Reviewerly/exHarmony huggingface dataset:
Project Management With High-Performance Work Teams

One of the factors that positively affect the objectives of a project is the performance level of the human talent that executes it. From the human point of view, a project can be defined as the action of a group of people focused towards achieving an outcome. This research work seeks to establish the general skills that a person who is part of a work high-performance team must have, that allow him or her to positively influence collective performance. A general skill describes the behavior and abilities of an individual, regardless of the degree of complexity that frames a job. This study was carried out with the support of 149 project managers from the Colombian software industry.
Path Planning Based on Differential Kinematics for Passing Through Small Opening by Transformable Multilinked Aerial Robot

In this paper, we propose a path planning method allowing a transformable multilinked aerial robot to pass through small openings. The majority of the related studies have used under-actuated and unfoldable multirotors to enable passing through narrow gaps by performing an aggressive motion to compensate for disadvantage of under-actuation. However, this approach is challenging in an unknown environment since an aerial robot needs to run-up and decelerate to perform agile maneuvers. Furthermore, the unfoldable frame cannot allow the robot to squeeze through small openings. To overcome these limitations, we have developed a transformable aerial robot with a multilinked structure in our latest work, along with a motion planning algorithm using a sampling-based method. In spite of the probabilistic completeness, the sampling-based method cannot guarantee the real-time property since the dimension of the configuration space related to the multilinked model is high. Thus, in this work, we apply differential kinematics to achieve instantaneous generation of the valid path by finite numerical integration. To validate our approach, we present an experiment employing a multilinked aerial robot composed from four links to perform online motion planning for a tight horizontal opening and track the desired squeezing trajectory instantaneously.
Determination of Corn Quality using the Decision Tree of C 4.5 Algorithm

Indojaya Agrinusa company, as one of the animal feed processing companies based on corn, trying to maintain the quality of the marketed products. Along with the rapid development of technology and information, it would be a great opportunity for any business to develop and promote the manufactured goods or products, all of it is inseparable from standard or quality as a benchmark for customers in selecting the goods or products which shall be used for their individual needs. In previous research, it had been tested using data mining techniques with Naive Bayes method in determining the quality standard of corn, where the application of this method resulted in predictions accuracy rate of 82.3% corn quality. While in this research, the testing was conducted by using the method of Decision Tree (C 4.5) and is obtained the accuracy result of prediction increased by 86.2%.
Actively Semi-Supervised Collaborative Filtering

Collaborative filtering (CF) has been widely used in various recommender systems, but often suffers from the problem of data sparsity which dramatically degrades the recommendation performance. In this paper, we propose a co-training style semi-supervised CF approach towards the task of rating prediction, which exploits a few observed ratings in conjunction with copious unobserved ones to reduce sparsity. In each round of co-training iterations, our approach utilizes two different neighborhood-based recommenders, each of which labels the unobserved data for the other recommender; in particular, the most informative unobserved examples are actively selected for labeling, and then the labeling confidence is estimated through validating the influence of the labeling of unobserved examples on the observed ones. Experiments results on the three datasets demonstrate that our approach can effectively exploit unobserved data to improve CF predictions, and achieves better performance than other counterparts.
Human-Computer Interaction: A Literature Analysis from 1998 to 2018 Using Automated Text Mining

The purpose of this study is to investigate the research themes and trends in the field of Human-Computer Interaction (HCI) in the last twenty years between 1998-2018. For this purpose, an automated text mining based content analysis has been implemented using probabilistic topic modeling on 33830 peer-reviewed journal articles published between 1998-2018, and that are indexed by SCOPUS database. As a result of this analysis, 53 topics specifying the themes and trends in the HCI field was uncovered. Besides, these discovered topics categorized within themselves, and a systematic taxonomy mapping the landscape of HCI research and practices in the past 20 years was presented. This taxonomy contains six main categories: "machinery systems", "HCI body of knowledge", "feature identification", "brain-computer interfaces", "interaction", and "medical". This study is expected to contribute to HCI research, practices, and investments in the future.
Binocular Depth Estimation Using Convolutional Neural Network With Siamese Branches

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods.
Application Education For Introduction Hijaiyah With Technology Augmented Reality Magic Card

