Reviewerly/exHarmony huggingface dataset:
Transaction Item Embedding by Maximizing A Joint Probability

Frequent pattern mining plays an important role in the data mining field. This topic has been studied for a long time. Most of the method which finds frequent pattern mining is high computational cost. In general, transaction data is sparse. Therefore, searching frequent itemsets in a dense part of transaction data is better than searching all transaction data. In this paper, we propose a method to embed items from transactions to a low dimensional vector space. We show the relationship between transaction data and a low dimensional vector space which is created by our method.
Weighted knn using grey relational analysis for cross-project defect prediction

Abstract Defect prediction plays important roles in detecting vulnerable component within a software. Some researchers have tried to improve the accuracy of software defect prediction so that it helps developer to manage resources (human, cost, and time) better. They focus on building the software defect prediction model only for a specific domain. To our knowledge, research on cross-project domains has not been carried out before. This research developed method to predict software defect for cross-project domains. Thus, the domain contains datasets with different number of features. To extend shorted features in a dataset, the method calculates the missing values. This research employed weighted KNN to fill in the missing value. The refilled datasets were then classified using naive bayes and random forest. This research also conducted a feature selection process to select relevant features for detecting defects by means of a comparative analysis of methods of selection of features. For the experimentation, this research used seven NASA public dataset MDPs. The results show that for imbalance data, naïve bayes combined with information gain (IG) or symmetric uncertainty (SU) feature selection produced the best balance, i.e. 0.4975. It also shows that for balance data, random forest combined with gain ratio (GR) produced the best balance, i.e. 0.7795. In general, the developed method performed relatively alike the previous method, which classify only specific domain, i.e. 0.4975. It even outperformed previous method for dataset PC2, i.e. 0.4033.
Reducing Search Space of Genetic Algorithms for Fast Black Box Attacks on Image Classifiers

Recent research regarding the reliability of Deep Neural Networks (DNN) revealed that it is easy to produce images that are completely unrecognizable to humans, but DNNs recognize as classifiable objects with 99.99% confidence. The present study investigates the effect of search space reduction for Genetic Algorithms (GA) on their capability of purposefully fooling DNNs. Therefore, we introduce a GA with respective modifications that is able to fool neural networks trained to classify objects from well-known benchmark image data sets like GTSRB or MNIST. The developed GA is extended and thus capable of reducing the search space without changing its general behavior. Empirical results on MNIST indicate a significantly decreased number of generations needed to satisfy the targeted confidence of an MNIST image classifier (12 instead of 228 generations). Conducted experiments on GTSRB, a more challenging object classification scenario, show similar results. Therefore, fooling DNNs has found not only easily possible but can also be done very fast. Our study thus substantiates an already recognized, potential danger for DNN-based computer vision or object recognition applications.
Multi-scale Feature Mapping Method Based on Clustering Convolution

Abstract In recent years, convolutional neural networks have attracted much attention due to their powerful image feature learning capabilities. However, in many cases, the network exhibits shortcomings such as difficulty in training and adjustment, and time-consuming training. This paper combines clustering and constrained Boltzmann machines to propose a multi-scale feature mapping method based on clustering convolution. Experiments were carried out on Cifar-10 and lung X-ray images. This method not only shortened the time but also improved the accuracy. Therefore, the proposed method is superior to the traditional feature extraction method.
Study on the Soundings Arrangement of Seafloor Topography Representation Model

Abstract The representation method for submarine topography is mainly the sounding notations combined with depth contours representation currently, and the sounding notation is the important part of the seabed terrain representation model. The most commonly intuitive and used seabed terrain representation model is the chart. The users understand the sea area through the interpretation of depth notation in chart, which contribute to achieve safe navigation and well-off exploitation. The density of bathymetric notation is specified in various regulations on charting that proposed by the state, thus the arrangement of soundings is only mentioned simply without stating any reason. Based on the characteristics of seabed topography and the features of hydrographic survey, the conditions that the soundings arrangement should satisfied are analysed, two types of soundings notation places which can meet the conditions are induced. Finally, the two arrangements are compared on the representation ability, and accuracy and feasibility of the rhombohedral arrangement are verified better by the results of the statistical calculation.
Abstract Layers and Generic Elements as a Basis for Expressing Multidimensional Software Knowledge

