Reviewerly/exHarmony huggingface dataset:
The Role of Attribute Ranker using classification for Software Defect-Prone Data-sets Model: An Empirical Comparative Study

Feature selection, is an issue firmly identified with size decrease of data-sets model. The target of feature selection is to recognize features in the data-set as significant, and dispose of some other feature as unimportant and repetitive data. Since feature selection diminishes the dimensionality of the data-sets model, it holds out the probability of increasingly successful and quick activity of data mining algorithm which can be worked quicker and all the more adequately by utilizing feature selection. In this research paper we will investigate feature extraction Principal Component Analysis (PCA) with attribute ranker search technique. In practice, Principal Component Analysis with attribute ranker search strategy isn’t just used to improve extra storage space or the computational accuracy and efficiency of the classification algorithm, however can likewise enhance the prescient presentation by diminishing the scourge of dimensionality — particularly on the off chance that we are working with software defect-prone or non-defected data-sets models. We have used 10 datasets models, these datasets models basically REPOSITORY model of NASA which contain binary class defected and non-defected datasets models. We have also used 6 classifiers for comparatively analysis between objective model and real datasets model. We illustrated the comparatively analysis between PCA Ranker Search method with No-PCA. We have also compared classifiers efficiency with each other. The most efficient and useful classifier are the Bagging and Multilayer Perceptron at all attribute ranking search method. But the comparatively analysis between the classifiers that Naïve bayes and MultiLayer Perceptron have well increased the correctly classified instances percentage in overall software fault forecast. One more comparison between the PCA Ranker Search Method and No-PCA that is attribute ranker search method is really good for increasing accuracy and efficiency for software fault forecast dataset model as compare the no-PCA method.
Image Steganography Using Bit Differencing Technique

The swift progression of evidence correspondence in contemporary time demands protected communication of data. Steganography obscures personal material in numerous record organisations, for example, photograph, content, sound, and audio–visual, impalpability, payload and vigour are the key complications on the way to steganography. This proposed work would give a nascent procedure that could be used for storing imperative data inside some cover metaphors by means of most significant bits (MSB) of the cover metaphors that have been presented. Firstly, the bits are numbered in increasing order, and bit number 6 is concerned over storing the confidential data which is based on the difference of bits 6 and 7. The result is generated on the basis of the difference whether the resultant bit is same as that of secret bit or not. If the resultant bit is distinct as compared to confidential pixel, the bit number 6 is modified and updated in the original bit. The outcome communicates the suggested model that must give good percentile of improvements in signal-to-noise ratio. Here, picture is fragmented into red, green and blue components where red components used for signalling on the way to store information in green or blue components of the portrait. The proposed method built on most significant bits (MSB) is used for safeguarding the organisation from unauthorised admittance, and the interlopers would not be intelligent to admittance the intimate data.
From Explicit to Implicit Dynamic Frames in Concurrent Reasoning for Java

In our earlier work we presented a method for formal verification of concurrent Java programs based on Dynamic Logic and symbolic permissions. Embedded within the explicit dynamic frames method realised through JML⁎ specifications, permissions to heap locations and the actual heap location values are tracked separately and require two independent and often overlapping frame specifications. This is in contrast to well established Separation Logic and sibling frameworks, where program frames are inferred from permission annotations that already provide implicit framing information. In this paper we show how to avoid redundant frame specifications and move towards the implicit framing approach in our method. We strive to keep as much as possible of the existing reasoning framework to preserve the general verification philosophy and implementation of our verification tool, the KeY verifier. We achieve our goal by only a small alteration of the existing proof obligation generation without changing any core part of the underlying logic, in particular, we maintain its closed character. However, even though specifications become more natural and less redundant, the indirect character of the specifications introduces a clear performance penalty for the verification engine. We then proceed to a brief discussion why, under our minimal approach assumptions, this extension is still not sufficient to translate Separation Logic specifications into our framework.
Value functions and two classes of binary codes

We state that the minimum and the maximum support weight of subcodes of a code are useful in several aspects. Based on the above findings, we will determine part of the above mentioned parameters for two classes of binary codes. We also present the separation property and calculate the trellis complexity of a class of binary codes.
Features of informatization of sociological knowledge

