Reviewerly/exHarmony huggingface dataset:
Rotation center calibration based on line laser rotating platform

Abstract Line lasers are of great significance in many fields such as industrial inspection, machine vision, cultural relics identification, mechanical design, and medical oral cavity. The three-dimensional reconstruction of the line laser can accurately obtain the three-dimensional information of the surface of the object, and quickly complete the three-dimensional contour reconstruction of the object to be tested. In the online laser rotation scanning, the object rotates around a certain point, which is a rotation center, and the calibration of the rotation center is the main factor that restricts the accuracy of the three-dimensional contour. At present, the calibration of the center of rotation is mainly obtained by the characteristics of a circle (ball, cylinder, cone, etc.). This paper mainly introduces the basic process of rotating scanning, rotating center, and rotating methods as well as their advantages and disadvantages. First, the basic process of rotating scanning is briefly introduced. Secondly, the definition and content of the rotating center are introduced. Then, the calibration methods of three rotating centers (ellipse fitting method, symmetry method, plane fitting method) are introduced. Finally, the advantages and disadvantages of the three calibration methods and their respective applications are summarized.
Bayesian Design of Sampling Set for Bandlimited Graph Signals

The design of sampling set (DoS) for bandlimited graph signals (GS) has been extensively studied in recent years, but few of them exploit the benefits of the stochastic prior of GS. In this work, we introduce the optimization framework for Bayesian DoS of bandlimited GS. We also illustrate how the choice of different sampling sets affects the estimation error and how the prior knowledge influences the result of DoS compared with the non-Bayesian DoS by the aid of analyzing Gershgorin discs of error metric matrix. Finally, based on our analysis, we propose a heuristic algorithm for DoS to avoid solving the optimization problem directly.
Text Classification Using Self-Structure Extended Multinomial Naive Bayes

With the advent of neural networks and its subfields like deep neural networks and convolutional neural networks, it is possible to make text classification predictions with high accuracy. Among the many subtypes of naive Bayes, multinomial naive Bayes is used for text classification. Many attempts have been made to somehow develop an algorithm that uses the simplicity of multinomial naive Bayes and at the same time incorporates feature dependency. One such effort was put in structure extended multinomial naive Bayes, which uses one-dependence estimators to inculcate dependencies. Basically, one-dependence estimators take one of the attributes as features and all other attributes as its child. This chapter proposes self structure extended multinomial naïve Bayes, which presents a hybrid model, a combination of the multinomial naive Bayes and structure extended multinomial naive Bayes. Basically, it tries to classify the instances that were misclassified by structure extended multinomial naive Bayes as there was no direct dependency between attributes.
Evaluation of Local Features for Near-Uniform Scene Images

The following topics are dealt with: feature extraction; learning (artificial intelligence); convolutional neural nets; image classification; medical image processing; image segmentation; edge detection; diseases; face recognition; object detection.
Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms

This chapter presents a study to identify with classification techniques and digital recognition through the construction of a prototype phase that predicts criminal behavior detected in video cameras obtained from a free platform called MOTChallenge. The qualitative and descriptive approach, which starts from individual attitudes, expresses a person in his expression, anxiety, fear, anger, sadness, and neutrality through data collection and feeding of some algorithms for assisted learning. This prototype begins with a degree higher than 40% on a scale of 1-100 of a person suspected, subjected to a two- and three-iterations training parameterized into four categories—hood, helmet, hat, anxiety, and neutrality—where through orange and green boxes it is signaled at the time of the detection and classification of a possible suspect, with a stability of the 87.33% and reliability of the 96.25% in storing information for traceability and future use.
Security-Related Commits in Open Source Web Browser Projects

The security of web browsers is of paramount importance, these days perhaps more than ever. Unfortunately, acquiring real data for security-related research is not an easy task, as access to sensitive information is rarely granted to researchers who are not members of a trusted security team. In this paper, we describe a method to mine security-related commits from open source software repositories, even if the reports of already fixed security issues have access restrictions, and we show the applicability of the method on two popular web browser projects. We also made the mined dataset available, listing more than 13,000 security-related commits, with which we hope to facilitate research on security-targeted bug prediction.
Detection of Fuzzy Text Duplicates in Large Amounts of Information

