Reviewerly/exHarmony huggingface dataset:
Improving Student Forum Responsiveness: Detecting Duplicate Questions in Educational Forums

Student forums are important for student engagement and learning in university courses but require high staff resources to moderate and answer questions. In introductory courses, the content can remain almost unchanged each year, so the questions asked in the course forums do not see a lot of variety over different iterations, which provides an opportunity for automation. This paper compiles a dataset of forum threads and meta-information of the participants from the Web Design and Development course at the Australian National University for the purposes of duplicate question detection in educational forums. A state of the art neural network model is trained on the dataset to measure its usefulness. An accuracy of 91.8% is achieved, which is on par with what is achieved on other datasets with similar features. A high performing neural network for this dataset could potentially be used to create a live system that detects and reuses answers for duplicate questions on course forums.
Paragraph-Level Hierarchical Neural Machine Translation

Neural Machine Translation (NMT) has achieved great developments in recent years, but we still have to face two challenges: establishing a high-quality corpus and exploring optimal parameters of models for long text translation. In this paper, we first attempt to set up a paragraph-parallel corpus based on English and Chinese versions of the novels and then design a hierarchical model for it to handle these two challenges. Our encoder and decoder take all the sentences of a paragraph as input to process the words, sentences, paragraphs at different levels, particularly with a two-layer transformer. The bottom transformer of encoder and decoder is used as another level of abstraction, conditioning on its own previous hidden states. Experimental results show that our hierarchical model significantly outperforms seven competitive baselines, including ensembles.
Implementing a Probabilistic Learner Model Into a Course Creation Application

In the context of e-learning systems, we can distinguish between two different types of settings. First, we have adaptable systems, which refer to the property of changing the system settings. The learner can change the behavior of the system. Then, the learner is able to customize the system in a specified way to fit the needs of users. The learner model is the key element to generate the adaptation of the system to each specific user. It is an internal representation of the user's properties through which the system is based in order to adapt to the needs of each user. The authors present in this chapter the implementation of a probabilistic learner model developed based on multi-entities Bayesian networks and artificial intelligence into a course creation application (COPROLINE) compatible with LMS-LD. The results presented in this work are arguments in our favor for the implementation of a learner model to endorse the adaptation into some learning situations that the learners have followed during a year of testing.
A New Fuzzy Belonging-based Multi-view K-means Clustering Algorithm

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. The feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.
Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification

One of the challenge in classification is classification in multimodal data. This paper proposed multi-codebook fuzzy neural network by using incremental learning for multimodal data classification. There are 2 variations of the proposed method, one uses a static threshold, and the other uses a dynamic threshold. Based on the experiment result, the multi-codebook FNGLVQ using dynamic incremental learning has the highest improvement compared to the original FNGLVQ. It achieves 15.65% margin in synthetic dataset, 5.02 % margin in benchmark dataset, and 11.30% on average all dataset.
Application of handheld X-ray fluorescence spectrometer for major element analysis and characterization of geological samples in Southern Thailand

Abstract The handheld X-ray fluorescence spectrometer (hXRF) was applied to major element analysis in the geological samples and the results were compared with the results of the wavelength dispersive X-ray fluorescence spectrometer (WDXRF). In addition, the analytical compositions were confirmed by X-ray diffractometer (XRD) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). In the case of pXRF analysis, eight geological reference materials were used to calibrate the analytical method and four known concentration samples were used to prove the method. In research, ten geological samples from Phuket province, Thailand were collected with different depths corresponding to O horizon (0-0.1 m), A horizon (0.1-0.3 m), B1 horizon (0.3-1.0 m), B2 horizon (1.0-2.0 m), C1 horizon (2.0-3.0 m), C2A horizon (3.0-5.0 m), C2B horizon (5.0-8.0 m), C3 horizon (8.0-12.0 m), D horizon (12.0-20.0 m) and RK horizon (&gt; 20.0 m). The elemental analysis results showed that the pXRF results were matched well with that WDXRF results. The concentration range in ten samples were Si (18-22 wt%), Al (12-15 wt%), Fe (1-4 wt%), K (0.4-3.2 wt%) and Ti (0.2-0.4 wt%). The concentration of some elements such as Ca and Mg on those ten samples are low and they are unable to analysed by hXRF. The XRD patterns revealed that all samples consisted of quartz, kaolinite, halloysite, illite, vermiculite and biotite.
Agile Experiences in a Software Development Extension Course at a Software Engineering Bachelor’s Degree

