Reviewerly/exHarmony huggingface dataset:
A New Score for Adaptive Tests in Bayesian and Credal Networks

A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, are used for adaptive tests as they allow to model the uncertainty about the questions and the skills in an explainable fashion, especially when coping with multiple skills. A better elicitation of the uncertainty in the question/skills relations can be achieved by interval probabilities. This turns the model into a credal network, thus making more challenging the inferential complexity of the queries required to select questions. This is especially the case for the information theoretic quantities used as scores to drive the adaptive mechanism. We present an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain. This makes considerably simpler the evaluation in the credal case, without significantly affecting the quality of the adaptive process. Numerical tests on synthetic and real-world data are used to support this claim.
Concurrent Composition of Differential Privacy

We initiate a study of the composition properties of interactive differentially private mechanisms. An interactive differentially private mechanism is an algorithm that allows an analyst to adaptively ask queries about a sensitive dataset, with the property that an adversarial analyst’s view of the interaction is approximately the same regardless of whether or not any individual’s data is in the dataset. Previous studies of composition of differential privacy have focused on non-interactive algorithms, but interactive mechanisms are needed to capture many of the intended applications of differential privacy and a number of the important differentially private primitives.We focus on concurrent composition, where an adversary can arbitrarily interleave its queries to several differentially private mechanisms, which may be feasible when differentially private query systems are deployed in practice. We prove that when the interactive mechanisms being composed are pure differentially private, their concurrent composition achieves privacy parameters (with respect to pure or approximate differential privacy) that match the (optimal) composition theorem for noninteractive differential privacy. We also prove a composition theorem for interactive mechanisms that satisfy approximate differential privacy. That bound is weaker than even the basic (suboptimal) composition theorem for noninteractive differential privacy, and we leave closing the gap as a direction for future research, along with understanding concurrent composition for other variants of differential privacy.
COMASNet: Precise Face Mask Detector Network

With the spread of the novel coronavirus in the world, COVID-19 is causing a global health crisis with countries already aware of the importance of the epidemic. According to the World Health Organization (WHO), wearing a mask is a necessary strategy for the prevention of COVID-19 infection. Due to this guideline, governments around the world are constantly asking people to wear masks in public places. However, it is currently inefficient to manually monitor if people are wearing a mask in public. Thus, to resolve this issue, in this paper we proposed a new robust and accurate model called COMASNet modified from the object recognition method (YOLOv5) to automatically detect if people are wearing a mask. Extensive experimental results on the proposed model gives us an accuracy of 98.1%. Besides this, our method has great advantages in identifying masks in a crowd compared with other models.
Fusion Filtering for Stochastic Systems with Correlated Noises and Deception Attacks

This paper is concerned with fusion filtering problem for a class of stochastic systems subject to both correlated noises and deception attacks. The system noise and measurement noises of different sensors are cross-correlated at the same time instant. During the data transmission over network, the deception attack signals are injected randomly, whose statues are described by a group of Bernoulli variables. The centralized fusion filter is proposed via an innovation analysis approach. In order to improve robustness and reduce computational burden, a distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. A simulation example is given to show the effectiveness of the proposed algorithms.
On the use of MFCC and SWT-based features for offensive speech detection in social media

Research on psychological comfort and serenity in social media becomes a necessity because of the excess of negative waves generated by the users. In this paper, we aim to distinguish between offensive and ordinary speech based on machine learning classification techniques. For this purpose, the VAM emotional audio database has been restructured and adjusted to the context of offensive speech detection. Besides, a feature fusion based on Mel Frequency Cepstral Coefficients (MFCCs) as well as Stationary Wavelet Transform (SWT) has been employed and K-nearest neighbors (KNN) algorithm has been used as a classification tool. Results show that the considered feature set has relatively great power in recognizing suspicious behavior, reaching 96.2% as the highest accuracy rate.
Minimizing Ancilla Inputs and Garbage Outputs of Reversible BCD Multiplier

