Reviewerly/exHarmony huggingface dataset:
Deep learning based search engine for biomedical images using convolutional neural networks

The development of efficient search engine queries for biomedical images, especially in case of query-mismatch is still defined as an ill-posed problem. Vector-space model is found to be useful for handling the query-mismatch issue. However, vector-space model does not consider the relational details among the keywords and biomedical image search space is not evaluated. Therefore, in this paper, we have proposed a deep learning based fusion vector-space based model. The proposed model enhances the biomedical image query similarity matching approach by fusing the vector space model and convolutional neural networks. Deep learning model is defined by converting the vector-space model to a classification model. Finally, deep learning model is trained to implement the search engine for biomedical images. Extensive experiments reveal that the proposed model achieves significant improvement over the existing models.
SELF-REGULATION AND ITS COMPONENTS AS A CONDITION OF READINESS FOR PROFESSIONAL SELF-DETERMINATION

The article presents the results of research on the relationship of readiness for professional self-determination and self-regulation in the context of building a professional perspective. The author reveals the specifi cs of professional self-determination in the period of study at a higher education institution. As a result, empirical studies have identifi ed differences in readiness for professional self-determination in accordance with the development of self-regulation skills in adolescence. Students not ready for professional self-determination are characterised by a weak formation of modelling processes, a focus on the hedonistic present, distrust of the world, and most often low self-esteem. Students ready for professional self-determination can distinguish signifi cant conditions for achieving their goals, focus on the future, and they are characterised by the ability to plan and achieve future goals. The author also revealed that the feature of professional selfdetermination of modern students is the great fl exibility of professional intentions, the mobility of interests, but the ability to self-evaluate their own abilities and capabilities, mastery of a certain type of activity remains at a rather low level. As a result, a considerable part of young men and women have unrealistic or unrealisable plans, an abstract, insuffi ciently meaningful, inconsistent, contradictory idea of a professional perspective.
Image translation with dual‐directional generative adversarial networks

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between input images and output images. However, due to the unstable training and limited training samples, many existing GAN-based works have difficulty in producing photo-realistic images. Herein, dual-directional generative adversarial networks are proposed, which consist of four adversarial networks, to produce images of high perceptual quality. In this framework, self-reconstruction strategy is used to construct auxiliary sub-networks, which impose more effective constraints on encoder-generator pairs. Using this idea, this model can increase the use ratio of paired data conditioned on the same dataset and obtain well-trained encoder-generator pairs with the help of the proposed cross-network skip connections. Moreover, the proposed framework not only produces realistic images but also addresses the problem where condition GAN produces sharp images containing many small, hallucinated objects. Training on multiple supervised datasets, convincing evidences are shown to prove that this model can achieve compelling results by latently learning a common feature representation. Qualitative and quantitative comparisons against other methods, demonstrate the effectiveness and superiority of the method.
Comparison of Segmentation Algorithms for Leukemia Classification

Leukemia is a deadly cancer that results from the proliferation of nondifferentiated white blood cells in blood as compared to the other two types of cells, i.e., red blood cells and platelets.These cells are known as blasts cells which overcrowd other cells rendering those cells as inefficient in their functions and are are themselves non-functional.This paper presents a comparative study of four different segmentation techniques on the images of peripheral blood smear and the classification of these images into diseased and healthy cells using the SVM classifier.The best result was obtained by a custom threshold method of segmentation with a classification accuracy of 96.89%.
Decision Support Model using FIM Sugeno for Assessing the Academic Performance

Assessing academic performance is a common way of evaluating and assessing the abilities of students in tertiary institutions.Usually it is practically performed based on the cumulative grade point average (GPA) at the end of each semester passed.Unwittingly there are many factors that are able to influence student performance results apart from GPA as a performance measure; i.e. gender, hometown, sibling, family status, residence, father education, mother education, family income, motivation, mileage, traveled time, transportation, scholarship, community, social media, and hang-out. Academic performance assessment is proposed through the decision support model (DSM) applying the fuzzy logic (FL) Sugeno technique. The model output generates a decision value (linear or constant equation) for academic performance based on the calculation of the measured fuzzy parameter value (ax) and conventional parameter value (bx). The DSM with the FLSugeno method is able to provide sharp output in assessing student academic performance.In this case, the model is able to be applied then to assist academics in higher education in determining educational strategies for students with poor academic performance results.
Effect of the scientific and educational space transformation on the system of interacting research libraries in the region

