Reviewerly/exHarmony huggingface dataset:
IM-Net: Semantic Segmentation Algorithm for Medical Images Based on Mutual Information Maximization

The medical image is often noisy, which makes it difficult to extract the image features from the medical image segmentation model. Because the noise is often generated randomly, it is difficult to use supervised information for denoising. In this paper, we focus on this challenging problem and propose an IM-Net algorithm for medical image segmentation based on mutual information maximization. The IM-Net can remove the noise and therefore improve the quality of the extracted feature by maximizing the mutual information between the extracted feature and the input image. IM-Net uses the Binary Cross Entropy with Logits Estimation to approach the true value of mutual information and uses a bilinear interpolation function as a discriminator to maximize the mutual information estimator. Extensive experiments are conducted and the IM-Net is compared with different methods to demonstrate the effectiveness of our model. Experimental results show that the training efficiency and segmentation precision are greatly improved.
Displacement Measurement Based on Computer Vision

In this study, a method to convert the position changes among light spots into displacement values by using digital image processing is proposed. Considering the precision and practical engineering environment, light spots are produced by an industrial laser source, and industrial digital camera is used as sensor to collect images with tiny displacements of target. The image binarization as one of digital image processing techniques is applied to the algorithm developed, and the least square fitting is used to achieve the transformation from image information to displacement information. Several simulation experiments and data processing were carried out to validate this method.
Reliability Optimization Allocation Method Based on Improved Dynamic Particle Swarm Optimization

Aiming at the reliability optimization allocation, the basic particle swarm optimization algorithm has the disadvantages of slow convergence and easy to fall into local extremum. This paper proposes an improved dynamic particle swarm optimization algorithm, which uses the extrapolation technique in mathematics to guide the evolution direction of particles. At the same time, the idea of transforming the multimodal function and dynamically adjusting the group size is introduced, and the elite set is used to preserve the optimal individual of each generation. Finally, this paper uses a certain type of sonar as an example to establish a reliability mathematical model, which uses the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm proposed in this paper. The results show that the proposed method has a stronger search ability, higher accuracy and better stability.
Efficient ACO Feature selection algorithm for Image stego anomaly detection based on SPAM features

The goal of this paper is to enhance the detection accuracy of steganalysis and to reduce the dimensions of high dimensional feature space by removing the irrelevant features. Feature selection is a significant area of research in steganalysis which can greatly inspires the accuracy in detection. In this paper we have proposed an Ant Colony Optimization feature selection (ACOFS- o( n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for an exploration on the enhancement of revealing (detection) accuracy. ACO is inspired by the artificial ants can pass through on a directed graph. We have chosen the ACO algorithm that can pass through the directed graph with o (2 n) arcs rather than o ( n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) arcs. This greatly reduce the processing time of features and reduced the dimensionality of the feature set extracted from the spatial domain namely Subtractive Pixel Adjacency Matrix (SPAM -686 dimensional), features extracted by steganographic algorithms S-Tools, nsF5, open stego, Hide & Seek. The experimental results show that it significantly improves the detection accuracy and significantly reduces the feature dimensions when comparing with renowned feature selection algorithms.
An efficient attribute-based authentication scheme with multiple authorities in public cloud

Abstract Currently, attribute-based authentication provides a feasible solution for fine-grained access control in cloud environment. However, the existing schemes can not solve the following problems at the same time, that is, how to ensure that the computation cost of the client does not depend on the size of underlying access structure, and how to introduce distributed authorities to manage and maintain the attribute universe. To solve the above problems, an efficient multi-authority attribute-based authentication scheme is proposed. The new scheme uses the technique of distributed attribute-based encryption to realize the access control of anonymous users, and reduces users’ computation burden by optimizing the standard implementation zero-knowledge proof and outsourcing users’ computing tasks in the authentication stage. Under the new definition of security, it can be proved that the new scheme is secure and satisfies many attractive properties, such as introducing distributed authorities, supporting outsourcing computation, satisfying attribute anonymity.
Educational Project Management and Competitive Advantages of Educational Programs

