Reviewerly/exHarmony huggingface dataset:
Predicting Online News Authorship by an Authorship Embeddings Space Method

In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification method named Authorship Embeddings Space model (AES) to predict the online news authorship between online news and authors. First, we propose an authorship space to represent the deep semantic relationship of news content. Second, we use an embedding learning method to perform the relationship between authors and news. Finally, we formulated an authorship prediction algorithm to identify the news authors based on the authorship embeddings. Experimental results on the online news dataset reveal that the AES model outperforms the baseline models.
The Paintbrush Coverage Problem

Autonomous vehicles become more and more popular in our daily life. Mobile computing schemes to be installed on these vehicles have drawn a lot of recent research interest. In this paper, we address the important path-planning problem for autonomous vehicles. We introduce and formulate the novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">paintbrush coverage problem</i> . We present a theoretical study on the minimum trajectory length of a paintbrush to cover an arbitrary convex region, which is derived as a function of the area of the region and the size of the cover. Three commonly-used patrolling/scouting methods, namely boustrophedon, spiral, and sector, are manifested in details as the potential solutions to the paintbrush coverage problem. The theoretical minimum trajectory lengths any algorithm can achieve are also demonstrated as the benchmarks for different shapes of regions.
Theory and Applications of Graph Neural Networks in Knowledge Acquisition

Knowledge acquisition is a process in which an artificial intelligence system acquires relevant knowledge from datasets. Knowledge acquisition extracts and forms knowledge from data of different structures. One type of data used to acquire knowledge is structured data, such as physics models, chemical structures, social network information, and traffic network information. Many structured data can be represented as graph structures with dependencies and attributes. In recent years, deep learning methods are applied to knowledge acquisition tasks. As a deep connection model, graph neural network (GNN) is suitable for processing data represented as graph structure since it can use nodes and edges to realize information interaction and obtain graph dependencies. In this review, we discuss GNN model and its variants. Experiments show that the variants of GNNs outperform traditional methods in many knowledge acquisition tasks.
Some Properties of Free Cyclic Codes over Finite Chain Rings

Free codes are investigated. It is illustrated that every free cyclic has some important proprieties and the minimal distance of every code depends upon its associated direct component.
Introduction to Re-Programmable Color-Changing Materials

Re-programmable materials, such as those which can change their color in response to external stimuli, hold the promise for a future in which objects will re-configure according to a user's needs. In this course, we will provide participants with an in-depth understanding of color-changing materials, brainstorm novel applications in HCI, discuss technical solutions to realize participants' ideas, and conduct a hands-on session with one color-changing system from our prior works. Our team is uniquely positioned to host this workshop since we combine expertise in materials, optics, computational geometry, and personal fabrication. At the end of this course, we will summarize the results as a research agenda for future work on re-programmable color-changing materials.
Automated Analysis and Construction of Feature Models in a Relational Database Using Web Forms

The Feature-Oriented Domain Analysis (FODA) approach introduced the feature model abstraction to represent software product lines. However, these abstractions are often difficult for mainstream developers to specify and maintain because most tools rely on specialized theories, notations, or technologies. To address this issue, we propose a design that uses mainstream Web technologies to enable users to construct syntactically and semantically correct feature models and mainstream relational database technologies to encode the models as directed acyclic graphs. The Web interface and relational database designs can form parts of a comprehensive, interactive environment that enables mainstream developers to specify, store, and update feature models and use them to configure members of product families.
GCFA: Graph Neural Networks on Collaborative Filtering Recommendation via Attention Mechanism

Ranging from Matrix Factorization to deep learning based methods, recommender systems usually obtain user's (or item's) embedding via mapping from existed features. However, the collaborative signal is not encoded in the embedding process. Graph Neural Networks (GNNs) have been proved to be powerful in learning on graph data. But building recommender systems on GNNs faces challenges: the user-item graph encodes both interactions and opinions; user item interactions have heterogeneous strengths. To address these challenges, we proposed a novel framework on collaborative filtering recommendation via attention mechanism (GCFA), which models heterogeneous user-item interaction strengths and exploits the user-item graph structure by propagating embeddings on it. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GCFA.
Tomographic three-dimensional position measurement of particles using a light field camera