Novosibirsk Region plays the leading role in the structure of the scientific-educational complex of the Siberian Federal District. There are higher educational institutions, hat provide professional training, re-training and advanced training of specialists, as well as scientific-research institutions of the Novosibirsk scientific center of the Siberian Branch of the Russian academy of sciences and institutions of other departments. The scientific and educational space of the region is in constant dynamics. The article objective is to characterize factors effecting the scientific and educational complex development in the region including scientific libraries as a part of its structure, and determine the trends of their further development. The dialectic approach, permitting to view library-information and educational spheres of the large scientific library activity in their unity and inter-conditionality, is the common methodological basis for the research undertaken. The authors identify features of the modern scientific and educational space in Novosibirsk region, its characteristics effect on scientific libraries development, and a number of trends of changing research and student contingents affecting the library and information services of these reading cohorts based on the analysis results of statistical data of regional and federal significance and publications. The integration process of leading universities and research institutions in the region contributes to the development of corporate relations of libraries with different statuses to converge the information support of industrial and disciplinary discourse in the educational and research environment. The authors highlight some trends that promote the changing role of libraries in the scientific and educational space: the mediation function priority between the ever-expanding information space and the society needs, deepening the differentiation of library technologies depending on the status and objectives of scientific libraries; they suggest measures to strengthen regional interlibrary cooperation.
A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon

Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. The candidate sentiment lexicons are regarded as the individuals that need to be optimized. Then, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high-quality sentiment lexicon in specific domains.
Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Abstract Concept Drift’s issue is a decisive problem of online machine learning, which causes massive performance degradation in the analysis. The Concept Drift is observed when data’s statistical properties vary at a different time step and deteriorate the trained model’s accuracy and make them ineffective. However, online machine learning has significant importance to fulfill the demands of the current computing revolution. Moreover, it is essential to understand the existing Concept Drift handling techniques to determine their associated pitfalls and propose robust solutions. This study attempts to summarize and clarify the empirical pieces of evidence of the Concept Drift issue and assess its applicability to meet the current computing revolution. Also, this study provides a few possible research directions and practical implications of Concept Drift handling.
iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

Abstract In recent years, $$\hbox {optical character recognition (OCR)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>optical character recognition (OCR)</mml:mtext></mml:mrow></mml:math> systems have been used to digitally preserve historical archives. To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an $$\hbox {OCR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>OCR</mml:mtext></mml:math> is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and $$\hbox {OCR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>OCR</mml:mtext></mml:math> capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable $$\hbox {System-on-Chip (SoC)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>System-on-Chip (SoC)</mml:mtext></mml:mrow></mml:math> based on anyOCR for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system: Text and Image Segmentation , which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized $$\hbox {FPGA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>FPGA</mml:mtext></mml:math> -based hybrid architecture of this anyOCR step along with its optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by 6.2 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> , while achieving 207 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> higher energy-efficiency and maintaining its high accuracy.
Towards an Implementation of Blockchain-based Collaboration Platforms in Supply Chain Networks: A Requirements Analysis

The competitiveness and speed of international markets have created significant pressure from competitors, forcing companies to collaborate with foreign companies.To address this situation, companies use supply chain networks (SCN) to concentrate on their core competencies while sourcing the remainder of (pre-)products or services.This situation often causes a lack of trust as the application of hard-to-trace illegal practices through complex SCN is a threat.The blockchain provides a solution for chaining data, enabling trust in its tamper-proof storage, even if there is no trust between business parties.Using blockchain also provides the opportunity to automate and monitor processes within digital SCNs in real-time.This paper aims to identify requirements for a blockchain-based collaboration platform in SCNs.We define the requirements based on a literature review and expert interviews.We use an additional survey to validate and prioritizes these 45 requirements.
Analyzing and Building an Advertising Image of the Library through User Interviews

Over the past decades, advertising for the library has become a mandatory part of the activity, but the approach to its creation is largely based on accepted stereotypes that express the most common ideas about the properties of such a complex phenomenon as the target audience of the library. Advertising can be expressed through an advertising image that contains not only objective but also subjective information; it uses associations, metaphors, emotions, visualization of something that has a generalizing character, i. e. a layer of implicit information is added to the exact data and read automatically. One of the problems of creating an advertising image may be that there are contradictions between the perception of the person who creates an advertising based on a stereotype and its recipient. The article reflects the experience of the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS) in the study of two groups — “Event visitors” and “Readers”. The study involved 326 people (254 visitors to mass events and 72 readers of the SPSTL SB RAS). The author tested the method of semi-formalized interview to get relevant information for better understanding the audience’s motivation and desires. It is assumed to use pre-made plan with a list of mandatory questions, while using general wording and their variations, “close to the text”, and observing the general logic in their sequence. The author analyses the data on distribution of users in both groups (“Event visitors” and “Readers”) by the age categories and employment areas; frequency of library visits; frequency of references to forms of work that are called in demand and found interesting in both groups; frequency of references to preferred information channels. The article presents the main positions for creating an advertising image of the library. The author shows the expediency of using the semi-formalized interview method to adjust the advertising image of the library and the campaign to promote events, resources and services based on it, both for individual groups and for several segments in parallel, if the library’s event or service does not have a specific addressee. The article reveals the method of work that can be applied in the analysis of the advertising image for other libraries.
Photovoltaic panels tilt angle optimization