The development of increasingly sophisticated technology, influences learning patterns and teaching systems. Especially in the introduction of hijaiyah letters, which are intended for early childhood. The current education system is very monotonous and less attractive so student learning interest is less effective. Often the teacher feels difficulties in determining the learning style that can increase students' interest in learning. For the community not a few Muslims who understand about the letter hijaiyah. Hijaiyah letters must be studied, especially all Muslims starting from early childhood. letter hijaiyah the first step is learned to facilitate reading the Qur'an. So with the use of augmented reality technology, educators are able to provide and foster student learning interest, especially in early childhood. In designing this AR using a card called the magic card. The work system of this educational application uses a magic card that already contains an attractive hijaiyah letter design, then the magic card containing the hijaiyah letter is given a marker and will later be captured via an Android-based smartphone and gives the hijaiyah letter object in the form of a virtual 3d object and provides mention of the letter. With AR Magic Card technology, it is able to provide interest in learning and facilitate student understanding in hijaiyah letter learning.
Scene-Based Priors for Bayesian Semantic Image Segmentation

Based on the observation that semantic segmentation errors are partially predictable, this study proposes a compact formula using the confusion statistics of a trained classifier to refine (re-estimate) the initial label hypotheses. The proposed strategy is contingent upon computing the classifier confusion probabilities for a given dataset and estimating a relevant prior on the object classes present in the image to be classified. This study provides a procedure to robustly estimate the confusion probabilities and explore multiple prior definitions. Experiments are shown comparing performances on multiple challenging datasets using different priors to improve a state-of-the-art semantic segmentation classifier. The study demonstrates the potential to significantly improve semantic labeling and motivates future work for reliable label prior estimation from images.
Neural Modeling With Guaranteed Input–Output Probability Distributions

Neural networks (NNs) are effective models for data-driven system identification. However, these data-based models do not give the probability properties of the system. Is it possible to use NNs to learn both the dynamics and input–output probability distributions to improve the modeling? In this article, we propose a special neural model, which combines the restricted Boltzmann machine (RBM) and the feedforward NNs. The stochastic RBM learns the input–output probability distributions. The feedforward NN learns the dynamics of nonlinear systems. For the input–output probability distributions, we give the calculation methods of their conditional and joint probabilities. The approximation abilities of this neural model are analyzed. We use two nonlinear systems to compare our guaranteed probability distribution method with the other black-box identification methods. The results show that this novel model is much better, when there are big noises and the dynamics of the system are complex.
Near-optimal search-and-rescue path planning for a moving target

Discrete search and rescue path planning for a moving target problem is computationally hard. Despite heuristic methods proposed so far, for which efficiency is further plagued by random episodic target kinematic behavior, qualifying real solution goodness and specifying optimality gap still remain elusive. In this paper a new alternate formulation is proposed to solve the search path planning problem for a moving target. Rather than tackling explicitly the original problem model, a remarkable property is exploited to solve a conjugate mixed-integer linear programming problem. The approach lies on a compact open-loop search path planning problem model with anticipated feedback to efficiently minimize probability of detection failure as opposed to traditionally maximize cumulative probability of success. Preserving path solution optimality, solving the conjugate problem proves very valuable in significantly reducing computational complexity. This is mainly achievable by virtue of the resulting problem objective property which concisely defines the function to be minimized in terms of terminal belief contributions only. For the first time optimal or upper bound solution computation are made possible in reasonable time for practical size problems. A network representation is further utilized to simplify modeling and facilitate constraint specification. Computational results show the strength of the approach for one and two searching agent problem instances.
An Open Identity Authentication Scheme Based on Blockchain

With the development of Public Key Infrastructure (PKI), there implements lots of identity management systems in enterprises, hospitals, government departments, etc. These systems based on PKI are typically centralized systems. Each of them has their own certificate authority (CA) as trust anchor and is designed according their own understanding, thus formalizing lots of trust domains isolated from each other and there is no unified business standards with regard to trust delivery of an identity system to another, which caused a lot of inconveniences to users who have cross-domain requirements, for example, repeatedly register same physical identity in different domains, hard to prove the validity of an attestation issued by a domain to another. Present PKI systems choose solutions such as Trust list, Bridge CA or Cross-authentication of CAs to break trust isolation, but practice shows that they all have obvious defects under existing PKI structure. We propose an open identity authentication structure based on blockchain and design 3 protocols including: Physical identity registration protocol, virtual identity binding protocol and Attribution attestation protocol. The tests and security analysis show that the scheme has better practice value compared to traditional ones.
Reducing link quality impact to encrypted packets by compressing the transmitted data

