Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning

Trypanosomosis, which is caused by the Trypanosoma parasite, is an infectious disease that affects both humans and animals. Today, the microscopic examination of a Giemsa or Wright stained blood smear from an infected individual is the standard procedure for diagnosis because of the straightforward nature of sample preparation. Unfortunately, this method is labor-intensive and prone to error, particularly resulting in false-negative scoring when parasite levels are low during chronic infections. Automating the detection of parasites in blood smear images can overcome sensitivity limitations related to a microscopic examination. We therefore propose a deep learning approach that aims at automatically classifying microscope images in terms of parasite presence or absence. To that end, we applied a ResNet18 model using a pre-processed dataset derived from microscope videos of unstained thick blood smears, with the blood smears originating from a mouse infected with Trypanosoma brucei. Our pre-processing strategy mainly involved image cropping and the application of a thresholding algorithm for facilitating effective model training. Moreover, our thresholding approach made it possible to observe a positive correlation between the percentage of parasite-related pixels in an image and the classification effectiveness.
Study on Prediction Model of Cement Precalciner Outlet Temperature

In order to establish of a stable and reliable outlet temperature prediction model for the cement precalciner, combined with several main operating parameters closely related to the decomposing furnace temperature, proposed an algorithm of support vector regression machine which parameters are optimized by the particle swarm optimization algorithm, and introduced the idea of adaptive inertia weight in particle swarm optimization algorithm to build decomposing furnace temperature prediction model. Through simulation, the proposed model is compared with the not improved model. Experimental results show that, the IPSO-SVR model has better forecasting ability, the correlation coefficient reached 0.7075, temperature prediction error absolute value less than 7°C, the error rate is less than 0.8%.
Attack and Defense Strategy for Infection Network System with Two Early Warning Mechanism

Abstract As the progress of IoT technology, Cyber-Physical System gradually becomes the basic pillar of people’s daily life. Contrary to its convenience, Cyber-Physical System could be easily broken by attackers and these attacks infect the system and affect more systems, thus interfering with people’s normal work and life. In this context, this paper establishes a system defense model for the propagable network with the warning mechanism as the core. The system defense model includes warning components, which trigger the first and second warning mechanisms, and components protection and camouflage components. After deducing the reliability of the defense model system, this paper considers the influence of different parameters about the system reliability and analyzes the relationship between warning components, camouflage components, and systemreliability. Based on the different parameters that affect the system reliability, this paper proposes the optimal defense deployment strategy under the attack optimal strategy and obtains the basic principles of the deployment of defense measures through numerical analysis. Finally, the simulation experiments of different systems prove the rationality and correctness of the proposed model.
Systematic Literature Review of Question Answering Systems

Increasing number of healthcare organizations are providing self-service for citizens with the help of online Question Answering (QA) systems on their websites. Citizens post questions to be answered by professionals and experts. The service may be run by professional experts, who first read the questions, which the visitors have asked online. They then write and publish the answers for the questioners thus helping other visitors looking for information to their similar questions. Another way to run such a QA service is to let visitors help each other. The quality of user-generated answers is based on confidence among the users, peer monitoring and rewarding mechanisms [1]. A systematic literature review according to Kitchenham et al. [2] is performed to find current solutions and implementations especially for Finnish language processing and QA web services. The literature review is organized to answer the main research question “How to formulate the search and information retrieval for data intensive Finnish language-based databases?” Study searched scientific databases and after screening and applying exclusion and inclusion criteria 14 articles were selected for the further study. This review gives some understanding of possible obstacles when designing a QA system for a specific foreign language other than English. Not all techniques developed for certain language can be utilized in Finnish because of the nature of the language [3].
FIR Filters in Signal Interpolation, Re-sampling, and Multi-Rate Processing

