Reviewerly/exHarmony huggingface dataset:
Uncertain Big Data Stream Clustering

Big data stream processing in real-time becomes very important for many IoT applications and modern cyber-physical systems. Sometimes such data streams are uncertain, which is caused by several reasons such as sensors inaccuracy and errors as well as, network faults. This fact leads to some problems during uncertain big data streams processing such as ensuring the results’ veracity. Such tools as Storm, Samza, Spark Streaming, and Flink, which allow work with data streams in real-time, do not initially provide mechanisms for uncertain data stream mining. So implementation and evaluation of data mining mechanisms are very important for modern cyber-physical systems. In this chapter, we focused on implementation and study different algorithms for uncertain big data stream clustering for Apache Spark Stream. We studied CluStream and UMicro clustering algorithms, made evaluations of their robustness to noise, performance, and scalability. Finally, we selected the best one of them.
Machine Learning for Breast Cancer Diagnosis and Classification Using Hand-Crafted Features

Analysis of biological processes (signals) is always probabilistic and statistical analysis. Machine Learning stemmed from advanced probabilistic and statistical analysis is the exact tool what the medical workforces are looking for. Research and studies in the area of Machine Learning algorithms enhances its accuracy; its healthcare applications in medical diagnosis can improve the treatment procedure. Healthcare application being directly related to human health, dimensionality reduction is very critical; it sometime triggers to panic losses; therefore, the complete feature involvement in the model development is challenging to enhance the reliability of the Machine Learning algorithm and accuracy of the algorithm. Depending upon the diagnosis area, Regression analysis leading to dimensionality reduction and Principal Component Analysis (PCA) to remove less significant features can be applied. Primarily, in cancer diagnosis, only noise removal is acceptable, not the feature with small significance removal and minimal human intervention is acceptable which could lead to reduced accuracy of diagnosis, so the following treatment procedure.
Security Risk Assessments and Shortfalls for Evaluating and Protecting Dynamic Autonomic Systems in Future Internet

Methodologically-sound security assessments are crucial for understanding a system in fulfilling the requirements, realizing its behavior, and identifying implications. A system is made resilient if and only if there is enduring confidence that it will function as expected. It is cyber secure if it displays this property in the face of an adversary. This chapter provides an explanation of various static security risk assessment methodologies (SSRAM) having long epochs for assessing and revisiting the risks and explains their strengths and weaknesses. The SSRAM will form the basis for elaborating on dynamic security risk assessment methodologies that must have very short epochs depending on the emergent threats and vulnerabilities to combat new or evolving threats. This is essential for handling cyber security of dynamic complex systems including future networks having a number of self-managing properties including self-protection for defending against malicious attacks.
The Optimization of Privacy Data Management Model In Big Data Era

The management of privacy data in big data era involves data collecting, data storage and data use. Current privacy data management studies focus on concrete techniques and seldom investigate various processes and subjects of privacy data management. In this paper, the basic structure and analytic models of privacy data management is analyzed based on the realization of big data analysis, and problems of imperfect management mechanism, increasing information disclosure risk and deficiencies of current models are analyzed. Based on the problems of privacy data management, the optimized schemes are proposed from perspectives of perfecing privacy data management mechanism, building secure data platform and optimizing data use structure.
Detection of Small Ships in Remote Sensing Images based on Deep Convolutional Neural Network

Remote sensing technology for detecting ships at sea can be widely used in maritime supervision, military reconnaissance, and ship rescue. It has very important application value. In this paper, we propose the YOLOv3-RS algorithm based on YOLOv3 algorithm to precisely detect small ships in remote sensing images. The YOLOv3-RS algorithm uses the K-medians algorithm to improve the clustering effect of the data set, and then uses DenseNet to increase the feature reuse rate, and finally improves the ResNet module to enhance the expressive ability of the network. The final results show that YOLOv3-RS algorithm has better detection performance than YOLOv3 algorithm.
On the Reform of Instrumental Course of "Cloud Classroom" in Normal Universities under the Internet Age

