Reviewerly/exHarmony huggingface dataset:
Visual-Based Pose Tracking in Weak Light

Visual tracking is a popular topic in computer vision, which has broad application prospects in military guidance, aerospace, video surveillance and analysis, medical diagnosis, intelligent robotics, human-computer interaction, and etc. Current tracking algorithms perform poorly in specific scenes, such as a target in condition of weak light. Thus, a pose tracking method based on visual information is put forward, to implement real-time visual tracking in weak light. More precisely, in proposed method, target pose parameters are known at the first frame in a given video sequence, additionally, edge features and point features are taken as visual features to improve the realtime of tracking. The experimental results demonstrate that our approach has better real-time performance of tracking in weak light.
Design and Implementation of a Smart Learning System for Agriculture Education Based on Scientific Inquiry

Rapid development of smart mobile device shows great potential to support learning, which is a frontier issue in the field of education technology. Aiming at specific problems of curriculum, and then developing particular system to support mobile learning has drawn great attentions. As an effective way for learning science, scientific inquiry has traditionally been the concern of researchers. A considerable amount of literature has been published on how to combine mobile learning with scientific inquiry. However, there are few researches on integrating mobile learning and scientific inquiry into agricultural curriculum teaching. In this paper, we intend to examine how to adapt scientific inquiry and smarting learning environment as the teaching scheme for the design of Practice Teaching in Agriculture Education Smart Learning System (PTAESL). Firstly, the paper analyzes scientific inquiry and smarting learning environment theory through literature review. Upon the foundation above, a smart learning system is designed and realized with using technologies like Java, MySQL database, etc. Then, technology details of the Location Based Service (LBS) learning resource recommendation, learning context awareness and structured inquiry cycle is introduced. In preliminary application on practice teaching of Plant Protection, teachers and students gave positive evaluation on the system. Finally, the implications and limitations of the study are also discussed.
Towards a Decision Support Framework for Forensic Analysis of Dynamic Signatures

This paper presents a preliminary easy to explain and effective framework for supporting dynamic signature analysis in forensic settings. The proposed approach is based on measuring similarities among signatures by applying Dynamic Time Warping on easy to derive dynamic measures. The long term goal of our research is to provide forensic handwriting examiners with a decision support tool to perform reproducible and less questionable inference.
The Formation Model Of Future Scientists Professional Training

Relevance of the research: The relevance of the problem under study is determined by the need for diagnostic monitoring of the training of future scientists and the insufficient development of methodological, theoretical, substantive, technological and scientific and methodological aspects of the formation of professional training of future scientists in the process of pedagogical activity. Research methods: The leading method for studying this problem is modeling, which allows considering this problem as a process of purposeful and conscious acquisition of intellectual culture by specialists, taking into account the peculiarities of cultural and educational environment for the implementation of diagnostic monitoring of future scientists professional training. Results of the research: The article presents a multidimensional model of the process of the formation of future scientists professional training, consisting of interconnected content-structural components, methods of forming a culture of intellectual work based on an anthropological approach, ensuring the systematic of managerial actions of subjects of the educational process, aimed at improving future scientists professional training. Practical significance: The model is aimed at a qualitatively new level of adaptation of teachers to new conditions of professional activity in the field of education and is focused on the development of scientific and methodological support for diagnostic monitoring of future scientists professional training.
A Quantitative and Comparative Analysis on Variations of a Method for Segmentation of Similar Images

Segmenting images is one of the most famous image processing and computer vision's challenge. In this work we present an approach to a method of segmentation of similar images based on hierarchical segmentation and texture information. An empirical study was conducted, it was made of three experiments. First experiment evaluated the $P$ param-eter's variation of the LBP texture extractor and the use of external edges of analyzed region; the second one evaluated the variation of eight gradients, being 7 colored gradients; and the last one compares results between Kullback-Leibler measure and Euclidean distance. The obtained results show the best configurations.
Classification of Thyroid Carcinoma using Sobel Edge Detection and Adaptive Neuro Fuzzy Inference System Approach

Bioinformatics applications in the medical world become human needs. Pathologists as analysts required computer-assisted to conduct a diagnosis. This research aims to create an application that could help pathologic to diagnose thyroid cancer. The diagnosis could recognize the types of papillary, follicular, and anaplastic thyroid cancers. The cancer image was extracted using Sobel edge detection to derive the numeric database value. The numeric value represented the number of cells, cell expenses, and features of malignant cells. Furthermore, the ANFIS classified hybrid was forward and backward. The backward stage would correct the forward value for processing image data. Train image and probe image would be validated by pathologist as an accuracy test. The results obtained had a quite high accuracy of about 80%. This research had still need to be continued through a combination of feature extraction, classification and optimization techniques for higher performance. This application required further development as the pathologist needs visually in enforcing the gold standard examination.
Blockchain and Energy Transition: What Are the Challenges for Cities?

