Reviewerly/exHarmony huggingface dataset:
Total Bending Method for Piecewise Smoothing Image Denoising

Since the seminar work by Rudin, Osher, and Fatami for total variation (TV) model, piecewise smoothing image is favored in a diversity of related fields. To recover a piecewise smoothing image, we propose a high order anisotropic geometrical model which we name total bending (TB) in a multiplicative strategy. TB model measures the bending degree of the image surface by approximating its second fundamental form. The analysis shows that TB is a rotation version of TV model in moving Frenet–Serret frame: TB norm measures the jumps along the normal direction in an adaptive local coordinate while TV measures the jumps along the vertical direction in a fixed Cartesian coordinate. TB reduces undesired staircase effect by recovering the horizontal and slope surface. The evolution diffusion of TB model inherits TV’s edge preserving ability. The experimental results show the performance of the proposed model quantitatively and visually.
Character input system based on gaze input using wearable camera

This research presents the character input system by using wearable camera based gaze estimation system (GES) for disorders. The GES is the system that can estimate the user's gaze direction. The proposed system uses the inside-out camera to get the user's gaze image. This system uses the gaze image to classify the character and create the word. Proposed system consists of two processes: Gazes estimation process and character recognition process. This research uses the Convolutional Neural Network (CNN) method to archive the good accuracy. By using the proposed method to train the model, the system was confirmed through evaluation experiments. The experimental results show the good accuracy of the proposed system compared with the previous research.
Converting Dependency Structure Into Persian Phrase Structure

Treebank is one of the important and useful resources in natural language processing represented in two different annotated schemas: phrase and dependency structures. There are many works that convert a phrase structure into a dependency structure and vice versa. Most of them are based that exploit the handcrafted head percolation table and argument table in predefined deterministic ways. In this article, we propose a method to convert a dependency structure into a phrase structure by enriching a trainable model of former hybrid strategy approach. By adding a classifier to the algorithm and using postprocessing modification, the quality of conversion is increased. We evaluate our method in two different languages, English and Persian, and then analyze the errors. The results of our experiments show a 46.01% reduction of error rate in English and 76.50% for Persian compared to our baseline. We build a new phrase structure treebank by converting 10,000 sentences of Persian dependency treebank into corresponding phrase structures and correcting them manually.
Pruning the deep neural network by similar function

Recent deep neural networks become deeper and deeper, while the demand for low computational cost model will be higher and higher. The exists pruning algorithm usually focus on pruning the network layer by layer, or using the weight sum as important score. However, these methods do not work very well. In this paper, we propose a unified framework to accelerate and compress cumbersome CNN models. We put it into an optimization problem to find a subset of the model which can produce the most comparable outputs. We concentrate on filter level pruning. Experiment shows that our method has surpassed the exists filter level pruning algorithm. Taking the network as a whole is better than pruning it layer by layer. We also have an experiment on the large scale ImageNet dataset. The result shows that we can accelerate the VGG-16 by 3.18× without accuracy drop.
$$\mathsf {Uhura}$$ : An Authoring Tool for Specifying Answer-Set Programs Using Controlled Natural Language

In this paper, we present the tool $$\mathsf {Uhura}$$ for developing answer-set programs by means of specifying problem descriptions in a controlled natural language which then are translated into answer-set programming (ASP) rules. The tool is aimed for supporting users not familiar with answer-set programming—or logic-based approaches in general—for developing programs. $$\mathsf {Uhura}$$ is based on a new controlled natural language called $$\mathsf {L}^{\text {U}}$$ , which is in turn an adaption of $$\mathsf {PENG}^{\text {ASP}}$$ , a controlled natural language employed in the PENG ASP system, developed by Guy and Schwitter, for solving computational problems by translating $$\mathsf {PENG}^{\text {ASP}}$$ statements into answer-set programs. In contrast to $$\mathsf {PENG}^{\text {ASP}}$$ , $$\mathsf {L}^{\text {U}}$$ allows for a more natural translation into ASP rules and provides also a broader set of pre-defined sentence patterns. $$\mathsf {Uhura}$$ is implemented in $$\mathsf {Java}$$ and employs $$\mathsf {DLV}$$ as backend answer-set solver.
An efficient deep neural network for classification in esophageal cancer histopathological images (CNN+LSTM)

