Reviewerly/exHarmony huggingface dataset:
Interval number ranking method considering multiple decision attitudes

Many interval number ranking methods cannot representthe different attitudes of decision makers with different riskappetites. Therefore, interval numbers are expressed in the RectangularCoordinate System (RCS). After mining the interval numbers in the RCS,the Symmetry Axis Compensation Factor, which is known as (lambda),was introduced, and the Equivalent Function of the Goal Interval Number(GIN) was deduced. Thus, the interval number ranking method consideringsymmetry axis compensation was defined along with its applicationprocedures. Additionally, the feasibility and effectiveness of thismethod were verified through examples. This method is intuitive andsimple and can represent multiple attitudes of decision makers withdifferent risk appetites.
Automated Generation of Synthetic in-Car Dataset for Human Body Pose Detection

In this paper, a toolchain for the generation of realistic synthetic images for human body pose detection in an in-car environment is proposed.The toolchain creates a customized synthetic environment, comprising human models, car, and camera.Poses are automatically generated for each human, taking into account a per-joint axis Gaussian distribution, constrained by anthropometric and range of motion measurements.Scene validation is done through collision detection.Rendering is focused on vision data, supporting time-of-flight (ToF) and RGB cameras, generating synthetic images from these sensors.Ground-truth data is then generated, comprising the car occupants' body pose (2D/3D), as well as full body RGB segmentation frames with different body parts' labels.We demonstrate the feasibility of using synthetic data, combined with real data, to train distinct machine learning agorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.
DKU-Tencent Submission to Oriental Language Recognition AP18-OLR Challenge

In this paper, we describe our submitted DKU- Tencent system for the oriental language recognition AP18- OLR Challenge. Our system pipeline consists of three main components, including data augmentation, frame-level feature extraction, and utterance-level modeling. First, we perform speed perturbation to increase the diversity and amount of training data. Second, we extract several kinds of frame-level features, including the hand-crafted acoustic features as well as the deep phonetic features. Third, we aggregate the frame-level features into fixed-dimensional utterance-level representation through i- vector and x-vector modelings. We also propose a deep residual network to obtain the utterance-level language posteriors in an end-to-end manner. Our submitted primary system achieves C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</inf> of 0.0499, 0.0146, and 0.0135 for the corresponding short- utterance, confusing language and open-set tasks on the evaluation set.
Effective Use of the Knowledge Management System in Improving Organizational Performance (Case Study in National Energy Company)

The study aims to know the application of Knowledge Management in national utility company, PT. PLN. Data collection is done through conducting interviews to several stakeholders / employees of PT. PLN that has retired and some still active. The data collection method uses focus group discussion, where it examines a two-way communication between director and employees to discuss the effective way to improve corporate performance. The findings are developed and selected idea are gathered in a special portal for knowledge sharing activities or knowledge of company. It is known as Knowledge Management System (KMS). The article addresses several important factors that leads to implementation and enables to change productivity and increase competitive advantage.
High Dimensional Robust Classification: A Random Matrix Analysis

This article proposes a random matrix analysis of the spectral properties of a new robust kernel matrix model adapted to elliptically distributed large dimensional data mixtures. It is shown that these kernel matrices, based on robust estimators of scatter, when finely tuned, can perform asymptotic non-trivial (unsupervised) classification while sample covariance matrices are ineffective. Unlike in conventional robust statistics wisdom though, the “maximally robust” estimators (such as Tyler's estimator of scatter) also break asymptotic classification feasibility. This entails the existence of an optimal robustness-classification trade-off which we discuss.
Characterizing multipartite entanglement by violation of CHSH inequalities

Entanglement of high-dimensional and multipartite quantum systems offer promising perspectives in quantum information processing. However, the characterization and measure of such kind of entanglement is of great challenge. Here, we consider the overlaps between the maximal quantum mean values and the classical bound of the CHSH inequalities for pairwise-qubit states in two-dimensional subspaces. We show that the concurrence of a pure state in any high-dimensional multipartite system can be equivalently represented by these overlaps. Here, we consider the projections of an arbitrary high-dimensional multipartite state to two-qubit states. We investigate the non-localities of these projected two-qubit sub-states by their violations of CHSH inequalities. From these violations, the overlaps between the maximal quantum mean values and the classical bound of the CHSH inequality, we show that the concurrence of a high-dimensional multipartite pure state can be exactly expressed by these overlaps. We further derive a lower bound of the concurrence for any quantum states, which is tight for pure states. The lower bound not only imposes restriction on the non-locality distributions among the pairwise-qubit states, but also supplies a sufficient condition for distillation of bipartite entanglement. Effective criteria for detecting genuine tripartite entanglement and the lower bound of concurrence for genuine tripartite entanglement are also presented based on such non-localities.
The $${\mathbb {Q}}$$-Korselt set of $$\mathrm {pq}$$

