Reviewerly/exHarmony huggingface dataset:
Statistical Approach for Classifying Sentiment Reviews by Reducing Dimension using Truncated Singular Value Decomposition

Human sentiments, the attitudes, thoughts, or the judgments backed by feelings are the most important factors for the field of science and business which can be expressed through the web. The rapid growth of unstructured sentiment data has created a very big challenge to provide a fast and cost-efficient model for sentiment analysis. The aim of this paper is to find positive and negative emotions from the public comments rapidly and effectively. In this paper, to vectorize the text reviews and to reduce the dimension of the vectorized data, Term Frequency-Inverse Document Frequency (TF-IDF) and Truncated Singular Value Decomposition (TSVD) is used respectively. An efficient approach has been provided using the logistic regression algorithm employed in a vector space model of affective knowledge which gives fast learning speed. In result, this proposed method has reduced the learning time by 42.63% with the reduction of dimension from 12209 to 100 and has reported almost the same accuracy which is 85.7% and 89.3% for the movie review and yelp restaurant review dataset respectively. In comparison to existing methods, this method has shown almost the same accuracy after dimension reduction. Our result showed that our proposed system is useful.
Robust worst cases for parity games algorithms

The McNaughton-Zielonka divide et impera algorithm is the simplest and most flexible approach available in the literature for determining the winner in a parity game. Despite its theoretical exponential worst-case complexity and the negative reputation as a poorly effective algorithm in practice, it has been shown to rank among the best techniques for solving such games. Also, it proved to be resistant to a lower bound attack, even more than the strategy improvements approaches, but finally Friedmann provided a family of games on which the algorithm requires exponential time. An easy analysis of this family shows that a simple memoization technique can help the algorithm solve the family in polynomial time. The same result can also be achieved by exploiting an approach based on the dominion-decomposition techniques proposed in the literature. These observations raise the question whether a suitable combination of dynamic programming and game-decomposition techniques can improve on the exponential worst case of the original algorithm. In this paper we answer this question negatively, by providing a robustly exponential worst case, showing that no possible intertwining of the above mentioned techniques can help mitigating the exponential nature of the divide et impera approaches. The resulting worst case is even more robust than that, since it serves as a lower bound for progress measures based algorithms as well, such as Small Progress Measure and its quasi-polynomial variant recently proposed by Jurdziński and Lazic.
Graph-Assisted Visualization of Microvascular Networks

Microvessels are frequent targets for research into tissue development and disease progression. These complex and subtle differences between networks are currently difficult to visualize, making sample comparisons subjective and difficult to quantify. These challenges are due to the structure of microvascular networks, which are sparse but space-filling. This results in a complex and interconnected mesh that is difficult to represent and impractical to interpret using conventional visualization techniques. We develop a bi-modal visualization framework, leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. This framework allows researchers to objectively interpret the complex and subtle variations that arise when comparing microvascular networks.
Scheimpflug Camera-Based Stereo-Digital Image Correlation for Full-Field 3D Deformation Measurement

To further extend the scope of stereo-digital image correlation (stereo-DIC) to more challenging environments, a novel Scheimpflug camera-based stereo-DIC is developed for full-field 3D deformation measurement, wherein the Scheimpflug condition, consisting of tilting the sensor plane with respect to the lens plane for the sake of larger depth of field (DOF) of the camera, is employed. The geometric imaging model of the Scheimpflug camera is described, on the basis of which a robust and effective stepwise calibration strategy is performed to calculate the intrinsic and extrinsic parameters of the stereo Scheimpflug rig. With the aid of a specially tailored stereo triangulation method and well-developed subset-based DIC algorithms, the three-dimensional shape and displacement of the specimen can be retrieved. Finally, practical experiments, including rigid motion tests and three-point bending tests, demonstrate the effectiveness and accuracy of the proposed approach.
Learning job representation using directed graph embedding

In recent years, embedding technologies have gained popularity in many areas of machine learning, such as NLP, computer vision, information retrieval, etc.. In this paper, we propose a latent representation of job positions consisting of job title and company pairs, which can capture not only similarity relations but also ordering relations among job positions. We first construct a directed graph of job positions from the user's job transition history in the resume data, then we train the job position embedding using an asymmetric relation preserving graph embedding algorithm. Experimental results on a career move prediction task using real-world data set demonstrated that the proposed embedding solution can outperform state-of-the-art embedding methods.
Development of Information Measuring Complex of Distributed Pulse Control System

