Reviewerly/exHarmony huggingface dataset:
Finding dense subgraphs with maximum weighted triangle density

Finding dense subgraphs from sparse graphs is a fundamental graph mining task that has been applied in various domains, such as social networks, biology, and spam detection. Because the standard formulation of this problem is difficult to solve owing to connections with the Maximum Clique Problem, some tractable formulations have been proposed. These formulations find a dense subgraph by optimizing some density function, such as the degree density or triangle density. In this paper, we introduce the weighted k-clique density, a novel formulation for dense subgraph extraction. We show that the problem of maximizing weighted k-clique density can be solved optimally in polynomial time by solving a series of minimum cut problems. For scalability, we also propose a more efficient greedy algorithm with performance guarantee. The experimental results on real-world network datasets show that, compared with established state-of-the-art algorithms, the proposed algorithm can find a much denser subgraph in terms of edge density and triangle density.
Classification Tree with Hybrid Splitting Mechanism

Binary Decision Trees (BDT), such as CART and C4.5, is one of the most commonly used classifiers, where each node of the tree can be split into a new layer with two child nodes. An alternative classification tree, Multi-Layer Classifier (MLC) has been proposed to take advantage of the underlying data structure. The MLC splits the parent node into 1 or 2 classified child node and an unclassified child node at each layer. Unlike BDT, MLC only allows the unclassified child node to be split into a new layer until a stop criterion is reached. In this study, an algorithm that switches between the two splitting mechanisms in the tree construction process is proposed. In the experiments of real cases, the proposed classifier has better performance than BDT and MLC in terms of the out-of-sample accuracy and Youden's Index.
Achieving Fine-Grained QoS for Privacy-Aware Users in LBSs

LBSs have been prospering over recent years. Although many LPPMs are proposed to solve specific problems under certain settings, it is still hard to satisfy the different demands of users due to the diverse requirements for quality of service (QoS) and privacy preservation. In this article, we propose an adaptive scheme to achieve fine-grained QoS for privacy-aware users in LBSs. Specifically, we construct a Bayesian-based classification model to identify the QoS requirements of users under different circumstances. QoS and privacy degrees provided by LBSs are carefully defined based on the concept of rate-distortion theory, which plays an important role in bridging the LPPMs with users' requirements. When users request the location service from the built-in GPS module in smart devices, we first specify their circumstances and requirements, then reply with the modified locations. We also design an efficient algorithm to dynamically choose the appropriate privacy strategy according to the QoS requirements of users in different circumstances. Experimental results show that our scheme can identify QoS requirements accurately and protect location privacy adaptively.
Analysis and Assessment of Dynamics of Neurocomputer Performance Measures

The article discusses the benefits of neurocomputer interfaces aimed at classifying patterns of EEG signals which are converted into output signals for controlling actuators. To detect and classify signals, artificial neural networks of various architectures are used to increase recognition efficiency. The accuracy of the biological signals depends on the condition how measurements were performed and filtered to prepare the data for training and testing of ANNs. The development of mathematical models and teaching methodology will facilitate neural networks testing of various types and configurations. It will also help to evaluate and improve the accuracy of the classifier as well as to eliminate and compensate for the shortcomings of various software products and libraries. The assessment of neurocomputer performance measures, even of a well-trained network implemented in the classifier, is complicated by identifying events in a dynamic measurement system. The creation of a system of adaptive filters and convolution of input signals made it possible to identify the necessary patterns in the EEG to facilitate the implementation of the neurocomputer interface.
Numerical solution of Blasius viscous flow problem using wavelet Galerkin method

In this article it is proposed to solve numerically the Blasius viscous flow problem of fluid mechanics using wavelet Galerkin method. The spline wavelet system is used for this purpose. It is assumed that the approximate solution lies in the space spanned by translates of the scaling function of the considered wavelet system. It is found that the numerical solution thus obtained is having high accuracy when compared to the benchmark solution given by Howarth.
Unstructured Malay Text Analytics Model in Crime

Abstract The concern in crime prevention to realize a safe environment for public’s welfare is one of the central agenda to government authorities. The increasing volume of crime reports with unstructured data that are heavily loaded with text presents the biggest challenge to human experts in analysing the data efficiently as it contains lots of hidden and valuable information such as modus operandi and common crime patterns. The capacity limitations in human information processing demand an effective data analytics model to uncover these hidden and potentially meaningful information from large-scale of data especially in Malay language for improving analysis and supporting decision-making in crime investigation. The objective of this paper is to propose Unstructured Malay Text Analytics Model to support serial crime investigation based on modus operandi patterns. The significance output of this paper is to assist experts especially Royal Malaysia Police in crime investigation based on modus operandi patterns analysis as one of the efforts in reducing rate of crime index.
Lifted Hybrid Variational Inference