Esophageal cancer is one of the leading causes of mortality worldwide. Early diagnostics are imperative of improving the chances of correct treatment and survival. Pathology examination requires time consuming scanning while often leads to a disagreement among pathologists. The computer-aided diagnosis systems are critical to improve the diagnostic efficiency and reduce the subjectivity and error of human. In this paper, a deep learning–based approach is proposed to classify the H&amp;E stained histopathological images of esophageal cancer. The histopathological images are firstly normalized to correct the color variations during slide preparation. Then one image patch is cut into five pieces and gets five corresponded features via convolutional neural networks (CNNs). Subsequently, the five features of one image patch are fed into the Long Short-Term Memory (LSTM) model for further feature extraction and integrated as one for the last classification. The experiment results have demonstrated the proposed approach is effective to classify the esophageal histopathological images with the accuracy of 84.5% which outperforming 10 percent than GoogleNet.
Design and Implementation of Secure and Transferable eCoupons

Electronic coupons are part and parcel of both traditional and electronic commerce. In this paper, we present the design and implementation of secure generation and redemption of electronic coupons which, unlike most existing coupons, ensures transferability between coupon holders in a secure and distributed manner as an added feature.
Table of Contents Recognition in OCR Documents using Image-based Machine Learning

The importance of automatic analysis of Optical Character Recognition (OCR) documents has been increasingly recognized to assist with efficient data managements and accessibility. However, most OCR documents are unstructured, making the analysis extremely challenging. A document's Table Of Contents (TOC) provides an overall structure of a document, such as chapters and appendixes. Hence, TOC recognition enables more effect analyze OCR documents effectively. Most existing related works are based on textual features, such as keywords and font sizes. However, textual-based TOC recognition in OCR often fail when OCR documents are complex. In this study, we develop a novel image-based machine learning approach for recognition of TOC, where one-dimensional horizontal projections of TOC are proposed for classifying TOC and non-TOC. To the best of our knowledge, this is the first work to recognize TOC by image-based analysis. We evaluated the proposed methods with PDF documents of thesis and dissertations. The experimental results show that our proposed methods outperformed others.
Studying the Schrödinger Equation, Implementation of Qubits and Visualization of Matrices Using Matlab

In this paper, we consider two key concepts that form the basis of quantum physics, in particular, the Schrödinger equation and the Born probability equation. A mental experiment that allows one to show the incompleteness of quantum mechanics in the transition from subatomic systems to macroscopic systems is also analyzed. It is proposed to use the MATLAB package, which makes it possible to solve the time-dependent Schrödinger equation for the infinite rectangular potential well of a harmonic oscillator, as well as for potentials that can be solved only numerically. The relevance of these studies is to find methods for the eigenvalues definition of the Schrödinger equation. The topicality of this research is explained either by the large number of both experimental and theoretical works devoted to these issues. The aim of the work is to determine the methods for finding the eigenvalues of the Schrödinger equation using the MATLAB package, which will allow to visualize the density matrix for a different number of qubits of the quantum scheme.
ISLAMIC CENTER DI LASEM

Lasem is one of the districts with a thick and unique history of Islam. The cultural mix between Islamic and Chinese culture is very close in the Lasem community. The history of Islam that is ingrained is one of the factors supporting the many Islamic boarding schools around Lasem sub-district. The existence of Islamic boarding schools is like something that must be maintained so that Islamic history in Lasem is not lost. An Islamic center is considered to be a complement to activities and supporting activities in Islamic boarding schools so that it can be even better. The planning and design of Islamic centers in Lasem focuses on supporting Islamic boarding schools in the vicinity so that they can carry out academic and non-academic activities better. The Islamic center is equipped with facilities that are very supportive for both students and future visitors. Keyword : Islam, Lasem, Religion
MANAGEMENT OF DEVELOPMENT OF PROFESSIONAL COMPETENCE OF THE TEACHER