The article assumes an analysis of information as one of the most important socially significant resources of modern civilization. The authors stressed that main paradigm characteristic of modern social theory can be expressed by the thesis about the impossibility of direct access to social phenomena. In modern civilization, information is becoming one of the most important socially significant resources. The authors think that its role as a strategic resource increases with the development of network technologies that accelerate data transfer and facilitate the search for necessary information. The authors are confident that the role of higher education in this process is key. But it itself is faced with a huge number of problems, one of which is improving the quality of its functioning and guaranteeing the results of its activities. This problem is closely related to the implementation of innovations. The authors suggest that innovation in education should be understood as an innovation designed to resolve the current problem situation in order to optimize the educational process, improve its quality or organize favorable conditions for the assimilation of material by students. Based on a critical approach to reform the current system of higher education the paper touches upon the need to change the process infornatization of humanities education.. The authors conclude that the development of humanities education in the information society is aimed at implementing an interdisciplinary approach, ie should ensure the effectiveness of the development and application of humanities knowledge and form a conscious responsible choice in a variety of cultural meanings, cultural self-determination.
Boundary Detection in Images in Intelligent Systems of Autonomous Vehicles Using Wavelet Transform

“Smart city” is one of the concepts within which modern scientific and technical areas are actively developing. This concept defines the tasks associated with the organization of various infrastructure objects' continuous monitoring to optimally divide resources and ensure security. One of these tasks is to ensure road safety involving autonomous vehicles. The solution to this problem involves various technologies, including computer vision technologies. One of the approaches in this area is based on machine learning methods. These methods consider objects models in images represented as attribute vectors. Boundary attributes are often used here. The formation of these attribute comes down to two steps the boundaries detection and their description in the form of descriptors. This paper describes an approach to the objects' boundaries detection in images in intelligent systems of autonomous vehicles, which is based on the use of wavelet transform. The method is based on determining the significance of the brightness change magnitude at some point at a certain level of the wavelet decomposition. For that, it is necessary to evaluate the contribution to the total image energy of the detailed coefficients corresponding to this point. The method determines the sequential refinement of boundaries, which is as follows: as the brightnesses of the original image's copies at different levels are interconnected, we assume that the boundary points at different levels correspond to each other. The proposed method is simple to implement, has a relatively high speed and the ability to flexibly configure for real operating conditions.
Supporting Medical Auxiliary Work: The Central Sterile Services Department as a Challenging Environment for Augmented Reality Applications

This paper reports on the central sterile services department (CSSD) as a potential new design space for future research in Human-Computer Interaction (HCI) and Augmented Reality (AR). Within the last 2 years, we explored processes, tools and user needs in this field to identify use cases with the capability of enhancing everyday work using AR head-mounted displays (HMD). The conducted research was focused on the potential of applying AR technology as a proof of concept in which 8 problem-driven use cases were identified. These use cases enable interesting aspects for future investigation and utilization of new technologies. In addition to that, this paper describes the insights into user groups, their tasks, challenges, and needs for work support in this specific domain. Furthermore, a sample application is introduced which demonstrates the possibilities of HMD-based AR in the CSSD.
Determining Gaze Behavior Patterns in On-Screen Testing

This study proposes a software system for determining gaze patterns in on-screen testing. The system applies machine learning techniques to eye-movement data obtained from an eye-tracking device to categorize students according to their gaze behavior pattern while solving an on-screen test. These patterns are determined by converting eye movement coordinates into a sequence of regions of interest. The proposed software system extracts features from the sequence and performs clustering that groups students by their gaze pattern. To determine gaze patterns, the system contains components for communicating with an eye-tracking device, collecting and preprocessing students’ gaze data, and visualizing data using different presentation methods. This study presents a methodology to determine gaze patterns and the implementation details of the proposed software. The research was evaluated by determining the gaze patterns of 51 undergraduate students who took a general knowledge test containing 20 questions. This study aims to provide a software infrastructure that can use students’ gaze patterns as an additional indicator of their reading behaviors and their processing attention or difficulty, among other factors.
Robust Prostate Cancer Classification with Siamese Neural Networks

Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to an extra dose of radiation. In this paper, we propose a new DL model, based on a Siamese neural network, able to withstand random noise perturbations. We use data coming from the ProstateX challenge and demonstrate the superior robustness of our model to random noise compared to a similar architecture, albeit deprived of the Siamese branch. In addition, our approach is also resistant to adversarial attacks and shows overall better AUC performance.
Training highly qualified personnel for professional activities in the context of digital learning

The article actualizes the solution of the problem of highly qualified teaching staff “Researcher. Lecturer - researcher” in the system of continuing education to carry out professional activities in the conditions of the emerging digital educational environment and presents the main components of a multi-level system of training lecturers for professional activities in the conditions of digital education. In developed countries of Europe, graduates of the third stage of higher education, who are awarded the degree of doctor of philosophy (PhD) at the end of their studies, are the main players in the field of forming a “knowledge society”, which is distinguished by a departure from template solutions in various fields of activity.
A state-of-art method for solar irradiance forecast via using fisheye lens

Abstract The purpose of this investigation is to enable the solar irradiance forecast function implementing a common camera devise instead of specialized instrument thereby serve for other researches. Development of various simulated tools requires higher accuracy surrounding weather condition data. Previous studies mainly focus on the improvement of precision for professional monitor equipment i.e. total sky imager, which is limited to the scope of users. In this research, a fisheye lens graph is rectified following a particular algorithm based on the image forming principle. Moreover, solar irradiance prediction adopts the advanced BP neutral network method being proved to be valid. Final results indicate that after rectifying the special perspective images under fisheye direction, colour threshold configuration could remarkably recognize the cloud image. The conclusion shows that common camera fisheye lens coupled with BP neural network successfully predict the solar irradiance.
A Step-by-Step Algorithm for Finding the Optimal Strategy for the Behavior of a Group of Robots

The solution of the multi-criteria problem, which includes the distribution of objectives, the planning of trajectories and the optimization of energy consumption, is considered in the realization of the collective interaction of robots. It is proposed to use a genetic algorithm according to the chosen conditions (constraints) and optimality criteria to find the best strategy for group behavior. The considerable difficulty in choosing how to control a team of autonomous mobile robots represents the distribution of tasks among agents that operate under conditions of parametric and information uncertainty, possess "modest" hardware, power and functionality. Therefore, the implementation of a multi-stage search for an optimal solution requires a specialized approach that takes into account the whole range of dynamic parameters, allowing for real-time target correction and degradation of robots until they fail. The basis of the proposed neurogenetic algorithm is a new algorithm for calculating the fitness function, in which the results of the neural network method of trajectory planning for a group of robots are used, as well as information about the initial charge of the batteries of robotic agents of the collective, the energy consumption of each agent and the preliminary estimation of the energy required by some agent to perform the individual tasks available to it. In order to ensure an acceptable performance of the algorithm and given the high dynamism of the external environment, it was decided to limit the search for solutions to only one step (the next working beat of the collective). The paper presents the results of the simulation of the task of finding the optimal behavior of robots, the algorithm of calculation of the specialized fitness function and the options of step-by-step search of the global strategy of distribution of tasks, which make it possible to increase the efficiency of the use of the team of robots due to the guaranteed production of the result while minimizing the total time of completion of all the tasks, as well as to increase the working time of the team due to the correct energy consumption.
Experimental Analysis of Optimal Bandwidth Allocation in Computer Vision Systems

This paper considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. In further contrast with prior work, it presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy.
Joint Matrix Factorization: A Novel Approach for Recommender System