Lifted inference algorithms exploit model symmetry to reduce computational cost in probabilistic inference. However, most existing lifted inference algorithms operate only over discrete domains or continuous domains with restricted potential functions. We investigate two approximate lifted variational approaches that apply to domains with general hybrid potentials, and are expressive enough to capture multi-modality. We demonstrate that the proposed variational methods are highly scalable and can exploit approximate model symmetries even in the presence of a large amount of continuous evidence, outperforming existing message-passing-based approaches in a variety of settings. Additionally, we present a sufficient condition for the Bethe variational approximation to yield a non-trivial estimate over the marginal polytope.
The Realization of Intelligent Knowledge Adaptive Learning Method in the Field of Substation Operation and Maintenance

Abstract In view of the lack of targeted and personalized knowledge learning in existing substation operators, this paper proposes an intelligent adaptive knowledge learning method in the field of substation operation and maintenance. First, an adaptive knowledge learning system is established including domain model, user model, teaching model and adaptive engine. In the user model, the item response theory (IRT) is used to estimate the learner’s master degree of knowledge points contained in the domain model, and then their learning needs are obtained. Meanwhile, the Bayesian network method is used to speculate the cognitive style of learners. On the basis of a comprehensive consideration of learning needs and cognitive style, the particle swarm optimization algorithm (PSO) in the teaching model is used to recommend the optimal learning resource for learners to realize the high-efficiency personalized knowledge learning in substation operation and maintenance.
Deblurring of Low Light Images using Light-streak and Dark Channel

Images taken by smartphones or handheld cameras in night or dark places are often blurry. To solve this, the device should be steady and have long exposure time. There are many existing deblurring techniques, but they often fail when the image does not have enough salient features. Thus using state of the art approach may result in failure and needs specific methods for deblurring of low light images. Light streaks are common in most of the low light images and are formed from point light sources. It contain more information regarding camera motion and blur kernels. Also while low light images contain dark pixels (very low intensity pixels), these pixels are not very dark when averaged with neighboring high intensity pixels during the blur process. The deblurring approaches based on light steaks and dark pixels are rare. Here a method is developed to select a light streak for kernel estimation and introduces a non-linear blur model that explicitly takes dark channel prior into account for estimating the blur kernel in an optimization framework.
Impact of Airborne Radar Uncertainties on Detect-and-Avoid Systems' Performance

Impact of sensor uncertainty on Detect-and-Avoid (DAA) systems' performance is investigated. Key metrics analyzed are the loss of DAA well clear ratio, the near-mid-air-collision risk ratio, the alert ratio, and the number of maneuvers per loss of DAA well clear. Sensitivity of these metrics to the magnitude of sensor uncertainty, pilots' selection of maneuver, and surveillance range is investigated. Benefits of a dynamic buffer around the DAA well clear separation boundary, computed based on track accuracies, are also analyzed. These metrics are computed from open- and closed-loop simulations of a large number of representative encounters between an unmanned aircraft system and a manned aircraft. Results show that sensor uncertainty degrades safety metrics considerably but has only a minor effect on the number of maneuvers per loss of DAA well clear. The number of maneuvers, nonetheless, can be reduced by a 5° buffer away from the edge of the range of conflict-resulting heading in pilots' selection of maneuver.
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems

Health monitoring is important for maintaining reliable information and communications technology (ICT) systems. Anomaly detection methods based on machine learning, which train a model for describing "normality" are promising for monitoring the state of ICT systems. However, these methods cannot be used when the type of monitored log data changes from that of training data due to the replacement of certain equipment. Therefore, such methods may dismiss an anomaly that appears when log data changes. To solve this problem, we propose an ICT-systems-monitoring method with deep learning models divided based on the correlation of log data. We also propose an algorithm for extracting the correlations of log data from a deep learning model and separating log data based on the correlation. When some of the log data changes, our method can continue health monitoring with the divided models which are not affected by changes in the log data. We present the results from experiments involving benchmark data and real log data, which indicate that our method using divided models does not decrease anomaly detection accuracy and a model for anomaly detection can be divided to continue monitoring a network state even if some the log data change.
Theory of Quantum Computation With Magnetic Clusters