This paper presents the results found in the application and adaptation of an agile approach to software development in a course called “Interdisciplinary Software Project IV” at the bachelor’s degree in Software Engineering from the Pontifical Catholic University of Minas Gerais over two years with twenty projects already done. In the fourth semester of the course, students have to develop a software with a social focus. They are introduced to real customers from the communities with real problems and need to incorporate useful knowledge of the current semester subjects into their system, with artifacts and documentation. Through the use of the Scrum framework, it was possible to observe several adaptations made to suit the academic context, although the practical reality of the course, and the nature of the clients for which the projects are developed. Throughout the paper are presented the results obtained and which can serve as an example for similar approaches.
Research on collaborative adaptive scheduling mechanism of cloud workflow task and virtual resources

Cloud workflow requires task assignment and virtual resource provisioning to work together in a collaborative manner for adaptive scheduling, thus to balance the interests of both supply and demand in the cloud service under the Service Level Agreements. Focusing on collaborative adaptive scheduling of cloud workflow task and virtual resources problem, we introduce fine-grained cloud computing system model and design a novel collaborative adaptive scheduling mechanism based on reinforcement learning and multi-agent society. Our results demonstrate the efficiency of the proposed scheduling scheme.
Code Clone Tracer (CCT): A Tracking Tool for Analyzing Human and Social Factors in Creating and Reusing Code Clones

This paper introduces a tool called CCT (Code Clone Tracer) which is designed to help practitioners and researchers analyze the impacts of human/social factors on software quality in creating and reusing code clones. CCT tracks a history of created and reused code clones for all the revisions in a distributed version control system such as Git. The tool also has features to analyze the relationship between bug-inducing changes and developers, working together with a bug tracking system such as Bugzilla and JIRA. This paper reports our pilot case study where CCT is applied to three open source projects (RxJava, c:geo, and Jackson databind). The results of the case study indicated that a small number of particular developers were involved in many of clone changes to resolve issues.
Kinematics and Dynamics Analysis of an Autonomous Three-wheeled Bicycle Modeling

Three-wheeled bicycle, also known as pedicab, is well known as an eco-friendly transportation in Indonesia. The pedicab is usually operated by utilizing human power to transport objects. It also has high mobility in urban areas which is useful for areas with a heavy traffic. In the future, pedicab which operated by human power can be replaced by autonomous controller. This study aims to develop a model of autonomous pedicab. The kinematics and dynamics model of the pedicab motion were developed and analyzed. Furthermore, autonomous pedicab model is built in the simulated environment. Main components, i.e. sensor, module processing, and engine were added to the conventional pedicab. Initial design of the autonomous pedicab is simulated in Robot Operating System (ROS).
Towards a gold standard dataset for Open Information Extraction in Italian

The following topics are dealt with: social networking (online); learning (artificial intelligence); text analysis; natural language processing; pattern classification; graph theory; data mining; sentiment analysis; Internet; recurrent neural nets.
Deep learning and voice comparison: phonetically-motivated vs. automatically-learned features

Broadband spectrograms of French vowels /A/, /a/, /E/, /e/, /i/, /@/, and /O/ extracted from radio broadcast corpora were used to recognize 45 speakers with a deep convolutional neural network (CNN). The same network was also trained with 62 phonetic parameters to i) see if the resulting confusions were identical to those made by the CNN trained with spectrograms, and ii) understand which acoustic parameters were used by the network. The two networks had identical discrimination results 68% of the time. In 22% of the data, the network trained with spectrograms achieved successful discrimination while the network trained with phonetic parameters failed, and the reverse was found in 10% of the data. We display the relevant phonetic parameters with raw values and values relative to the speakers' means and show cases favouring bad discrimination results. When the network trained with spectrograms failed to discriminate between some tokens, parameters related to f0 proved significant.
Monitoring Concept for a High-Speed Machine Application with a Magnetically Levitated Rotor System