Over the last decades, designing reversible arithmetic circuits is one of the interesting research areas because of its ability to reduce power consumption in the circuits. This paper proposes two new design approaches of reversible binary-coded decimal (BCD) multiplier. The realization of such BCD multiplier has been achieved through binary multipliers, multiplexers, and a binary-to-BCD converter. Four types of multiplications, viz. [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] multiplications, have been utilized for such binary multiplication and are implemented parallelly as a combined multiplier to reduce ancilla inputs (AIs) and garbage outputs (GOs). We also propose a novel reversible BCD adder for a reversible binary-to-BCD converter with reducing AIs and GOs. The first design of the reversible BCD multiplier is integrated with the proposed BCD adder in the binary-to-BCD converter. Furthermore, the proposed reversible BCD adder is modified to reduce the AIs and the GOs, which is then integrated into the second design of the reversible BCD multiplier. The results offer appreciable reductions of AIs and GOs by at least [Formula: see text]16% and [Formula: see text]43%, respectively, compared to the existing designs found in the literature.
Web Content Authentication: A Machine Learning Approach to Identify Fake and Authentic Web Pages on Internet

The Internet is evolving very fast and is gaining user base enormously. With the advent of low-cost devices and connectivity to access the Internet, it has become a common medium for communication. Various social media platforms and blogging Web sites are prevalent in the Internet today. This gave rise to fake news, unreliable Web content, etc., to prosper because of the absence of a system to validate the authenticity of the content. Also, hackers and unauthentic authors are taking advantage of the ranking system of popular search engines by modifying the content and other necessary factors, as to bag the first position of the search result. This paper introduces certain theoretical concepts that can be used to identify authentic Web sites by giving a trust factor to Web content. Also, with these factors modeled as input to a machine learning algorithm which outputs a trust factor for the Web site, it becomes easy for the Web surfers to pick the right Web site displayed to them by search engines. This paper also contributes ideology in creating a new dataset which can be used to train different ML models to identify unauthentic Web sites and Web blogs.
Tracklet and Signature Representation Using Part Appearance Mixture Approach in the Context of Multi-shot Person Re-Identification

Recognizing persons in a video surveillance scene in the real world is attractive and is now showing an increasing interest. The task of person Re-Identification (Re-ID) consists in assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. This chapter addresses the problem of high variance in a person’s appearance by represent it as a set of multi-modal feature distributions modeled by Gaussian Mixture Model (GMM). We tackle also the Re-ID challenges that consist in occlusions, illumination changes and person’s orientation/pose. To this end, we extract the tracklets, i.e trajectories of persons. From these tracklets, we compute the signature and represent it based on the approach of Part Appearance Mixture (PAM) in the context of Multi-Shot person Re-Identification. Experiments and results on two public datasets and on our own dataset show good performance. An evaluation of the quality of this signature representation is also described.
Cooperative Tracking of Hypersonic Target with Bearing-Only Measurements

In this paper, we study cooperative tracking problem in hypersonic missile defense scenario. To this end, we propose an enhanced cubature Kalman filtering algorithm for cooperative state estimation of the incoming target with bearing-only measurements, the proposed cooperative filtering scheme is fully decentralized and works under any balanced and connected communication topology. Simulation results verified performance of the proposed algorithm.
Multi-Domain Image Super-Resolution Generative Adversarial Network for Low-Resolution Person Re-Identification

Person re-identification (ReID) is an important task in video surveillance application. To address the issue that various low-resolutions and scale mismatching always exist in the real world, a multi-domain image-to-image translation network, termed Multi-Domain image Super-Resolution Generative Adversarial Network (MSRGAN), is proposed to learn the mapping relationship between the various low-resolution domains and the high-resolution domain. MSRGAN can ensure that the transferred image has a similar resolution as in the target domain. It is also able to keep the identity information of images from low-resolution domain during the translation. In addition, a novel ReID model, termed CSA-ReID in which channel attention and spatial attention module are introduced, is designed to learn resolution-invariant deep representations. The proposed method achieves 90.7% rank-1 accuracy and 96.4% rank-5 accuracy on multiple low-resolutions Market-1501 dataset. The experimental results prove that the proposed method achieves promising generalization ability and accuracy compared with the state-of-the-art methods.
Development of Music Theory Analytic Platform in Mobile Environment based on Mobile Computing

Music is not only an important part of culture, but also the spiritual and cultural heritage and wisdom crystallization of human beings. Music theory expresses human feelings and life in different countries, nationalities and times, and expresses people's feelings and wishes. Therefore, it is the most direct and effective way to spread Chinese voice, tell Chinese stories, carry forward Chinese spirit and show Chinese cultural confidence with music. This paper proposes a computing migration algorithm, selects a reasonable program decomposition granularity and designs a program decomposition algorithm to complete the construction of mobile computing model. The model can collect the trajectory data uploaded by the mobile terminal. After sorting the collected data, the analyst can call the algorithm interface in the system to analyze the trajectory data.
MCF Tree-Based Clustering Method for Very Large Mixed-Type Data Set