We propose a complete, quantitative quantum computing system that satisfies the five DiVincenzo criteria. The model is based on magnetic clusters with uniaxial anisotropy, where two-state qubits are formed utilizing the two lowest lying states of an anisotropic potential energy. We outline the quantum dynamics required by quantum computing for single-qubit structures, and then define a measurement scheme in which qubit states can be measured by sharp changes in current as voltage across the cluster is varied. We then extend the single-qubit description to multiple qubit interactions, facilitated specifically by an entanglement method that propagates the controlled-not quantum gate.
A Memory Management Strategy Based on Task Requirement for In-Memory Computing

For in memory computing framework, the memory allocation category cannot adjust the proportion of memory according to different data processing scenarios, and there are limitations in terms of memory usage and performance. In order to solve the issue, we analyzes the spark memory model, defines the concepts of available memory amount, job execution time, data inclination, execution area demand, execution area allocation amount, execution area overflow amount, task fairness, task demand degree, etc., presents the theoretical analysis, proposes the memory optimization algorithm based on the execution area on demand allocation, and determines the memory allocation amount according to the size of task input data. On the basis of slightly modifying spark source code, the memory allocation of storage area minimization and execution area for tasks is optimized to reduce cache loss and memory overflow and improve task execution efficiency.
The Study of Computer Simulation as a Method of Cognition in the Teaching of Technical and Natural Sciences

An article is devoted to investigation of computer simulation as the method of active learning for the technical specialties students. Methodological recommendations on the use of the computer simulation methods, its formalization and limitations and the features of Computer Simulation as a Method of Knowledge Synthesis are considered. The proposed approaches were applied in educational process for the masters’ programs «Radio Engineering» and «Applied Informatics», whic is carried out on the basis of software and hardware of the Space monitoring center of the Ural Federal University.
Self-Bootstrapping Pedestrian Detection in Downward-Viewing Fisheye Cameras Using Pseudo-Labeling

Downward-viewing fisheye cameras have attracted much attention in surveillance systems due to the wide coverage and less occlusion. However, pedestrian detection in downward-viewing fisheye cameras remains an open problem due to a lack of large-scale labeled dataset. Furthermore, it's time-consuming and labor-intensive to label a downward-viewing fisheye dataset manually. To address this, we propose a self-bootstrapping pedestrian detection method, which automatically pseudo-labels downward-viewing fisheye images by making full use of spatial and temporal consistency of pedestrians in the cameras to improve the accuracy of pedestrian detection. We segment the downward-viewing fisheye images into two regions and propose the pseudo-labeling methods for them progressively: a cyclic fine-tuned detector for the oblique region and a visual tracking method for the vertical region. Combining the pseudo-labels from two regions, we fine-tune the network for better accuracy. Experimental results show that the proposed approach reduces time consumption by about 95% compared with the labor-intensive manual labeling while it still reaches competitive and comparable Average Precision (AP).
Representation of Chiseling Operation Using Force Display with Two Degree-of-freedom Admittance Control

This study addressed an advanced force display control system for a virtual surgical training simulator. In oral and orthopedics surgeries, a surgeon uses a chisel and a mallet for chiseling or cutting hard tissues such as bones and teeth. In order to construct the virtual reality training simulator for the chiseling and the cutting operations, it is necessary that the force display device as a human-machine interface has high stiffness and high responsiveness against the impact forces generated by pounding the chisel with the mallet. Therefore, we develop the force display device with ball-screw for obtain the high realistic sensation, and 2 degree-of-freedom (DOF) admittance control is applied for the moving parts of the force display device to react instantaneously. In the analysis of the force display control system using 2 DOF admittance control, the responsiveness can be enhanced by increasing the gain on high-frequency region in the feedback controller. However, to increase the gain on the high-frequency region, the force display device can be vibrated by the excitation of the sensor noise and the vibrational mode in this device. Therefore, we apply PD control using the incomplete derivative term to reduce the excessive high gain of high-frequency region in the feedback controller. And, to obtain the chiseling operation with high realistic sensation, the virtual model in the feedforward controller is applied the spring-mass-damper system with movement of supported object. The efficacy of the proposed device and control system is verified by creating the virtual experiences to operate the chisel.
Proof by Assumption of the Possible in <i>Prior Analytics</i>, 1.15; How Not to Blend Modal Frameworks

The present paper aims to show that the reconstruction of the formal framework of the proofs in Pr. An. 1.15, as proposed by Malink and Rosen 2013 (‘Proof by Assumption of the Possible in Prior Analytics 1.15’, Mind, 122, 953-85) is due to affront a double impasse. Malink and Rosen argue convincingly that Aristotle operates with two different modal frameworks, one as found in the system of modal logic presented in Prior Analytics 1.3 and 8-22, and one occurring in many of Aristotle’s works, such as the Physics, De Caelo and the Metaphysics. However, they misconstrue the latter framework. More precisely, they misconstrue the domain of significance of what they call the ‘Principle of Necessitation’. As a consequence, bringing the two frameworks into one results into a contradictory modal logic. On the other hand, if the Principle of Necessitation is rectified, the proofs put forward by Malink and Rosen in the same paper are no longer available.
Satisfied User Ratio Prediction with Support Vector Regression for Compressed Stereo Images