Fuzzy duplicates are texts that have undergone some adjustments and modifications, but retain a semantic similarity with the originals. The problem of detecting texts that are significantly similar to each other (fuzzy duplicates) arises when building plagiarism search systems, news aggregators and other intelligent systems. Currently, a number of methods have been developed to detect such documents. However, these methods allow either to quickly detect similar documents with a decrease in accuracy, or to search with high accuracy but for an unacceptably long time. The work is devoted to the development of methods for fuzzy duplicates detection that provide work with large amounts of information with minimal time and high search accuracy.
Multi-robots Trajectory Planning Using a Novel GA

One of the biggest challenges encountered in mobile robots is in the planning of their trajectory, as their development is directly related to the greater autonomy of robots. In this work a solution for multi-robot path planning problem is presented, the problem modeling is performed using the combination of the team orienteering problem and the problem of the multiple backpack, this combination allows each robot to have an individual limitation, the proposed solution was developed using genetic algorithms.
Performance and energy efficiency of the analysis of the growing synchrophasor in a high-perfomance computing environment

Synchronized phasor (synchrophasor) measurements for power systems are is very useful tool for engineers in analyzing dynamic events in the grid which is not possible with traditional SCADA measurements and was codified in IEEE C37.118 Synchrophasor for Power Systems and IEEE 60255-118-1-2018 Synchrophasor for power systems - Measurements standards. Online and retrospective processing of data collected from hundreds and thousands of sources of synchrophasor measurements is a complex computational task. A Python based framework for digital signal processing with its parallel implementation was developed to use it in analysis of growing synchrophasor measurements. An implementation shows satisfactory performance and parallel scaling. The energy-efficient regimes of its parallel execution were found. Ways of further optimization are identified. The results of numerical experiments are presented.
Quality Assessment for Natural and Screen Content Images

Quality assessment (QA) of screen content images (SCIs) has gained more and more popularity. SCIs are very different from natural images (NIs) which have been dealing with by most researchers in the literature. QA methods specifically designed for NIs also can be used to evaluate the quality of SCIs. Yet, their performances are unsatisfactory. This may due to the statistical differences of SCIs and NIs. In this paper, SCIs and NIs QA methods in the literature are being compared and studied for both SCIs and NIs benchmarked databases. It is found out that methods that incorporate gradient features work well for both SCIs and NIs. This points out a possible way to utilize gradient features to come out with a QA method that works for both SCIs and NIs simultaneously. Hence, application related to SCIs and NIs such as deep learning and multitasking for person tracking system can be improved with the QA method.
Some misconceptions frequently detected in students of compiler construction courses

This paper reports some misconceptions that are frequently detected in students of introductory compiler construction courses. Compiler construction is a topic included in many university computer science programs. It deals with the design and implementation of programming and other computer languages. These misconceptions have been identified by the author when teaching the topic at the Complutense University of Madrid (Spain). The misconceptions are classified according to the different stages approached in compiler construction: lexical level, syntactic level, semantic level. At the lexical level the following misconceptions are documented: (i) incorrect identification of token types; (ii) transitions with more than one symbol in the state transition diagrams; and (iii) inadequate treatment of keywords. Misconceptions at the syntactic level include: (i) confusing non-determinism and ambiguity; (ii) considering ε (the empty string) as a terminal symbol; (iii) misinterpreting operator precedence; (iv) attempting to perform type checking at the syntactic level; and (v) grammar transformation errors. Finally, the following misconceptions are reported at the semantic level: (i) transformations that do not preserve semantics; (ii) difficulties in determining the abstract syntax trees of complex sentences; (iii) incorrect interpretation of how the symbol table works during the analysis of lexically-scoped languages; (iv) variable addressing errors; and (v) errors related to the translation of complex designators. Identifying these errors is very useful in developing instructional strategies to help students avoid them.
Cancer Cell Detection Using Projective Transformation for Effective Segmentation

The research paper focuses on providing review of existing cancer detection techniques and the advancements that could be done for effective segmentation. The process in which many images of the similar variety are engaged and combined at distinct instance, from diverse angles and by distinct sensors is called as image registration. The idea proposed in this paper, is the image registration of MRI and CT scan of the same cancer affected patient followed by segmentation using `Projective Transformation'. It includes Marker-Controlled Watershed Segmentation. This is a process wherein, objects in touching images are divided into separate objects. This research is aimed at finding an efficient way to detect the cancer growth at different stages which in turn will help doctors to reduce the death rate due to cancer.
A Fault Location Method Considering Distribution Network Partition Based on Deep Learning

