Reviewerly/exHarmony huggingface dataset:
Active Bayesian Assessment of Black-Box Classifiers

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that labels may be scarce and costly to collect). In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. We begin by developing inference strategies to quantify uncertainty for common assessment metrics such as accuracy, misclassification cost, and calibration error. We then propose a general framework for active Bayesian assessment using inferred uncertainty to guide efficient selection of instances for labeling, enabling better performance assessment with fewer labels. We demonstrate significant gains from our proposed active Bayesian approach via a series of systematic empirical experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets.
Ontology alignment: Simulated annealing-based system, statistical evaluation, and application to logistics interoperability

The primary motivation of this dissertation is to investigate how to enable interoperability in the logistics domain by the aid of ontology alignment. More in detail, the primary research objective of this dissertation is To address interoperability between heterogeneous IT systems in logistics by using ontology alignment. To accomplish the objective, we first look into the literature of ontology alignment using a quantitative approach to get a thorough understanding of the available literature and its progress. We particularly identify several research gaps that are studied in the subsequent chapters that serve the objective of this dissertation. An important lesson learned from the literature analysis is that there are two segregated communities that form ontology alignment but work independently and with minimal interactions with each other. Based on the identified research gaps, we develop a new system, SANOM (simulated annealing-based ontology matching), that addresses the non-deterministic polynomialtime( NP) ontology alignment problembased on thewell-knownevolutionary algorithm, simulated annealing. SANOMis equippedwith an extended Soft TF-IDF (termfrequencyinverse document frequency) string similarity metric that can also detect linguistic similarity among the names of entities in two ontologies in question. Structural similarity metrics are also taken into account that increase the alignment performance for more complex ontologies. Simulated annealing with a warm initialization is used as the matching strategy to find an optimal solution to the ontology alignment problem. The experiments show that SANOM has a very competitive performance with the best systems that participated in the ontology alignment evaluation initiative (OAEI) and is particularly faster than other evolutionary algorithm-based alignment systems. To come to a better understanding of which alignment system is preferred, we develop several methods for evaluation and comparison of alignment systems using different statistical techniques and multi-criteria decision-making methods (MCDM). We first study the frequentist approach for comparing alignment systems. More in detail, we compare different statistical tests for comparing alignment systems over single or multiple benchmarks and propose a proper test based on the number of benchmarks and alignment systems. While these techniques are more reliable than those being currently in use, it suffers from the drawbacks of making decisions based on p-values, which can be addressed by Bayesian statistics.
Multiple conclusion linear logic: cut elimination and more

Abstract Full intuitionistic linear logic (FILL) was first introduced by Hyland and de Paiva, and went against current beliefs that it was not possible to incorporate all of the linear connectives, e.g. tensor, par and implication, into an intuitionistic linear logic. Bierman showed that their formalization of FILL did not enjoy cut elimination as such, but Bellin proposed a small change to the definition of FILL regaining cut elimination and using proof nets. In this note we adopt Bellin’s proposed change and give a direct proof of cut elimination for the sequent calculus. Then we show that a categorical model of FILL in the basic dialectica category is also a linear/non-linear model of Benton and a full tensor model of Melliès’ and Tabareau’s tensorial logic. We give a double-negation translation of linear logic into FILL that explicitly uses par in addition to tensor. Lastly, we introduce a new library to be used in the proof assistant Agda for proving properties of dialectica categories.
Asynchronous parallel adaptive stochastic gradient methods

Stochastic gradient methods (SGMs) are the predominant approaches to train deep learning models. The adaptive versions (e.g., Adam and AMSGrad) have been extensively used in practice, partly because they achieve faster convergence than the non-adaptive versions while incurring little overhead. On the other hand, asynchronous (async) parallel computing has exhibited much better speed-up over its synchronous (sync) counterpart. However, async-parallel implementation has only been demonstrated to the non-adaptive SGMs. The difficulty for adaptive SGMs originates from the second moment term that makes the convergence analysis challenging with async updates. In this paper, we propose an async-parallel adaptive SGM based on AMSGrad. We show that the proposed method inherits the convergence guarantee of AMSGrad for both convex and non-convex problems, if the staleness (also called delay) caused by asynchrony is bounded. Our convergence rate results indicate a nearly linear parallelization speed-up if $\tau=o(K^{\frac{1}{4}})$, where $\tau$ is the staleness and $K$ is the number of iterations. The proposed method is tested on both convex and non-convex machine learning problems, and the numerical results demonstrate its clear advantages over the sync counterpart.
The Role of ICT in Teaching Processes in Bulgarian Schools