Abstract The encrypted traffics are sensitive to losses, while wireless link in prone to error. Although solutions have been proposed in each TCP/IP stack layer, error or losses is always exist. In order to reduce losses of part(s) of encrypted data, bytes or packets should be as many as possible received by the receiver. This paper examines data compression impact in reducing losses of encrypted data. NS-2 simulator is employed to evaluate the study by using the 802.11 radio link. Simulations show that the 3DES decryption rate increases when compression applied. Packet losses can be reduced about 0.32% and 0.54% for TCP and UDP subsequently. The successful encrypted data rises in average 0.3% and 1.3% for both TCP and UDP.
Fusion of Spatio-temporal Information for Indic Word Recognition Combining Online and Offline Text Data

We present a novel Indic handwritten word recognition scheme by fusion of spatio-temporal information extracted from handwritten images. The main challenge in Indic word recognition lies in its complexity because of modifiers, touching characters, and compound characters. Hidden Markov Models (HMMs) are being used to model such data due to their ability to learn sequential data, however, the recognition performance is not satisfactory. We propose here a Long Short-Term Memory (LSTM)-based architecture for offline Indic word recognition. Offline recognition methods usually involve spatial data, whereas it has been observed that online recognition schemes show better performance than the offline methodologies. Online information usually refers to the temporal information obtained from the strokes of the pen tip while writing, which is missing in offline word images. In this article, an effort has been made to extract the online temporal information from offline images using stroke recovery and later it is combined with spatial information in LSTM architecture. During recognition, the character models are trained using both offline and extracted pseudo-online handwritten data separately. Finally, a novel fusion scheme has been used to combine them together. From the experiment, it is noted that recognition performance of handwritten Indic words improves considerably due to the fusion scheme of spatial and temporal data.
A Solution to Improve Object Detection for Images Captured by UAV-mounted Camera

Compare to traditional object detection, such as facial detection or general object recognition, the Unmanned Aerial Vehicle (UAV) object detection system is designed to detect and recognize targets with small scale from height. This paper proposes a solution combines neural network with multiscale feature maps and data augmentation to improve object detection for UAV using. The proposed network achieves 0.618 m AP and 16 fps on aerial image dataset.
The Boomerang Uniformity of Power Permutations x<sup>2</sup> <sup>k</sup> <sup>−1</sup> over F<sub>2</sub> <sup>n</sup>

The boomerang uniformity is a safety index of S-boxes, which measures the resistance of block ciphers to the boomerang attack. In this paper, we investigate the boomerang uniformity of power permutations of the form x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k-1</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sub> . As a result, the relationship between the differential uniformity and the boomerang uniformity of x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k-1</sup> is given. Especially, for even n, the boomerang uniformity of power permutation x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> is completely determined.
Optical design method for afocal achromatic systems

We describe the method for optical design of afocal achromatic systems that keep their characteristics stable in a wide thermal range without refocusing. Usually, thermal stabilization of the back focal length of an optical system is considered, but these methods are not directly applicable for afocal systems. The method proposed is based on the idea of using a proper combination of optical materials and powers of optical elements to keep the whole system as an afocal one in the desired temperature range. As an additional advantage, the method has shown that afocal systems have enough parameters to achieve achromatic correction and may take into account the F-number of optical elements, which is important for the aberration balance. The research presents both theoretical statements and the example of a laser beam expander for dual wavelengths. The method can also be used to design afocal achromatic compensators that help to achieve thermal stability.
Deep Vectorization Convolutional Neural Networks for Denoising in Mammogram Using Enhanced Image

Mammography is an X-ray image of the breast which has been widely used for the management of breast cancer. However, in many cases, it is not easy to identify a sign of cancer as tumour or malignancy due to clouding various noise patterns caused by the low dose radiation from the X-ray machine. Mammogram denoising is an important process to improve the visual quality of mammogram to help the radiologist’s diagnosis when they screening mammogram. This paper introduces denoising deep vectorization convolutional neural networks using an enhanced image from direct contrast in a wavelet domain for training. Then, Denoised mammogram is obtained from mapping between the original and enhanced image. Mammogram image from the mini-MIAS database of mammograms was used in this experiment. The experimental results demonstrate that the proposed method can effectively suppress various noises in mammogram both qualitative and subjective test by comparison to traditional denoising methods.
Using clickstream data to enhance reverse engineering of Web applications