Enormous intellectual efforts are being invested into producing software in its executable form or, more precisely, a form from which this executable form can be automatically derived, commonly known as a source form (usually code, but may be a model, too). On the other hand, it is inherently complex to restore the ideas upon which software has been built. Moreover, it is usually not possible to produce software in its source form directly without producing a number of documents, diagrams, or schemes. All these artifacts, including the program code, represent software knowledge necessary for maintaining existing software and for building further systems in a given domain. But these software knowledge sources are disconnected from each other making it hard to navigate between them and to devise conclusions based on their relatedness, which is essential for their effective use. In this paper, a new approach to versatile graphical software modeling based on abstract layers and generic elements and its use in modeling multidimensional software knowledge and interrelating its pieces is proposed. The approach is supported by a prototype tool called InterSKnow, which targets mainly internal representation. This enabled to evaluate the approach from two perspectives: the efficiency of searching for software knowledge in software models and its comprehension. The results are generally plausible to the approach proposed here compared to Enterprise Architect as a representative of traditional, state-of-the-art software modeling tools.
Efficient Neural Network For Real Semantic Segmentation

At present we have the research innovations that can viably empowers the driver to keep away from smashing regardless. Advance driving assistance system (ADAS) enables autonomous vehicle to transform into a reality. One of the essential advancements of ADAS is semantic segmentation and we are witnessing fundamentally in semantic segmentation generally by the virtue of deep learning. The ability to play out the pixel wise image segmentation progressively is of the principal significance in ADAS applications. The goal of semantic segmentation is to identify the exact region that the object occupies. In this proposed work ENet (efficient neural network) model is used for semantic segmentation for a superior scene understanding and then we are dissecting the execution of this model by doing hyper-parameter tuning.
Learning Regularization Parameters of Radial Basis Functions in Embedded Likelihoods Space

Neural networks with radial basis activation functions are typically trained in two different phases: the first consists in the construction of the hidden layer, while the second consists in finding the output layer weights. Constructing the hidden layer involves defining the number of units in it, as well as their centers and widths. The training process of the output layer can be done using least squares methods, usually setting a regularization term. This work proposes an approach for building the whole network using information extracted directly from the projected training data in the space formed by the likelihoods functions. One can, then, train RBF networks for pattern classification with minimal external intervention.
Image dehazing using spatially displaced images

Images of outdoor scenes are often degraded by particles and water droplets in the atmosphere. Haze, fog, and smoke are such phenomena due to atmospheric absorption and scattering. Most image dehazing (haze removal) methods proposed for the last two decades employ image enhancing or restoration approach. Different variants of local adaptive algorithms for single image dehazing are suggested. These methods are successful when a haze-free image has higher contrast compared with that of the input hazy image. Other haze removal approaches estimate a dehazed image from the observed scene by solving an objective function whose parameters are adapted to local statistics of the hazed image inside a moving window. In this presentation we propose a new dehazing algorithm that utilizes several scene images. These images are captured in such a way to be spatially shifted relatively each other. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of hazed images.
Graph Compression by Tree Grammars and Direct Evaluation of Regular Path Query

This paper proposes a graph compression method that embodies a good trade-off between compression ratio and direct evaluation (of regular path queries) without decompression. A given directed graph is decomposed into a set of trees, each of which is compressed to a tree grammar by TreeRePair. When a set of tree grammars G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,⋯G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> and a deterministic selecting graph automaton (as a query) M are given, M is directly applied to production rules of G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,⋯,G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> without decompression. Duplicate computation can be avoided by memorization. We conducted experiments on real-world graph data. The result shows that while the compression ratios of the proposed method do not outperform previous research (supporting only simple queries), CPU time for the proposed direct query evaluation tends to be shorter for larger graphs compared with a naive method.
Stability Control of a Quadcopter Fixed to a Base

The present paper proposes a system based on the usage of Proportional, Integral and Derivative (PID) Control for stabilizing a quadcopter attached to a metal base. A smartphone Graphic User Interface (GUI) was created so the user can adjust manually, or automatically, the three gains that are characteristic of this type of control (proportional, integral and derivative gains). This system is intended to be seen as an educational concept for exploring PID Control. The user can change each of the three PID gain values and observe what are the consequences reflected on the behavior of the system. To test the latter, an attempt of tuning the three gains in order to stabilize the quadcopter was performed. The system reacted as expected, taking into account the theoretical knowledge about each of the PID gains and their influence on the system response. Overall, the final outcome of this work is an educational tool, designed and implemented by students in a compulsory curricular unit, which allows the user to understand how a PID controller works in an intuitive and practical way.
The Improved Intelligent Optimal Algorithm Based on the Artificial Bee Colony Algorithm and the Differential Evolution Algorithm