The aim of this exploratory note is to draw the first outlines of the experimental use of blockchains for energy at the local level: to what extent can blockchains impact the energy transition of cities? How can cities and their inhabitants, thanks to this technology, become a producer of energy and consume all or part of this same energy? This exploratory note provides an overview of the types of energy this technology could apply to, as well as potential uses in the territories. Multiple experiments seem to be already launched by public and private actors, for example in the sector of municipal buildings, exchanges between private actors or in the energy sector for the (electric) mobility of people. The opportunities and vigilance of blockchain technologies for communities are discussed. In addition, perspectives to establish if and when a massification of this technology at the local level could be possible are presented.
Document Analysis And Classification Based On Passing Window

In this paper we present Document analysis and classification system to segment and classify contents of Arabic document images. This system includes preprocessing, document segmentation, feature extraction and document classification. A document image is enhanced in the preprocessing by removing noise, binarization, and detecting and correcting image skew. In document segmentation, an algorithm is proposed to segment a document image into homogenous regions. In document classification, Neural Network (Multilayer Perceptron- Back propagation) classifier is applied to classify each region to text or non text based on a number of features extracted in feature extraction. These features are collected from different other researchers’ works. Experiments were conducted on 398 document images selected randomly from printed Arabic text database (PATDB) which was selected from various printing forms which are advertisements, book chapters, magazines, newspapers, letters and reports documents. As results, the proposed segmentation algorithm achieved only 0.814% as ratio of the overlapping areas of the merged zones to the total size of zones and 1.938% as the ratio of missed areas to total size of zones. The features, that show the best accuracy individually, are Background Vertical Run Length (RL) Mean, and Standard Deviation of foreground.
A Data Mining Study on League of Legends

The popularity of computer games is increasing with developing technology. Online games, where people interact with each other, are particularly preferred. Among the online games, League of Legends is one of the paced and highly competitive games. In such games, the champion's choices stages and selection restrictions are important. Data analysis should be performed well in order to predict the result of the game. Data mining methods can be used in data analyzes. In this study, the effect of the choices, restrictions and in-game events in future games on winning or losing the game will be predicted. Some of the classification algorithms have applied to the data set. As a result of the studies, it was determined that there is a connection between the player's choices and the winning or losing of the game.
On the Classification Consistency of High-Dimensional Sparse Neural Network

Artificial neural networks (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features and output variables. This results good performance in many application areas such as classification and prediction from magnetic resonance imaging, spatial data and computer vision tasks. Most commonly used ANNs assume availability of large training data compared to dimension of feature vector. However, in modern days applications, such as MRI applications or in computer visions the training sample sizes are often low, and may be even lower than the dimension of feature vector. In this paper, we consider a single layer ANN classification model that is suitable for low training sample. Besides developing the sparse architecture, we also studied theoretical properties of our machine. We showed that under mild conditions, the classification risk converges to the optimal Bayes classifier risk (universal consistency) under sparse group lasso regularization. Moreover, we proposed a variation on the regularization terms. A few examples in popular research fields are also provided to illustrate the theory and methods.
Reconocemos nuestras características y cualidades

This sufficiency work has been prepared for students of the fourth grade of Primary Education of the city of Trujillo in the year 2019, with the theme entitled: We recognize our characteristics and qualities, in which we have taken into account everyday situations in which Students daily establish interpersonal relationships observing that they have different characteristics and qualities.In the preparation of the session, we have worked with the pedagogical and didactic processes of the Social Personnel area to promote expected and meaningful learning.The strategies used were designed to promote the active and meaningful participation of all students.It is intended at all times to awaken the interest and motivation of the student in the area of Social Personnel, since it is a very important and valuable area.
Mobile Tactical Forces: Experiments on Multi-broker Messaging Middleware in a Coalition Setting

Manso, Marco; Guerra, Barbara; Freire, Fernando; Jansen, Norman; Chan, Kevin; Toth, Andrew; Bloebaum, Trude Hafsoe; Johnsen, Frank T..Mobile Tactical Forces: Experiments on Multi-broker Messaging Middleware in a Coalition Setting. International Command and Control Research and Technology Symposium (ICCRTS) proceedings 2019
Performance Analysis of Parallel Differential Evolution Algorithm with Cooperative Emigrant Creation Strategy