As the twenty-first century is dominated by information and communication technologies (ICT), they are a fundamental factor for innovation both in social and economic sectors. Education has an essential role in this process, as it is responsible for the establishment of the necessary knowledge and skills in students today, who will be the key driver in future development. ICT has been widely introduced to school programs for students to learn, but the meaningful integration of technology into classrooms is not strongly evident. Until recently, the use of these technologies in education practice is not explored in details. Many studies point out that teachers are the key determinant to the effective use of ICT in teaching practice. However, the issue of how students perceive the efforts of teachers in this direction have to be further explored. There are a lot of related studies worldwide, but very few empirical research has been done to investigate this issue in the context of Bulgaria. The authors make a scientific study on the role and impact of ICT to comprehensively demonstrate the effects of technology tools on the learning process. For that purpose, a survey that applies multiple case study methodology is performed, where both teachers and school students were the units of sampling used. The goal is to explore their views concerning the impact and importance of technology in education. The presented research formalizes and assesses the main aspects that refer to the use of new technologies in the education process. The current paper focuses on several issues concerning the application of ICT resources and tools in science, technology, engineering and mathematics (STEM) and other subjects. The results point to the importance of providing appropriate conditions for effective teaching, so teachers can employ a wide range of technology resources to support many different pedagogical strategies to enhance the comprehension of students.
Collaborative Filtering Method with the use of Production Rules

this paper proposes a new collaborative filtering method with the calculation of unknown similarity coefficients between users via the application of production rules, which aims to improve the work quality of recommendation systems, to develop of production rules for the developed system. The methods used are: graph theory, the theory of algorithms, mathematical statistics, object-oriented programming, and fuzzy logic. The developed systems are new collaborative filtering method with the definition of unknown similarity coefficients between users through the application of production rules was developed, software for the implementation and testing of this method was developed, experiments on the developed software was conducted. The production rules to determine unknown similarity coefficients in recommendation systems was proposed. The new method of collaborative filtering with the application of production rules has been developed to find unknown similarity coefficients between users that may be possibly used to improve the work quality of the recommendation system. The conducted experiments showed that the developed method enhances the quality indicators of the recommendation system, such as item space coverage and the total number of predicted preferences of users
Correction factors for non-uniform large-area reference sources

Based on uniformity measurements of large-area reference sources used in calibration procedures of surface contamination monitors, an investigation was carried out to obtain a method that estimates the bias originated from surface source intensity distribution deviation from the ideal uniform distribution and corrects it. It relies on correcting the estimated instrument efficiency by applying correction factors driven from the uniformity distribution profiles of the sources used in calibration procedure. Simulations of the monitor calibration procedure are run for 2 distinct surface source distributions: the real and the ideally uniform distributions. Correction factors are driven from counting rate estimates obtained from each source representation. In order to evaluate adequacy of this proposition it was validated against a method proposed by the NPL in the Good Practices Guide No.14.
FPGA Based Hardware Design for Noise Suppression and Seismic Event Detection

In this paper, we present a Field Programmable Gate Array (FPGA) based design for suppression of high frequency noise and detection of seismic events in a fast-arriving P-wave for the development of an early earthquake warning system. The key motivation of this research was to design an accelerated hardware platform to reduce the response time of the early warning system. It consists of two types of FIR filter: low pass FIR filter and another moving average filter (short term and long term). We explore three different architectures for the FIR low pass filter in order to optimize delay and area. The ratio of the short term averaging and long term averaging is compared with a pre-defined threshold level to detect seismic event. The entire design is implemented in Xilinx Vivado 2015.4 platform targeted towards ZED board. The design is validated through carefully generated seismic signals.
Data Tagging in Medical Images: A Survey of the State-of-Art

