Reviewerly/exHarmony huggingface dataset:
Image restoration and denoising using block-based singular-value derivative

Singular-value decomposition (SVD) can be used to estimate the point spread function (PSF) of a blurred image to denoise the image, but it does not consider the local difference of the image; moreover, the selection of recombination order or rank is related to the estimation of the PSF and the recovery effect. An optical image restoration and denoising method with white Gaussian noise using the block-based singular-value derivative is proposed in this paper. First, PSF is estimated using the average energy spectrum of the singular-value vector of the ideal image, which is based on SVD properties, starting from the discrete degenerate model. Next, the inverse filtering is used to restore the image. Finally, the denoised image is obtained using block-based SVD filtering and combination, in which the singular-value recombination order is determined by the singular-value derivative. The experimental results demonstrate that the proposed method has a higher PSNR (peak signal-to-noise ratio) than other methods, and it can get a more clear image.
Non-Written Enrolment System using Classification Methods

Institutions of higher education nowadays has different technique to organize their enrolment system (e.g. Written or non-written test). Classification methods are used to predict student's GPA. These GPA's prediction is used to determine whether students are accepted or not. Classification process include 2 phases, learning phase and classification phase. In learning phase training data are analyse by classification algorithm. Tested data is use to estimate the accuracy of classification rules. Aim of this study is to compare datamining classification methods i.e. C4.5 algorithm and naive Bayes algorithm implemented on non-written enrolment system. This study shows that naive Bayes has better accuracy than C4.5 algorithm.
Quantitative palynological analysis of the E2a and E2b goniatite biozones (Arnsbergian, Mississippian) in mudstones from the Southern Pennine Basin (U.K.)

We performed a quantitative palynological analysis of Arnsbergian (Namurian, Late Mississippian) mudstone intervals, potentially prospective for unconventional hydrocarbons. While many palynological studies exist on these stratigraphic intervals in the Widmerpool Gulf and the Edale Basin (sub-basins of the Pennine Basin), very few studies perform full statistical analyses. Using the Carsington Dam Reconstruction C3 (Carsington DRC3, Widmerpool Gulf) and the Karenight-1 (Edale Basin) boreholes, we show that the combination of quantitative palynological data and detrended correspondence analysis (DCA) can aid biozonation and additionally, provide paleoecological constraints to the Arnsbergian mudrocks. The studied interval in Carsington DRC3 was assigned to the TK miospore biozone and a hitherto undescribed peak in the fresh water alga Botryococcus was recorded. Using relative abundances of hinterland species, mainly from the genus Florinites, both boreholes could be correlated and a more confident assignment of the TK miospore biozone covering an interval containing goniatite biozone E2b in Karenight-1 was achieved. The techniques used in the current study should be especially valuable for assessing borehole materials where the recovery of macrofossils, like goniatites used as the main biostratigraphic tool in the Namurian, can be very low. Future studies should focus on the same stratigraphic interval from different sub-basins of the Pennine Basin to further assess the applicability of quantitative palynological analysis combined with DCA as a stratigraphic tool for potentially prospective mudstones.
UAV Task Assignment Algorithm Based on Splitting and Reorganization

With the development of e-commerce, one of the essential ways of logistics distribution in the future is the distribution by UAVs. The limitations of the UAVs' resources and the distribution of express delivery need to be considered when delivery multiple merchandises. If the UAV task assignment is only based on the location of the delivery point, more UAVs are needed to accomplish the task. Therefore, it is necessary to divide the merchandises at the same distribution point. The goal of this paper is to use the minimum number of UAVs while minimizing the total distance traveled. This paper considers the two constraints of the maximum payload and cruising range of the UAV and models it according to these two constraints. Then the group-split-reorganize method is proposed to separate the packets that meet the constraints and optimize the intragroup path. Finally, experiments confirmed the feasibility and effectiveness of the method.
A Hybrid Framework Based on PSO and Neutrosophic Set for Document Level Sentiment Analysis

Opinion mining for document level has undergone different testing process in the last few years. A lot of research work has contributed for analysis of such reviews to extract the sentiment associated with the texts. Fuzzy technologies and machine learning are the most explored area for sentiment analysis. In this paper, Particle Swarm Optimization (PSO), an Evolutionary algorithm has been hybridized with Neutrosophic Set concept to generate a ternary classifier. PSO has a simple and robust method for finding out global optima on huge sample points. The sentiment of a large text is classified as a ternary value: Positive, Negative or Neutral. This method is suitable to classify large sized text. This hybridization has not been dealt in the literature. Large sized positive, negative and neutral text have been generated from existing review comments of products and plots of movies. This method will be highly useful for classifying documents like review comments on research papers from different researchers, political analysis on a subject or police verification on any news for sentiment polarity finding and many more.
TAVO: A Tree-like Analytical View for OLAP