"Internet" technology has been developing rapidly and rising rapidly. It mainly relies on Internet technologies such as big data, cloud computing, social network, search engine, etc., to realize the new network mode of information accommodation and information intermediary function. In order to better adapt to the development of the times, we need to improve the ideological and political education system of colleges and universities with the times. Based on this, this paper analyzes the development of the teaching mode in the "Internet" era, explores the challenges of the traditional college instrumental music teaching mode in the "Internet" era, expounds the advantages of the combination of "Internet" technology and college instrumental music teaching, and further puts forward practical and feasible reform measures of instrumental music teaching mode in Internet colleges and universities in the "Internet" era This paper analyzes the innovation of classroom teaching mode of instrumental mu.
Guided-LORE: Improving LORE with a Focused Search of Neighbours

In the last years many Machine Learning-based systems have achieved an extraordinary performance in a wide range of fields, especially Natural Language Processing and Computer Vision. The construction of appropriate and meaningful explanations for the decisions of those systems, which are in may cases considered black boxes, is a hot research topic. Most of the state-of-the-art explanation systems in the field rely on generating synthetic instances to test them on the black box, analyse the results and build an interpretable model, which is then used to provide an understandable explanation. In this paper we focus on this generation process and propose Guided-LORE, a variant of the well-known LORE method. We conceptualise the problem of neighbourhood generation as a search and solve it using the Uniform Cost Search algorithm. The experimental results on four datasets reveal the effectiveness of our method when compared with the most relevant related works.
Explore the Development of Software Engineering Technology Under the Background of the Internet Era

In network information age, people cannot do without software engineering technology in their daily production and life. However, with the software engineering technology continuous development, problems have also arisen. Especially with the gradual increase of Internet users, the demand for software is increasing, and the requirements are getting higher and higher. Therefore, while the software engineering technology is developing steadily, it is necessary to continuously reflect, find, and solve problems in time. This article mainly starts from the problems in the development of software engineering technology in the era of network information, proposes effective strategies to promote the development of software engineering technology, and elaborates its future development trends. Hope to provide valuable references for other scholars to study software engineering technology related issues.
Developing a deep learning estimator to learn nonlinear dynamic systems

Process complexities are characterized by strong nonlinearities, dynamics and uncertainties. Modeling such a complex process requires a flexible model with deep layers describing the corresponding strong nonlinear dynamic behavior. The proposed model is constructed by deep neural networks to represent the process of state transition and observation generation, both of which together constitute a stochastic nonlinear state space model. This model is evolved from the variational auto-encoder learned by the stochastic expectation-maximization algorithm. To solve the complexity of posteriors for dynamic processes, the posterior distributions with respect to state variables are constructed by a forward-backward recurrent neural network. One example is given to validate that the proposed method outperforms the comparative methods in modeling complex nonlinearities.
Pareto Optimal Approach for Contrast Adjustment in Gray Level Images Using IDS Algorithm

Image enhancement/contrast adjustment plays an important role in almost every image processing system. The main aim of the contrast adjustment is to enhance image quality by maximizing the information content in the image. Many researchers implemented heuristic approaches for image enhancement using maximization of objective function based on image quality metrics. And such objective function may fail to yield proper enhancement for a given image (either under enhanced or over enhanced). Because the parameters of the image transformation function are selected based on overall value of the objective function but not individual objectives in the objective function. That means the parameter values so chosen may not satisfy all the objectives of the objective function, and this may lead to improper enhancement of the image. In this context, this paper presents a Pareto optimal approach-based gray level contrast adjustment using Improved Differential Search Algorithm (IDSA) named Multi-Objective Improved Differential Search Algorithm (MOIDSA). Image enhancement is treated as a three-dimensional optimization problem, and MOIDSA is used to solve it. The input image quality is enhanced by simultaneous objectives which is a blend of image performance measures and quality metric as a three-dimensional optimization approach will yield best-compromised solution by satisfying all the individual objectives. This algorithm is tested on some standard test gray level images, and results compared with well existing algorithms have proven its superiority.
Paradox of AlphaZero: Strategic vs. Optimal Plays

This article analyzes AlphaZero-type algorithms quantitatively from the viewpoint of local and global optimal sequences of play on a 7×7 board. Through targeted evaluation of the AI agent, the authors reveal the strategic, that is, winrate-dominated, nature of such algorithms, and expose thereby certain inherent obstacles against optimal play. Possible remedies are then explored, leading to techniques that may help further quantitative analysis of those algorithms and for the search for optimal solutions, on 7×7 as well as larger boards.
Clusterwise Regression Model for Statistical Downscaling to predict Daily Rainfall using Gamma Distribution