At the present stage of development of automation systems, there is an acute problem of fast information processing. Often this is difficult to do due to the imperfection of the mathematical apparatus and modern means of automation. Therefore, modern technical solutions need refinement. And technical devices in the appropriate technical support. The article synthesizes the law of temperature field distribution in an isotropic rod based on impulse transition functions - the Green function. Mathematical modeling of the obtained law was carried out on a hybrid supercomputer using NVidia CUDA technology. Based on the mathematical modeling, the conclusion is made about the possibility of optimizing the temperature regimes in the considered control system. Conclusions about the number of heating elements and the number of members of the Fourier series are made. The materials of the article underwent the procedure of introduction to industrial enterprises All obtained software systems have patents registered in the manner prescribed by law.
The Use of Asymmetric Numeral Systems Entropy Encoding in Video Compression

Modern image and video compression technologies includes many compression methods both lossless and lossy. Entropy encoding has a special place among lossless compression methods - it performs final data compression before its conversion to output bitstream. Huffman coding was used for these purposes, but arithmetic coding is widely used nowadays. This algorithm allows to achieve higher compression rate than Huffman coding, however, it is inferior in speed. New method called ANS (Asymmetric Numeral Systems) was proposed a few years ago. This statistical coding algorithm is comparable in terms of compression ratio with arithmetic coder and in speed with Huffman coder. ANS is currently used in such data compressors as Zstandard (Google), LZFSE (Apple), DivANS (Dropbox), etc. In this paper, it is proposed to use ANS in wavelet video codecs. The research and development of an effective compression method of wavelet transform coefficients with usage of ANS entropy encoding was performed.
A method of formation of structural elements in the task of structural identification of interval discrete models of the atmosphere pollution processes by harmful emissions of motor vehicles

The method of structural identification of interval discrete dynamic models based on a behavioral models of a bee colony is considered in the paper. It is shown that in order to increase its efficiency, it is expedient to pre-process the input data obtained in an interval form. It is proposed to use the “mountain” clustering method for this purpose.
Cyber-physical System of the Mobile Robot’s Optimal Trajectory Planning with taking into account Electric Motors Deterioration

The article is devoted to the development of a cyber-physical planning system for the optimal trajectory of a mobile robot, taking into account the technical state and deterioration of its electric motors. The relevance of using mobile robots and control methods has been described. The relevance of the cyber-physical diagnostics system and control of mobile robots application has been justified. The structure of the cyber-physical system and applied diagnostic method, including five levels has been described. Most attention was focused on the task of optimizing the movement of a mobile robot. Three robot's movement trajectories along a rectangular field was considered, their comparative analysis according to the parameters of speed and wear is made. Graph theory and probabilistic algorithms are proposed to make a mathematical model of the optimal definition of the trajectory.
Nonconvex Low Rank Approximation With Phase Congruency Regularization for Mixed Noise Removal

Mixed noise removal from a natural image is a challenging task since the complex noise distribution usually is inestimable. Many noise removal methods based on the low rank approximation have an excellent image denoising performance and are effective for recovering the images corrupted by Gaussian noise. These methods based on the additive white Gaussian noise(AWGN) model are sensitive to the outliers and non-Gaussian noise, such as the salt-and-pepper impulse noise (SPIN) and random valued impulse noise (RVIN). Such methods for mixed noise removal, however, are less effective in preserving image structures and tend to undesired staircase artifacts. This paper presents a novel Nonconvex Low Rank Model with Phase congruency and overlapping Group sparsity regularization (NLRM-PG) for mixed noise removal. Moreover, an efficient optimization algorithm under the alternating direction method of multipliers and majorization minimization framework is proposed to solve the NLRM-PG model. We demonstrate that the proposed method is effective for preserving local irregular structures and it reduces staircase artifacts with the two types of mixture noise, namely, AWGN+SPIN and AWGN+RVIN. Both qualitative and quantitative experiment results on synthetic noisy images and real noisy images illustrate that the proposed method can remove mixture noise in images more efficiently than the existing methods can do. And the results also outperform those obtained by using the competing state-of-the-art methods, particularly for the removal of high-density impulse noise.
Weakly-Supervised Sparse Coding With Geometric Prior for Interactive Texture Segmentation