High-speed machine technology with magnetically levitated rotor systems is gaining attention in the industry. Control electronics with high calculation capacity together with accurate displacement sensors are of key importance in the quest for high-performance machines. In the most critical industrial processes, it is vital to have a remote monitoring system capable of providing measurement data for analysis. This paper proposes an Internet of Things (IoT) based monitoring concept for a high-speed machine with a magnetically levitated rotor system that employs the measured rotor position data for diagnostics. The unbalance in the rotor system is monitored by the sliding discrete Fourier transform (SDFT), and the processed data are stored in the cloud.
Multi-person 3D Pose Estimation from Monocular Image Sequences

This article tackles the problem of multi-person 3D human pose estimation based on monocular image sequence in a three-step framework: (1) we detect 2D human skeletons in each frame across the image sequence; (2) we track each person through the image sequence and identify the sequence of 2D skeletons for each person; (3) we reconstruct the 3D human skeleton for each person from the detected 2D human joints, by using prelearned base poses and considering the temporal smoothness. We evaluate our framework on the Human3.6M dataset and the multi-person image sequence captured by ourselves. The quantitative results on the Human3.6M dataset and the qualitative results on our constructed test data demonstrate the effectiveness of our proposed method.
A novel ε-sensitive correlation indistinguishable scheme for publishing location data

Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.
An Image Deblurring Algorithm Based on Edge Selection

In the image deblurring problem, as the blurred images usually have less gradient total contrast than the clear images, the maximum a posterior estimation method prefers to the non-blur interpretation. Hence, this paper focuses on how edge selection can affect blurring kernel estimation, and an image deblurring algorithm based on edge selection is proposed, which can be used to estimate blurring kernel and latent clear image simultaneously under the framework of maximum a posterior probability estimation. Experimental result shows that the proposed algorithm can estimate the optimal blurring kernel, and the restored latent clear image can get better visual experience.
The anthropology of creativity in the context of S.N. Bulgakov’s sophiology

This paper deals with Bulgakov’s doctrine on the human being and creative work. The reason why it is possible to interpret and understand Bulgakov’s conception of creativity in the light of anthropology is justified in the paper. It is indicated that many researchers of Bulgakov's philosophy did not make an explicit connection between anthropology and creativity and did not raise the question why man is capable of creativity. Anthropology and the concept of creativity are reconstructed using Bulgakov's texts. The role of Sofia in the creative process and her role in human life as a whole are determined. The change of the ontological status of man as a result of the original sin is analyzed. The specificity of Bulgakov's understanding of the creative act and its influence on man is revealed. The impact of creativity on a person is analyzed in the paper. It is proposed to consider artistic creation separately from self-creation, as it is fundamentally different from artistic creativity. It is emphasized that according to Bulgakov, self-creation can lead a person to salvation and even to Holiness. It is argued that self-creation as the implementation of one's own idea-norm is the true meaning of human life. Attention is drawn to the tragedy of creativity, which every person-creator experiences. In conclusion, it is pointed out that in the future the concept of Bulgakov's creativity can be ap-plied to the evaluation of works of art. The article concludes that, according to Bulgakov's philosophy, the main characteristics of a person that make him capable of creativity are his freedom, genius and talent. This way the importance of creative activity, both for an individual and for the whole world, is proved and the eschatological role of creativity is indicated.
Graph-Based Representation of Customer Reviews for Online Stores

The purpose of this paper is to investigate the graph-based representation of the data required for the vector space model (VSM) and PMI (pointwise mutual information)-enriched VSM used for text mining (e.g. text classification). The transformation of a dataset containing free text reviews for online stores in a graph-based form is described and its format that allows to be used by Neo4j graph database management system is proposed. Queries for retrieving the data required for training text mining models are considered; the steps and the actions for their modification when receiving new data are specified. The advantages of the proposed graph-based representation in regard to the maintenance and the extraction of current data needed for retraining data mining models are summarized, in order to prevent loss of performance of results from the execution of the respective data mining task.
BALD-VAE: Generative Active Learning based on the Uncertainties of Both Labeled and Unlabeled Data