Online Analytical Processing provides multidimensional, summarized views of business data. It enables users to drill down or roll up from a level to another level in a dimension, which forms an analytical path/thread. In many scenarios, this analytical path is very useful for users to keep their analysis in context. But most visualization tools display only the result in one chart after drilling down or rolling up. The analytical context or the intermediate process is lost or indirect. This paper presents a novel OLAP visualization approach called TAVO - a Tree-like Analysis View for OLAP. It visualizes the multi-dimensional data by various charts and organizes them in a tree, where each node is a chart and connects other charts when a user navigates along a dimension by levels or to another dimension. By this way, the drill-down/roll-up semantic relationship is clearly expressed. Users can easily explore relative history queries by rolling up in a chart. We implemented TAVO by dynamically generating multidimensional expressions (MDX) on the fly and then visualizing the results by visualization components such as Microsoft Office Web Component. The system prototype demonstrates the effectiveness of TAVO.
English Teacher Information Resource Affordances in the Context of Big Data

The focus of research on educational information resources in the past ten years in China lies in the design, development, application and sharing of resources, the construction of an information-based teaching environment, and the learning methods, learning concepts and learning of learners in an information environment. Behavioral analysis is the main focus, and few studies focus on teachers’ research on the psychological characteristics and application behavior of information resources. Therefore, the focus of this study is on the supply of information resources by foreign language teachers in colleges and universities in the context of big data. From the perspective of teachers’ perception and supply of information resources, this paper explores the key factors and influence mechanisms that affect teachers’ use of foreign language information resources, and provides suggestions for policy formulation and professional development of foreign language teachers.
Protuberance of depth : Detecting interest points from a depth image

Detecting distinctive interest points in a scene or an object allows estimating which details a human finds interesting in advance to understand the scene or the object. This also forms the important basis of a variety of latter tasks related to visual detection and tracking. In this paper, we propose a simple but effective approach to extract the feature from a depth image, namely Protuberance of Depth (PoD). The proposed approach semantically explores the inherent feature representing three-dimensional protuberance by using depth which only contains two-dimensional distance information. Our approach directly allows detecting consistent interest points in a depth image. The experimental results show that our method is effective against the isometric deformation and rotation of a depth region and is applicable for real-time applications.
Stacking-based deep neural network for Facial Expression Recognition

We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deep neural network with feedforward network architecture. Thur, choosing trainable learning modules is the core to effectively build S-DNN in an end-to-end manner. Inspired by the manifold learning archetype, we implement a Patch Discriminative Analysis(PDA) as a basic learning model, followed by hashing and block histogram on the top, which sample image in a low discriminative space, and finding an efficient representation of the training data. As those self-learnable models trained, a low dimensional discriminative feature is implicitly learned, which proves to be useful in facial expression recognition. Experimental results on the facial expression dataset(CK+) show that the proposed model is superior to its counterparts, capable of achieving state-of-the-art performance.
Identification of hyperparameters with high effects on performance of deep neural networks: application to clinicopathological data of ovarian cancer

Recent advances in deep learning have emerged as an effective approach for precision medicine. The applications of deep learning to medicine have been applied mainly to medical image data but not clinicopathological data. One of challenges of deep learning model to clinicopathological data is to optimize hyperparameters to get high predictive power. In this study, we identified hyperparameters of deep learning model that have large effects on power. Specifically, we focused on predicting platinum-based chemotherapy response for ovarian cancer patients. As a performance metric, we used the area under the curve. We optimized six hyperparameters: the number of hidden layers, number of hidden units, optimization algorithm, weight initialization, activation function, and dropout rate. We also identified significant interaction effects between hyperparameters. We successfully found the combination of hyperparameters having large effects on prediction. These optimal combinations are expected to increase the prediction accuracy for the response to chemotherapy for a variety of cancer patients.
Method of estimation of hidden correlation of narrowband noise signals

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.
The role of Increasing the Number of Iterations for Numerical Methods upon Digital Image