The article presents the study results of students’ involvement in the transformation and development of intra-group interaction in the process of mastering an educational program. The key tool is students’ project work aimed at solving significant educational problems. Project management in the educational program is based on distributed leadership. However, based on the results of surveys, interviews, and product analysis of students ’project activities, the problem of students’ unwillingness to interact in the project group is revealed. The study purpose is to identify the impact of student engagement on the development of intra-group interaction in project groups. The correlation analysis has shown that students’ lack of readiness for interaction correlates with a low level of their involvement in project activity, which leads to the process formalisation and project activity results. The solution to this problem gives the educational program competitive advantages due to the formation of students’ competencies of interaction in project groups and distributed leadership.
Tensor Algebras and Symmetric Algebras

Tensors are creatures that we would prefer did not exist but keep showing up whenever multilinearity manifests itself.
General System Function

In this chapter the following basic questions are considered: natural fractal algebraic systems through series of examples from mathematics, natural sciences, telecommunications,the concept of quasi-fractal homomorphism and target quasi-fractal of a system, system target functions,regulation of a quasi-fractal system. Regulatory functions of the system. Theorems explaining the appearance of mutations.
Image Retrieval Based on Block Motif Co-Occurrence Matrix

Motif co-occurrence matrix (MCM) is one of the commonly used image features descriptions. However, MCM has two shortcomings. One is that it doesn't meets translation invariance, and another is that different sub-blocks can be represented by the same motif. In order to overcome the two shortcomings, an image retrieval method based on blocked motif co-occurrence matrix (BMCM) is proposed. BMCM divides the image into five regions firstly, and then it extracts the quantized HSV color histogram feature, MCM feature and local binary pattern feature from each of the five regions. Considering that different attributes and contents of the image are described by different characteristics, this paper achieves image retrieval through a weighted fusion of the above three features. Experimental results in Corel 1k standard image library show that the proposed method has higher precision and lower computation complexity compared with MCM, BCTF and MCMCM algorithm.
An efficient, dense and long-range marker system for the guidance of the visually impaired

In this paper, we address the problem of making a mobile/smartphone camera sensitive to distant fiducial markers. To this end, we carefully design a novel visual marker that is both dense and readable from large distances. The main novelty of the proposed marker is the combination of a quaternary color-based coding system with robust methods for reading the color patterns included in each frame once it is detected. These patterns include a CRC whose length grows linearly, whereas that of the message grows quadratically. Our experiments show that the proposed bundle marker-vision algorithm outperforms the alternatives in terms of distance and angle and also that it is very robust to changes in lighting conditions, thus making it a good intelligent system for guiding people with visual impairments in their day to day use of public transportation systems.
Visual Classification of Formula One Races by Events, Drivers, and Time Periods

Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.
A Practical Implementation of Searchable Encrypted Audit Logging System

In this paper, we propose a practical implementation of dynamic SSE scheme specialized for audit log. Audit log is a collection of data about the action performed by operators or users. Since audit log contains a lot of sensitive information, confidentiality and integrity are required. These requirements could be met by encrypting the audit log, but it would prevent the auditor from seeing inside. By using symmetric searchable encryption (SSE) scheme, it is possible to disclose only the parts of the auditor’s interest while maintaining confidentiality. However, several problems occur when applying a typical dynamic SSE scheme to an audit log. One is the efficiency of building the secure index, and the other is to keep forward secrecy when auditing the log. In this paper, we describe how to implement an SSE efficiently according to the characteristics of audit logs.
Text Detection by Fusing Text Edge Semantics in Arbitrary Shapes

In this paper, we propose a method of fusing text edge semantics (FTES) for text semantic segmentation. FTES divides an image containing text into text semantic region, edge semantic region and background semantic region, where edge region is as a transitional region that splits text region from background region. At the same time, we design a text semantics segmentation network FTES-Net to detect arbitrarily shaped text regions in an images. We perform experiments on two public datasets containing a large number of non-linear text regions, and the results show that our proposed text region detection method can achieve comparable results.
Text Retrieval Technology Based on Keyword Retrieval