A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.
Information/Control – Control in the Age of Post-Truth

Guest editors Stacy E. Wood, James Lowry, and Andrew J Lau introduce the issue on Information/Control – Control in the Age of Post-Truth.
DEVELOPMENT OF MOBILE-LEARNING MEDIA ON BASIC ELECTRICITY AND ELECTRONICS SUBJECT

in the learning process of basic electricity and electronics subject, students face problems in understanding the material because it is considered as difficult without supported of adequate media. Then, the time allocation for basic electricity and electronics learning in the classroom is only four hours in a week, thus the material delivering is considered as not optimally to students. Therefore, teaching learning process needs to be supported by suitable and adequate media. The purpose of this research is mobile based learning media that is valid, practical and effective for Basic Electricity and Electronics subjects. The method of this research is Research and Development (R&amp;D). This research and development used the Borg and Gall model that consists of several stages. The instrument used in this research is validation questionnaire that was given to material experts and media experts. The results obtained from this development research are a learning media based on mobile learning in the Basic Electricity and Electronics subjects. Based on research developed media are valid media, with the results of media validation of 0.84 and material validation of 0.74. The practical value of the results of the teacher's response of 94% and student responses of 84.24% were categorized very practical. From the posttest scores students get effective results that is 0.00 which is smaller than 0.005 which means that it is significant from the value of the experimental class and the control class.
Evolving Hyperparameters for Training Deep Neural Networks against Adversarial Attacks

Deep neural networks have been found to be vulnerable to adversarial attacks. To address this challenge, this paper adopts the evolutionary multi-objective approach to the learning process, and manages to achieve a balance between learning accuracy and robustness against adversarial attacks. In addition, we propose to minimize the model complexity together with the adversarial training loss to defend against fast gradient signed method attacks. Our experimental results using two deep neural networks models, LeNet-5 and VGG-11, on MNIST and CIFAR-10 datasets, respectively, confirm that the proposed methods are effective in improving the robustness of deep learning models against adversarial attacks.
Multi-Event Modeling and Recognition Using Extended Petri Nets

This paper addresses the event modeling and recognition problem in video surveillance systems using the system net on the Petri Net (PN) formalism. The single event on the foundation of prior knowledge is first modeled via arranging event schemes from the design document. Then, finite sequential runs (FSRs) determined by semantic features in training clips drive elements of the proposed single event model. Finally, the multi-event model is built automatically from single event models via a proposed integration method using multi-level features (including high-level semantic features and low-level features like numerical characters of individual trajectories). We provide a novel solution to event conflict issue through an extended system net where the resultant event type is determined by the decision tree technique. The comparison between the proposed methodology and other approaches in the literature is reported via experiments on an acknowledged public dataset.
Parallel Whale Optimization Algorithm for Maximum Flow Problem

Maximum Flow Problem (MFP) is considered as one of several famous problems in directed graphs. Many researchers studied MFP and its applications to solve problems using different techniques. One of the most popular algorithms that are employed to solve MFP is Ford-Fulkerson algorithm. However, this algorithm has long run time when it comes to application with large data size. For this reason, this study presents a parallel whale optimization (PWO) algorithm to get maximum flow in a weighted directed graph. The PWO algorithm is implemented and tested on datasets with different sizes. The PWO algorithm achieved up to 3.79 speedup on a machine with 4 processors.
Analysis and improvement of evaluation indexes for clustering results

Clustering algorithm is the main field in collaborative computing of social network.How to evaluate clustering results accurately has become a hot spot in clustering algorithm research.Commonly used evaluation indexes are SC, DBI and CHI.There are two shortcomings in the calculation of three indexes.(1) Keep the number of clusters and the objects in the cluster unchanged.When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged.When changing the objects in the cluster, the three indexes will change tinily.This shows that the three indexes unable to evaluate the clustering results very well.Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI.Through testing on standard data sets, three new indexes can better evaluate clustering results.
ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS OF E-LEARNING COURSE USING IN THE HIGHER EDUCATION PROCESS IN UKRAINE