This chapter is focused on methods of signal re-sampling. Such task occurs very frequently in systems working with signals acquired with many sampling rates, for example, in acoustical digital mixers or digital multi-rate telecommunication transceivers (during frequency up/down conversion). We learn about signal up-sampling (interpolation) and down-sampling (decimation). Designing special low-pass filters for digital interpolators (sample expanders) and decimators (sample reducers) is discussed. For high-order re-sampling usage of one long interpolation/decimation filter is not optimal: in this case a cascade of shorter filters is preferred, what is presented. Finally, fast polyphase filter structures for signal up/down-sampling are introduced. They omit filter weights multiplications by signal samples equal to zero during signal interpolation and calculation of samples to be removed during signal decimation. The chapter ends with explanation of Farrow filter technique of Lagrange signal interpolation and with description of dyadic wavelet filter banks (discrete wavelet transform).
Computer Assisted Surgical Educational System Using Virtual Reality

This paper describes a computer assisted surgical educational system using VR (virtual reality). We made a model to represent bleeding and 3D anatomical human body model to be observed using Unity virtual space. Three able-bodied experimental subjects tested the system with each model. From the experimental results of the bleeding model, we were able to confirm that the blood comes from a cut when we touch any part of the model with a scalpel, surgical knife. From those of the human body model, it was found that we can observe the 3D anatomical human body model from various angles in VR development environment.
Improving Pedagogical Competence of Future Specialists

The formation of a modern teacher is a fully developed, well-educated person, capable of using modern techniques and technologies. The role of educators in society and their role in educating future generations poses important requirements for the process of training teachers for higher education. Therefore, one of the most important ideas of technology is the notion that the result is guaranteed. Y.A.Komensky called the result-learning mechanism, the learning process a didactic machine. In shaping the learning process for the full performance of a didactic machine with high results in education, it is necessary to create the following:
A Hybrid Attention Based Autoencoder for Chinese Text Abstractive Summarization

Salient information selection is one of major difficulties for generating summaries with adequate salient information. To address this serious problem in Chinese text summarization task, we put forward a novel method for abstractive summarization. In this paper, we try to adjust the local and global information between the source text and summary, and construct a hybrid attention mechanism. The hybrid attention mechanism can dynamically leverage both of the local and global information. For conveniently quantifying their contributions, we use a gating scalar to integrate both sources of the local and global attention. Then, we combine the hybrid attention mechanism with a supervise autoencoder. We evaluate our model on a popular Chinese social media dataset, LCSTS. Experiments results on the LCSTS demonstrate that the proposed method is effective in the aspect of summary informativeness.
FEENET: A Real-Time Semantic Segmentation via Feature Extraction and Enhancement

As a pixel-level prediction task, semantic segmentation need rich spatial information. However, most popular real-time network architectures tend to compromise spatial resolution to increase speed, but the accuracy is greatly reduced as a result. Therefore, we propose a novel Feature Enhancement Module (FEM) to extracted and enhanced the future map before the image down-sample on the backbone. Meanwhile, since the low-layer have rich detail information and high-level contain more semantic information, we propose a Feature Extraction and Fusion Module (FEFM) to fuse low-level and high-level feature representation. Based on the FEM and FEFM, we introduce a real-time semantic segmentation network FEENET. Experiments on Cityscapes and CamVid datasets demonstrate that the proposed FEENET achieves a balance between speed computation and accuracy. Without additional processing and pre-training, it achieves 75.47% Mean IoU on the Cityscapes test dataset with only 4.35G Flops and a speed of 94 FPS on a single RTX 2080Ti card. Code is avilable at https://github.com/favoMJ/FEENet .
Graph Model Proposals for Capturing Meta-information Within Professional Network Data

Any successful analytics platform is typically backed-up by a fitting data persistence layer. In a world where storage cost is no longer an issue, deduplication and compact representations go against the idea of enabling the analysis of large streams of information. The focus should instead be on choosing a solution that naturally maintains the structure of data both when storing as well as when processing it. Thus, given their expressiveness and ease of use, graph databases have now become a widely-popular approach. In the context of professional network information, this paper presents a succession of graph data models that should enable faster retrievals while building an extensible foundation for enabling boundless improvements. By the means of a synthetically constructed dataset, several validations are performed, demonstrating how the querying process can be optimised for achieving improved execution times. While indeed expansive in terms of space consumption, the proposed techniques are highly-adaptable and are likely to benefit a large range of scenarios.
A Real Time Analysis of Offensive Texts to Prevent Cyberbullying