We propose the first method to predict the Satisfied User Ratio (SUR) for compressed stereo images. The method consists of two main steps. First, considering binocular vision properties, we extract three types of features from stereo images: image quality features, monocular visual features, and binocular visual features. Then, we train a Support Vector Regression (SVR) model to learn a mapping function from the feature space to the SUR values. Experimental results on the SIAT-JSSI dataset show excellent prediction accuracy, with a mean absolute SUR error of only 0.08 for H.265 intra coding and only 0.13 for JPEG2000 compression.
Analysis of Suitable Approaches for Data Mining Algorithms

Data mining is the knowledge discovery method by examining the huge bulks of information from numerous perspectives and summarizing it into valuable data; data mining has become an important component in numerous fields. It is used to recognize hidden patterns in a huge data set. In this paper, we are using three techniques of Data mining i.e. classification, clustering, and regression. The paper helps users to identify the best algorithm suitable for their dataset along with their advantages and disadvantages. It also shows the accuracy of the best four algorithms for classification and regression, and for clustering, it shows the number of clusters for different clustering algorithms. The performance of algorithms depends on the size of datasets. As the size of the dataset increases, the performance will also increase. This will reduce the work of users finding the best algorithm using different tools like weka, orange, TPOT, etc. The advantages and disadvantages can help users identify the single best algorithm for further analysis. This system also helps the user to identify target columns from the dataset and various dispensable columns in the dataset.
Efficient Online String Matching Based on Characters Distance Text Sampling

Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields, like natural language processing, information retrieval and computational biology. Sampled string matching is an efficient approach recently introduced in order to overcome the prohibitive space requirements of an index construction, on the one hand, and drastically reduce searching time for the online solutions, on the other hand. In this paper we present a new algorithm for the sampled string matching problem, based on a characters distance sampling approach. The main idea is to sample the distances between consecutive occurrences of a given pivot character and then to search online the sampled data for any occurrence of the sampled pattern, before verifying the original text. From a theoretical point of view we prove that, under suitable conditions, our solution can achieve both linear worst-case time complexity and optimal average-time complexity. From a practical point of view it turns out that our solution shows a sub-linear behaviour in practice and speeds up online searching by a factor of up to 9, using limited additional space whose amount goes from 11 to 2.8% of the text size, with a gain up to 50% if compared with previous solutions.
Design and Evaluation of 3D Selection in Mobile VR Environments

Virtual Reality (VR) applications are now widely applied in various scenarios and areas, such as tourism, education, manufacturing, and digital entertainment. Although there many different VR devices and platforms, e.g., HTC Vive, Oculus Rift and PS VR, the mobile VR based on smartphones and low-priced VR goggles are still the most popular VR platform for VR games and videos. However, many fundamental interaction techniques in mobile VR environment are still quite challenging. In this paper, we design and evaluate the usability of 3D selection technique in mobile VR environments. We evaluate the user performance on a selection task with different navigation speed settings and operation modes, and we then proposed a selection task performance model for 3D user interfaces in mobile VR environments based on Fitts' Law. The model indicates that the target distance from the 3D viewpoint has the most significant influence on selection time.
The Role of the Version Control Information in Code Comprehension

Most software comprehension frameworks use the source code as the main resource for information retrieval. Advanced code comprehension process, however, requires the utilization of the full knowledge portfolio of the software system. In this paper we investigate how the version control information of the project can be utilized for extending our apprehension of large legacy systems providing a better understanding of the software under examination. We show that some of the hidden structural connections between the elements of the program can be revealed most easily by the development history of the system. A prototype implementation of the method using git version control information has been implemented as an open source extension of the CodeCompass software comprehension framework.
An Improved BKW Algorithm For LWE With Binary Uniform Errors

The Learning with Errors (LWE) Problem has received much attention since its introduction and been widely used in cryptography. However the error sampled from Gaussian distribution affects efficiency of application based on LWE, the LWE variants with errors taken from uniform distribution came into being in 2013, which has an asymptotic complexity analysis. An improved BKW algorithm has been proposed to the LWE problem with binary uniform errors, and a study of the complexity of the algorithm is given in the paper. As a result, new bounds are provided for the concrete hardness of cryptography based on the variant, and parameter choices for quiet a few important algorithm.