In this paper, a fault location method considering distribution network partition based on deep learning is proposed, in which the Tensorflow framework is employed to establish and construct the fault location model of the distribution network. This method firstly collects the current and voltage data to form fault data vectors through the Feeder Terminal Unit. Combined with the complex network theory, each node degree is calculated to represent the node priority, and the topology of the distribution network is partitioned to form each regional model. Secondly, it builds a feature extracting network and a Deep Neural network to mine the mapping relations between fault data vectors and fault sections and form the final fault location model through training. Case studies show that compared to the back propagation (BP) neural network model and the support vector machine (SVM) model, the deep learning model has faster convergence speed and higher fault location accuracy.
An Incident Identification Method Based on Improved RCNN

An emergency is a sudden and harmful event. It is of great significance to quickly identify the event and reduce the harm caused by the event. In this paper, the current advanced recurrent convolutional neural networks (RCNN) are utilized, but the traditional model cannot effectively identify the event, and the accuracy rate is not good enough. In order to solve this problem, the recurrent neural network and activation function part of the traditional model are improved, and through experimental comparison, the optimal model in the training model is selected. Finally, the accuracy of the model is 90%, the recall rate is 92.55%, and the F1 value, a metric that combines accuracy and recall, is 91.26%, which proves that the improved model has good effects.
An Improved Particle Swarm optimization based Neural Network Training for Classification

Artificial Neural Networks (ANNs) have been successfully applied to solve various classification problems. To perform the classification task,neural network training is essential. Multiple parameters are associated with neural network training and setting these parameters are a very challenging task. Several meta-heuristic algorithms are employed to train the network. In this paper, Particle Swarm optimization (PSO) is used to train the network to optimize the training error while solving a classification problem. Performance of the proposed method is evaluated on ten different data sets which are in supervised. The experimental result shows a significant performance of the proposed methodology compared to traditional neural network training approaches.
Modeling CBR using Python for Football Matches

Case-based reasoning(CBR) is a growing topic in machine learning. CBR is a system which contains 4Rs, retrieve, reuse, revise and retain. When a problem enters the system, all the cases which are similer to the problem are retrieved, then adaptation process is used and revised and then stored in the casebase. The observed problems of the system are lack of knowledge, adaptation is very difficult and different domain need different adaptation process bor better result. In this paper, we get to know what is case-based reasoning and its adaptation methods. The literature survey tells that every domain may need different adaptation methods. We have used a few adaptation methods in python to a football match dataset and found which adaptation method gives the best accuracy. We have used myCBR for getting retrieved cases.
Applications of Optimal Stopping Algorithm for Social Graph Based Recommendation

Recommendation Systems are becoming one of the most used tools in online marketing. In this paper, basic item based recommendation algorithms were extended to make use of a user's social circle's influence in deciding the recommendations. Depending on the social network of a user, the items to be recommended were selected. Apart from this, his own previous ratings of movies were considered to get an assessment of his taste and this was added up to this ego-network's preferences to get a list of recommendations. An optimal stopping algorithm was then implemented on top of this recommendation system. This algorithm helped us pick users better from the ego-network. In the end, a general recursive optimal stopping algorithm is proposed, providing an efficient way to search for best set of users to recommend movies from throughout the social network.
Design of Chest Cuirass for Negative Pressure Ventilation of Neonates

Non-invasive external ventilation provides breathing support that is more akin to the physiological function of the lungs. Chest cuirass ventilator, which is available in the market, has not in portfolio cuirasses from patients under 1,8 kg, so it is not usable for neonatal patients. This project aims to design a cuirass for extremely immature infants that would provide ventilation support of the respiration using negative external pressure. The cuirass was made by technology of 3D printing.
Compositional Data Analysis in Chemometrics

This article presents an introduction to the field and methods of compositional data analysis. The basic concepts and ideas are explained, and the approach is motivated from a geometrical perspective. Multivariate methods that are relevant in chemometrics are considered from the point of view of compositional data analysis, such as principal component analysis, multivariate outlier detection, as well as regression and classification, also for the high-dimensional case. The article includes several examples which illustrate the use of this methodology for practical data analysis.
Stability Margin Estimation for Tracked Dozer Used in Defence Applications