This paper proposes an improved ABC algorithm to avoid the phenomenon of premature convergence of the basic artificial bee colony (ABC) algorithm. By combining the mutation operators of differential evolution (DE) algorithm, the new search equations of employed bees and onlookers are designed. Besides, a selective probability is leaded into the former one and a development coefficient is added in the latter one. In order to demonstrate the availability of the proposed ABC algorithm, simulations are conducted on a set of benchmark functions. The simulation results show that the improved ABC algorithm performs better than the basic ABC algorithm, DE algorithm and particle swarm optimization (PSO) algorithm.
Multichannel audio steganography based on MPEG surround using direct sequence spread spectrum

Audio steganography is a technique for embedding hidden message on the audio signal. Several techniques are currently available, proposed as methods to hide secret messages on audio signals and integrated into the audio encoding system. In this paper, a data hiding technique is proposed working based on MPEG Surround (MPS), a multichannel audio encoding standard that is very popular for spatial or three-dimensional (3D) audio coding. Direct sequence spread spectrum (DSSS) is integrated with MPS to embed a secret message into the downmix signal of multichannel audio that generated from MPS encoder. The result of experiments shows that the audio that produced by the proposed system is still in acceptable quality and signal synchronization can run smoothly. The mean value of the signal to noise ratio (SNR) of the audio signal is 16.67 dB. The secret message can be successfully extracted with a mean value of bit error rate (BER) of 3.38%, and normalized correlation (NC) is 95%.
Fuzzy Controller for Solar Cell Optimization

Abstract This study describes the Fuzzy Logic Control for the movement of Solar Cells that use fuzzy logic algorithms to achieve intelligent and flexible knowledge-based systems in the design of hardware to achieve efficiency in controlling the movement of solar cells that are better and maximize the function of the solar cell motor works based on time input. Fuzzy Logic allows mapping from the input given based on time to an output. involves components of variables, membership functions, Fuzzy Logic operators and if-then rules. And with an analysis it can produce optimum output of Solar cell rotation.
Automatic Tissue Type Classification in Large-Scale Microscopic Images Using Zernike Moments

In this paper, we propose an approach in identification of histological sections of human tissue in large-scale microscopic images on the basis of sample tissue fragments from the image. The method uses pattern recognition properties of Zernike moments in the form of image descriptors consisting of sequences of Zernike moments. The goal is to acquire a robust and precise method that allows for identification of the original source of the tissue fragments in the microscopic images. The approach relies on machine learning to perform the final identification of the tissue subject from the constructed image descriptors. The method is verified by a series of experiments on a set of microscopic slides of histological sections. The results and their analysis are presented in the conclusion of the paper.
Increasing indicators of effectiveness of the higher educational institution by regulation of the threshold scores of the unified state examination

The paper describes improving the effi ciency of educational activities of higher education institutions on the example of enrolment to the South-west state university in 2015-2017.The main effi ciency criterion of educational activity is grade-point average evaluation of persons accepted for full-time study.This criterion is set by the Ministry of Science and High Education of the Russian Federation.It is shown on the example of statistical data of the unifi ed state examination in Kursk region in [2015][2016][2017][2018].The implementation of this criterion is determined by admission structure of higher education institutions (the list of specialties, entrance tests, state-funded places).Increasing of grade-point average is possible by threshold scores increasing for one or several subjects (entrance tests).The analysis shows that such approach allows GPA increasing of enrolled students but at the same time leads to income decrease of the educational organization due to the reduction of accepted students.There are diagrams that show dependence of GPA from threshold score change of such subjects as Russian language, Mathematics and other subjects.The diagrams also show income decline at increase in threshold scores in specifi ed cases.GPA increase of the unifi ed state examination by changing the threshold score is economically inappropriate as it infl uences other effi ciency indicators (assessment of fi nancial and economic activity, average salaries).
Neural eye-processing computer based on FPGA technologies

