Reviewerly/exHarmony huggingface dataset:
An Improved Sequential Pattern mining Algorithm based on Large Dataset

So far, there are many classical algorithms for sequential pattern mining. In these algorithms, PrefixSpan algorithm is one of the most widely used algorithm, the algorithm USES the prefix projection technology, effectively avoid the candidate item, to a certain extent, improve the efficiency of mining, however, need to construct a large number of projection database PrefixSpan algorithm, and constructs the projection database not only need to consume a lot of memory, and need to add a lot of scanning time, therefore, in this paper, the PrefixSpan algorithm is improved, and put forward the ISPA algorithm, this algorithm can greatly reduce the number of projection database building and thus improve the efficiency of sequential pattern mining First, by comparing the mining results of the two algorithms, it is found that ISPA algorithm can find the most important sequence pattern, thus satisfying. Secondly, experiments are performed on three aspects: different support, types of data sets, and size data sets. It is verifies that the ISPA algorithm is better than the PrefixSpan algorithm.
Centralized Trajectory Tracking Controller for a Multi-robot System

This work shows the development and implementation of a centralized trajectory tracking system for multi-robot systems, which is based on a kinematic trajectory controller for unicycle robots and subsequently transforming it to differential kinematics, a wireless network is also created for the communication of the master with the slave robots, additionally a method of prevention and avoidance of collisions between the robots is implemented.
Approaches for Efficient Query Optimization Using Semantic Web Technologies

Query optimization system proposes an answer-driven approach to information access. Most of the query optimization system aims for information retrieval required by natural language queries. Queries are generally asked within a context, and answers are provided within that specific context. RDF is a general proposition language for the Web, joining data from diverse resources. SPARQL, a query language for RDF, can join data from different databanks, as well as papers, inference engines, or anything else that may reveal its expertise as a guided classified chart. Because of lack of proper architectural circulation, the existing SPARQL-to-SQL translation techniques have actually trimmed a lot of restrictions that decrease their toughness, effectiveness, and reliability. These constraints include the generation of ineffective or perhaps incorrect SQL inquiries, lack of official history, and bad applications. This paper recommended a structure which made use of by an ontology-based moderator system to provide the well-defined semantical design, which (i) supplies a distinct SPARQL semantics used to rewrite the question in SQL; (ii) ontology-based expertise is created for rapid accessibility as well as equate question revising SPARQL to SQL for reliable information retrieval in semantic Internet data of big dataset; (iii) hybrid query optimization framework is proposed for query handling technique for the effective access of customized details on the semantic Internet making use of bundled ontology expertise and also inference engine.
An Attention-based Sequence Learning Model for Scene Text Recognition with Text Correction

Recognizing text from images taken in natural scenes is a challenging task and a hot research topic in computer vision. Unlike traditional optical character recognition (OCR), words in natural images often possess irregular layout (e.g. arbitrarily orientation, blurring, perspective distortion) which are difficult to recognize. In this paper, we develop a novel method consisting of a text recognition network and a text correction component, which is more robust to irregular text. The text correction component rectify the text of an input image to a more "readable" text. The text recognition network is a more "location aware" attention-based sequence learning model that take the rectified image as input and recognize the text. The entire networks are trained jointly by only images and word-level annotations. The standard Softmax loss function only considers the separability between classes but does not restrict the aggregation within classes. Therefore, we adopt a new loss function based on the Softmax loss function to enable the model to learn more discriminative features, reduce misjudgments and improve accuracy. Extensive experiments on seven popular standard benchmarks, demonstrate the proposed method is comparable to state-of-the-art performance.
Development of communication science, computer science and cybernetics in the 1940s – 1950s

