Reviewerly/exHarmony huggingface dataset:
Adaptive Approach for Density-Approximating Neural Network Models for Anomaly Detection

We propose an adaptive approach for density-approximating neural network models, the alternative use of neural models in anomaly detection. Instead of modeling anomaly indirectly through reconstruction error as is common in auto-encoders, we propose to use a neural model to efficiently approximate anomaly as inferred by k-Nearest Neighbor, which is popular due to its good performance as anomaly detector. We propose an adaptive approach to model the space of kNN inferred anomalies to obtain a neural model with comparable accuracy and considerably better time and space complexity. Moreover, the neural model can achieve even better accuracy in case of noisy data as it allows better control of over-fitting through control of its expressivity. The key contribution over our previous results is the adaptive coverage of kNN induced anomaly space through modified Parzen estimate, which then enables generating arbitrarily large training set for neural model training. We evaluate the proposed approach on real-world computer network traffic data provided by Cisco Systems .
Karakterisasi Nanokomposit Poly(Lactic Acid)-Spent Bleaching Earth Regenerasi Termodifikasi

Development of renewable resource-based polymers attracts attention to solve environmental problems due to the build up of polymer (plastic). Poly(lactic acid) (PLA) is one of the most widely used polymers which have good biodegradability and processability. The addition of fillers to the PLA matrix aims to improve the characteristics of the PLA, such as mechanical properties of nanocomposites of PLA. Thus, PLA can be use as substitution of fossil fuel-based polymer. Spent Bleaching Earth (SBE) can be used as a filler after regeneration process. SBE was extracted and oxidized to take its oil content. Then, SBE was modified with urea solution to increase the interlayer distance. In this study, structure of nanocomposites was characterized using X-Ray Diffraction and mechanical properties of nanocomposites were characterized using Universal Testing Machines. X-Ray Diffraction characterization results show that PLA-SBE nanocomposite and PLA-modified regenerated SBE nanocomposites do not form new peaks, so SBE and modified regenerated SBE is intercalated and partially exfoliated in the PLA matrix. The degree of intercalation/exfoliation is indicated by the results of characterization of mechanical properties. The mechanical properties of PLA-SBE nanocomposite are lower than neat PLA, whereas the mechanical properties of PLA-modified regenerated SBE nanocomposites are higher than neat PLA. The best mechanical properties of nanocomposites were obtained for PLA-5% modified regenerated SBE, with elongation and tensile strength, 3.26%, and 42.22 MPa, respectively.&#x0D; Keywords: nanocomposites, poly(lactic acid), regeneration, spent bleaching earth
Multi-modal Summarization

Kato, TsuneakiMulti-modal summarization is a technology that provides users with abridgments of topics of interest. Such abridgments consist of organized text and informative graphics. These summarizations have two roles. One is to assist the users to review and understand their topics of interest. The other is to guide users both visually and verbally in their exploratory search. To establish this technology, it was necessary to integrate several research streams. These included information access, information extraction, and information visualization; all of these technologies had been developing rapidly since the beginning of the twenty-first century. MuST was a workshop, the main theme of which was research on multi-modal summarization of trend information. It was not an evaluation workshop and did not present the participants with a specific task, because at the time when the workshop was conducted, multi-modal summarization was merely an agglomeration of yet-to-be-developed technologies that had not yet been fully synthesized. Rather than sharing a task, the MuST workshop shared a data set. Making an annotated corpus shared as its unifying force, the workshop encouraged cooperative and competitive researches on trend information. Several innovations emerged from the workshop. These covered trend information extraction, visualization as information access interface and as data analysis method, linguistic summary generation from charts, and trend mining.
Weighted Particle Swarm Optimization with T-Distribution in Machine Learning Applications

Inspired by the biological development of evolution, Particle Swarm Optimization (PSO) is recognized as a simple and popular practice used for the optimization process. It is a population-based algorithm that has been applied to a broad number of problems in real-life and the foundation of this method is laid by Eberhart and Kennedy in 1995. This paper gives a layout of the advantage of putting a further modification supported by T-distribution in a modified pso for the furtherance of getting a more satisfactory outcome. Basically, the work surveys two modifications, i.e., moderation of traditional weight factor by an efficacious nonlinear inertia weight and again optimizing it with the T-distribution which conjointly delivers the global solution of higher quality and it is also finer in convergence. By analyzing the simulation results, it can also be verified that weighted pso with supported by T-distribution predominates all the cases of general pso.
The Big-O Problem for Labelled Markov Chains and Weighted Automata