Let N be a positive integer, $${\mathbb {A}}$$ be a nonempty subset of $${\mathbb {Q}}$$ and $$\alpha =\dfrac{\alpha _{1}}{\alpha _{2}}\in {\mathbb {A}}{\setminus } \{0,N\}$$ . $$\alpha $$ is called an N-Korselt base (equivalently N is said an $$\alpha $$ -Korselt number) if $$\alpha _{2}p-\alpha _{1}$$ is a divisor of $$\alpha _{2}N-\alpha _{1}$$ for every prime p dividing N. The set of all Korselt bases of N in $${\mathbb {A}}$$ is called the $${\mathbb {A}}$$ -Korselt set of N and is simply denoted by $${\mathbb {A}}$$ - $$\mathcal {KS}(N)$$ . Let p and q be two distinct prime numbers. In this paper, we study the $${\mathbb {Q}}$$ -Korselt bases of pq, where we give in detail how to provide $${\mathbb {Q}}$$ - $$\mathcal {KS}(pq)$$ . Consequently, we finish the incomplete characterization of the Korselt set of pq over $${\mathbb {Z}}$$ given in [4], by supplying the set $${\mathbb {Z}}$$ - $$\mathcal {KS}(pq)$$ when $$q <2p$$ .
The Characteristics of Professional Pedagogy Orientation

This study aims to investigate the characteristics of professional orientation in pedagogy. Based on the works of I.S. Yakimanskoy, E.V. Bondarevskaya, A.A. Melik-Pashayeva, I.N. Semenova, V.K. Zaretsky and other researchers, the authors clarified the principles of personal orientation of educational activities that contribute to the development of the professional orientation of students of pedagogical specialties at the university. The article also presents the author’s complex of pedagogical conditions ensuring the development of the professional orientation of students of pedagogical specialties in a higher educational institution. The complex includes the following components: identification and formation of value orientations of students of pedagogical specialties; creating in higher education educational environment that promotes self-realization of the individual; accounting of students' subject experience; development of pedagogical abilities of future teachers; development of professional skills of university teachers as the basis for the formation of professional orientation of students of pedagogical specialties. The article also describes the results of experimental work, which confirmed the effectiveness of the developed complex of pedagogical conditions that ensure the development of the professional orientation of students of pedagogical specialties in a higher educational institution on the basis of personal orientation of educational activities.
Deformable alignment of longitudinal postoperative brain GBM scans using deep learning

Longitudinal brain alignment is critical for disease monitoring and adaptive treatment planning in glioblastoma (GBM) patients. However, the current methods are either non-adaptive to pathological brains, or time and laborintensive. Here, we aim to develop a novel deep-learning-based framework for longitudinal postoperative brain GBM scan registration. The proposed pathology adaptive registration framework (PARF) adopts a double UNET architecture: a 2D 7-level UNET, <i>NET<sub>seg</sub></i>, for pathology segmentation, and a 3D 5-level UNET, <i>NET<sub>reg</sub></i>, for unsupervised image registration, connected through a spatial transformer and a volume combiner. <i>NET<sub>seg</sub></i> was first trained separately and then combined with <i>NET<sub>reg</sub></i> for pathology adaptive registration training. In aggregated registration testing of PARF, 36 registrations from 18 intra-subject pairs of post-operative follow-up MR scans were selected, and the results were compared to those from current state-of-the-art methods as well as non-adaptive <i>NET<sub>reg</sub></i> alone. PARF is significantly faster and more accurate than comparison methods, in terms of sum-of-squared differences, segmentation alignment dice coefficients, and landmark mislignment errors. PARF may pave the path for various clinical and research applications that depend on the accurate registration of GBM longitudinal images.
Multi-Model Deep Learning for Cloud Resources Prediction to Support Proactive Workflow Adaptation