The following topics are dealt with: learning (artificial intelligence); mobile robots; robot vision; object detection; path planning; cameras; image classification; emotion recognition; SLAM (robots); feature extraction.
A New Approach to Image Enhancement by Intensity Transformation

Intensity transformation is most known technique of image processing in the spatial domain and is widely applied to enhance images in various real-time applications. However, the known techniques of intensity transformation have a number of disadvantages, which significantly limit their use in automatic mode. A new approach to image intensity transformation based on equalizing the distribution of brightness increments of objects in the image is proposed. The proposed approach provides an effective increase in the image contrast without the emergence of unwanted artifacts and without decreasing the contrast of objects with small sizes and has several advantages compared with the well-known technique of histogram equalization. The results of experimental research confirm the effectiveness of the proposed approach for image enhancement in automatic mode.
Using Financial Valuation Techniques to Minimize Waste in Requirements Scoping

This paper presents our initial experiences with employing option theory and NPV techniques for optimizing waste reduction in requirements scoping. Inspired by financial market theories, we analyze a large requirements scoping decision making history from the mobile handset domain. We outline how we can optimize waste reduction in requirements scoping by modeling the neutral, positive and negative scenarios, giving each of the scenarios appropriate budget and development team commitment.
Understanding and Designing for Privacy in Wearable Fitness Platforms

There has been increasing use of commercial wearable devices for tracking fitness-related activities. These devices sense and collect a variety of health and fitness data, which can be shared by users with other people and organizations. Yet, sharing personal data collected by these devices imposes several privacy concerns, ranging from private information exposure and repurposing, to aggregation and inferences. We do not fully understand people's sharing practices and privacy behaviors in the context of these ubiquitous devices. To address this limitation, my dissertation investigates the sharing of data collected by these devices in order to design solutions that support users' sharing needs and enhance their privacy. Preliminary findings indicate that users do not consider much of the data collected by these devices as sensitive, though they voice concerns about the possibility of abusing their data.
OF NEON LIGHT: MULTIPHONIC AGGREGATES ON THE ELECTRIC GUITAR

Abstract Considerable research has been made into the harmonic properties and playability of woodwind multiphonics, while the utility of string multiphonics has received far less attention. In recent years, however, there has been an increasing amount of interest in the topic, and several publications have been devoted to acoustic guitar multiphonics. Primarily written for non-guitarist composers, these studies range from the scientific to the practical. Variously, they describe the sonic qualities of the multiphonics, discuss methods of performing them, or examine their spectral content and morphology. Until now, published research into guitar multiphonics has been limited to the acoustic guitar and has examined only its three lower strings. In this study, we analyse multiphonics on all six strings of the electric guitar and present a catalogue of harmonic aggregates on strings 3–1. We test these multiphonics on five different guitars and examine their response to three commonly used analogue effect pedals (compression, overdrive and distortion). In order to precisely indicate the spectral components and harmonic nodes, we have used the Extended Helmholtz-Ellis JI Pitch Notation (HEJI).
A New Information Extractor for Profiled DPA and Implementation of High Order Masking Circuit

Profiled DPA is a new method combined with machine learning method in side channel attack which is put forward by Whitnall in CHES 2015. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> The most important part lies in effectiveness of extracting information. This paper introduces a new rule Explained Local Variance (ELV) to extract information in profiled stage for profiled DPA. It attracts information effectively and shields noise to get better accuracy than the original rule. The ELV enables an attacker to use less power traces to get the same result as before. It also leads to 94.6% space reduction and 29.2% time reduction for calculation. For security circuit implementation, a high order masking scheme in modelsim is implemented. A new exchange network is put forward. 96.9% hardware resource is saved due to the usage of this network.