Abstract In such an era of informatization and big data where the Internet has greatly enriched the sources of information of people and changed the way they get information, search engine is widely used and it results from the combination of text retrieval and Web. Based on the research on Boolean model, vector model and probability model, this paper deduces a text retrieval algorithm using keyword retrieval, which can realize keyword retrieval of text documents in text database.
Information Fusion in the Applications of Workflow Management System

Information fusion is increasingly used in workflow. Information fusion can be used to design, validate, execute and visualize scientific workflows and their results. Recently, there are many workflow systems based on information fusion, which can be divided into activity-oriented workflow information fusion, process-oriented workflow information fusion and topic-oriented workflow information fusion from different perspectives. They are also surrounded by information fusion and workflow in various fields of society. This paper mainly discusses various kinds of work. Different modes of information fusion between streams.
Predictive Technique Of Task Scheduling For BigData In Cloud

In the current era, Big data utilizes MapReduce strategies in task scheduling, most notably, Apache Hadoop, a programming library and framework that considers the distributed processing of enormous data across clusters of computers using simple programming models. Still, there persist longer wait times with MapReduce technique. Because, the scheduling in Cloud with huge data causes frequent obstructions to effective computing, prompting prolonged makespan, longer waiting time and expenses acquired by the client and the server end. Task Scheduling with a huge measure of information data can cause obstacles to proficient processing. The motive of this work is to accomplish the desired solution to overcome the issues obstructing the effective CPU/task Scheduling of VMs in the Cloud environment with massive data without the use of any complex algorithms. This paper presents a predictive task scheduling approach and introduces PCA (Principal component analysis) and utilizes nine different Machine classifiers and compares the results of the accuracy and time obtained by each ML classifiers with and without the use of PCA. Results are visualized and the percentage variation of comparison is discussed. Experiments are carried out in the Hadoop Environment, using MapReduce the dataset is generated and ML classifiers are executed in Python.
Experience in Project-Oriented Professional Activities in The Digital Educational Environment of a School

Today, education is being modified to meet the requirements of the digital environment. The educational function of the school is already being implemented using digital means. Therefore, it is possible to predict attempts to implement the upbringing function of the school in a digital environment. The purpose of the present research is to test professionally-oriented projects as a measure of pedagogical support for students’ professional choice in the context of the school’s digital educational environment. The methods to study professional interests included the use of the survey form proposed by G.V. Rezapkina and the questionnaire of N.S. Pryazhnikov. The method of creating professionally-oriented projects has become an instrumental method of pedagogical support for professional choice. The results of the study included examples of tasks that could be used to organize professionally-oriented projects in the context of the school’s digital educational environment. The experience of implementing these projects shows that the development of technologies for pedagogical support of professional choice employing the digital educational environment of the school is quite promising.
Collaborative filtering over evolution provenance data for interactive visual data exploration

In interactive visual data exploration, users rely on recommendations on what data to explore next. EVLIN is a system that recommends queries to retrieve these data for the next exploration step, paired with suited visualizations. This paper extends EVLIN by combining its content-based recommendations with recommendations leveraging collaborative filtering to improve the effectiveness of recommendation-based visual data exploration. The recommendations rely on evolution provenance, which tracks users’ interactions during interactive visual data exploration. As more users explore a dataset, the evolution provenance of individual user explorations is incrementally integrated into a multi-user graph, for which we present match and merge algorithms. To compute collaborative-filtering recommendations, we present a search algorithm and optimizations to efficiently search queries similar to a current user’s query in the multi-user graph and give preference to queries that have been previously explored in an exploration step succeeding those similar queries. Our experimental evaluation studies the efficiency and effectiveness of the solutions proposed in this paper and demonstrates that using the full system with both content-based and collaborative-filtering recommendations enabled allows for effective interactive visual data exploration.
Secure Cognitive Protocols for Data Fusion in Transformative Computing