Due to its numerical procedure, an expected error occurred in applying the numerical algorithms. A challenging attempt toward using such approximation methods on digital image has been utilized here. Bisection, Newton Raphson, and secant methods are some numerical techniques which have been used here for certain considerations to test their evaluation upon a digital image through investigating the role of increasing the number of iterations each time. Results show that Bisection method is the best numerical technique than other used techniques in its approximated use upon an image with least and stable absolute and relative errors. Increasing the number of iterations didn't affect its numerical procedure while an alternative errors with a bell shape resulted in the case of Newton- Raphson and secant methods. Absolute error seems to be more sensitive estimator than relative one toward detecting the suitable number of iterations that caused a least significant error.
Anomaly Detection in Credit Card Transactions

We live a significant increase in the use of credit cards which leads to a high number of fraudulent transactions. The detection of fraudulent transactions carried out by the credit card is an important application in anomaly detection. The use of the credit card has not only several advantages but also losses and damages that may reach billions of dollars. However, existing approaches and methods are not optimized for detecting anomalies. When facing large volume of data, these methods remain limited resulting in a very high percentage of unsupported anomalies. The purpose of this paper is to compare these different techniques in order to choose the most adequate one to detect real-time anomalies in credit card transactions. We have opted for Isolation Forest which not only achieved a high-level of detection accuracy and AUC Score but also increased the rate of fraudulent transaction detection and minimized the percentage of incorrect fraud classifications. In this paper, we propose an anomaly detection model in order to predict and detect fraudulent transactions over time.
A Sundanese Characters Recognition Based on Backpropagation Neural Network Approach

The Sundanese script is the original script of the archipelago inheritance which should be recognized and preserved. This study is to recognize the Sundanese script pattern with the Backpropagation Neural Network (BPNN) algorithm. This research starts from image acquisition, image preprocessing process which consists of normalization of size, thresholding, image sharpening, binarization and thinning, feature extraction, analysis by the BPNN method. This test obtained an accuracy rate in recognizing Sundanese script patterns of 95.23%. The most optimal variant of network architecture is the variation of learning rate of 0.02 and the number of hidden layers of 90 layers so that an MSE of 4.99×10-10 is obtained with a learning time of 2 minutes 45 seconds. So, the BPNN method can be recommended to recognize Sundanese script patterns.
Convolutional Neural Network using Stacked Frames for Video Classification

We propose a Convolutional Neural Network model with stacked frame images for video classification. In this research, one of the challenges is that each video is more than 3,600 seconds long. We extracted ordered frames by skipping some frames from each video. Under our assumption, it is not practical to train each video with all the extracted frames as input because it can cause building a huge model. Hence, we created a median filter layer for reducing the number of frames before training. In the experiment, we extracted 500 frame images from each video. In the reduction process with a median filter layer, we were able to reduce the number of frames from 500 frame images to 50 median images. In the training process, we trained our model that used binary classification to classify seven classes of TV programs. With 1,092 video clips of TV programs, our model achieved 79.38% average accuracy.
The bang calculus revisited

Call-by-Push-Value (CBPV) is a programming paradigm subsuming both Call-by-Name (CBN) and Call-by-Value (CBV) semantics. The essence of this paradigm is captured by the Bang Calculus, a term language connecting CBPV and Linear Logic. This paper presents a revisited version of the Bang Calculus, called λ!, enjoying some important properties missing in the original formulation. Indeed, the new calculus integrates permutative conversions to unblock value redexes while preserving confluence. A second contribution is related to non-idempotent types. We provide a quantitative type system for our λ!-calculus, giving upper bounds to the length of the reduction to normal form plus its size. We also explore the properties of this type system with respect to CBN/CBV translations. Last but not least, the quantitative system is refined to a tight one, which transforms the previous upper bound into two independent exact measures for the reduction length and the normal form size respectively.
Insight About the Use of AR and VR for Trainees

The chapter gives an overview about the use of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) for trainee in company. In this vein, the case of BOSCH company is summarized. The chapter provides insights about the use of these technologies in Bosch company. Future research directions and conclusions are presented. In the future, AR will have transformational impact on Humans in the Digital World. The augmentation of human mind and human perception with digital tools will lead to have transformational impact on humankind. New applications will be provided for: Maintenance &amp; Production Training, Remote Support (expert), Real-time machine/equipment status, &amp; monitoring with failure analysis using an OPC unified architecture. Also, Product development (preview products before development), Production line simulation (preview production lines work steps before industrialization phase), and creation of collaborative classrooms.
A Study on the Suitability of Applying Active Contour Evolution Models in Segmenting and Delineating Boundaries in Medical Images