The tracing particle plays a key role in measuring velocity of flow field. The accuracy of tracing particle's spatial localization determines the accuracy of flow field velocity measurement. The traditional multi-camera technology for photographing tracing particles is costly and time consuming. A light field camera is used to collect particle images, and improves tomographic inversion algorithm to obtain the three-dimensional spatial position of particles faster and more accurately. Based on imaging principle of the light field camera system, a forward tracking model of light emitted by particles is established to realize the imaging simulation of particles. On the basis of this model, a tomographic inversion model is constructed to reversely trace the ray and realize the mapping between non-zero pixels and spatial voxels. A descending dimension method is proposed to calculate the weighting matrix. Multiplicative algebraic reconstruction technology is improved by combined with the similar triangle principle. The Gaussian Blob model is used to determine center position of the voxel with largest intensity value to obtain the three-dimensional position of particles. Experiments indicate that the improved MART algorithm which is used to perform three-dimensional inversion of particles can significantly increase calculation efficiency under the same accuracy level. It is more suitable for applications requiring high calculation speed. The x-axis coordinate error is ±0.06mm. The y-axis coordinate error is ±0.07mm. The z-axis coordinate error is ±0.58mm.
Application of micro-lecture combined with Simbaby teaching model in neonatal practical teaching

ObjectiveTo investigate the effect of Micro-lecture combined with Simbaby teaching model in the practical teaching of neonatology for undergraduates.MethodsA total of 160 five-year medical undergraduates from grade 2013 to grade 2015 were divided into group A (the control group) and group B (the experimental group), with 40 male students and 40 female students in each group. Students in group B received Micro-lecture combined with Simbaby teaching model, while students in group A received traditional teaching method. In group A, teachers adopted traditional teaching method with the use of common teaching models, then students practiced on these models following the demonstrations from teachers. In group B, students previewed the contents before class in micro-lecture, then teachers made Simbaby to be used as case models and explained to the students as how to use the model in actual class. Students made diagnosis and gave treatment to the Simbaby after physical examination. At last, teaching effects were evaluated by questionnaire survey and theoretical test. The SPSS 24.0 was used to conduct t-test and chi square test.ResultsThe average scores of theoretical test (88.40±9.40) and skill test in group B (79.53±8.84) were significantly higher than those in group A (84.86±9.50) and (72.35±9.70), with statistically significant differences (P 0.05).ConclusionMicro-lecture combined with Simbaby teaching model can help students comprehend the basic theories of neonatal disease and improve their clinical operation skills, self-learning ability and clinical thinking ability, which is worth to be promoted in neonatal clinical teaching.Key words: Micro-lecture; Simbaby; Newborn; Clinical teaching
Temporal Recursive Propagation Network for Action Recognition

Efficient spatio-temporal information modeling is the key to action recognition. Present state-of-the-art suffers from the trade-off of spatiotemporal information modeling capability and model complexity. In this paper, we propose a novel temporal recursive propagation network (TRP) which can efficiently encode and fusion spatiotemporal information. TRP Module can be inserted in existing 2D CNN architectures such as ResNet and MobileNet. Abundant experiments show that TRP enjoys the performance over 3D CNN at lower computational cost than 2D CNN. We evaluate the proposed TRP on the large action recognition benchmark dataset UCF-101. TRP outperforms the state-of-the-art methods on UCF-101 from scratch.
HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement

This study explores the problem of co-location judgement, i.e., to decide whether two Twitter users are co-located at some point-of-interest (POI). We extract novel features, named HisRect, from users' historical visits and recent tweets: The former has impact on where a user visits in general, whereas the latter gives more hints about where a user is currently. To alleviate the issue of data scarcity, a semi-supervised learning (SSL) framework is designed to extract HisRect features. Moreover, we use an embedding neural network layer to decide co-location based on the difference between two users' His-Rect features. Extensive experiments on real Twitter data suggest that our HisRect features and SSL framework are highly effective at deciding co-locations.
Tajweed Segmentation Using Pattern Recognition, Extraction and SURF descriptor Algorithms

Abstract This paper proposes a method of detection and recognition algorithms and recitation of the Qur’an. The methods and algorithms are packaged into a software system application to allow a person to learn recitation and how pronunciation is good and right and real-time. Any recitation of the Qur’an written in a different color of the letters and punctuation instead of recitation and tajweed each have different shapes. Variable color is used as a reference for the segmentation process forms of recitation and SURF algorithm used for feature extraction process forms. Feature any form of recitation is stored in the data base (knowledge base) accompanied by explanatory text recitation of data and audio pronunciation files. In the process of recognition when the user enters a query tajweed image, Euclidian distance is used to measure the similarity between the query and the shape feature tajweed recitation shape features that exist in the database.
A method of hidden faults opposition for FPGA-based components of safety-related systems