The article is substantiated the pedagogical conditions for the e-learning course using (ELC) in the educational process of the Ukrainian higher education institutions (HEI). There had been found out that systemic, competent, personality-oriented and activity-based approaches are common in the HEI, that indicated the priority orientation in the development and using of the ELCs for complexity, practical orientation, personification in the organization of the educational process. The peculiarity of the selection of pedagogical conditions for the application of the ELC is emphasised on the formation of information and computer literacy, value guidance, cognitive interest in future specialists of different specialties, due to the specificity of their professional training. The research methods are applied some logic and structural analysis for the structure determining of construction of ELCs on the MOODLE platform in HEI; analytical-system method, pedagogical experiment for substantiation and determination of pedagogical realization expediency of pedagogical conditions in the process of using ELC during professional training of different-specialty students in HEI The structure of the ELC "Educational Policy" (National Dragomanov National Pedagogical University) and "General Pedagogy" (Boris Grinchenko University of Kyiv) on MOODLE platform for students of specialty 01 “The Educational Sciences” has been characterized. In view of the practical experience of designing and using the studied ELCs, the following structure is didactically logical: an IT course; a lecture course; some assignments for seminars and practical classes; an independent-working unit; a block of final progress tests for students etc. The improving the content recommendations of the ELC are offered. The pedagogical realization expediency of certain conditions in the process of vocational students training in educational and professional programs for the speciality “The Educational Sciences”01 Education / Pedagogy and 03 Humanities was experimentally investigated. The analysis of the results of pedagogical experiment on the efficiency of the using of ELC is presented, which testifies to the increase of the level of professional competence formation of the HEI-studied students
Trends in traffic delinquency rates in the Republic of Serbia

Enforcement of radical legislative measures in order to improve traffic safety in the Republic of Serbia, which are implemented for some time, should result in a reduction of the rate of the traffic delinquency.This paper analyses trends in traffic delinquency rates in the Republic of Serbia with the aim to show trends of traffic delinquency rates by looking at the total number of traffic accidents and traffic offenses, the structure of traffic delinquency rates, as well as the share of traffic offenses within the overall crime in Serbia for period 2014-2018.The aim of this paper is to analyse the statistics, to obtain the right picture about existing situation, trends and structure of traffic delinquency, as well as about the effectiveness of preventive and repressive measures implemented, as well as the possible need to take further steps towards combating the traffic delinquency.
An Extended Approach for Generating Unitary Matrices for Quantum Circuits

In this paper, we do research on generating unitary matrices for quantum circuits automatically. We consider that quantum circuits are divided into six types, and the unitary operator expressions for each type are offered... | Find, read and cite all the research you need on Tech Science Press
Application Analysis of Artificial Intelligence in Computer Network Technology

Artificial Intelligence (AI) is a kind of intelligent behavior similar to human being which helps human being to participate in dangerous and complicated work directly. The effective application of computer network technology has changed people's production and life style, and technological innovation has paid more attention to intelligence and humanity, leading to that artificial intelligence has become a development trend. This paper analyzes the general situation of artificial intelligence and its application and future development in computer network technology, hoping to provide help for related work.
A Color Compensation Method Using Inverse Camera Response Function for Multi-exposure Image Fusion

Multi-exposure image fusion is a method for producing an image with a wide dynamic range by fusing multiple images taken under various exposure values. In this paper, we discuss color distortion included in fused images, and propose a novel color compensation method for multi-exposure image fusion. In the proposed method, an inverse camera response function (CRF) is estimated by using multi-exposure images, and then a high dynamic range (HDR) radiance map is recovered. The color information of the radiance map is applied to images fused by conventional multi-exposure imaging to correct the color distortion. The proposed method can be applied to any existing fusion approaches for improving the quality of the fused images.