Scientific workflows are complex, resource intensive, dynamic in nature and require elastic cloud resources. To support these requirements, cloud resources' prediction schemes forecast resource scarcity and therefore support proactive workflow adaptation. In this paper, we propose a proactive workflow adaptation approach supported by a Deep Learning based prediction of cloud resources' usage. The model uses an algorithm to evaluate and privilege the most appropriate prediction model for resource utilization violations for each task of the workflow. Then, it recommends the proper adaptation actions to maintain the Quality of Service (QoS) for the entire workflow. Runtime monitoring of cloud resources data is continuously fed into Machine Learning models including GRU, LSTM, and Bi-directional LSTM for predicting the future task resource utilization values. The algorithm evaluates the resources' prediction using a number of metrics, such as RMSE, MAE, and MAPE. The prediction model achieving the highest accuracy is selected to determine the needed cloud resources. We conducted a series of experiments to evaluate our approach and the results demonstrate that the proposed Multi-Model predicts properly the cloud resource usage as well as suggesting their adaptation actions to guarantee the required workflow QoS.
Thai Keyword Extraction using TextRank Algorithm

Information extraction (IE) refers to automate the extraction of structured information such as entities, relationships between entities, and attributes describing entities from unstructured sources. Keyword extraction is the part of information extraction to discover implicit and potentially important keywords in underlying unstructured natural-language texts. Due to the inherent characteristic of Thai written language which does not explicitly use any word delimiting characters, identifying individual words. In this paper, an alternative method for word-formation for noun phrase recognition is proposed. The word-formation is improving keyword extraction using the compound noun pattern. We use the word-formation to applying the TextRank algorithm to group the noun phrase, there are selected as candidates to calculate in the algorithm. The experiments are 2,727 documents in the banking domain from social online such as Facebook, Twitter, and online news. The experimental results yield 30.73% of accuracy with significant improvement by word-formation.
Analysis on the Evaluation Model of the Teaching Effect of Innovation and Entrepreneurship Course Based on Big Data

In order to improve that effect of the teaching of the innovation and start-up course, through the analysis of the effect of the teaching of the innovative start-up course, the quantitative evaluation of the effect on the teaching of the innovative start-up course is realized, and a method for evaluating the effect of the innovation and start-up course teaching based on the big data analysis is put forward. To analyze the problems and countermeasures of the teaching of the innovation and start-up course. a large-data statistical average analysis model of the effect of building an innovative entrepreneurship course teaching is constructed, a sample regression analysis method is combined to carry out the high-effect data analysis on the teaching of the innovative start-up course, and the decision-making objective function of the effect evaluation on the teaching of the innovative start-up course is constructed, The quantitative regression analysis of the effect of the innovative start-up course teaching is carried out by adopting the method of convergence rule evaluation, and the large-data feature extraction and the relevance description are carried out in combination with the descriptive statistical analysis result. The statistical feature of the effect of the innovative start-up course teaching is applied to the pattern recognition and feature selection by means of the average mutual information clustering method, and the effect evaluation of the innovation and start-up course teaching based on the big data analysis is realized. The simulation results show that the confidence level of the effect evaluation is high, and the evaluation result is accurate and reliable.
Adopting Trust in Learning Analytics Infrastructure : A Structured Literature Review

One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA).
Eyeball Model Construction with Head Movement Compensation for Gaze Tracking Systems

The gaze tracker has become an important human machine interface, while it remains challenging for the users to move their head randomly. That is, the system performance appears significantly inferior due to the motion of a human head. However, the head movement compensation cannot be simply formulated as an eye detection problem. The minor error of the eye corner detection algorithm leads to an unacceptable result for limbus circle matching since the incorrect eyeball model is adopted. In this paper, we further explore the 3-D eyeball model anchored by the inner eye corner point. The relative location between the eyeball center and the inner eye corner is analyzed. This feature is used to guide the eyeball model construction as well as the limbus circle matching. The experimental results show the proposed approach allows a range of the estimation error for eye detection and the final limbus circle matching performance has been remarkably improved.
A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation

The existing approaches achieved remarkable performance in many computer vision applications like moving object segmentation (MOS), classification, etc. However, in presence of infrequent motion of foreground objects, bad weather and dynamic background, the accurate foreground-background segmentation is a tedious task. In addition, the computational complexity is a major concern, as the data to be processed is large in case of video analysis. Considering the above mentioned problems, a novel compact motion saliency based cascaded encoder-decoder network is proposed for MOS. To estimate the motion saliency of current frame, background image is estimated using few neighbourhood frames and subtracted from the current frame. Further, to estimate prior foreground probability maps compact encoder-decoder network is proposed. The estimated foreground probability maps are undergoes the problem of spatial coherence where visibility of foreground objects is not clear. To enhance the spatial coherence of obtained foreground probability map, cascaded encoder-decoder network is incorporated. The intensive experimentation is carried out to investigate the efficiency of proposed network with different challenging videos from CDnet-2014 and PTIS database. The segmentation accuracy is verified and compared with existing method in terms of average F-measure. In addition, the compactness of proposed method is analysed in terms of computational complexity and compared with the existing methods. The performance of proposed method is significantly improved as compared to existing methods in terms of accuracy and computational complexity for MOS task.
Professional and Pedagogical Culture and Teachers’ Professional Competence as Components of their Professionalism