This publication presents the emergence of the new sciences that are most important for today’s world: communication science, cybernetics, the theory of information, and the theory of the noosphere in the 1940s – 1950s. The purpose of this article is to analyze the total scientific achievements in Eurasia at the time of the Second World War. This was a bright phenomenon in the formation of new revolutionary theories. Works of Chicago-based and Frankfurt-based schools of thought, the theoretical concepts of T. Adorno, M. Horkheimer, H. Lasswell, P. Lazarsfeld, аnd other researchers laid the foundations of the communication science and contributed to the breakthrough in a number of the key subject areas. A system approach to and a comparative analysis of the causes and subsequent consequences of the achievements at that time for today’s world served as a methodological basis for a comprehensive consideration of large-scale studies of the past. The scientific novelty of this historic study consists in the interdependence and complementarity of the theoretical and practical achievements in the 1940s and rethinking of their importance in the structure of concepts in the 20th century. Industrial and military goals associated with the automatic management and communication processes required fundamentally new approaches and achievements. When World War II broke out, N. Wiener worked on these problems aiming at creating a computer, which pushed him to the idea that the principles of managing biotic and abiotic systems are the same and to the cybernetic concept development. In the mid-1940s, J. von Neumann built the first digital computer. In 1945-1947, A. Turing worked, as an inventor of “a universal machine”, on the “electronic brain” project and was the first to develop a number of programs for it. In 1942, C. Shannon published his work dedicated to the theory of information permitting a constellation of researchers to lay the foundations of the theory of communication. V. Vernadsky’s noosphere concept proposed in 1944 was particularly important. At present, the ideas of that period are gaining new importance as a basis for the single planetary management system.
Eye-Gaze Prediction Considering Text Reading Speed for Visual-Tactile Content

This study is attempts to develop interactive visual-tactile content that uses eye-gaze movement to present vibrotactile feedback (VTF) on text. Since text is independent of time, reading speed varies depending on the individual reader. Therefore the timing of VTF must be considered when designing interactive content. Assuming that timing is affected by reading speed, we administered a survey to determine the preferred timing of VTF for each subject. The survey results indicate that there is a relationship between timing and reading speed.
Multi-Label Visual Feature Learning with Attentional Aggregation

Today convolutional neural networks (CNNs) have reached out to specialized applications in science communities that otherwise would not be adequately tackled. In this paper, we systematically study a multi-label annotation problem of x-ray scattering images in material science. For this application, we tackle an open challenge with training CNNs - identifying weak scattered patterns with diffuse background interference, which is common in scientific imaging. We articulate an Attentional Aggregation Module (AAM) to enhance feature representations. First, we reweight and highlight important features in the images using data-driven attention maps. We decompose the attention maps into channel and spatial attention components. In the spatial attention component, we design a mechanism to generate multiple spatial attention maps tailored for diversified multi-label learning. Then, we condense the enhanced local features into non-local representations by performing feature aggregation. Both attention and aggregation are designed as network layers with learnable parameters so that CNN training remains fluidly end-to-end, and we apply it in-network a few times so that the feature enhancement is multi-scale. We conduct extensive experiments on CNN training and testing, as well as transfer learning, and empirical studies confirm that our method enhances the discriminative power of visual features of scientific imaging.
Security enhancement for MPLS data-plane forwarding based on dynamic signature label calculation

Cloud service gradually becomes the main trend for online business, and the security of core ISP networks between the cloud and customers is very important. The MPLS protocol is proposed to promote the forwarding performance in ISP networks without enough security consideration. However, network security becomes more and more important in the core and edge of Internet. In order to enhance the security of for MPLS data-plane forwarding in ISP networks, a dynamic signature label calculation method is proposed in this paper. It uses HMAC signature of MPLS packet data to calculate the dynamic label values. Every label switch router verifies the signature and recovers the original label for forwarding. Security analysis and comparison result shows that our method can defend several security attacks, such as label eavesdropping attack, packet insertion/replay attack and packet tampering attack.
Performance Evaluation of Iterative Denoising Algorithm Based on Variance Stabilizing Transform and Wavelet Thresholding

The restoration of images degraded by noise is one of the most important tasks in image processing. This paper deals with the recovery of an image from a Poisson noisy observations. More precisely, we have combined an iterative denoising algorithm based on Variance Stabilizing Transform (VST) with the conventional Wavelet Thresholding technique. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and riltered through a VST scheme and wavelet thresholding. Experimental results show the effectiveness of the proposed method for denoising images corrupted by Poisson noise. Performance assessment is provided.
Intellectual algorithms and information systems for the education process

This article discusses the modern approach to the learning process - the use of intelligent information systems in the education system and their algorithms. The use of intelligent information systems in education will enhance the effectiveness of education and the formation of talented future personnel. One of the most pressing problems of our time is the development of targeted, informative and applied technologies for the specialization of sciences as a new approach to the organization of the process of professional and individual development of students in the educational process.
A Hybrid Connectionist/ Substitution Approach for Data Encryption