Transformative computing is a new computational paradigm, which join communication technologies with sensor devices, and artificial intelligence methods. It allows to acquire sensor signals and evaluate those using different AI technologies towards determining some analytics factors. Application of cognitive systems in such analysis can extend possibilities of semantic analysis and evaluation of signals towards performing semantic evaluation of their meaning. Data fusion using cognitive approach can facilitate fast analysis, management, and creation new areas of such systems application in security and management protocols.
Bases of G-V Intuitionistic Fuzzy Matroids

The purpose of this paper is to study intuitionistic fuzzy bases (IFBs) and the intuitive structure of a G−VIFM. Firstly, the intuitionistic fuzzy basis (IFB) of a G−VIFM is defined; then the h-range and properties of an IFB are presented and a necessary and sufficient condition of a closed G−VIFM is studied. Especially, a necessary and sufficient condition of judging an IFB is presented and the intuitive tree structure of a closed G−VIFM is proposed and its properties are discussed.
Software Supply Chain Analysis Based on Function Similarity

Abstract The supply of open source and open source components is growing at an alarming rate, while vulnerabilities in open source components are everywhere. Software supply chain analysis aims to discover third-party components and open source code used in a software, and analyze the software’s dependence on components. In this paper, we propose a software component analysis method and a known vulnerabilities detecting method. By scanning the open source components of the binary file and conducting vulnerability analysis, the known vulnerabilities are detected. This paper mainly solves the problem of detecting known vulnerabilities in the supply chain of binary files. We conducted a case analysis and achieved good results.
Beyond Plain and Extra-Grammatical Morphology: Echo-Pairs in Hungarian

This paper presents an investigation of echo-pairs in Hungarian. Echo-pairs are formed by duplicating a base with an altered initial consonant and have diminutive, playful, or intimate connotations (e.g., cica [t͡sit͡sɒ] "cat" → cica-mica [t͡sit͡sɒ-mit͡sɒ] "cat.dim"). Echo-pairs are commonly seen as an example of extra-grammatical morphology in the literature. Our goal in looking at this phenomenon is to gain a better understanding of the morphological mechanisms underlying extra-grammatical phenomena and shed new light on the distinction between plain and extra-grammatical morphology. We analyze data from (a) a collection of echo-pairs extracted from a large corpus of online texts and (b) a large-scale online nonce-word experiment with close to 1,500 participants. Our results reveal two key phonological patterns in the data and some additional systematic variation across words and experimental stimuli. We compare two different models of morphology, the Minimal Generalization Learner and the Generalized Context Model, in terms of their ability to capture this variation. We find that echo-pair formation is best captured by lexicon-oriented models such as the Generalized Context Model, but only when they rely on a structured similarity metric that encodes broader generalizations about the data. Our results do not support a clear-cut distinction between extra-grammatical and plain morphological processes, and we suggest that some of the peculiar characteristics of extra-grammatical phenomena such as echo-pair formation may simply follow from their special function and the limited set of contexts in which they appear.
Predicting User Influence in the Propagation of Toxic Information

With the advances of information technology, the Internet has become an indispensable part of life. At the same time, toxic Information has become virulent and common on the Internet. Such information propagation can have a negative impact on individuals, organisations and the society. Traditional approaches, such as detecting texts and posts with toxic Information will eventually generate 'dark pools in which the online propagation of toxic information will flourish. In this study, we pay attention to influential users who evidently affect others in the activities related to toxic information. A method of predicting user influence was proposed. Compared to the existing literature, user influence is assessed on the basis of users' text-based and behaviors-based characteristics rather than the network structures only. Moreover, whether the influential users have always been those with strong connections on the social networking site is also discussed. The effectiveness of the proposed method is demonstrated in two real-world datasets.
An Art-Directed Workflow for Transferring Facial Action Coding Between Models with Different Mesh Topologies

In this paper, we present a simple and intuitive approach to transfer existing FACS (Facial Action Coding System) based facial rig and its data set on to a different facial model that has a different mesh topology to support rigging and animation pipeline for visual effects films, 3D animations, and video games. Facial rigs are custom built for each character and are time consuming and complex to create, and even minor tweaks on the model typology may cause the rigging process to start from scratch. In order to maximize the data reuse, this system can apply one set of data onto a variety of human or creature facial rigs.