Reviewerly/exHarmony huggingface dataset:
Development and usability evaluation of virtual guide using augmented reality for Candi Gunung Gangsir in East Java

Abstract Despite being one of Indonesian cultural and historical heritage, Candi Gunung Gangsir failed to attract many visitors especially from the younger generations. Lack of attraction and supporting facilities in the vicinity is one of the reasons. This research aims to develop an interactive virtual guide enabled by augmented reality that will increase the attractiveness of the object. The methods used in this research are product design and development steps with consideration of human computer interaction, and usability. Primary data of customer needs are collected on and off site through questionnaires and interviews. Customer needs are then used to generate several product concepts to choose 1 final concept from through concept selection methods. Literature review and interviews are conducted to create an accurate story line for the virtual guide. The chosen concept is then developed to become a final application which also tested against the customer needs through a survey method. The result of this research is an offline Android-based augmented reality application to scan the 17 markers to show a3-D virtual guide character on the marker and inform the visitor about history of that particular spot. The usability testing was conducted to 20 respondents and revealed that the application can attract them to visit Candi Gunung Gangsir.
Anomaly Detection and Cause Identification based on Sensor Data

Maintenance tasks allow us to find faults in its early stage, and extend service life of facilities. Now, facilities and systems, which maintenance tasks are done under status monitoring, tend to be increasing, but there are still a lot of facilities that are executed under time plan maintenance. In this study, anomaly detection method is proposed with forecast error variance decomposition based on data provided from each sensor assembled in facilities.
BreakOut: “Sigue la pista”

espanolLos cambios producidos en la sociedad propiciados por el uso de las tecnologias de la informacion y la comunicacion hacen necesario que se revise la metodologia que la escuela tradicional sigue en la ensenanza de las Matematicas. La estimulacion a la que los jovenes estan expuestos desde edades tempranas y el desarrollo tecnologico innato en ellos hacen dificil y poco recomendable que se imparta la materia de Matematicas planteando la labor del profesor como un narrador de contenidos matematicos que no interactua con los alumnos. Es por este hecho que las corrientes metodologicas actuales estan dirigidas al diseno de actividades motivadoras, empleando metodologias activas que permitan que alumnos y profesores interactuen. En este trabajo se describe como se ha puesto en marcha una nueva actividad con los estudiantes de tercero y cuarto de la ESO en la materia de Matematicas orientadas a las ensenanzas academicas. “Sigue la pista” es una actividad que plantea una metodologia activa en la que se utilizan tecnicas de gamificacion y propician la actitud positiva de los alumnos a traves del juego. La actividad resulto exitosa: los alumnos tuvieron una gran participacion, mejoraron sus resultados academicos, desarrollaron el gusto por la materia y adquirieron una vision global de todas las asignaturas que cursaban durante el ano. EnglishThe changes that have occurred in the society in general, based on the use of information and communication technologies determines the necessity of changing the methodology traditionally used to teach mathematics. Teenagers are familiar with technological development and continuously stimulated since early stages of their life’s, therefore, is difficult and not recommended to assume the role of the teacher of Mathematics as a narrator of mathematical content that does not interact with their students. This fact produces that current methodological innovations are focused on the design of motivating activities, by the use of active methodologies that allow students and teachers to interact. This paper presents the results of putting in practice a new activity with the students of third and fourth form of ESO in Mathematics. “Sigue la pista” translated as “Follow the clue” is an activity where gaming techniques are used, which tend to produce positive attitudes of the Students. The activity proposed was very successful: the students participate actively, they improved their academic results, increased their attraction to Mathematics and they acquire a global view of all the subjects they were enrolled in during that year.
An Interactive Watershed-Based Approach for Lifelog Moment Retrieval

The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; social networking (online); video signal processing; convolutional neural nets; object detection; image representation; image classification; image segmentation.
The pedagogical implications of the socially relevant university

From the founding of the first universities in the Middle Ages to the modern university in the period of globalization, universities have been created according to the prevailing social, cultural, economic and political conditions.Contemporary universities are faced with numerous demands from the external environment which expects social problems to be solved through education. In this context goals of education are also subject to constant redefinition and alignment with the needs of new social realities. The aim of the paper is to examine the implications of a socially relevant university from the perspective of two approaches: (a) concerns about the future of the university, the very idea of the university itself, and the reduction of the role of universities as critics of society; (b) the importance and necessity of opening a university to the community in order to contribute to society. Both approaches have been permeated within the analytical approach to the challenges facing the modern university, as well as within the study of the social relevance of the university which is linked to the third mission of the university. We then present the current initiatives that advocate the opening of universities towards the environment, the project Institutional Framework for the Development of the Third Mission of the University, as well as the Strategy for the Development of Education in the Republic of Serbia until 2020. Through elaboration of the relevant literature, we also present the pedagogical implications of a socially relevant university. In conclusion, we emphasize that the pluralism of analytical and critical approaches is the only adequate way to consider the relation of the university and its environment as a whole.
Applying Markov Models to Identify Grammatical Patterns of Function Identifiers