Off-road tracked vehicles like dozers and excavators are predominantly used for road formation & snow clearance operations in High Altitude Areas (HAA). These operations are a part of day-to-day activities in forward areas prone to snowfall, landslides and avalanches. Stability of tracked dozer deployed for such earth moving operations is a major concern, because it involves the risk of losing equipment and human life. To avoid such instability scenarios and to get informed about vehicles state, Force-Angle Stability measure is used. It computes the stability index of the tracked dozer taking ground reaction forces and vehicles center of mass as input from the multibody dynamic simulation in MSC ADAMS ATV software. The stability estimation for the dozer maneuvering on roads containing ditch, downslope and uneven terrains is addressed for the proximity of tipover prediction.
Development and Calculation of a Computer Model and Modern Distributed Algorithms for Dispersed Systems Aggregation

In this work, an attempt has been made to eliminate the contradiction of the Smoluchowski equation, using modern distributed algorithms for creating calculation algorithm and implementation a program for building a more perfect model by changing the type of the kinetic equation of aggregation taking into account the relaxation times. On the basis of the applied Mathcad package, there is a developed computer model for calculating the aggregation of dispersed systems. The obtained system of differential equations of the second order is solved by the Runge-Kutt method. The authors are presetting the initial conditions of the calculation. A subsequent analysis was made of the obtained non-local equations and the study of the behavior of solutions of different orders. Also, this research can be aimed at the generalization of the proposed approach for the analysis of aggregation processes in heterogeneous dispersed systems, involving the creation of aggregation models, taking into account both time and space non-locality.
Employee Information Security Practices

Employee information security practices are pivotal to prevent, detect, and respond to security incidents. This article synthesizes insights from research on challenges related to employee information security practices and measures to address them. The challenges identified are associated to idiosyncratic aspects of communities and individuals within organizations (culture and personal characteristics) and to systemic aspects of organizations (procedural and structural arrangements). The measures aimed to enhance systemic capabilities and to adapt security mechanisms to the idiosyncratic characteristics and are categorized as: (a) measures of training and awareness; (b) measures of organizational support; and (c) measures of rewards and penalties. Further research is needed to explore the dynamics related to how challenges emerge, develop, and get addressed over time and also, to explore the interplay between systemic and idiosyncratic aspects. Additionally, research is needed on the role of security managers and how it can be reconfigured to suit flatter organizations.
A Multi-level Access Control Scheme Based on Attribute Encryption for Big Data

In order to meet the needs of flexibility and security in big data access, this paper first processes the access objects from folders, files and metadata respectively. Then, aiming at the security and efficiency problems in attribute encryption, this paper uses the processing strategy of public key encryption and private key decryption, and gives the attribute revocation. Finally, the experimental results show that the proposed scheme is feasible and effective.
Improvement of Software Productivity by The Integrated Software Test Management System

Development of derivative products and software in short term has been required in semiconductor apparatus. The integrated software test management system has developed to prevent regression and shorten development cycle. This paper discusses about the system. After that, it mentions the result of applying the system to software development project of a semiconductor inspection apparatus. The effectiveness of reducing workload of integration testing and improvement of software quality have been found by this validation.
Measuring Research in RSS Feed Literature

The study analyzes the publications on the research literature on RSS feed during 2008-2018. It is found that 175 publications only brought out by the researchers in the core area of computer science, library science, and engineering related field of research. The study analyzes that information science and library science areas are seen as the predominant areas, which have a plurality (39; 28.2%) of the publications distributed in the field. Shell International Ltd has the most (10; 5.71%) publications. USA occupied the top country. It contributed (10; 48%) of the publications on RSS Feed during the period of study.
Emerging Technologies, Information Provision, and Libraries

The advent of computers and information communication technologies (ICTs) has drastically changed the acquisition, storage, and retrieval of information. Further, information can be accessed through various media as new technologies continue to spring up. Since information can be accessed anywhere as long as one has internet connectivity, this has resulted in many users not using the libraries and other information centers. The decline in the use of libraries is worrying, hence the need to understand how the emerging technologies have changed information provision in the libraries. The chapter addresses challenges that affect the provision of the needed services in the libraries. Finally, alternative services that libraries are adopting in order for them to remain relevant in this information age are outlined. This chapter provides insights on how libraries can continue to play their role as information providers and how they can fit into the present information age.
The General Principles of Quantum Key Integration into Data Network Part 1

In this paper, we have considered the examples of classical cryptography. Here we describe ciphers and classical encryption and decryption algorithms. We specify the basic methods and evolution vectors for cryptography and cryptanalysis. We also outline the basic principles of quantum cryptography. You may see how quantum key distribution system works as well as the main types of attacks on quantum communication systems. We have described the quantum key distribution system interaction with hardware encrypter. During this research, we have conducted a review of the requirements for the stability of the developed quantum key integration algorithm.