The paper is devoted to the problem of hidden faults, which is inherent in safety-related systems aimed at ensuring the functional safety of high-risk facilities to counter accidents.The problem of hidden faults is considered from the perspective of a resource-based approach as a problem of growth from a lower level of replication to the next level of diversification in the development of models, methods and means.Computer systems in critical applications have risen to the level of diversification in the division of the operating mode into normal and emergency, in the input data and structurally functional checkability, which for digital components have become different in these modes.Digital components continue to be traditionally stamped based on matrix structures that reflect the level of replication.The lag of the components from the development of the system creates a problem of hidden faults which can be accumulated during the normal mode and reduce the fault tolerance of the components and the functional safety of the system in emergency mode.We propose a method of counteracting hidden faults by raising components to the level of diversification in the promising field of FPGA designing.The proposed method uses the natural version redundancy inherent in the program code of the FPGA projects with LUT-oriented architecture.The method generates and selects versions of the program code, reducing many hidden faults of short circuits between neighboring inputs of LUT units.Possible hidden faults are eliminated by increasing the checkability of the FPGA project in normal mode and the trustworthiness of the results calculated in emergency mode.
Extending LINE for Network Embedding With Completely Imbalanced Labels

Network embedding is a fundamental problem in network research. Semi-supervised network embedding, which benefits from labeled data, has recently attracted considerable interest. However, existing semi-supervised methods would get biased results in the completely-imbalanced label setting where labeled data cannot cover all classes. This article proposes a novel network embedding method which could benefit from completely-imbalanced labels by approximately guaranteeing both intra-class similarity and inter-class dissimilarity. In addition, the authors prove and adopt the matrix factorization form of LINE (a famous network embedding method) as the network structure preserving model. Extensive experiments demonstrate the superiority and robustness of this method.
Applying Machine Learning to a Conventional Data Processing Task

Though machine learning (ML) can be applied to a wide spectrum of applications, it has been hardly used and evaluated in the context of conventional data processing tasks. Such conventional data processing tasks are characterized by a set of calculations that follow strict rules, such as in accounting or banking applications. This paper quantitatively evaluates how software which is automatically generated by ML methods and tools compares to software programmed by hand. The assessment of poker hands according to Texas Hold'em rules is a representative example for conventional data processing tasks, because of the various exceptions how to assess and compare hands. For some hand values, the rank (two, three, ... king, ace) of the cards is relevant and the suit (club, diamond, heart, spade) irrelevant, and vice versa. This paper shows how an accuracy of 100% can be achieved for assessing poker hands according to Texas Hold'em rules, with a small set of labeled training data compared to the number of possible hands. We also evaluate quantitatively the effect of the labeling quality on accuracy.
Coaching in foreign language training of social sciences students as a facilitator of their personal and professional development

The coaching model of training in the field of Arts is an element of the psychological support in a pedagogical activity. Such a support is primarily aimed at creating personal and value guidelines, evolving students’ potential in the field of self-determination and self-development, including, while studying a foreign language, in the content, goals and objectives such components as psychological assistance to students in solving their educational and daily problems, overcoming barriers and demotivators for learning a non-native language. The coaching model allows to teach more effectively and develop students’ ability to study on their own and think critically, to find a way out in difficulties or dangerous life situations. The specifics of the activity oblige a coach to plan classes in a life-affirming manner, to introduce innovations competently and warily, to resist stereotyping, to mitigate negative feelings, self-doubt. At the end of the article, a number of psychological and pedagogical conditions are proposed for putting into practice the coaching model of foreign language training.
Facial Expression Classification and Recognition Based on Improved Hybrid CNN-ELM Model

In order to further improve the classification accuracy and computational speed of facial expression recognition, this paper proposes an improved facial expression classification and the recognition algorithm based on the hybrid CNN-ELM model. This model uses convolutional neural network (CNN) to learn convolution features of facial expressions, and feeds them to the extreme learning machine (ELM) for face expression classification and recognition. Experimental results show that the model has an accuracy of 91.3% in the JAFFE data set and 89.1% in the fer2013 data set respectively. Compared with CNN algorithm and Gabor feature extraction + ELM algorithm, this model has better test accuracy.
Living Paper: Authoring AR Narratives Across Digital and Tangible Media