One of the promising areas in the field of image processing and analysis is the hardware implementation of neural network for processing and analyzing of images based on FPGA technologies. The structural scheme of the multifunctional calculator was developed. It contains MxN cells in the form of matrix, a formation block of signs that has N control nodes, input of clock impulses, input of reset, output of common feature of zero and outputs of features of zero by columns and rows of matrix. The described structure is a classifier and it was written to the FPGA crystal<sup>1</sup>. The main task is to provide processing and analysis of images in real time. Hardware implementation allows to use it in many types of activities: biomedical engineering, industry, aerospace sphere, etc<sup>2</sup>.
Design of Object Edge Detection System Based on FPGA

Aiming at the problems of poor real-time performance and insufficient resource utilization of the software moving image edge extraction system, the FPGA device was used as the platform to design the image format conversion, image grayscale, median filtering, edge extraction and other algorithms by Verilog language, so as to complete the image acquisition and processing functions. The test shows that the dynamic edge detection has good real-time performance and tracking effect, and the resources are fully utilized to meet the design requirements.
Improving the Security of Visual Challenges

This article proposes new tools to detect the tampering of video feeds from surveillance cameras. Our proposal illustrates the unique cyber-physical properties that sensor devices can leverage for their cyber-security. While traditional attestation algorithms exchange digital challenges between devices authenticating each other, our work instead proposes challenges that manifest physically in the field of view of the camera (e.g., a QR code in a display). This physical (challenge) and cyber (verification) attestation mechanism can help protect systems even when the sensors (cameras) and actuators (a display, infrared LEDs, color light bulbs) are compromised. In this article, we consider skillful adversaries that can capture the correct challenges (our system is sending) and can re-create them in the response to try fooling our verification system, and we propose new algorithms to detect these powerful attackers. Also, we introduce new visual challenges that make harder for anti-forensics attackers to succeed, and we present experimental results showing how our system is robust against a variety of attacks ranging from naive attacks to more sophisticated anti-forensics attackers.
Large object vision measurement system based on a fixed connection between a camera and a total station telescope

Vision measuring, involving the measurement of the three-dimensional shape and color of large objects using stereo vision-based methods, is a challenging task. In this study, a freely adjustable vision measurement system for large objects is developed by connecting a camera to a total station telescope via a fixed mount. In this system, a camera is focused on the target object to be measured in far-range scenarios. A high-precision calibration method is proposed based on non-parametric camera models. In addition, a point cloud-based three-dimensional reconstruction method is developed by integrating with the non-parametric camera models. The experimental results showed that the proposed system has high precision and robustness. When measuring objects from a distance of 8 m in an indoor scenario, the error in the maximum spatial distance measurement between two corner points on the calibration board is less than 0.24 mm. The maximum relative measurement error is less than 0.09%. When measuring an object 20 m away in an outdoor scenario, the average error in the spatial distance measurement between two arbitrary points in an 11 m×4 m testing region is 5.6 mm. The average relative error in the distance measurement is 0.12%.
Proportionate Study of Neuron Models for Implementation of Neural Encoder

Neuromorphic Engineering is the interdisciplinary branch where various designs are innovate with the use of VLSI technology. This design named as Artificial Neural System that included vision system, head and eye system, auditory system and autonomous robot design. In recent years, neuromorphic computing and VLSI technology merges to used in robotics applications. Biological behavior of neuron is implemented using MOSFET structure which is the part of VLSI technology [1]. One of the key component of neu-romorphic computing is the neural encoder. Comparative study of various neuron models for proposed temporal encoder. Then mathematical implementation of Hodgkin- Huxley (HH) and Leaky Integrate & Fire (LIF) neuron model in the one of the suitable technique of neural encoder. This implementation has performed using an MATLAB software. After implementation this two model encoder comparative study has been performed for selection of better temporal encoder which further used in auditory spiking neuron.
High-speed 3D imaging with three binary patterns using Hilbert transform

The number of patterns used to reconstruct one 3D image is of great importance to high-speed 3D shape measurement. This paper presents a method to reduce the number of patterns by using Hilbert transform with only three binary patterns. Two fringe patterns at different frequencies and one DC component pattern are used for absolute phase retrieval. We directly applied Hilbert transform to the difference maps generated by subtracting the DC component from the fringe patterns. Then, with two-frequency phase unwrapping method using geometric constraints, the absolute phase is retrieved robustly. We designed a system to capture 2D images at 6,000 Hz, and achieved 3D geometries at 2,000 Hz. Experimental results verified the success of the proposed method.