Reviewerly/exHarmony huggingface dataset:
Risk Assessment For Enterprise Application In The Insurance Sector

With the rapid pace of digital transformation, many organizations have realized the imminent cyber threats and risks that may impact the organization. In particular, these threats may come from applications used to support business transactions in the organization, and various threat modeling has been widely utilized since its introduction. This research employs STRIDE and DREAD threat modeling methods to assist the risk assessment process of business applications used in the insurance sector to determine the risk score and appropriate risk mitigation to the highly valued assets in the organization.
ARE YOU WHO YOU SAY YOU ARE? COMPUTER SCIENCE AND THE PROBLEM OF DIVINE SELF‐AUTHENTICATION

Abstract A vital axiom of Reformed theology is that God is who God claims to be, and that he acts to “self‐authenticate” his identity to humans through the internal witness of the Holy Spirit. But how ought theologians to represent and make sense of the multiform nature of divine self‐authentication (DSA)? And can their models account for the various kinds of evidence God utilizes in acts of DSA, or explain why doubt and deception about divine identity persists despite it? Operating at the intersection of theology and computer science, this study examines how modern authentication technologies can deepen theological reflection upon the nature, evidence, and efficacy of DSA. It proposes that computer authentication “trust systems”: (1) offer a valuable schematic for representing the variegated structural patterns inherent in DSA; (2) shed critical light on the forms and functions of evidence used in DSA; and (3) provide strategies and practical measures for offsetting the continuing risk of deception about human and divine identity.
Monitoring Student Performance Through an Agile Project-Based Assessment Strategy for Distance Higher Education

This article presents an observational qualitative study regarding the use of agile methodologies in implementing project-based student assessment procedures in higher education courses. The study comprised 32 students enrolled in an online biology class in a public university in Rio de Janeiro, Brazil. Students underwent two types of activities: first using traditional project-based learning, second using an agile approach based on the EduScum methodology. Results demonstrated that the agile methodology enforced student engagement, especially collaboration aspects. Interactions between students and teachers became more acute and straightforward. Moreover, it became easier for teachers to monitor students' performance throughout the course. This study provides highlights to enable schools to improve students' assessment procedures in distance education courses.
Combined Application of Hash, Public Key and Blockchain Technology in Electronic Money Transactions

research-article Combined Application of Hash, Public Key and Blockchain Technology in Electronic Money Transactions Share on Authors: Qiang Li Chongqing University of Arts and Sciences, China Chongqing University of Arts and Sciences, ChinaView Profile , Xiaoqing Hu Chongqing City Vocational College, China Chongqing City Vocational College, ChinaView Profile , Xinqiang Ma Chongqing University of Arts and Sciences, China Chongqing University of Arts and Sciences, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1790–1794https://doi.org/10.1145/3482632.3484040Online:22 November 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Protecting AI: We Built the Brains, but What About Helmets?

Artificial intelligence models introduce attack surfaces that current defenses simply do not protect. But much like helmets, new tools, and techniques are emerging to safeguard the smarts.
Fraud Prevention and Detection on Heterogeneous Information Networks with Deep Graph Infomax

Fraud is ubiquitous in both institutions and private companies, costing $1.9 billion in losses in the US, in 2019 alone. Fraud detection introduces a way to mitigate these losses while ensuring better security and enabling trust between all parties. However, it frequently comes at a great cost of resources needed to locate and oppose fraudulent cases manually. This cost also grows exponentially with the size of a company's financial transaction network. In this work, we propose a novel framework for automatic fraud detection that relies on institutions' readily available data, that aims at reducing the cost of resources outlined above. We evaluate our framework by comparing it against a baseline result and show an increase of 37% in performance expressed as F1-score while providing highly desirable characteristics such as online learning capability and a reduction of 82% in training time on commodity hardware.
Environmental Concern to save Langgar Tinggi Heritage Mosque at Kampung Pekojan