Abstract This study presents the development of clusterwise regression with gamma distribution and normal distribution. This clusterwise regression model is expected to be a solution for high variance data in order to minimize the error rate in modeling. The first study is simulation of clusterwise regression with gamma distribution and normal-gamma mixture. This simulation is divided into two-cluster and three-cluster simulations with gamma distribution, normal distribution, and mixed normal-gamma distribution with different parameters. The application of clusterwise regression was carried out at four rain stations in West Java, including Bandung, Bogor, Citeko, and Jatiwangi rain stations by first doing variable reduction using principal component analysis.
Colvis—A Structured Annotation Acquisition System for Data Visualization

Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive “subject-verb-complement” set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of the limitations, the current state of the method and the upcoming steps for this annotation interface.
Interactive Fusion for Multi-exposure Images

Exposure fusion methods are effective for directly fusing multi-exposure images to one high-quality image. Most exposure fusion methods can generate a more natural and visually pleasing image than tone-mapping methods, but they do not preserve so much fine details as tone-mapping methods, especially in the brightest and darkest regions. In this paper, a more flexibility exposure fusion method is proposed so that users can manipulate details according to their preference. Our method extracts different amount of details only from the most underexposed image and the most overexposed image by using local Laplacian filtering before multi-resolution weighted blending. Compared with previous methods, our interactive detail-variable method is much more flexible and effective on detail-preserving in the brightest and darkest regions.
“Continuing Library and Information Education” – the Conference inspired by Valentina A. Minkina, or On the role of the individual in history

In March, 2021, Valentina A. Minkina would turn eighty. Valentina A. Minkina is a prominent scientist, doctor of pedagogy, Professor of Information Management Department that she headed from 1991 to 2004. In memory of Valentina Minkina, the Library and Information Faculty of Saint-Petersburg State Institute of Culture holds the annual International Scientific and Methodological Conference “Continuing LIS Education”. Due to the COVID-19 pandemic, many professional events had to move to online or were deferred to a later date, and this conference, too. It is hard to overestimate the contribution of Valentina Minkina made to the library science. The authors review in brief her research efforts as the leader of the library scientific school and a wonderful mentor. The authors also review the origins and development of the conference. Among other library professional forums, the conference is unique as it focuses on professional education and training staff for libraries and information organizations, which is due not in no small way to Minkina’s ideas and the faculty’s traditions. The most significant publications in the periodicals on the conference are listed.
De-anonymizing Social Networks Under Partial Overlap: An F-score Based Approach

This paper studies social network de-anonymization problem, which aims to identify users of an anonymized network by matching its user set with that of another auxiliary sanitized network. Prior arts primarily assume that both networks share exactly the same set of users, as opposed to many real situations of partially shared users in between. Different from the full matching case that only needs to take care of increasing the number of correctly matched pairs, the case of partial overlapping imposes additional demand on avoiding the wrong matches of those who do not have accounts across networks.To this end, we establish a new cost function, which we call the structural F-score to incorporate both the structural commonness and difference across networks. Intrinsically, the structural F-score computes the ratio of link agreements and disagreements, thus serving as the harmonic mean of precision and recall for any given matching function. Theoretically, we show that for networks parameterized by node overlap t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and link overlap s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , as long as the mean degree of networks grows as Ω(t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> log n), maximizing the structural F-score provably ensures the perfect matching, where the nodal precision and recall are both maximized to 1. Algorithmically, for small-scale networks, we propose a two-step heuristic of F-score based de-anonymization, which firstly finds the optimal full matching between networks and then removes those pairs hindering structural F-score maximization. Due to the universal adaptability of the structural F-score, we further extend the algorithm to large-scale networks via a progressive matching process. Empirical results also validate the effectiveness of our methods in terms of improving the nodal F-score.
Using Text Data Mining to Enhance the Literature Search Process for Novice STEM Researchers