Storytelling is an important means for children to build literacy while sharing beliefs, values, and cultural norms. Our work investigates how augmented reality (AR) can fit into creative storytelling practices. We introduce Living Paper, a system for authoring AR narratives that span both digital and tangible media. Our augmented storybook prototype integrates animated AR characters from hand drawings with programmable LED lights that shine through the pages. Living Paper combines the flexibility of digital objects with the tangibility of physical cues to enable the creation of immersive and shareable stories.
Special Issue on Context‐Aware Computing: Theory and Applications

Summary The special issue with the title of “Context‐Aware Computing: Theory and Applications” edited by Prof. Phan Cong Vinh is presented to researchers in the field of context‐aware computing. This issue does not introduce a methodology for formal specification and reasoning in context‐aware computing. It presents theoretical bases in context‐aware computing and connects basic reasoning problems to other fundamental issues in context‐aware computing. In short, the special issue tries to get answer to the following question: “How far can it go in context‐aware computing specification and reasoning by representing context‐aware computing with formal notions in process algebras sense and reasoning with formal logics?”.
Digital object identifier (DOI) application for rice germplasm collection at Yogyakarta AIAT

Abstract In 2014, local rice exploration survey identified 76 local rice accessions from Yogyakarta. These local rice collections have been stored in Yogyakarta AIAT cooler facilities. Yogyakarta AIAT has assigned the digital object identifier (DOI) to 55 of its local rice collections. The assignation of DOI will be useful for the local rice collection for their availability for transfer with the Standard Material Transfer Agreement (SMTA) in the Multilateral System (MLS) of Access and Benefit-Sharing (ABS) of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA).
Discretization Using Combination of Heuristics for High Accuracy with Huge Noise Reduction

Over the years, several algorithms for discretization have been devised, but the problem of efficient, accurate discretization still remains an open problem. This paper proposes a novel discretization algorithm, called SPID5, based on combination of two heuristics, one being local and the other global, both being supervised and their combination resulting in a significant synergy. The local heuristic is the well-known information gain of the continuous attributes, and the global heuristic is a novel concept of iterative reduction of noise in the data set. The reduction of noise is achieved by reducing successively pseudo deletion count of the data set to be discretized. The performance of SPID5 algorithm is compared with that of three well-known and time-tested discretization algorithms, using six state-of-the-art classifiers and 35 real-world data sets from the standard UCI data repository. Performance of SPID5 compares favorably with that of all the three existing discretization algorithms it is compared with, not only in terms of classification accuracy but also in terms of noise reduction in the data sets.
Application of Image Recognition Technology in Automatic Teaching of Humanoid Educational Robots

In recent years, more and more robots are being applied to school and home widely. Teaching and playback are a universal method to develop robot functions. For purpose of simplifying the process of teaching humanoid educational robots, a method of human motion and gesture recognition, based on digital-image processing technology, is proposed for automatic teaching of robots. In this method, raw images taken by camera are converted to human body gesture binary images by using the technology of image subtraction and image preprocessing. Then, the body gesture images are mapped to body mathematical models by the skeletonization and recognition algorithms. Finally, the control commands of robots are calculated and drive robots to do the similar actions. Compared with other methods of robot teaching, the teaching method by using digital-image processing and recognition technology has an easier usage and lower cost. By series of experiments, it is seen that this method has a good performance on teaching robots.
Constructing Higher Inductive Types as Groupoid Quotients

In this paper, we show that all finitary 1-truncated higher inductive types (HITs) can be constructed from the groupoid quotient. We start by defining internally a notion of signatures for HITs, and for each signature, we construct a bicategory of algebras in 1-types and in groupoids. We continue by proving initial algebra semantics for our signatures. After that, we show that the groupoid quotient induces a biadjunction between the bicategories of algebras in 1-types and in groupoids. We finish by constructing a biinitial object in the bicategory of algebras in groupoids. From all this, we conclude that all finitary 1-truncated HITs can be constructed from the groupoid quotient. All the results are formalized over the UniMath library of univalent mathematics in Coq.
Recommending Tourism Destination using Collaborative Filtering

Abstract Tourism is among the interesting area of research as more people traveling to various destinations worldwide. With the vast amount of places to visit, there is a problem for tourist to determine which place is most suitable for them in terms of their preferences. However, it can be solved by the means of technology. This paper propose the use of recommender techniques to help tourist in selecting the most preferable destinations. A web-based application was built as an interface with the tourist. The result shows that the system is functioning as it expected. Future directions are discussed.