On the basis of the analysis of scientific literature the concept of teacher’s professional competence, professional and pedagogical culture as important and inalienable components of his/her professionalism is considered in the article. The components of teacher’s pedagogical culture, its features, levels of formation and classification of teacher’s pedagogical values are analyzed.
FBA-AMNET: Foreground-Background Aware Atrous Multiscale Networks for Stereo Disparity Estimation

In this paper, we propose novel networks for stereo disparity estimation. First, deep features are extracted using efficient depthwise-separable convolutions. Next, the stereo matching costs are calculated from the deep features with a novel extended cost volume. Then, rich multiscale contextual information is aggregated with the proposed atrous multiscale network (AMNet). The proposed foreground-background aware network (FBA-AMNET) is trained with an iterative multi-task learning strategy to discriminate between foreground and background objects at multiple scales. The proposed networks advance the state of the art on challenging disparity estimation benchmarks, such as the KITTI 2012, KITTI 2015, Sceneflow, and Middlebury stereo benchmarks.
RECOGNITION OF FACIAL EXPRESSION BY UTILIZING FEED FORWARD ARTIFICIAL NEURAL NETWORKS

One of the vital requirements of person to person communication is facial expression which are an output of inner emotions. To understand human behaviour, it is important to understand the facial expressions. There are wide varieties of emotions expressed by face. This paper intends to review on use of Feed forward Artificial Neural network in recognition of various facial expressions like happiness, sad, anger and surprise.
Relative Entropy Pre-Fitting Model for Noisy and Intensity Inhomogeneous Image Segmentation

This paper reports an improved Active Contour Model (ACM) for image segmentation. Despite the significant development of the ACMs, their performances for the noisy and intensity-inhomogeneous images are still deficient. To tackle the intensity-inhomogeneity and noises in segmentation, new construction of local pre-fitting for evolving active contours is proposed. Two locally pre-fitted images are constructed from the local intensity estimation, and the relative entropy measures are used to define the local energy functional that provides statistical information of these images with the original image. Thereby, the desired contour evolution of the proposed model is expedited, and its noise-immunity is increased. The proposed model has demonstrated more initialization robustness, faster contour evolution and higher segmentation accuracy over the prominent ACMs for all the test images both with and without noise and intensity-inhomogeneity.
Enhanced HDR Image Reproduction Using Gamma-Adaptation-Based Tone Compression and Detail-Preserved Blending

We propose image synthesizing using luminance adapted range compression and detail-preserved blending. Range compression is performed using the correlated visual gamma then image blending is performed by local adaptive mixing and selecting method. Simulations prove that the proposed method reproduces natural images without any increase in noise or color desaturation.
MACHINE LEARNING APPROACHES FOR HEALTHCARE DATA ANALYSIS

Breast cancer is the most common cancer in women worldwide and it remains the most common cause of cancer-related death in woman globally. Machine Learning techniques have been proven to be of great help in prognosis and diagnosis of various health related issues. This work constitutes a comparison of five machine learning (ML) algorithms: Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Decision Tree (DT), Random Forest (RF) on the Breast Cancer Wisconsin Diagnostic (BCWD) dataset. Features were extracted from the digitized images of FNA tests on a breast mass. Results show that Random Forests performs better among all the models across different classification metrics such as accuracy, precision, recall, and f1-score.
The Effectiveness of Blended Learning in Improving Students

The advancement of technology and the age of the industrial revolution 4.0 today have changed the way people learn and acquire information. The availability of internet access and the large use of computers and smartphones in a campus environment can be directed to carry out the learning reform and to obtain the course learning outcomes. Therefore, blended learning as one method of choice was adopted to be implemented to achieve the learning outcomes. The study aimed to investigate the effectiveness of blended learning to improve students' English speaking ability. The participants of the study were 48 fourth-semester students, and the study was conducted for one semester in the English Business Conversation class. The research methods used were quantitative and qualitative. The use of quantitative in this research was to measure students