Abstract Located in Tambora, West Jakarta, Kampung Pekojan has an essential role as a part of historical stories back to the colonialism era in Batavia, now known as Jakarta. As the residential area of Muslim for the Arabs, The Indian, and the indigenous people, Pekojan has six cultural heritage mosques, one of them is Langgar Tinggi. However, the yearly flood event eventually become a problem and it endangered Langgar Tinggi and its surrounding. Using descriptive qualitative methods by collecting data which includes literature studies, interviews with the experts, this paper is expected to provide the necessary information and data about how to handle a flood event around Kampung Pekojan and save Langgar Tinggi mosque as an important cultural heritage and a place that holds history.
Specific patterns in the number of lines of<i>The Sumerian Temple Hymns</i>

Strict formal restrictions have appeared in literature for centuries. A close consideration of The Sumerian Temple Hymns, a united set of poems devoted to Sumerian-Akkadian pantheon and attributed to Enheduanna, High Priestess and Princess of Akkad (twenty-fourth–twenty-third centuries B.C.E.), demonstrates that the total number of the lines in these hymns is an exact multiple of one hundred, and each half of the hymns contains exactly half of the total number of lines of all hymns. Sequential subsets of the hymns demonstrate several other quantitative patterns associated with numerical representation of the Mesopotamian deities. These features could only be constructed on purpose, through intentional poetic work, which makes these hymns the earliest known example of the literature of formal restriction.
Deciphering drivers of ICT use in agricultural education

Information and Communication Technology (ICT) is an important element in the education scenario to prepare citizens for the future. Since, its inception factors influencing the use have not been studied more, hence an exploratory research study has been framed for finding those factors among the students of CCS Haryana Agricultural University, Hisar, Haryana during 2018-19. The data were collected from 200 respondents using structured interview schedule and analyzed using standard methodology. The findings revealed that the institutional factors were perceived highest with composite index value (CIV) of 70.18, followed by personal (67.35), technical (59.15) and economic constraints (47.33). Although, these factors could be overcome through implementing remedies suggested by students like; teaching should be through ICTs, followed by improved internet connection, training programme related to use of ICTs, etc. but an effective national level policy related to infrastructure facilities, software licensing, availability of high quality ICT gadgets at subsidized rates, free and regular training programmes, etc. are possible ways to deal with these factors especially during COVID-19 pandemic. The correlation and regression of different variables, i.e. age, education, family education, scientism, annual expenditure, mass media exposure, information seeking behaviour and risk orientation exhibited negative and significant effect with their perceived personal factors at 0.05 level of probability. Hence, the paper recommends that effective utilization of ICT depends not only upon the available ICT resources, but also effective utilization of the same.
Evaluation of Attention Mechanisms on Text to Speech

Attention mechanisms have been widely used in sequence to sequence tasks. Among those tasks, attention-based neural text to speech synthesis with monotonic property has shown a powerful ability to generate natural speech. This paper introduces three different attention mechanisms designed to utilize the strict monotonic property and evaluates them in a multi-speaker TTS task.
Instructional design in foreign language teaching

This paper offers a presentation of two original multimedia educational resources with interactive online tasks in the German language developed within the course “Teacher”s and translator”s electronic resource” in cooperation with the students of the Faculty of Foreign Languages and International Communication of Tver State University. The purpose of our projects assumes, in the first place, deliberate formation of a learning environment meeting the needs of a target audience. In the process of working over the projects, the authors took into consideration the principles of instructional design that served as a theoretical basis for the development of educational materials. Instructional design is viewed by the authors of the article as a separate branch of practical pedagogy which primarily focuses on the application of pedagogical knowledge in practice, namely, development of high-quality efficient educational resources with the use of modern information technologies. The distinctive feature of the theory of instructional design is consideration of not so much the cognitive parameters and mental processes characterising the learning process as the efficient methods and learning formats stimulating these cognitive processes. The most important result of our work is the conclusion that the projects developed with regard for the principles of instructional design under the ADDIE model contribute to enhancement of students’ cognitive abilities and increase the amount and quality of information assimilated by them; at the same time, the learning becomes more systemic and practice-oriented from the first stages of project analysis and planning. The novelty of our approach specifically lies in practical treatment of instructional design as a tool for development of learning materials, invigoration of learning activities and tutor support of students.
The Research on Classification of Small Sample Data Set Image Based on Convolutional Neural Network