A literature search can be an arduous process, especially for novice researchers. We have developed a tool that allows a researcher to rank order a list of references that are returned by a keyword-based search engine, based on similarity to known exemplars. This significantly accelerates literature searches by novices. Our research question was: can we produce a text-analytic tool that, when used by an inexperienced scholar, rank-orders a list of references against an exemplar, so that the time needed to find relevant literature is reduced, and the literature survey section of their paper will be superior. An experiment was set up where one course section used the tool to produce the literature review section of a thesis proposal, and the other class used traditional literature research tools. We surveyed both sections to self-report the time used for the literature search. We found some time savings by some of the students using the tool. We also provided blind, randomly selected pairs of completed proposals to SME faculty who teach that same class to assess the quality of the literature sections of the samples. We found that the tool-using section of students reported significantly less time to do the literature search, and the quality of their literature review produced had a significantly higher quality.
Repetition and variation in the Catalan translation of Virginia Woolf's<i>The years</i>: a corpus-based approach

The present study is a corpus-based contrastive analysis of the use of repetitive patterns in Virginia Woolf's novel The Years (1937) and its Catalan translation (1988) by Maria-Antònia Oliver. For our study, we build a parallel corpus in which both the source text and the translation are aligned, and then focus on three kinds of repetition: (a) The question Where am I?; (b) the phrase hum in time to + N, and (c) the construction ‘verb indicating stillness (sit, stay …) + for a moment + verb of perception.' All these patterns are stylistic devices that Woolf uses for the representation of time in its diverse and overlapping manifestations: as an external measurable dimension and as an internal continuous stream. The use of a parallel corpus allows us to study how all occurrences of these repetitions have been translated into Catalan and to identify the semantic prosody surrounding the three expressions in both English and Catalan. Finally, our corpus-based approach provides evidence for a description of Oliver's translation method, which we define as intentionally literal, though mindful of the numerous stylistic and lyrical effects that can characterize Woolf's prose.
Application Countermeasures of AI Technology in Tax Collection Management

Accompanied by a growing number of taxpayers, the taxpayer’s tax collection is also becoming increasingly large and complex, the tax authority will AI research achievements of intelligent technology used in tax collection and administration system, aims to change the existing pattern of hotline, hotline to reduce costs, improve the efficiency of collection and management, to be enhanced social tax compliance, alleviate the relationship of the taxpayers and publicans, improve taxpayer satisfaction. To promote the establishment of the new model of “artificial intelligence + tax governance”, in order to promote the reform of the tax collection and management system, improve the intelligence of the AI intelligent system to promote the reform of the tax collection and management system, accelerate the establishment of a modern tax collection and management system.
A Combined Approach for Text Detection in Images Using MLP Neural Networks and Image Processing

Text detection in images is an important research area that has attracted the attention of many researchers. Due to the growing image databases, information retrieval from databases in less time and with maximum precision is more important. The low level features which are extracted from the images and videos include metrics of color, texture and shape, although these features can easily be obtained, they do not have enough accuracy to extract the content. Today, text retrieval in images is an important part of content recovery. Detection of vehicle plates, text ads or video frames, book title and addresses in mailing envelopes are among text mining applications. In the current article a method for combing image processing and neural networks for text detection in images is presented. In this approach, after detecting text location in image using MLP (Multiple Layer Perception) neural network structure, the text in the images is detected. Finally, the performance of two neural network MLP and LVQ (Learning Vector Quantization) for text detection are compared. The results show that the MLP neural network with suggested implemented algorithm in this network has a better performance.
Mortality prediction for ICU patients with individualized single classification method

In intensive care units (ICUs), mortality prediction is crucial to help doctors select appropriate diagnosis and treatment methods and reasonably allocate medical resources. However, the scoring system currently used by hospitals may not be suitable for every patient. Therefore, personalized diagnosis is trending because it responds to a wide range of needs. This study proposes a new combination of dynamic time warping (DTW) and one-class extreme learning machine (ELM) to improve the mortality prediction accuracy, termed as one-class DTW-ELM, where DTW is used to find similar cases for new patients, and while one-class ELM is adopted for fast and accurate model building. The testing results on the real physiological data from PhysioNet demonstrate that the area under curve (AUC) index of one-class DTW-ELM model is 0.9739, with the Lift of 8.1081 and the G-mean of 0.8137. The accuracy rate is 0.9583, with specificity and sensitivity of 1 and 0.6622, respectively. As mentioned, one-class DTW-ELM can accurately predict the future physiological state of a patient by using early physiological parameters.