Cyberbullying is one of the leading causes of mental health issues in the younger population, often leading to depression, stress, and suicidal tendencies. Often it is observed that timely intervention can bring awareness towards unintentional cyberbullying. This paper presents an approach to the prevention of cyberbullying via social networking. The objective of this work is real-time detection of the degree of offensiveness and alerting the user typing the message. We also propose corrective measures, by displaying an alternative word for any offensive word that is typed in the suggestion box in real-time. This enables the user to prevent unintentional cyberbullying. The proposed solution is integrated into an app, that displays the relevant statistics as well as visualization of the user typing pattern. The model to detect the offensiveness percentage can achieve 97.77% accuracy and is the backbone of the entire approach.
Subject Librarians Vs Collection Data: How to Use Data to Visualize a Small Monograph Collection

To understand an Anthropology print collection, a librarian at an urban university created data visualizations. The librarian analyzed the Library of Congress Classification call numbers GN, publication decade, checkout rage, gender of author and title keywords for analysis. The analysis found an overemphasis of books in certain geographical areas, authors skewed male, and during the years 2013–2019 only 200 books were checked out and 75% of the collection was not checked out. The goals of the study are to visualize a print collection quickly, increase librarian confidence in using spreadsheets, and advocate for strong collection development.
Detection of AAC compression using MDCT-based features and supervised learning

Audio files are frequent targets of malicious users who seek illegal profit trading with fake-quality content. For increasing the confidence in the integrity of audio files, the detection of fake-quality content is an important task. This paper proposes a method for detecting Advanced Audio Coding (AAC) compression on suspicious WAV files, in which the variance of the Modified Discrete Cosine Transform (MDCT) characterises four compression bitrates: uncompressed, 64 kbps, 128 kbps, and 256 kbps. This scheme takes advantage of the reduction of the variance of the high-frequency MDCT coefficients in compressed signals. Data obtained from MDCT coefficients generate a high-dimensional feature space. Hence, Principal Component Analysis, followed by Linear Discriminant Analysis, is used for projecting the high-dimensional data onto a lower-dimensional space. Besides, six supervised learning algorithms are compared for classifying four compression bitrates. The experiments show that using audio samples with 20 seconds and 1024 MDCT coefficients, an accuracy of 93% is reached with a Bayesian classifier. Collaterally, the detection between uncompressed and compressed signals attains an accuracy of 97% with Multinomial Logistic Regression. In conclusion, the proposed approach can detect previous AAC compression and can be potentially used when it is unfeasible to recover the suspicious signal completely.
Richter's Predictor: Modelling Earthquake Damage Using Multi-class Classification Models

Natural calamities like earthquakes cause damage to life and property. Estimation of damage grade to buildings is essential for post-calamity response and recovery, elimination of the tedious process of manual validation and authentication of property damage before granting relief funds to people. By considering basic aspects like building location, age of the building, construction details and it's secondary uses, taken from the Gorkha earthquake dataset, this paper explores various multi-class classification machine learning models and techniques for predicting the damage grade of structures. The proposed architecture of the model involves three major steps, Feature Selection, XGBoost Classifier, and Parameter Tuning. The paper presents the results of the experiments with feature engineering, training variations and ensemble learning. The paper delves into the analysis of each model, to understand the reason behind their performance. This paper also infers the agents that play a major role in deciding the seismic vulnerabilities of the buildings. The proposed classifier in the paper provides significant input to understanding earthquake damage and also provides a paradigm to model other natural disaster damage.
A trajectory data publishing algorithm satisfying local suppression

Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.
Framework For Information Analysis of the Scientific Literature using Machine Learning

One of the dynamically up growingversatile research field is Visual analytics which can beproductively used in the area of Visual Information System Management. The visual cortex is the mostelevated data transfer capacity data channel into thehuman mind and people are known to better hold dataintroduced outwardly. The papers and the scientific documents which are submitted and published todaycontain a huge amount of alpha-numeric data. Information Collectionand Information Management system play an important role inany kind of scientific visual data system. It is a crucialprecondition to search and find data on theproductivity of the process. For a decade and more, ScientificScholarly papers and documents are published very frequently. So, it is necessary to develop a frameworkto search and find the paper to identify according to the sub domain and user request. This paper offerings main tasksexisting in Visual Information Analysis area(the combination of text-mining and image processing)and offers the best suitable novel solutions to thesechallenges.
Random Access in Persistent Strings and Segment Selection