The events taking place today in the world make them lively and unconditionally respond to them: what was yesterday was too much, today it is not enough. In the information society, it becomes increasingly difficult to find the required unique information. Instead, the number of technological discoveries is steadily increasing. Given the fact that the Ukrainian educational system is currently looking for its own modern way, the changes take place quickly and sharply. There is a problem in creating such technologies for training pedagogical workers that would be able to fulfill educational tasks. The article deals with innovative technologies for raising the professional competence of pedagogical workers of institutions of general secondary education. An important component is the improvement of the professional skills of the teacher. In particular, learn to navigate in modern trends and apply them in practice: to equip teachers with the knowledge and skills of organizing innovation, implementing information and communication technologies, creating author programs, writing textbooks, improving the level of methodological and psychological training. Based on the results of the research carried out at secondary school number 6in the Brovary we can see the level of pedagogical competencies, as well as the advantages and disadvantages of attracting modern technologies into the educational process. The research distinguishes and analyzes modern approaches to managing the teacher's professional competencies: project expertise, methodical service and project methodology
S$^\mathbf{4}$L: Self-Supervised Semi-Supervised Learning

This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that our approach and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels.
Sequentially Cutting Based the Cluster Number Determination for Spatial Feature Classification

Clustering the spatial feature of the object region plays an important role in object modeling, detecting and tracking etc. However, many clustering methods adopt the pre-set cluster number, which cannot adapt to full automatic system. In this paper, a novel frame work for adaptively determining the number of clusters is proposed based on hierarchically kernel cutting. We firstly extract the value contour of object region and rank the peaks of the contour in descending order. And then we utilize a group of gauss kernels located at peaks to sequentially segment the contour into several subintervals. When the residual area being not cut is lower than a threshold value, the cutting process is compulsively terminated. Furthermore, we merge adjacent kernels according to the intersection area ratio and take the retained kernel number as the cluster number k. We finally classify the object region with k and K-means algorithm. Both theoretical reasoning and experimental comparing illustrate the proposed method is rational, adaptive and efficient.
Full batch loss for person re-identification

Distance metric learning between pairs of samples is the key issue for the person re-identification(re-ID). Recently, convolutional neural networks (CNNs) significantly is employed to improve performance by learning deep semantic features which requires a large amount of labeled data for train the deep model. For person re-ID task, the training data is scarce. To the end, how to use the limited data to achieve the optimal deep model is a challenging problem. We observe that sample's similarity information and identity information are complementary. Therefore, a full-batch loss function is proposed in this paper, which promotes the distance relationship of individual sample pairs in a batch to the distance matrix in all samples to make full use of the sample similarity information. At the same time, it also makes full use of the sample's identity information by integrating the identification loss function. We conduct experiments on the two public person re-ID benchmarks: Market1501 and DukeMTMC-reID. The results clearly demonstrate that our proposed full batch loss model produces more discriminative descriptors for person re-ID, which outperforms well established baselines significantly and offer new state-of-the-art performances
MODELING OF FIBER-OPTIC COMMUNICATION CHANNEL FOR QUANTUM CRYPTOGRAPHIC SYSTEMS

The existing methods of information delivery to the strategic and tactical management of many government agencies are expensive, not always reliable and efficient. Therefore, quantum cryptographic systems (QCS) have been actively developed in recent years. However, there are problems with the use of the QCS associated with the reliability of information transfer. First, the existing fiber-optic communication channels (FOCC) are not designed to transmit single-photon signals, which leads to the complexity of their cryptographic protection. The second is insufficiently methodically developed calculation of energy losses and errors in the evaluation of the characteristics of information transfer in FOCC QCS. In article the analysis of the energy loss factors in the classical fiber-optic channel is carried out and the additive loss formula is discussed in detail. Then we consider the fiber-optic channel of quantum information transmission with the use of integrated optical devices. The additive formula of optical losses in such a channel is discussed. The features of losses in integrated optical devices are shown. The features of quantum cryptographic system of information transmission are considered. As a result, the model of FOCC QCS taking into account energy losses is presented, which allows competently in theoretical terms and visualize the passage of information through modern quantum cryptographically secure telecommunications while providing control in government structures.
Camera calibration method based on an isosceles right triangle planar pattern

Camera calibration is one of the key steps of 3D reconstruction. Aiming at improving the accuracy and solving the complex problems of camera calibration, based on the camera model, a practical approach of camera calibration based on an isosceles right triangle planar pattern was presented. The camera coordinate system was set as the world coordinate system. Five camera intrinsic parameters needed to be calibrated. The planar pattern contained three apexes of the triangle and two arbitrary points on two right angle sides. More than six pattern images were captured from different poses, the improved Harris detector was used to obtain the sub-pixel coordinates of the corners. Established the constraint equations according to the right angle, camera intrinsic parameters can be calculated. This method reduced the requirements of pattern and was simple and convenient to operate. Experiment shows that the approach is accurate.
Car evaluation data classification using modified rule antecedent fuzzy neural networks