The authors believe that the hybridization of two different approaches results in more complex encryption outcomes. The proposed method combines a symbolic approach, which is a table substitution method, with another paradigm that models real-life neurons (connectionist approach). This hybrid model is compact, nonlinear, and parallel. The neural network approach focuses on generating keys (weights) based on a feedforward neural network architecture that works as a mirror. The weights are used as an input for the substitution method. The hybrid model is verified and validated as a successful encryption method.
Multi Sensor Data Fusion With Risk Assessment

In robotic field, autonomous navigation safely is a timely subject. Therefore, many researchers try to elaborate a reliable system to respond this exigency. Among these systems, we mention multi sensor data fusion. The data fusion is an active field of research, It is almost applied in all domains. it is resolved by several manner. The best represented model of multi sensor fusion by excellence is the human fusion using the five senses. However, in our paper this problem is tackled based on an interesting method applied often in the risk management strategy. In our work, we have designed a new scheme of multi sensor data fusion using risk assessment of a three suggested sensors GPS, INS and Mono-SLAM. Based on these risks, we generate a three parameters dynamically used within an adaptive complementary filter. The aim of our work is to obtain an estimated pose from the three poses provided by the three sensors, but more safely than using ordinary methods of fusion. The simulation of the proposed solution shows a promising results in severe situations. Anyway, the fusion based on this approach at least gives a similar result to the ordinary complementary filter. However, the biggest improvement of the risk assessment reflects amelioration on the pose quality.
A Progressive Sampling based Approach to Reduce Sampling Time

Analytics plays vital role in Data Science. It involves finding trends and patterns from the huge repository of data. Scanning huge amount of data consumes lot of time, which can be reduced by sampling. In this paper we have demonstrated effectiveness of Progressive sampling wherein the sample size is gradually increased till it reaches a desired accuracy. By applying an algorithm based on Rademacher average to mine frequent datasets using Progressive sampling, we have shown that the runtime and the sampling time is considerably reduced as compared with static sampling.
The effect of using scientific ecosystem material of experimental method towards students’ scientific attitude

Abstract Students’ scientific attitude is one of the factors which affect students’ achievement. By using the pretest-posttest method, the purpose of this research was to know the significant effect of using experimental methods towards students’ scientific attitude. This research was conducted on April 9th – May 7th, 2019 at Junior High School 1 Baitussalam. The sample of this research was 28 students that were taken by using purposive sampling technique. In collecting the data, this research used questionnaires which analyzed by using SPSS 20 version and Microsoft excel. The instrument of this research was 36 items of questionnaires. The result of this research found that there was a positive significance effect by using experimental methods towards students’ scientific attitude which described (result = 7.08 &gt; table = 2.03)
Improvement of Faster-RCNN Detection Algorithms for Small Size Line Accessory Equipment

Abstract Aiming at the problem that the image of patrol inspection is affected by angle and distance, the target of small size accessory equipment in the image is small, and the recognition rate of current algorithm is low. An improved Faster-RCNN detection algorithm for small size accessory equipment of patrol image is proposed. By adjusting the structure of Faster-RCNN network, we can overcome the shortcomings of the current algorithm. Firstly, the deep residual network ResNet50 is used to replace VGG16 network. At the same time, the bottom and high-level features of convolution network are applied to the selection of candidate regions, so as to improve the utilization of effective information of targets, and then to improve the detection precision of small-sized targets. The results show that the detection accuracy of the improved Faster-RCNN model is 4.2% higher than that of the original algorithm.
Pitch Accent Placement on Function Words in Koreans’ English Utterances

This study aimed to investigate the relation between pitch accent placement and word classes such as content and function words. Korean English learners were asked to participate in listen-and-repeat practice between pretest and posttest to examine learning effects on pitch accent realization. Korean English learners put pitch accents on much more function words in pretest utterances than native English speakers. However, the use of pitch accents on functions words decreased statistically significantly in posttest utterances. In addition, Korean English learners tended to put pitch accents on the first words of intermediate phrases more often than native English speakers regardless of information status such as new, given, contrastive information. Finally, the use of L* in Koreans English utterances increased after the practice in some of Korean English learners. It can be argued that Korean English learners benefitted from the listen-and-repeat practice in pitch accent realization.
Resolution Conversion of Volumetric Array Data for Multimodal Medical Image Analysis

