite graph that connects the users to the items they like and exploit such connections to identify items that are interesting for the target user. In this work we propose a hybrid semantics-aware recommendation method that aims to improve classical graph-based approaches in a twofold way: (i) we extend and enhance the representation by modeling a tripartite graph, that also includes descriptive properties of the items in the form of DBpedia entities. (ii) we run graph embedding techniques over the resulting graph, in order to obtain a vector-space representation of the items to be recommended. Given such a representation, we use the resulting embeddings to cast the recommendation problem to a classification one. In particular, we learn a classification model by exploiting positive and negative embeddings (the items the user liked and those she did not like, respectively), and we use such a model to classify new items as interesting or not interesting for the target user. In the experimental evaluation we evaluated the effectiveness of our method on varying of different graph embedding techniques and on several topologies of the graph. Results show that the embeddings learnt by combining collaborative data points with the information gathered from DBpedia led to the best results and also beat several state-of-the-art techniques.
MAPSS an Intelligent Architecture for the Pedagogical Support: Experiment, Results &amp; Discussion

In this paper, the authors have experimented the MaPSS (Multiagent pedagogical support system) which is an adaptive architecture based on ontologies and multi-agent systems for the presentation of the pedagogical support in its principal tasks: assessment of the knowledge, analyze of results and adapt remediation. In previous works, the authors have designed an ontology to present the relationship between the test questions and the concepts to evaluate and between those concepts and their remedial activities contents. Also, they have presented the operation of each agent (IT or human) of the system and the principle of collaboration between them. &#x0D; The experience consists on the use of an implemented prototype of MaPSS to support learners of Moroccan qualified secondary school in the domain of algorithmic and programming, and the impact of its use on the improving cognitive decision making to adapt learning.
Relevance Metric for Counterfactuals Selection in Decision Trees

Explainable Machine Learning is an emerging field in the Machine Learning domain. It addresses the explicability of Machine Learning models and the inherent rationale behind model predictions. In the particular case of example-based explanation methods, they are focused on using particular instances, previously defined or created, to explain the behaviour of models or predictions. Counterfactual-based explanation is one of these methods. A counterfactual is an hypothetical instance similar to an example whose explanation is of interest but with different predicted class. This paper presents a relevance metric for counterfactual selection called sGower designed to induce sparsity in Decision Trees models. It works with categorical and continuous features, while considering number of feature changes and distance between the counterfactual and the example. The proposed metric is evaluated against previous relevance metrics on several sets of categorical and continuous data, obtaining on average better results than previous approaches.
MapReduce rationality verification based on object Petri net

As a powerful distributed data processing mechanism, MapReduce supports abundant parallel applications that process massive data on computer clusters.To process the massive data efficiently and correctly, a rational design for the MapReduce procedure is desired.An irrational MapReduce procedure can cause great waste of computing resources and even paralyze the execution system.With the wide application of MapReduce, the unavoidable drawback of irrational MapReduce procedures becomes increasingly serious.To solve this problem, a method for verifying the rationality of a MapReduce procedure before executing it on a computer cluster is proposed.This method constructs the rationality criteria for MapReduce, and then studies an automatic approach for modelling MapReduce with an executable model object Petri net (OPN).Finally, the approaches for automatically identifying the rationality criteria by analyzing the consequence of model execution is developed.The results from extensive case studies demonstrate that the proposed method is feasible and effective.
TOM: A Threat Operating Model for Early Warning of Cyber Security Threats

Threat profiling helps reveal the current trends of attacks, and underscores the significance of specific vulnerabilities, hence serves as the means for providing an early warning of potential attacks. However, the existing approaches on threat profiling models are mainly rule-based and depend on the domain experts’ knowledge, which limit their applicability in the automated processing of cyber threat information from heterogeneous sources, e.g. the cyber threat intelligence information from open sources. The threat profiling models based on analytic approaches, on the other hand, are potentially capable of automatically discovering the hidden patterns from a massive volume of information. This paper proposes to apply the data analytic approaches to develop the threat profiling models in order to identify the potential threats by analyzing a large number of cyber threat intelligence reports from open sources, extract information from the cyber threat intelligence reports, and represent them in a structure that facilitates the automated risk assessment, and hence achieve the early warning of likely cyber attacks. We introduce the Threat Operating Model (TOM) which captures important information of the identified cyber threats, while can be implemented as an extension of the Structured Threat Information eXpression (STIX). Both the matrix-decomposition based semi-supervised method and the term frequency based unsupervised method are proposed. The experiment results demonstrate a fairly effectiveness (accuracy around 0.8) and a robust performance w.r.t different temporal periods.
A Baseline Interactive Retrieval Engine for Visual Lifelogs at the NTCIR-14 Lifelog-3 Task

This paper describes the work of DCU research team in collaboration with University of Science, Vietnam, and University of Bergen, Norway at the Lifelog task of NTCIR-14. In this paper, a new interactive retrieval engine is described that supports faceted retrieval and we present the results of an initial experiment with four users. Following this initial experiment, we implement a list of changes for a revised interactive retrieval engine for the LSC2019 comparative evaluation competition. The interactive retrieval system we describe utilises the wide range of lifelog metadata provided by the task organisers to develop an extensive faceted retrieval system.