Given two weighted automata, we consider the problem of whether one is big-O of the other, i.e., if the weight of every finite word in the first is not greater than some constant multiple of the weight in the second. We show that the problem is undecidable, even for the instantiation of weighted automata as labelled Markov chains. Moreover, even when it is known that one weighted automaton is big-O of another, the problem of finding or approximating the associated constant is also undecidable. Our positive results show that the big-O problem is polynomial-time solvable for unambiguous automata, coNP-complete for unlabelled weighted automata (i.e., when the alphabet is a single character) and decidable, subject to Schanuel’s conjecture, when the language is bounded (i.e., a subset of w_1^* … w_m^* for some finite words w_1,… ,w_m). On labelled Markov chains, the problem can be restated as a ratio total variation distance, which, instead of finding the maximum difference between the probabilities of any two events, finds the maximum ratio between the probabilities of any two events. The problem is related to e-differential privacy, for which the optimal constant of the big-O notation is exactly exp(e).
Utility of scatterplot patterns of automated hematology analysers in white blood cell disorders – A comparative study with peripheral blood smears

Background: Advances in automated blood cell counting offer new possibilities to analyse leukocyte differentials by fluorescence RNA/DNA staining combined with scattered light intensity technologies.&#x0D; Aims and Objectives: The purpose of this study is to identify the scatterplot patterns in normal cell counts and various White Blood Cell (WBC) disorders and to assess their efficacy compared to a peripheral blood smear (PBS) in the diagnosis of WBC disorders.&#x0D; Materials and Methods: A three-phased descriptive study analysing a total of 933 EDTA blood samples. The total leucocyte count, differential leucocyte count, scatterplot patterns as obtained by automated cell counter and PBS were studied in 405 normal samples (Phase 1) and 415 samples with WBC abnormalities (Phase 2). In phase 3, the scatterplots of 113 abnormal flagged samples were analysed and provisional diagnosis made in each case which was then compared with the diagnosis on the PBS. Sensitivity, Specificity, Positive and Negative predictive values (PPV &amp; NPV) were calculated.&#x0D; Results: A grid-based standard scatterplot of normal samples was generated as a reference plot in phase one. Based on the scatterplots obtained in known WBC abnormalities studied in phase two, the basic abnormal patterns specific to each of the individual abnormalities were discerned. The phase three study showed excellent correlation between scatterplots and PBS findings in neutrophilia, eosinophilia, lymphocytosis and leukopenia (sensitivity 92-100%, specificity 98-100%). Abnormalities suspected to be leukemia on the scatterplots had a relatively low sensitivity and PPV.&#x0D; Conclusion: WBC scatterplots can be used as a screening tool as it correlates well with various WBC disorders in the PBS.
Measuring the Business Value of Cloud Computing: Emerging Paradigms and Future Directions for Research

Much of the research on measuring the business value of cloud computing examines cloud computing from the perspective of a centralised commodity-based aggregated conceptualisation of cloud computing, largely based on the NIST reference architecture. Advances in new processor architectures and virtualisation combined with the rise of the Internet of Things are not only changing cloud computing but introducing new computing paradigms from the cloud to the edge. These new paradigms present both opportunities and challenges, not least managing complexity several orders of magnitude greater than today. Yet, academic research on measuring the business value of cloud computing is lagging practice and remains far removed from these innovations. New research is required that explores the relationship between investments in new cloud computing paradigms and business value, and the measurement thereof. This chapter explores a selection of these new paradigms, which may provide fruitful research pathways in the future.
Ensemble lemmatization with the Classical Language Toolkit

Because of the less-resourced nature of historical languages, non-standard solutions are often required for natural language processing tasks. This article introduces one such solution for historical-language lemmatization, that is the Ensemble lemmatizer for the Classical Language Toolkit, an open-source Python package that supports NLP research for historical languages. Ensemble lemmatization is the most recent development at CLTK in the repurposing and refactoring of an existing method designed for one task, specifically the backoff method as used for part-of-speech tagging, for use in a different task, namely lemmatization. This article argues for the benefits of ensemble lemmatization, specifically, flexible tool construction and the use of all available information to reach tagging decisions, and presents two use cases.
Containerization of Server Services