An empirical study to evaluate the effectiveness of using Markov chains in finding and predicting the grammatical patterns of function identifiers found in source code is presented. The study uses a specialized part-of-speech tagger to annotate function identifiers extracted from 20 C++ open-source systems. A dataset of 93K annotated unique function identifiers is created for analysis. The analysis includes using a first-order Markov chain to model part of speech tag sequences of the identifier names, using a probability transition matrix. The evaluation of the model is via a 10-fold cross validation over the entire set of annotated function identifier names. The preliminary results are promising in terms of applicability and accuracy. The model achieved an accuracy median of 91.53% in predicting the most common part of speech tag on a test set. Future work involves utilizing these results in creating a quality assessment and automatic repairing tool for source code function identifiers.
An Architecture for Enabling IoT Edge Devices to Allow Scalable Publishing of Semantic Linked Data

The Internet of Things (IoT) is growing exponentially and is creating enormous sets of data that often reside in proprietary formats and storage containers. Linked Data and the Semantic Web has been around for a long time but adoption by industry has been slow. Linked Data research tends to have an academic bias and real world applicability is often neglected. Based on our experience with ubiworx, an IoT framework used in several real world projects, we propose an architecture using intelligent edge gateways, scalable cloud micro-services and semantic adaption layers to bridge the gap between legacy sensor data acquisition and publishing linked data to ontologies such as Semantic Sensor Network (SSN) and Smart Appliances REFerence ontology (SAREF). In combination, the edge gateway paradigm and the layered cloud micro-services paradigm intersect to provide an accelerated path to publishing linked sensor data while achieving the scalability required by the explosive growth of the IoT and doing so with the best efficiency of resources. Dynamically creating Linked Data serialisations from rapidly changing sensor data sets is a key enabler for adoption of linked data principals in the IoT space.
MFDC‐net: Multifeature‐based deep convolutional neural network for single image haze removal

Summary When it comes to the dehazing method based on the ambient light scattering model, there are ill‐conditioning problems of great concern. Therefore, the inaccurate estimation of the global atmospheric light and transmittance can bring about hue error phenomena, halo artifacts, and image noises. In this paper, we present an effective and efficient approach to solve above problems. Firstly, we adopt single‐threshold segmentation method and quad‐tree algorithms, which can break through the limitation that the dark channel prior theory (DCP) is not suitable for sky region, to quickly and accurately locate and estimate the global atmospheric light. Secondly, we design a new deep convolutional network architecture, which can effectively suppress the halo effect of image edges and restrain image noises, to optimize transmittance. Numerous of outdoor and synthesis haze image test results indicate that the optimized approach could shorten the processing time greatly. Besides, with more accurate global atmospheric light value and transmittance, the image noise and the halo artifact can be effectively suppressed and removed respectively, the hue error phenomenon improved, and the image contrast significantly enhanced in the dehazing results.
The Hidden Elegance of Causal Interaction Models

Causal interaction models such as the noisy-or model, are used in Bayesian networks to simplify probability acquisition for variables with large numbers of modelled causes. These models essentially prescribe how to complete an exponentially large probability table from a linear number of parameters. Yet, typically the full probability tables are required for inference with Bayesian networks in which such interaction models are used, although inference algorithms tailored to specific types of network exist that can directly exploit the decomposition properties of the interaction models. In this paper we revisit these decomposition properties in view of general inference algorithms and demonstrate that they allow an alternative representation of causal interaction models that is quite concise, even with large numbers of causes involved. In addition to forestalling the need of tailored algorithms, our alternative representation brings engineering benefits beyond those widely recognised.
Evaluation of Students' Acceptance of the Leap Motion Hand Gesture Application in Teaching Biochemistry