Collaborative filtering (CF) is the most classical method for recommender system, but it is usually suffered from limited performance by the sparseness of user-to-item rating data. Recently, due to the powerful learning feature representation ability, deep learning components are used to leverage auxiliary information to assist recommendation. However, most existing models based on deep learning are incomplete so that merely extracting the item latent representation and ignoring the user parts. Besides, different data are not chosen from current models. This paper proposes a novel probability framework, named as joint matrix factorization (JMF). There are three components in JMF. Firstly, the modified multilayer crossing version of the factorization machine (MFM) is designed to extract the user latent factors based on user behavior information. Moreover, MFM is a general method which can be used to accomplish many tasks in terms of machine learning. Secondly, a modification of Long Short-Term Memory (LSTM), named as bidirectional LSTM (BLSTM), is used to extract the item latent factors of a document sequence from both front and back directions. Finally, we tightly integrate BLSTM and MFM into probabilistic matrix factorization (PMF) to form JMF. Compared with the classical matrix factorization and other integration models, JMF extracts document data as well as user behavioral data as item vectors and user vectors. Extensive experiments on five real-world datasets show the proposed model has better performance compared with the state-of-the-art recommendation methods.
Siamese Network Object Tracking Algorithm Based on Squeeze-and-Excitation

Object tracking method based on Siamese Network has shown great performance, which expressed the tracking problem as dependency relationship between target template x and search template z. How to extract the characteristics of the target is a crucial problem. We explicitly model channels interdependencies within modules and adaptively calibrate the feature response between channels to enhance the representation of the entire network. To enable the network to perform feature recalibration, global information is learned to selectively enhance useful features and suppress few less useful features. We proposed a Siamese Network tracker based on SE-ResNet-50. In the training phase, meta-learning was introduced to solve the problem of lack of training data, so that the model can achieve a better result on an extremely small amount of data. Our method overcame the limitations of Siamese Network through weighted cross-correlation. Experimental results illustrate our algorithm is much better than some popular trackers in solving the problems of occlusion, illumination change and object deformation.
Remote Monitoring of Physiological Parameters used in Ambient Assistive Technologies

Remote monitoring the progress or deterioration of the patient's physiological parameters at their homes is vital for medical personnel or caretakers to provide medical assistance in case of an early alert caused by some unusual activity that may be happen. The main purpose of the article is to propose a generalized architecture, including specifications for medical devices used, for design and implementation of an alert based medical system. The system will be used for remote monitoring of physiological parameters of patients and/or elderly people at their homes, as a part of a more complex Home Automation System and Medical Monitoring System. The system will be used with patients especially suffering from chronic illness or elderly people that need to be monitored from medical point of view. Briefly the proposed system contains wireless medical sensors and devices connected together to a gateway used to acquire and process the raw data transmitted by the medical devices, performs local processing and activates the alerts when necessary and forwards the measurements to the cloud for storing and further processing.
Prescribed convergence analysis of recurrent neural networks with parameter variations

Recurrent neural networks are designed to be convergent to the desired equilibrium point for their applications. Network parameter variations lead network states to other different points. So this paper discusses the prescribed convergence problem of recurrent neural networks with parameter variations. Firstly, we recurrent neural networks’ equilibrium point variation principles when parameters are changed. Then we design one track controller to make recurrent neural networks be convergent to the prescribed equilibrium for known parameter variations. Next, we present one adaptive controller to lead network states to the desired equilibrium for unknown parameter variations. At last, two examples are given for validating the presented methods.
Establishing a Tripartite Intelligent Reading Platform Connecting Schools, Families, and Students: An Approach with Chinese Characteristics to Promote Students’ Reading

Reading is important for students, but despite many efforts made to promote it, Chinese students generally do not spend much time reading. We believe the bottleneck lies in the difficulty of retaining students’ reading data, which makes it difficult for teachers and parents to know, manage, and guide students’ reading behaviors. Therefore, we establish a tripartite intelligent reading platform connecting schools, families, and students: schools assign reading tasks and give instructions on the platform; students “punch in” on the platform before and after the reading tasks; parents can get to check on their kids’ performance through the platform and also keep encouraging and supervising them to read. The platform has now been used in 110 schools in Guangzhou, and its significant effects are proved by large-scale survey statistics. Compared with traditional ways that aim at arousing students’ reading interest in the first place but are often eventually defeated by students’ massive compulsory homework under China’s educational system, our approach using mandatory methods is of great Chinese characteristics. In the beginning, the students might be “forced” to read by family-school collaboration. But gradually, external motivation would be transformed into internal motivation; students are becoming actively engaged in reading. In the future, based on the platform, Chinese governments and research institutes are preparing to establish large databases concerning reading and student’s development, which will hopefully make great contributions to educational decision-making and scientific research.