We consider compact representations of collections of similar strings that support random access queries. The collection of strings is given by a rooted tree where edges are labeled by an edit operation (inserting, deleting, or replacing a character) and a node represents the string obtained by applying the sequence of edit operations on the path from the root to the node. The goal is to compactly represent the entire collection while supporting fast random access to any part of a string in the collection. This problem captures natural scenarios such as representing the past history of an edited document or representing highly-repetitive collections. Given a tree with n nodes, we show how to represent the corresponding collection in O(n) space and $O(\log n/ \log \log n)$ query time. This improves the previous time-space trade-offs for the problem. Additionally, we show a lower bound proving that the query time is optimal for any solution using near-linear space. To achieve our bounds for random access in persistent strings we show how to reduce the problem to the following natural geometric selection problem on line segments. Consider a set of horizontal line segments in the plane. Given parameters i and j, a segment selection query returns the j th smallest segment (the segment with the j th smallest y-coordinate) among the segments crossing the vertical line through x-coordinate i. The segment selection problem is to preprocess a set of horizontal line segments into a compact data structure that supports fast segment selection queries. We present a solution that uses O(n) space and support segment selection queries in $O(\log n/ \log \log n)$ time, where n is the number of segments. Furthermore, we prove that that this query time is also optimal for any solution using near-linear space.
Retracted: Data visualization technique to study the conceptual metaphors in <i>Divan of Hafiz</i> and <i>Bustan of Sa'adi</i>

From the traditional perspective, metaphor would be regarded as a literary and ornamental device. Hence, the poets would resort merely to metaphor for the purpose of creativity. Many famous Persian poets such as Attar, Moulana, Sa’adi, and Hafiz focused on mystical literature. An important question is that weather the views of these poets regard to using conceptual metaphors differ or not. In this research, we respond to this question about the two famous books of Sa’adi and Hafiz, by using statistics and data mining techniques. For this purpose, the poems of ‘Divan of Hafiz and Bustan of Sa'adi’, two famous Persian poets, are studied. At first, the conceptual metaphors of these books are separately investigated by using data visualization technique. Then, the conceptual metaphors given in these books are compared by applying data visualization technique and Chi-square test. The results showed that these books are significantly different from each other in view of applied conceptual metaphors.
Evaluating the compliance of modern electronic banking and digital cryptocurrency systems with the information society's requirements

Subject. The digital economy emerged as a new generation of financial instruments, such as cryptocurrencies, were invented and proliferated, which were able to counteract global challenges. Those who oppose to the legitimization of digital assets and their integration into the payment infrastructure do not point out material advantages and support drastic transformations of the existing financial system. However, assuming very risky digital payments, the scope of cruptocurrency still grows. The article presents the outcome of intellectual text analysis of feedback left by users of electronic banking and digital cryptocurrency systems. Doing so, we determined to what extent they are satisfied with various systems. Objectives. The study is intended to provide the theoretical and methodological rationale for, and practically test the model that determines key themes in analyzable non-structured big data and allows to automatically evaluate the satisfaction of users with various payment systems. Methods. We resorted to the formal logic, systems approach, methods of comparative analysis, text mining and latent semantic analysis. Results. We analyzed reviews uploaded to www.banki.ru and www.otzovik.ru through parsing, stop word elimination, stemming, probabilistic thematic modeling based on the latent semantic analysis. We assessed to what extent users are satisfied with various systems by examining their reviews through the text tone analysis, the k-nearest neighbor algorithm and automated scoring of unrated reviews. Conclusions and Relevance. Text mining of unstructured big data shows that digital platforms, notwithstanding their infancy and high risks, already mostly satisfy social needs as compared to electronic banking systems, which determines the reasonableness of integrating them into the payment system to unlock their potential.