In recent years, Differential Evolution (DE) algorithm that is one of the most important delegate of the evolutionary algorithms have been successfully used for solving different optimization problems with its both serial and parallel variants. In this study, the performance of DE algorithm powered with cooperative emigrant creation strategy was investigated over the different types of numerical problems and obtained results were compared with the results of the serial DE and parallel DE algorithm supported with conventional emigrant creation strategy. Experimental studies showed that the convergence speed of the parallel DE algorithm is significantly improved with the cooperative emigrant creation strategy and the proposed parallel DE algorithm produces better results compared to the serial DE algorithm and parallel DE algorithm with the conventional emigrant creation strategy.
Deskripsi Fisik Tokoh Novel Layar Terkembang Karya STA dan Belenggu Karya AP

This research was aimed to describe the physical description of the main characters in the novel of Layar Terkembang by Sutan Takdir Alisjahbana and Belenggu by Armijn Pane and their use as teaching material in high school. This research used descriptive qualitative method. The results of this study indicate the physical description of the main characters in the novel Layar Terkembang and Belenggu in terms of accelerating the presentation of characters, which are presented gradually, the method of presenting characters is presented analytically, types of descriptions use subjective descriptions, and descriptions of symbolic signs. The results of this study are teaching materials that can be used in novel learning in 12th grade high school even semester in order to make the students understand that a character is as one of the intrinsic elements found in the novel Layar Terkembang by Sutan Takdir Alisjahbana and Belenggu by Armijn Pane with Basic Competence 3.9 Analyzing content and language Feature of the novel. Penelitian ini bertujuan untuk mendeskripsikan deskripsi fisik tokoh utama dalam novel Layar Terkembang Karya Sutan Takdir Alisjahbana dan Belenggu Karya Armijn Pane dan pemanfaatannya sebagai materi ajar di SMA. Penelitian ini menggunakan metode deskriptif kualitatif. Hasil penelitian ini menunjukkan deskripsi fisik tokoh utama dalam novel Layar Terkembang dan Belenggu ditinjau dari akselerasi penyajian tokoh yaitu disajikan secara berangsur, metode penyajian tokoh disajikan secara analitik, jenis deskripsi menggunakan deskripsi subjektif, dan deskripsi jenis tanda simbolik. Hasil penelitian ini berupa materi ajar yang dapat digunakan dalam pembelajaran novel di SMA kelas 12 semester genap yang bertujuan agar siswa mampu memahami tokoh sebagai salah satu unsur intrinsik yang terdapat pada novel Layar Terkembang Karya Sutan Takdir Alisjahbana dan Belenggu Karya Armijn Pane dengan Kompetensi Dasar 3.9 Menganalisis isi dan kebahasaan novel. Kata kunci : deskripsi fisik tokoh, novel, dan materi ajar
Low-Rank Laplacian Similarity Learning

Graph Laplacian methods are shown to be effective to reveal low-dimensional manifold which concealed in high-dimensional data features. However, the quality of constructed graph affects the final performance of graph Laplacian similarity. In this paper, we propose a low-rank Laplacian similarity learning method with local reconstruction restriction and selection operator type minimization. A low-rank constraint is added to the graph Laplacian matrix. An iterative algorithm is proposed to optimize the low-rank Laplacian similarity learning method. The proposed method is applied to clustering, classification and semi-supervised classification. Extensive experiments demonstrate the effectiveness of the proposed method.
EMPLOYING GENRE BASED APPROACH WITH AUDIO DIALOGUE TO ENHANCE STUDENTS’ SPEAKING PERFORMANCE

This study was aimed at investigating whether the implementation of Genre-Based Approach through Audio Dialogue can significantly enhance students’ speaking performance of SMPN 2 Muara Tiga and finding out the students’ responses toward the implementation of Genre Based Approach with Audio Dialogue. To achieve the purposes, the present study employed a quasi experimental research design. The population of the study is 100 students. By employing random sampling technique, 40 students were selected to be the sample of the study, comprising 20 students in class VIII-1 as an experimental class, and 20 students in class VIII-3 as a control class. To collect the data, the instruments employed were a series of tests, namely, pre-test and post-test, and also questionnaire. The data obtained from pre-test and post-test were analysed by employing SPSS 23 to find out t-test value of two classes, namely, experimental and control class. In addition, the data obtained from the questionnaire’s result were also analysed by employing SPSS 23 to examine the students’ responses of experimental class toward the use of Genre Based Approach with Audio Dialogue. The result of t-test showed that t test (4.269) is higher than t table  (2.086). Employing independent samples t test, the sig. 2-tailed value (0,007) < (0.05) revealed that H a is accepted and H 0 is rejected. Additionally, the students gave a very positive responses (90%) toward the use of Genre Based Approach with Audio Dialogue. To sum up, the implementation of Genre based Approach with audio dialogue is effective in enhancing students’ speaking performance in terms of accuracy, comprehensibility, and fluency and motivation. It is recommended that future related research conduct a study with integrated skills.
A deep interactive segmentation method with user interaction-based attention module and polar transformation