Abstract Businesses are increasingly confronted with server-related problems. More and more, businesses are enabling remote working and need to rely on network services. The provision of network services requires rebuilding the network infrastructure and the way employees are provided with data. Web applications and server services use common dependencies and require a specific network configuration. This often involves collisions between network ports and common dependencies’ configuration. This problem can be solved by separating the conflicting applications into different servers, but this involves the cost of maintaining several servers. Another solution may be to isolate applications with virtual machines, but this involves a significant overhead on server resources, as each virtual machine must be equipped with an operating system. An alternative to virtual machines can be application containerization, which is growing in popularity. Containerization also allows to isolate applications, but operates on the server’s native operating system. This means eliminating the overhead on server resources present in virtual machines. This article presents an example of web application containerization.
Intelligent Learning Support for Reusability Oriented Knowledge Organization in Programming Learning

Recently, the demand for programming education is increasing worldwide. Enhancing intelligent tutoringsystems (ITSs) in programming education is therefore very important. For a computer to intelligently support suchlearning, it is desirable that it be adaptive to individual learning. In ITS research, learning effectiveness is enhancedby (A) controlling features of the question or problem to be asked by indexing based on characteristics of targetdomains, or by (B) making appropriate interventions such as feedback by grasping problem-solving processes basedon explainable problem-solving models.
Differentially private $k$-means clustering via exponential mechanism and max cover

We introduce a new $(\epsilon_p, \delta_p)$-differentially private algorithm for the $k$-means clustering problem. Given a dataset in Euclidean space, the $k$-means clustering problem requires one to find $k$ points in that space such that the sum of squares of Euclidean distances between each data point and its closest respective point among the $k$ returned is minimised. Although there exist privacy-preserving methods with good theoretical guarantees to solve this problem [Balcan et al., 2017; Kaplan and Stemmer, 2018], in practice it is seen that it is the additive error which dictates the practical performance of these methods. By reducing the problem to a sequence of instances of maximum coverage on a grid, we are able to derive a new method that achieves lower additive error then previous works. For input datasets with cardinality $n$ and diameter $\Delta$, our algorithm has an $O(\Delta^2 (k \log^2 n \log(1/\delta_p)/\epsilon_p + k\sqrt{d \log(1/\delta_p)}/\epsilon_p))$ additive error whilst maintaining constant multiplicative error. We conclude with some experiments and find an improvement over previously implemented work for this problem.
Implementasi Model Pembelajaran Numbered Head Together Berbantuan Media Roda Putar untuk Meningkatkan Hasil Belajar Siswa

This study aims to improve student learning outcomes of student, and teacher teaching skills. Subject to be taught in this study are Social Sciences and Indonesia Language through the Numbered Head Together model, which is assisted by a Turning Wheel media. Learning outcomes are the ability of the skilss, attitude and skills that students get after they receive the tratment given by the teacher so that they can contribute that knowledge in their daily lives. Numbered Head Together learning media are learning process activities where each student is grouped and numbered in the head of activeness of students in searching, processing repoting information in class so that students can go to the front of the class or be presented and report information that has been learned at this time. Spinning Wheel learning media is a media in the form of a wheel or circle which is divided into several sections in which there are questions, this media can attract attention, interest, and motivation to learn so that the learning process will be fun and optimal. The subjects in this study were students of class V SD 2 Tenggeles, totaling 34 students consisting of 20 female students and 14 male students.
Using Graphlet Spectrograms for Temporal Pattern Analysis of Virus-Research Collaboration Networks

We introduce a new method for temporal pattern analysis of scientific collaboration networks. We investigate in particular virus research activities through five epidemic or pandemic outbreaks in the recent two decades and in the ongoing pandemic with COVID-19. Our method embodies two innovative components. The first is a simple model of temporal collaboration networks with time segmented in publication time and convolved in citation history, to effectively capture and accommodate collaboration activities at mixed time scales. The second component is the novel use of graphlets to encode topological structures and to detect change and persistence in collaboration activities over time. We discover in particular two unique and universal roles of bi-fork graphlet in (1) identifying bridges among triangle clusters and (2) quantifying grassroots as the backbone of every collaboration network. We present a number of intriguing patterns and findings about the virus-research activities.
Use of Open Access Journals for Improved Research Output by Academic Librarians in Universities in North Central Nigeria