Due to advances in Web technologies, existing Web applications are rewritten or replaced by new ones. As a result of either ad hoc or agile development, many of them lack proper technical documentation. Nevertheless, the domain knowledge built into these applications is valuable, which is why the reverse engineering, an activity aimed at detecting software components and their interrelationships to provide multiple views of software systems at a higher level of abstraction, of existing Web applications is becoming an important issue. Apart from the static reverse engineering based on examining the system's source code, analyzing the dynamic aspect of Web applications often proves worthwhile. One important source of data the dynamic analysis of a Web application can be based on are HTTP server log files. User sessions, results of clickstream analysis, and session reconstruction in particular, can be used as the basis for the first automatic step of reverse engineering, employed in order to gain a quick insight into Web application's source code. It is shown how clickstream data can be used to reveal not only the intensity of connections between individual Web application's source code artifacts but also the overall structure of a Web application. The extracted structure, based either on code artifacts names or their usage, is presented visually as an ATG, with code artifacts belonging to the same application module grouped together. Because session reconstruction is an inherently probabilistic process and thus in general produces noisy data, clustering the code artifacts becomes a challenging task. It is shown that multidimensional scaling and even a simple graph drawing approaches yield better representation of the application transition graph than hierarchical clustering. The method was tested against the results obtained by an expert (the author of the Web application used as a test case). Additionally, the method can also be used for verifying the structure obtained by manual reverse engineering of the application's source code.
TRANSFORMATION OF THE ROLES OF UKRAINIAN UNIVERSITIES TEACHERS IN STUDENT-CENTRED LEARNING AND TEACHING

The article deals with the problem of the readiness of Ukrainian academic staff to transform the role of teacher in student centred learning and teaching. Our research was aimed at studying the readiness of Ukrainian teachers to transform the role of teacher in student-centered learning and teaching. Despite the fact that student-centred approach is confirmed both in normative educational documents and in the theory and practice of European higher education, the teachers of Ukrainian universities are not well-informed about the transformation of the teacher's role in student-centred learning. And, consequently, the change of the authoritarian role of the teacher to the delicate support of the individual educational search of each student goes hand in hand with some difficulties. The empirical material of the research was collected by the method of questioning of academic staff from Ukrainian universities and students of the Central Institute of Postgraduate Pedagogical Education of the National Academy of Sciences of Ukraine. The results of the study confirmed our hypothesis: Ukrainian university lecturers in the majority of cases are not ready to use flexibly different strategies for interaction with students in order to activate, stimulate and encourage the active educational activities of each student in student-centred learning and teaching.
Performance of TB health center service officers based on compensation effect, supervision and competence using machine learning technique

Abstract Tuberculosis as a major health problem in Indonesia, need to evaluate the procedure to solved and increase the solution. The objectives of the research were to discover the correlation of performance of TB Health center service based on compensation effect, supervision and competence. The population consisted of 78 correspondence were taken as the sample through cluster random sampling technique. The study aims to the identification and evaluates the officer competence, supervision, and compensation of TB problem in the Medan Area, by using Machine learning technique to test the correspondence. The results of the research concluded, there was a significantly positive correlation between compensation effect, supervision and the competence with the performance of TB Health Center officers.
A Streamlined Pipeline to Enable the Semantic Exploration of a Bookstore

Searching in a library or book catalog is a recurrent task for researchers and common users alike. Thanks to semantic enrichment techniques, such as named-entity recognition and linking, texts may be automatically associated with entities in some reference knowledge graph(s). The association of a corpus of texts with a knowledge graph opens up the way to searching/exploring using novel paradigms. We present a pipeline that uses semantic enrichment and knowledge graph visualization techniques to enable the semantic exploration of an existing text corpus. The pipeline is meant to be ready for use and consists of existing free software tools and free software code contributed by us. We are developing and testing the pipeline on the field, by using it to access the catalog of a bookstore specialized in ancient Rome history.