The tilt angle of solar panels is significant for capturing solar radiation that reaches the surface of the panel. Photovoltaic (PV) performance and efficiency are highly affected by its angle of tilt with respect to the horizontal plane. The amount of radiation reaching the surface of a PV panel changes with the changes in its tilt angle, hence adding a solar tracking system will maximize the amount of solar radiation reaching the surface of a PV panel at any time during the day, however, integrating solar tracking system will increase the total cost and maintenance of any PV system. Thus, using an optimized fixed tilt angle is the solution to element the initial, maintenance, and operation costs of a solar tracking system. Yet, the fixed angle is location-specific because it depends on the daily, monthly, and yearly location of the sun. In this study; daily, monthly and seasonally angles are calculated mathematically and the amount of incident radiation on the surface of the PV panel is measured along with its voltage. By comparing the practical measurements of the output voltage of PV panels, an optimized tilt angle is decided.
Latent-variable Approaches Utilizing Both Item Scores and Response Times To Detect Test Fraud

Abstract There is a growing interest in approaches based on latent-variable models for detecting fraudulent behavior on educational tests. Wollack and Schoenig (2018) noted the presence of five types of statistical/psychometric approaches to detect the three broad types of test fraud that occur in educational tests. This paper includes a brief review of the five types of statistical/psychometric approaches mentioned by Wollack and Schoenig (2018). This paper then includes a more detailed review of the recent approaches for detecting test fraud using both item scores and response times—all of these approaches are based on latent-variable models. A real data example demonstrates the use of two of the approaches.
Review of Information Retrieval: Models, Performance Evaluation Techniques and Applications

Information Retrieval (IR) is a field that concerns the structure, memory, analysis, and access to pieces of information. It has a wide application in various areas like search engines, communication systems, information filtering, medical search, etc., and helps design efficient and secure applications. This area has been a surge of research from the last few years due to data mining's unparalleled success, deep learning in computer vision, blockchain technology, etc. Core models, performance evaluation techniques, IR system applications, and its role in blockchain technology have been proposed in this literature, calling the need for a broad survey to focus the research in this promising area. This paper fills the space by surveying the state of art approaches with deep learning models, query expansion techniques used, and the use of private information retrieval in blockchain technology. This survey paper includes different IR models like boolean model, vector space model, probabilistic model, language model, N-gram model, fuzzy model, Latent Semantic Indexing (LSI) Model, Bayesian network, Evolutionary algorithm-based models and Machine Learning based models. Applications of IR systems along with different datasets are also included to provide further research in this field.
Generic SDE and GA-based workload modeling for cloud systems

Abstract Workload models are typically built based on user and application behavior in a system, limiting them to specific domains. Undoubtedly, such a practice creates a dilemma in a cloud computing (cloud) environment, where a wide range of heterogeneous applications are running and many users have access to these resources. The workload model in such an infrastructure must adapt to the evolution of the system configuration parameters, such as job load fluctuation. The aim of this work is to propose an approach that generates generic workload models (1) which are independent of user behavior and the applications running in the system, and can fit any workload domain and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. We propose two approaches for workload estimation, the first being a Hull-White and Genetic Algorithm (GA) combination, while the second is a Support Vector Regression (SVR) and Kalman-filter combination. Thorough experiments are conducted on real CPU and throughput datasets from virtualized IP Multimedia Subsystem (IMS), Web and cloud environments to study the efficiency of both propositions. The results show a higher accuracy for the Hull-White-GA approach with marginal overhead over the SVR-Kalman-Filter combination.
Harmonic analysis of annual global irradiation in the cities of India

Intermittent renewable energy sources, installation and maintenance of sophisticated instruments to measure irradiation in remote locations has become a serious concern, so this study model the irradiation by harmonic analysis. The evaluated analysis recognises the suitable harmonic equation for the cities, which would sustainably enhance energy usage. Time series analysis involves the correlation of Fourier technique to meteorological parameters such as irradiation and precipitation. This process’s application highlighted the predominant contribution of yearly harmonic in a productive way rather than the subsequent harmonics. This idea allows obtaining a time series function that estimates irradiation’s possible value for any day of the year in a geographical location. The interpretation of global irradiation’s spatial characteristics over twelve sites in India includes the estimation of amplitude, percentage variance, and phase angle of the harmonics. The first and second harmonic components contribute larger than 91% of the irradiation variance in studied locations. Statistical error metrics evaluate the developed harmonic equations’ performance based on ranking technique. Global Performance Indicator identifies the appropriate harmonic equation using scaled statistical metrics. The study found a mean bias error closer to zero, and a lower value of root mean square error indicate a close estimation to measured values. However, the mean absolute percentage error varies between 1.9491% (Chennai) and 22.2184% (Goa) specifies overestimation due to overcast and cloudy conditions.
A General Framework for the Derandomization of PAC-Bayesian Bounds

PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers. However, when applied to some family of deterministic models such as neural networks, they require a loose and costly derandomization step. As an alternative to this step, we introduce three new PAC-Bayesian generalization bounds that have the originality to be pointwise, meaning that they provide guarantees over one single hypothesis instead of the usual averaged analysis. Our bounds are rather general, potentially parameterizable, and provide novel insights for various machine learning settings that rely on randomized algorithms. We illustrate the interest of our theoretical result for the analysis of neural network training.