Several clustering methods have been proposed for analyzing numerous mixed-type data sets composed of numeric and categorical attributes. However, existing clustering methods are not suitable for clustering very large mixed-type data sets because they require a high computational cost or a large memory size. We propose a novel clustering method for very large data sets using a mixed-type clustering feature (MCF) vector with summary information about a cluster. The MCF vector consists of the CF vector and a histogram to summarize the mixed-type values. Based on the MCF vector, we propose an MCF tree, along with a distance measure between the MCF vectors representing two clusters. Unlike previous studies that summarize a data set based on a fixed memory size, we estimate a small initial memory size of the data set for building the tree. Then, the memory size is adaptively increased to estimate a more accurate threshold by reflecting the size reduction in the re-built tree. Our theoretical analysis demonstrates the efficiency of the proposed approach. Experimental results on very large synthetic and real data sets demonstrate that the proposed approach clusters the data sets significantly faster than existing clustering methods while maintaining similar or better clustering accuracy.
Canine Behavior Interpretation Framework Using Deep Graph Model

Humans have long aspired to understand dog behavior. While research on the Calming signal has achieved substantial progress in understanding dog behavior, it remains an unfamiliar concept to non-expertise. Therefore, in this paper, we introduce a framework for analyzing dog behavior, which defines the interrelationship between dog postures through a graph model without any additional devices but a camera. First of all, our framework classifies the dog posture in frame units, using a machine learning model based on the position information of the dog’s body part in the video captured by the camera. We then analyze dog behavior using graph models that define interrelationships among classified dog postures. We expect that our approach will help non-expertise to understand dog behavior.
Research on the Application of Artificial Intelligence Technology in Audit Under the Background of Big Data

In the era of big data, artificial intelligence technology is gradually applied to audit, which requires auditors to have both traditional audit knowledge and modern technology. Based on this, this paper takes the application of artificial intelligence in audit from the perspective of big data as the research object. Firstly, it expounds the concept and development of big data and artificial intelligence, and then discusses the specific application of artificial intelligence in audit under the background of big data by analysing typical artificial intelligence systems. Finally, it analyses the future development and challenges of artificial intelligence to the audit profession.
Development and Evaluation of Scoring Support System in Writing Test for Subject Teachers and Classroom Teachers

The purpose of this study is to develop and evaluate a system to support the scoring of writing tests in primary and secondary schools employing the subject-teacher and the classroom-teacher systems. To this end, we employ the previously proposed image-skewered method, in addition to an area setting package function, to address commercially available tests used by classroom teachers and a multiple class selection function to score tests completed by students from multiple classes taught by the same subject teacher. Further, a function is added to operate only in the case of incorrect answers to reduce the number of operations required to assess the correctness of answers. To evaluate the system, the time taken to manually score actual test papers was compared with the time taken to do so using the proposed system. The automatic scoring method exhibited a time reduction by 44.0% for subject teachers and 42.9% for classroom teachers. These results indicate that the system functions effectively as a scoring support system to reduce the time consumed for scoring tasks for teachers working in primary and secondary schools.
Efficient Depth-Included Residual Refinement Network for RGB-D Saliency Detection

RGB-D saliency detection aims to segment eye-catching objects from images with the help of depth. Although many excellent methods raised, it is still difficult to locate salient objects accurately and efficiently, which lies in two challenges: (1) It is difficult to seamlessly and efficiently integrate cross-modal features from RGB-D inputs; (2) Low-quality depth maps have a serious negative impact on the final prediction results. The existing methods use two backbone networks to extract saliency features, which also introduce much redundancy. To address issues, we propose a simple and efficient deep feature refinement module to extract complementary depth features. We also design a depth correction module to filter out noisy depth input adaptively. Experiments with 13 recently proposed methods on 7 datasets demonstrate the effectiveness of the proposed approach both quantitatively and qualitatively, especially in efficiency and compactness.
Automatic Data Sheet Information Extraction for Supporting Model-Based Systems Engineering