This paper presents an early stage of the Leap Motion controller regarding user acceptance in the teaching and learning process. The Leap Motion is a new device for a hand-gesture-controlled user interface. For appropriate evaluation, a novel experiment and questionnaire were created utilizing 35 Biochemistry undergraduate students in Enzymology from the Universiti Putra Malaysia. The subjects participated in the user experiment and performed several tasks, such as rotating, translating and zooming in and out on the molecules. The tasks were performed using the Molecules application on an Airspace platform. The research compared the performance of Leap Motion with mouse interaction. As a result, 79.2% of the respondents gave a positive opinion about the Leap Motion because of its ease of use, acceptance, effectiveness and accuracy. These students were excited and looked forward to implementing the Leap Motion in class. Thus, the Leap Motion controller can potentially be used as a teaching tool for a better learning experience of the biomolecule.
Node-Edge Bilateral Attributed Network Embedding

This paper addresses attributed network embedding which maps the structural information and multi-modal attribute data into a latent space. Most existing network embedding algorithms concentrate on either node-oriented modeling or edge-oriented modeling, resulting in unilaterally capturing information from nodes or edges. However, there is no effective method to bilaterally extract node attributes cooperated with edge attributes, which delineates the outline and detail of social network. To this end, we propose a novel Node-Edge Bilateral Attributed Network Embedding method named NEBANE. Regarding each edge as a specific node, we construct a pioneering node-edge-node triangular structure for bilateral information modeling on both nodes and edges. Furthermore, we envisage a pairwise loss which maximizes the likelihood of connected node pairs and of connected node-edge pairs to measure the node-node and node-edge similarity. Empirically, experiments on two real-world datasets, including link prediction and node classification, are conducted in this paper. Our method achieves substantial performance gains compared with state-of-the-art baselines (e.g., 4.21%–13.65% lift by AUC scores for link prediction).
Visual analytics on big data of urban traffic trajectories

Abstract With the rapid development of Smart city and Big data concepts and technologies, how to quickly and accurately organize and reflect traffic data information has become the main topic of current research for urban traffic trajectory big data with increasing geometric quantity. Traffic trajectory visualization is favored for its ability to visually and vividly reflect traffic trajectory information. In this paper, the main research results of visual analysis of urban traffic trajectories in recent years are summarized from three main methods: direct visualization, aggregate visualization and feature visualization. The problems and future prospects of urban traffic trajectory data visual analysis research are summarized.
Requirements Elicitation with a Service Canvas for Packaged Enterprise Systems

We present a technique for eliciting requirements based on the use of a service canvas and the results of its application in the early phase of a customer relationship management integration project. The project was a collaboration between a research group and two industry partners. We describe (1) our service canvas, (2) how we designed a set of workshops to elicit the requirements, (3) the support tools used for running the workshops, and (4) the resulting canvas, listing the customer relationship management requirements, that was the basis for the project proposal. We explain how, as participant observers, we conducted the project and how we collected and analyzed the data. We describe what worked well and the lessons we learned. We outline some practical problems that remain unsolved.
Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems

As a prominent application of artificial intelligence in healthcare, medical dialogue systems have a promising future. In this paper, we built a medical Frequently Asked Questions (FAQ) dialogue system using an XGBoost classifier based on handcrafted features. Moreover, we propose to improve accuracy in domain-specific information retrieval by adding a topic judgment module to general sentence similarity prediction. As the size of the labeled medical corpus in Chinese is very limited, we built a corpus of Chinese medical FAQ from mvyxws https://www.mvyxws.com/ and annotated a small part of it. We applied our topic judgment module to the XGBoost model, a general sentence similarity model, and Baidu short text similarity API. Testing on two datasets, the models can be improved by adding the topic judgment in most cases.
Problems of application of Pareto set for choosing optimal strategies of university development in world university rankings

The position of the university in the rankings determines its attractiveness not only for applicants, but also for business. Therefore, each university is interested in maximum profitability from resources invested in the development. Growth of university in world university rankings can be presented as a multi-objective problem. According to Edgeworth-Pareto principle, the best solution to this problem should be found in Pareto set, which consists of optimal nondominated vectors. This article addresses the problems of application of Pareto set for choosing optimal strategies of university development in world university rankings.
A Hybrid Social Mining Approach for Companies Current Reputation Analysis

This paper presents an approach for company’s reputation analysis using data mining techniques. It obtains knowledge from huge data written about these companies and available publicly on the internet. It is done by extracting data from social media, such as twitter, containing relevant company’s mentions. Then, data is then injected into the first layer where it is classified into a positive and negative classes using machine learning, specifically artificial neural network. It takes tweets after preprocessing as an input, then outputs the sentiment of each tweet. The result will be the general reputation and perception of the company for a given timeframe. To further understand these results, the analyzed data is then transferred to a second layer of analysis where consumers to identify products, services, and announcements that lead to the positive of negative perception. Using Term Frequency (TF), this will result to ranked list of most mentioned words in each of the negative and positive classes. This will be valuable for companies to identify points of weakness and strength, advertisement impressions, and strategic decisions impact.
Sparse Matrix Completion for Effective Recommendation System