Interactive segmentation that extracts a specific foreground selected by the user input is widely employed in many user-interactive applications such as image editing and ground-truth labeling. In general, most interactive segmentation methods iteratively refine the previously obtained result using additional user interactions because they often produce unsatisfactory results with a single user input. A recently developed convolutional neural network (CNN)-based interactive segmentation method called deep interactive object selection has achieved high segmentation accuracy with fewer user interactions than earlier non-CNN-based approaches. However, the computational efficiency of deep interactive object selection deteriorates due to the repetitive feature extraction stage for each user interaction. Furthermore, the deep interactive object selection requires graph cut as a post-processing step to refine the boundary segments. To solve this problem, this paper presents a deep CNN-based interactive segmentation method employing an effective and simple user interaction-based attention module that does not require the repetitive feature extraction. In addition, we adopt Cartesian to polar coordinate transformation to further improve the segmentation performance. Experimental results demonstrate that the proposed interactive segmentation method is superior to the conventional ones in terms of segmentation accuracy and computational efficiency.
Research and Application of Configuring Monitoring User Group of FREQUENTIS in Beijing Area Control Center

In recent years, the contradiction between the demand for rapid development of China's civil aviation transport industry and the ability of operation support safely is quite prominent. Ensuring the safe operation of air traffic control system, accelerating air traffic flow and promoting the rapid and healthy development of aviation have become urgent problems for civil aviation development. This paper makes an in-depth analysis and research on the operational risk caused by the lack of security mechanism of TMCS part of FREQUENTIS Voice Communication Systems in Beijing Area Control Center, formulates a feasible solution and carry out the practical application and evaluation in Beijing Area Control Center. Finally, it proves that monitoring user group in TMCS is an effective and feasible solution.
Performance of bottom-up visual attention models when compared in contextless and context awareness scenarios

Visual Attention Models are usually tested using collections of natural images that have intentionally salient objects and obvious context information. On the other hand, in the literature, few algorithms have considered datasets with non-context information to modeling attention. Moreover, Visual Attention Models haven’t been well-measured considering both contextless and context-awareness environments. In this paper, we compare some well-known Bottom Up visual attention models performance using contextless and context aware datasets, using the Pearson Correlation Coefficient as a method to assess the efficiency of each Visual Attention Model in terms of accuracy and eye fixations predictions. The best algorithm outperforms the others by reaching 59,1% and 43,8% of correlation with ground truth information in the contextless and context awareness datasets respectively.
FMRI image segmentation based on hidden Markov random field with directional statistics observation model

In this paper we consider the problem of segmentation of three-dimensional fMRI images within the Bayesian framework with Markov Random Field (MRF) as the prior distribution and von Mises-Fisher distribution as the likelihood. Usually, the learning of such models is a complicated task and the exact inference is impossible in practice. To fit the proposed model, we apply the mean field approximation on the inference step in the EM algorithm. Some numerical examples are presented to illustrate the proposed method.
The Multi-Agent Programming Contest 2018 - A Third Time in the City

We present the thirteenth edition of the annual Multi-Agent Programming Contest, a community-serving competition that attracts participants from all over the world. Participants have to design a program that controls entities in a specifically designed scenario. This challenge is interesting in itself and also well-suited to be used in educational environments. Usage of multi-agent technology is encouraged and allows for comparison of different multi-agent systems and also conventional approaches. This time, five teams competed using strictly agent-based as well as traditional programming approaches.
Stomach cancer detection using CIELAB color space

Stomach cancer is the fourth most common cancer in the world. In addition, this type of cancer ratio is increasing in Turkey. Therefore, in this study, results of preliminary studies of a vision-based approach is presented. In this approach, RGB - CIELAB color space conversion method is applied to the endoscopy images in the data set by using MATLAB software platform. Preliminary results indicate that the applicability of the CIELAB conversion method in the detection of gastric cancer yields satisfactory results and that computer assisted systems can be developed to assist specialists.
Data Librarianship at UNC Greensboro [Poster]

Poster presented at the The Southeast Data Librarian Symposium (SEDLS) at Tulane University on October 11th. SEDLS is intended to provide a low-cost opportunity for librarians and other research data specialists to gather and explore developments in the field of data librarianship, including the management and sharing of research data, this poster reflects the challenges in education and hiring for the position.