To describe modeling objects in Model-Based Systems Engineering (MBSE) tools, physical properties of these objects are often provided only in data sheets, which are not truly machine-readable. Previously, we proposed a product data hub to exchange spacecraft product information between manufacturers and various MBSE tools. However, issues with heterogeneous structures and semantics of information, such as differences in data format and vocabularies, persist. Using ontologies to maintain product descriptions can mitigate the heterogeneity problem by providing semantic descriptions and supporting different vocabularies for a single concept. To automatically and semantically obtain information from documents that contain tables, lists, and text, we developed an ontology-based information extraction tool. We present how to use the Data Sheets Annotation Tool (DSAT) for, either manually or automatically, extracting information from data sheets, and populating a database with the obtained data. Particularly, we emphasize on the usage of DSAT as a user interface for improving ontologies, which, in turn, are used for a (better) information extraction from the data sheets. Although DSAT is initially created for supporting collaborative systems engineering, it is not limited to the domain of spacecraft design. It can also be applied to other domains, where information needs to be extracted from a multitude of heterogeneous sources.
Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient

Abstract We investigate the potential for deep learning to create a transfer function from T1 to T2 magnetic resonance imaging sequences using data collected from an asymptomatic patient. Neural networks were trained on images of a human left hand, and then applied to convert T1 images to T2 images for the associated right hand. Analysis showed that the most accurate neural network considered the features in the surrounding ∼1 cm when converting to T2, hence indicating that the neural network was able to identify structural correlations between the sequences. However, some small features measuring &lt;2 mm differed, and grid patterning was evident from the images. While using deep learning for sequence transformations could enable faster processing and diagnosis and in turn reduce patient waiting times, additional work, such as synergising physics-based modelling with neural networks, will likely be required to demonstrate that deep learning can be used to accurately create T2 characteristics from T1 images. In addition, since the present work was conducted using data collected from a single patient, further example datasets collected from patients with a range of different pathologies will be required in order to validate the proposed method.
ON COOPERATIVE GAME APPROACH TO PROFIT SHARING IN A FEW PROJECTS MANAGEMENT PROBLEM

The article is consider three different mechanisms of project’s profit sharing, assuming that the projects have common resource pool and both resources and profit may be distributed at arbitrary way without losses. The resources and profit distribution mechanisms are based on cooperative game theory thesis. As three different alternatives, such cooperative game solutions, as Shapley value, nucleolus ant τ-value are proposed. The calculation routine is delivered by easy typical example.
Generalized canonical correlation analysis for labeled data

Multi-view learning is a method that is used to extract standard features from different information sources in various fields such as medical data analysis, computer vision, and web data analysis. Canonical correlation analysis (CCA), a dimensionality reduction method used for the data acquired in multi-view learning, can extract a low-dimensional space where the correlation between two multivariate data is high. However, CCA has the following problems. First, it is difficult to perform dimensionality reduction while taking advantage of the label information attached to the data. Second, CCA is an analysis method for two sets of data; it cannot be directly applied when we have three or more datasets. Discriminative canonical correlation analysis (DCCA) can be used to solve the first problem. It enables the dimensionality reduction of two datasets while reflecting the label information. Further, generalized canonical correlation analysis (GCCA) can be used to solve the second problem. It calculates canonical correlation variables, which are the products of parameters and data, for three or more datasets, and assumes that all datasets aggregate to a piece of shared information so that the parameters aggregate the information of each data. However, DCCA and GCCA do not simultaneously solve the problems of CCA. Therefore, in this study, we extend DCCA and propose a dimensionality reduction method using the label information for three or more datasets. We validate the usefulness of the proposed method through a simulation study.
Analysis of Farmers’ Perception and Attitudes of Water Quality in Margodadi Embung, South Lampung

Abstract Margodadi Embung (retention basin) is water pond that was designed for irrigation and conservation which is located in South Lampung, Indonesia. This Embung is surrounded through numerous farm fields, consisting of the cassava plant, corn plants, palm trees, and paddy fields. This research was aimed to know the farmers’ perception and attitudes of water quality in Margodadi Embung, South Lampung. Sampling was conducted using random sampling on farmers’ around the Embung, with the parameter of respondent profile, farming attitudes, knowledge about the Embung, and perception of the Embung water quality. The outcomes confirmed that the farmers had been using manure and chemical fertilizers for his or her fields, and also, they wiped clean their farm animals in Embung after plowing on the farm. Most of the farmers thought that the water quality is still good and fortunately, it was proved with the DO and BOD parameters that still be permitted according to Government Regulation no 82, 2001. However, it was indicated that the knowledge of the farmers of water quality will influence the perception and attitudes of the farmers towards the Margodadi Embung.