This paper aims to clarify statistical and geometric properties of linear resolution conversion for registration between different resolutions observed using the same modality. The pyramid transform for higher-dimensional array with rational-order is formulated by means of tensor decomposition. For fast processing of volumetric data, compression of data is an essential task. Three-dimensional extension of the pyramid transform reduces the sizes of the volumetric data by factor 2. Extension of matrix expression of the pyramid transform to the operation of tensors using the mode product of a tensor and matrix derives the pyramid transform for volumetric data of the rational orders. The pyramid transform is achieved by downsampling after linear smoothing. The dual operation of the pyramid transform is achieved by linear interpolation after upsampling. The rational-order pyramid transform is decomposed into upsampling by linear interpolation and the traditional pyramid transform with the integer order. By controlling ratio between upsampling for linear interpolation and downsampling in the pyramid transform, the rational-order pyramid transform is computed. The tensor expression of the volumetric pyramid transform clarifies that the transform yields the orthogonal base systems for any ratios of the rational pyramid transform.
Adaptive tracking algorithm based on target moving speed

Abstract To solve the problem of tracking failure caused by fast object movement, we proposed an adaptive tracking algorithm based on object movement speed on the basis of ECO-HC (ECO for hand-crafted feature version). First, correlation filtering method was used to obtain the distance of the maximum response point of adjacent frames. Then the speed of the object was judged by the distance of the maximum response point of the adjacent frame. Then the number of scale filter factors and interval frames updated by the template were adjusted on the basis of the object’s speed. The comparative experiment on the VOT2016 benchmarks showed that our tracker improved the accuracy and robustness of object tracking.
Knowledge Based Modelling of Complex Interconnected Systems

The present paper discusses the use of intelligent control for modelling complex interconnected processes. The latter usually have many inputs and outputs and can be found in various areas of application. While part of the inputs are measurable, others are not due to the presence of stochastic environmental factors. For this reason such kind of processes operate under uncertainty. The latter is addressed in this paper by intelligent systems that use probabilistic and fuzzy network structures.
Categorical Interpretation of Modal Structures under Bisimulation

Abstract In this work we summarise the concept of bisimulation, widely used both in computational sciences and in modal logic, that characterises modal structures with the same behaviour in terms of accessibility relations. Then, we offer a sketch of categorical interpretation of bisimulation between modal structures, which comprise both the structure and the valuation from a propositional language.
Status Observation Agent Node for Secure Sharing Data Store in Autonomous Decentralized Network

Almost all the businesses are utilizing web based services using cloud computing facility. These transactions of business utilize not only legal currency but also crypto currency. The security to protect these financial transactions which are connecting each guild services are important topics today. So every business of this type requires that highest security solution for their business transactions. This paper proposes secure sharing data store model using Status Observation Agent (SOA) node in Autonomous Decentralized Network System. The data to be shared is divided into multiple parts using Secure sharing algorithms and SOA node would select anonymous nodes in the same community of decentralized network and store the multiple divided parts of data in encrypted form. SOA will keep the Node path, and even the owner does not know where the data is placed by SOA. The concept of SOA in Autonomous Decentralized Network is shown and the process of storing and retrieving data is explained.
Algorithms Used for Scene Perception in Driverless Cars

Research on developing Driverless/Autonomous cars have been going on from a long time now, still there are many functionalities on which a human cannot be completely replaced, for example recognizing a scene and reacting to it properly, handling different real-time situations, going according to other cars with drivers inside them, etc. This paper will highlight the issues and their solutions related to the domain of scene perception. The issues related to the same were gridding, speed efficiency, and complex scene recognition. Solutions that should be implemented to solve the related problems are Pyramid Scene Parsing Network (PSP Net), Convolutional Neural Network (CNN), Hybrid Dilated Convolution (HDC) and SegNET and Efficient Neural network. Each algorithm has its own ownership and unique way of identifying the scene, therefore they can be compared along the parameters of scene perception. These solutions have been evolved with time, implementing these technical aspects into the current cars can help replace a human with a driverless car with the highest possible precision.
ICT Terminologies In Learning English

Corpus linguistic will be the one of latest and fast method for teaching and learning of different languages. Proposed article can provide the corpus linguistic introduction and give the general overview about the linguistic team of corpus. Article described about the corpus, novelties and corpus are associated in the following field. Proposed paper can focus on the using the corpus linguistic in IT field. As from research it is obvious that corpora will be used as internet and primary data in the field of IT. The method of text corpus is the digestive approach which can drive the different set of rules to govern the natural language from the text in the particular language, it can explore about languages that how it can inter-related with other. Hence the corpus linguistic will be automatically drive from the text sources.