Active contour model has been used to segment and delineate boundaries in different types of medical images. It is a popular class of image segmentation and boundary delineation method due to its ability to fit a curve to an object boundary by iteratively expanding or contracting its boundary estimate. In this study, we provide an analysis of the suitability of applying active contour models in segmenting and delineating boundaries in medical images. As a case study, we used morphological Chan-Vese and morphological Geodesic Active Contour models to improve the accuracy of manually developed label images of axial view lumbar spine MRI images. The images contain labels for intervertebral disc, posterolateral element, and thecal sac regions and the context of the experiment is to improvement the segmented regions accuracy so that it can be used to diagnose lumbar spinal stenosis. Our experiment shows that morphological Geodesic Active Contour model performs better than morphological Chan-Vese, however both models still produce worse segmentation results than are needed for lumbar spine stenosis detection algorithm.
Extracting Word Embeddings via Joint Learning of Syntagmatic and Paradigmatic Structure

In this work, we explore the latent distributional semantic between each given word and its context in a way of reconstruction, and propose an Auto-encoder architecture to encourage the model pay attention to the interactions of Syntagmatic and Paradigmatic structure of context (SPC). In particular, SPC represent the syntagmatic words of the context through a vocabulary co-occurrence matrix, and extract a likely embedding for a better joint predicting of all paradigmatic words to fill in the input context. During the error back propagation, our model focus on the salient statistical structure by allowing a sub-network to approximate the nonzero sub-matrix. Finally, we trained our model on a public Wikipedia corpus and evaluated on word analogy, word similarity tasks and documents clustering and classification. The results show that our method yields almost 4% improvement on word analogy and nearly 3% improvement on word similarity tasks compared to state-of-the-art methods. The evidences demonstrate that SPC outperform baseline and the state-of-the-art methods on all tasks.
Variational inference for multiplicative intensity models

We extend variational inference approximation of probability density functions to multiplicative intensity functions. For Bayesian nonparametrics, this provides a computationally efficient alternative to the blocked Gibbs sampler described in Ishwaran and James (2004). Simulation results are presented to demonstrate performance.
Fuzzy Semantic-Based String Similarity Experiments to Detect Plagiarism in Indonesian Documents

Plagiarism is a topic of concern in the world of education. One way to overcome plagiarism is to make comparisons between documents. Due to a large number of documents, extrinsic plagiarism detection frameworks are needed to make comparisons of documents in large numbers. On the other hand, there is intelligent plagiarism in which plagiarists try to hide their actions by one of them is replacing words with semantics. Therefore, this study applies an extrinsic plagiarism detection system with a Fuzzy Semantic-Based String Similarity method which is divided into three stages, namely Preprocessing, Heuristic Retrieval (HR), and Detailed Analysis (DA). In the preprocessing stage, the removal of irrelevant characters, the division of text based on sentences, stemming, tokenization, and the elimination of stopwords were performed. The search for pairs of candidate documents in the HR stage used fingerprints and Jaccard similarity. DA stage applied fuzzy semantic based-similarity. Experiments were carried out by comparing the level of document similarity between Jaccard similarity in the HR stage and fuzzy semantic-based similarity in the DA stage because both were able to produce a level of document similarity. The results show that fuzzy semantic-based similarity is better than Jaccard similarity because it can detect semantic similarities in the form of synonyms.
An Efficient Model for Text Sentiment Analysis

With the rapid development of text sentiment analysis, the demand for automatic classification of electronic documents has grown by leaps. In this paper, we propose a technique for text sentiment classification using term expression attention along with sentiment word embedding vector, which can represent both semantics and sentiment information. Firstly, the sentiment vector is constructed by Long Short-Term Memory(LSTM) and attention mechanism. Then, it is combined with the semantics vector obtained by Word2Vec to form sentiment word vector. Meanwhile, we introduce the concept of expression attention, which is inspired by term frequency- inverse document frequency (TF-IDF). Finally, the sentiment of the text is classified by TextCNN, whose input is sentiment word vector. The model is evaluated by using small, widely used sentiment and subjectivity corpora. The results show that the proposed model out-performs several methods without sentiment vectors or using sentiment dictionaries to generate sentiment vectors.