In this era of information retrieval, the revenue of the e-commerce system mainly rely upon how intelligently information is being processed and decision are being made. In this paper, the problem of unavailability of complete information is targeted for information processing and decision-making in e-commerce domain. The existing system which works on a similar phenomenon is known as recommendation system which becomes the most evolving subject in the area of electronically operated markets. However, the decision-making capability and suggestive nature of these techniques have improved the overall output of the electronic market, but lacking behind where sufficient information is not available. In literature, most of the existing schemes are designed using collaborative filtering and content-based recommendations with KNN and K-means. But, huge increment in the number of online users and their varied pattern of purchasing goods increase the sparsity in information matrix due to which neighbor selection is getting more problematic. The proposed recommended system embeds the collaborative filtering method to complete the obtained incomplete matrix which lifts the aforementioned problem of high sparsity in data. The proposed scheme is verified and validated over different datasets and achieve significant results over existing schemes.
Analysis of Most Significant Bands and Band Ratios for Discrimination of Hydrothermal Alteration Minerals

Band ratios are one of the most common features for discrimination and identification of minerals using remotely sensed data. The band ratios to be used are determined mainly by spectral analysis and trial experiments. In the present work, we systematically examine the best features and best band ratios using Bhattacharyya distance based optimal band analysis. The experiments indicate that the ASTER nine bands and band ratios within the wavelength range of 2200 nm to 2300 nm are significant for hydrothermal alteration minerals.
Evaluation of Search Performance of Evolutionary Computation by Transfer Entropy

In Evolutionary Computation, the search space made from genotype and the search space made from phenotype is usually quite different. This study tries to reveal the relation among genotype space, phenotype space, and fitness landscape using transfer entropy. The preliminary experiment shows a promising result.
Security Vulnerabilities of OpenStack Cloud and Security Assessment Using Different Software Tools

New security challenges are raised because of cloud computing when contrasted with customary on-start as a result of its multi-occupant virtual condition on each cloud layer, namely Platform as a Service—PaaS, Infrastructure as a Service—IaaS, or Software as a Service—SaaS. Open clouds are utilizing restrictive cloud programming and security is generally kept up by issuing organizations. Security remains a concern for private clouds. Numerous components influence the cloud mis-configuration and integrity that could emerge on the grounds that security is kept up by an outsider. The target of this investigation is to inspect the territory of OpenStack cloud specifically. This will give a more noteworthy comprehension of in what way cloud computing capacities and any kinds of issues of security emerge in that. The investigation comprises three sections; in the primary section, the foundation of cloud computing and OpenStack is described. In the second section, OpenStack architecture is described. In the third section, known vulnerability exploitation and mitigation strategies are presented along with an assessment of various vulnerabilities in OpenStack is conducted utilizing top security scanners namely Metasploit and OpenVAS in an attempt to finding new vulnerabilities.
The Introduction of the PBL That Crosses Departments at the National Institute of Technology, Suzuka College

At the National Institute of Technology, Suzuka College, the curriculums of departments were reconsidered all together in advance reorganization of the Advanced Engineering Courses in 2017. “The Basic Design of the Study” which is Problem Based Learning was introduced for the 2nd grade in all departments. We planned the class for the purpose of experiencing research activities for students, as follows. Students do not depend on a field of own department and choose the study subject in which one is interested. A study target is established, and it’s studied to the target. Research activities will be reported appropriately. This paper is the practice report and proposal that “the Basic Design of the Study” was conducted at Suzuka College.
Reinforcement Learning on Robot with Variational Auto-Encoder

Reinforcement learningChen, Yiwen enables robot to learn plentiful skills through training.Yang, Chenguang The control based on end-to-end reinforcement learning outputFeng, Ying joint angle to the robot with image as input. However, using the deep network for end-to-end reinforcement learning makes it hard to converge and need much training time. And it’s also difficult to expand to other tasks because of custom-designed network. In this paper, we propose a reinforcement learning structure with variational auto-encoder that can be applied to different goals and reduce training time. Firstly the auto-encoder is trained with images that capture random robot actions. Then the reinforcement learning network is trained with the latent space vectors from auto-encoder instead of raw image. After finish training, the robot can reach a state similar to the state in expected image that we input.