Texture segmentation is about dividing a texture-dominant image into multiple homogeneous texture regions. The existing unsupervised approaches for texture segmentation are annotation-free but often yield unsatisfactory results. In contrast, supervised approaches such as deep learning may have better performance but require a large amount of annotated data. In this letter, we propose a user-interactive approach to win the trade-off between unsupervised approaches and supervised deep approaches. Our approach requires the user to mark one pixel in each texture region, whose label is directly propagated to its neighbor region. Such labeled data are of very small amount and even partially erroneous. To effectively exploit such weakly-labeled data, we construct a weakly-supervised sparse coding model that jointly conducts feature learning and segmentation. In addition, the geometric constraints are developed for the model to exploit the geometric prior on the local connectivity of region boundaries. The experiments on two benchmark datasets have validated the effectiveness of the proposed approach.
Minimizing Communication Cost for Virtual Machine Placement in Cloud Data Center

In order to achieve high performance and throughput, most of the computing applications nowadays are developed in a distributed computing environment. Such an environment is easily provided by a cloud computing platform. The distributed applications running on cloud demand huge network resources for transfer of data along with computing resources (Virtual Machines). Efficient allocation of virtual machines on hosts can help in the reduction of active hosts which lead to a decrease in deployment cost as well an energy consumption of data centers. We emphasize that while allocating VMs to hosts the communications among the VMs (in the case of distributed applications) cannot be ignored altogether. A placement algorithm which takes into account the cost of physical hosts as well as the communication among VMs can significantly reduce i) the cost of network resource usages ii) energy spent in communication iii) time to complete data transfer and thus improve performance. We have given an ILP formulation of this VM placement problem and then proposed a novel heuristic algorithm to achieve a near optimal solution. We have also analyzed the performance improvement of the proposed algorithm over a few well-known algorithms.
Automatically Generating Fix Suggestions in Response to Static Code Analysis Warnings

Static code analysis tools such as FindBugs and SonarQube are widely used on open-source and industrial projects to detect a variety of issues that may negatively affect the quality of software. Despite these tools' popularity and high level of automation, several empirical studies report that developers normally fix only a small fraction (typically, less than 10% [1]) of the reported issues-so-called "warnings". If these analysis tools could also automatically provide suggestions on how to fix the issues that trigger some of the warnings, their feedback would become more actionable and more directly useful to developers. In this work, we investigate whether it is feasible to automatically generate fix suggestions for common warnings issued by static code analysis tools, and to what extent developers are willing to accept such suggestions into the codebases they're maintaining. To this end, we implemented a Java program transformation technique that fixes 11 distinct rules checked by two well-known static code analysis tools (SonarQube and SpotBugs). Fix suggestions are generated automatically based on templates, which are instantiated in a way that removes the source of the warnings; templates for some rules are even capable of producing multi-line patches. We submitted 38 pull requests, including 920 fixes generated automatically by our technique for various open-source Java projects, including the Eclipse IDE and both SonarQube and SpotBugs tools. At the time of writing, project maintainers accepted 84% of our fix suggestions (95% of them without any modifications). These results indicate that our approach to generating fix suggestions is feasible, and can help increase the applicability of static code analysis tools.
Spatio-Temporal Video Segmentation

This Chapter considers approaches to video skimming into semantically-consistent segments of video streams, which are highly redundant and weakly structured data. In such a way, one of the promising ways is spatial-temporal segmentation as frame partitions represent certain spatial image content. Also, properties were formulated and proved which ultimately determine the characteristics of permissible segmentation transformations when searching for a compromise between over and undo segmentation. Temporal segmentation of multidimensional time series has been examined, which enables structuring video streams and significantly reduce the amount of data that will require online processing. For this, multidimensional time series analysis theory was used, since a completely natural video representation is a sequence of frames, followed by their combination into groups of frames (shots) with the same content. It was shown that various approaches can be used to detect shots with homogeneous characteristics, which are based on VAR models, exponential smoothing and predictive models.
Pāṇinian Syntactico-Semantic Relation Labels