The use of the journals especially open access journals is important in every academic environment for research as it affect publication output of academics including librarians.This paper examined the use of open access journals for improved research output by academic librarians in universities in North-Central Nigeria.The paper adopted ex-post facto research design and a selfconstructed questionnaire was used to collect data.Population of the study was made up of 209 academic librarians in 13 (7 Federal and 6 State owned) universities in North-Central Nigeria.The data was analysed using mean and standard deviation to answer research questions while Pearson's product moment correlation coefficient was used to test the hypothesis at 0.05 level of significance.It was found that, the number of open access journals that were in high use by the librarians were slightly lower than those in low use.It was also discovered that, academic librarians derived a lot of benefits from the use of open access journals and that, research output of the academic librarians was higher in conference papers (2.22) followed by chapters in books and coauthored textbooks (2.20 each), textbooks (2.18) and lastly articles in learned journals (2.00).It was recommended among other things that, University libraries in North Central Nigeria should make a substantial subscription to relevant full text databases and release their content to public domain for use by colleagues so as to increase their use of the journals.
Research on Image Edge Detection Method Based on Multi-sensor Data Fusion

In the process of defect detection of metal components, defect edge detection is very important, and the accuracy of abnormal edge directly affects the subsequent inversion evaluation. Aiming at the fuzzy problem of defect edges in metal component detection images, this paper proposes an image edge detection algorithm based on multi-sensor data fusion. The algorithm uses the detection results of three types of sensors: ultrasonic, eddy current and magnetic flux leakage to perform feature extraction, and wavelet transform is used for image fusion. Modular maximum edge detection is performed on the fused data, and the experimental results prove the effectiveness of the proposed algorithm.
Analysis of Indonesian Language Learning Obstacles in Primary Schools

&lt;p style="text-align:justify"&gt;Learning Indonesian in elementary schools can help students get to know themselves, their culture and other cultures, express ideas, participate in society, and discover and use the analytical and imaginative abilities that are present in them. In the implementation of Indonesian language learning there are many gaps, so it is less able to achieve the specified learning objectives to the maximum. This research was conducted in class 5 of Totosari Elementary School No. 102 Surakarta, Indonesia. The purpose of this research is to explore obstacles in the process of learning Indonesian in elementary schools. This research uses a case study approach. Data collection was carried out by observing the Indonesian learning process, interviewing, and examining the average student test scores. The collected data were analyzed using interactive analysis according to Miles and Huberman. The results of his research are 1) The teacher has not used the media in the learning process especially Indonesian Language learning; 2) Learning is carried out is still conventional; 3) Students in learning tend to memorize subject matter. Through learning innovations that are in accordance with the characteristics of elementary school students are expected to be able to create meaningful learning.&lt;/p&gt;
Selective Pseudo-Labeling Based Subspace Learning for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a research hot recently, which utilizes the data form existing source project to construct prediction model and predicts the defect-prone of software instances from target project. However, it is challenging in bridging the distribution difference between different projects. To minimize the data distribution differences between different projects and predict unlabeled target instances, we present a novel approach called selective pseudo-labeling based subspace learning (SPSL). SPSL learns a common subspace by using both labeled source instances and pseudo-labeled target instances. The accuracy of pseudo-labeling is promoted by iterative selective pseudo-labeling strategy. The pseudo-labeled instances from target project are iteratively updated by selecting the instances with high confidence from two pseudo-labeling technologies. Experiments are conducted on AEEEM dataset and the results show that SPSL is effective for CPDP.
Formation of students’ professional identity during university years: Stages of developing professional commitment

УДК 378+37
A Distributed Event Extraction Framework for Large-Scale Unstructured Text

Event extraction is an important subtask of information extraction. The goal of event extraction is to quickly extract events of a specified type from a large amount of textual information. Many excellent models and algorithms have been proposed since ACE released the event extraction task in 2005. Most of them are based on the dataset published by ACE and have contributed to the accuracy of event extraction to a certain extent. In practical applications, the processing object of the event extraction task is large-scale text data. However, as far as we know, there is currently no effective model for using multiple computers for event extraction. In this paper, we propose a model for event extraction based on inter-cloud computing technology. The experimental results prove that our method reduces the time consumption and also gets better accuracy than advanced models.
Time Series Prediction Using Random Weights Fuzzy Neural Networks

In this paper, we introduce Random Weights Fuzzy Neural Networks as a suitable tool for solving prediction problems. The generalization capability of these randomized fuzzy neural networks is exploited in order to estimate accurately the sample be predicted from a multidimensional input. The latter is obtained by applying an embedding technique to the time series, which selects only the meaningful past samples to be used for prediction. We tested the proposed approach on real-world time series pertaining to the application context of power delivery. We proved the efficacy of the proposed approach by comparing its forecasting accuracy with respect to other prediction systems based on well-known data-driven regression models.