Computational intelligence-based techniques have been successfully applied to many real world problems. In this paper, we apply a modified rule antecedent fuzzy neural networks to the problem of Car Evaluation data classification. Modified rule antecedent fuzzy neural networks can ensure a concise knowledge base with a reduced number of rules by allowing union in the rule antecedent. Genetic algorithms optimize the binary connections of the modified rule antecedent fuzzy neural networks and then gradient-based learning performs fine-tuning of the optimized binary connections. To validate the performance of the modified rule antecedent fuzzy neural networks, Car Evaluation data available on the Machine Learning Repository site at the University of California at Irvine is used.
CrossEncoder: Towards 3D-Free Depth Face Recovery and Fusion Scheme for Heterogeneous Face Recognition

As a worthwhile trade-off between fully 2D and fully 3D based face recognition (FR), 2D/3D asymmetric face recognition has recently emerged which involves matching two face representations from these two alternative modalities. Different from most previous work which rely on accurately registered 3D face templates, we address this issue by proposing a novel multi-task deep convolutional neural network (CNN) architecture based on 2.5D images. With an autoencoder-like pipeline and a specially formulated criterion, our approach is capable of parallelizing real-time 2.5D face image reconstruction and discriminative face feature extraction. Further, through both qualitative and quantitative experiments on commonly used FRGC 2D/3D face database, we demonstrate that our framework could achieve satisfactory performance on both tasks while being drastically efficient.
When visual object-context features meet generic and specific semantic priors in image captioning

In this work, we propose a novel encoding-decoding based image captioning framework, which improves the performance by jointly exploring the visual object-context features, generic and specific semantic priors. In the encoding of RNN, we extract the semantic attributes, object-related and scene-related image features first, and then feed them sequentially to the encoder of RNN, which considers the rich general semantic and visual object-context representation of images. To incorporate the testing specific semantic priors in the decoding of RNN, we apply cross-modal retrieval to find the most similar captions of the testing image in the visual-semantic embedding space of VSE++. The BLEU-4 similarity is utilized to evaluate the similarity between the generated sentence and the retrieved captions, which incorporates the sentence-making priors to the testing-specific reference captions. The evaluation on benchmark dataset Microsoft COCO shows the superiority of our algorithm over the state-of-the-art approaches on standard evaluation metrics.
Multi-local Feature Target Detection Method Based on Deep Neural Network

In application of video surveillance system, the algorithm ofLi, Guojie object detection is affected by occlusion easily, and the results on small object are not satisfying. This paper develops a multi-local feature object detection method based on deep neural network. The image is used as input to calculate the position and category probability of the object through a single network calculation, which improves the operating efficiency. The core of the methodWei, Wenxue is to extract multiple local features of the target for detection. When the target is partially occluded, it can identify the target by the unoccluded patch. In addition, the high-levelSun, Wen and low-level features in the convolutional network integrate to improve the detection effect on small targets. Experimental results show that the proposed method has a good effect on the detection of occluded targets and small objects.
A foreground detection algorithm based on improved three-frame difference method and improved Gaussian mixed model

In video foreground detection, the frame difference method bears a fast detection speed and strong timeliness. However, the detection foreground target is not complete enough, tending to result in voids and poor robustness. Although the Gaussian mixed model does well in detection, a ghost image is easily brought forth when it starts foreground target detection towards sudden motion. As far as these problems are concerned, a foreground detection algorithm based on improved Gaussian mixed model is proposed in this paper. The foreground region detected by the Gaussian mixed model is matched with the one detected by the improved three-frame difference method with the matched foreground reserved. Then the unmatched one is regarded as a 'ghost' region. The background model of the region is updated and the mean of the maximum weighted Gaussian model is replaced in an usage of the pixel of the corresponding area, thus breaking the obstacles in traditional method for detecting holes and 'ghosting' problems. The experimental results have shown that the proposed algorithm has better robustness and accuracy in different backgrounds, and the precision and recall excel that of traditional algorithms.