We present in this paper a list of dependency relations based on Pāṇini's grammar for Sanskrit.The important feature of this list is that most of the relations represent well defined semantics that can be extracted from the surface string without any extra-linguistic information.
Revealing student satisfaction related to academic information services using the Kano model

Abstract Information technology is one of the campus facilities. The information system department evaluates the use of information technology periodically by measuring user satisfaction. This study uses the Kano model to reveal student satisfaction related to the utilization and improvement of information technology in academic information services. Representation of the Kano quality category shows that the availability of conventional academic information services increases student satisfaction. The existence of an information technology that provides self-service practices without space and time constraints can further enhance student satisfaction.
Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents

This paper presents a distributed evolutionary learning control based on social wound treatment for mobile robot navigation using an integrated multi-robot system comprised of simulated and physical robots. To do so, this work proposes an extension of the population-based metaheuristic wound treatment optimization (WTO) method into a distributed scheme. In addition, this distributed WTO method is implemented on the multi-robot system allowing them to experience the environment in their own and communicate their findings, resulting in an emergence intelligence. We implemented our proposal using the combination of five simulated robots with one physical robot for tuning a navigation controller to move freely in a workspace. Results showed that the solution found by this multi-robot system aims using the output controller in the physical robot for successfully achieving the goal to move the robot around a U-maze, without applying any transfer learning approach. We consider this proposal useful in evolutionary robotics, and of great importance to decrease the gap related to transfer knowledge in robotics from simulation to reality.
On pruned, quantized and compact CNN architectures for vision applications

With the influx of several kinds of mobile electronic devices alongside the increasing popularity of deep learning networks in performing computer vision tasks, it is natural that demands for delivering them on smaller devices will increase. The authors of this paper review and experiment with compact models (MobileNet V1 and V2, ShuffleNet V1 and V2, FD-MobileNet)) and selected methods of pruning and quantization of popular Convolutional Neural Network (CNN) through transfer learning tasks. They further propose a hybrid technique of per layer pruning and quantization called Pruned Sparse Binary-Weight Network (PSBWN). The performance of these four techniques are evaluated on image classification tasks on the Caltech-UCSD Birds 200, Oxford Flowers 102 and CALTECH256 which are all publicly available benchmark datasets with focus on the trade-offs among the number of Floating Point Operations (FLOPS), model sizes, training and inference times against accuracy using the same computation resources.
A Multi-Density Clustering Algorithm Based on Similarity for Dataset With Density Variation

Clustering has been widely used in the fields of knowledge discovery, pattern recognition and artificial intelligence.However, discovering clusters in spatial databases is still a challenging task, especially when the shape, size, and density of clusters vary a lot.Existing algorithms have sensitive parameters, clusters must be separated far enough from each other and rich prior knowledge about datasets is required.In this paper, we propose algorithm DENSS, which performs clustering on the basis of the similarity of neighbour distribution and the number of shared neighbors for two objects.Algorithm DENSS can mine clusters that differ in densities, and within a cluster the local densities are reasonably homogeneous.Adjacent objects are separated into different clusters by significant change in densities.To verify the effectiveness of the algorithm DENSS, synthetic and real-world datasets are used for testing, and it has been compared with seven clustering algorithms.Experimental results show that the proposed algorithm has a relatively high efficiency, robustness and effectiveness, and is remarkably superior to the seven algorithms.This algorithm is universal and can rapidly and efficiently identify the clusters of different densities, shapes and sizes even in the presence of noise and outliers for any object feature types.
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.
Geometric Rules in Infinitary Logic

Large portions of mathematics such as algebra and geometry can be formalized using first-order axiomatizations. In many cases it is even possible to use a very well-behaved class of first-order axioms, namely, what are called coherent or geometric implications. Such class of axioms can be translated to inference rules that can be added to a sequent calculus while preserving its structural properties. In this work, this fundamental result is extended to their infinitary generalizations as extensions of sequent calculi for both classical and intuitionistic infinitary logic. As an application, a simple proof of the infinitary Barr's theorem without the axioms of choice is shown.