This paper is aimed to study the problem of small sample image recognition. We use the initialization method of dictionary filter to replace the traditional Convolutional Neural Network (CNN) initial filter method so that the dictionary filter can learn its model structure during the CNN training process and the CNN model can extract a more Multi-effective feature map. For the image classification stage, using GRNN to replace BP neural network can enhance the ability of model classification processing, accelerating the speed of model convergence, and comprehensively improving the recognition accuracy of the model. This article extracted a small amount of data in large data set for experiments, and use the ORL face database as verification simultaneously, the experimental results and analysis are given. Compared with the fine-tuning through migration learning, it has increased by 3.84%, compared with the use of fully connected classifiers, it has increased by 4.58%, which proves the accuracy of the proposed method.
Graph Algorithm for Anomaly Prediction in East Java Student Admission System

Before the zoning policy, students or their parents tend to choose a recognized school with high educational quality despite its distance. New Student Admissions or Penerimaan Peserta Didik Baru (PPDB) is a school zoning enrollment system that aims to reduce the student travel distance. The online-based PPDB system requires home location input supplemented with legal documents as validation mechanism. However, falsifying home residence or enrollment fraud could not be identified by the PPDB system. This study examines the possible fraud cases from the PPDB enrollment ranks data. The ranks data forms a graph relationship between the registrant and the school. Every data contains a longitude-latitude point, and it is the main factor for accepting based on PPDB policy. The process is trying to analyze the connection between distance gap distribution derived from the ranks data, with the concurrent fraud cases. Because the distance gap distribution still has a missing value on several gap points, it is useful to use KDE (Kernel Density Estimation) to estimate those unknown values. KDE will result in estimated distance gap distribution. The distance gap distribution is affected by the residence location that is plotted on a geo map. When there's an uncommon location of some registrant it will create fluctuation on the distance gap distribution. The gap distribution value exceeds the estimated distance gap distribution from this situation and will be detected as an enrollment fraud. The process to detect enrollment fraud is handled with a graph algorithm. The graph algorithm traverses the graph data and gets ranked registrant from a school. The data are grouped every two meters and check whether its count does not exceed the estimated distance gap distribution. The graph algorithm builds over the PPDB system and tests several manipulated residence locations. It could detect those manipulated data and has a fast process since it only took less than one second.
Research on Interactive Design of Tourism Brand Based on Augmented Reality Technology

This thesis researches how the interactive design rebranding tourism brands. It proposes the use of augmented reality cascading technology and multi-dimensional information display technology to sort out human information, geographic information, ecological information, cultural traditions and other information for tourist destinations, and finally use Professional development technology displays this information in a visual design. This study mainly discusses from three aspects: 1. Augmented reality technology enables tourism interactive experience to realize the transformation from surface to three-dimensional; 2. Augmented reality technology enhances the versatility of travel interaction; 3. Augmented reality technology optimizes the visitor experience of digital travel, thus driving the transformation of travel brands and realizing the new mode of tourism interaction and integration.
E2EET: from pipeline to end-to-end entity typing via transformer-based embeddings

Entity typing (ET) is the process of identifying the semantic types of every entity within a corpus. ET involves labelling each entity mention with one or more class labels. As a multi-class, multi-label task, it is considerably more challenging than named entity recognition. This means existing entity typing models require pre-identified mentions and cannot operate directly on plain text. Pipeline-based approaches are therefore used to join a mention extraction model and an entity typing model to process raw text. Another key limiting factor is that these mention-level ET models are trained on fixed context windows, which makes the entity typing results sensitive to window size selection. In light of these drawbacks, we propose an end-to-end entity typing model (E2EET) using a Bi-GRU to remove the dependency on window size. To demonstrate the effectiveness of our E2EET model, we created a stronger baseline mention-level model by incorporating the latest contextualised transformer-based embeddings (BERT). Extensive ablative studies demonstrate the competitiveness and simplicity of our end-to-end model for entity typing.
Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise

In the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.
Asymptotics for Markov chain mixture detection

Sufficient conditions are provided under which the log-likelihood ratio test statistic fails to have a limiting chi-squared distribution under the null hypothesis when testing between one and two components under a general two-component mixture model, but rather tends to infinity in probability. These conditions are verified when the component densities describe continuous-time, discrete-state-space Markov chains and the results are illustrated via a parametric bootstrap simulation on an analysis of the migrations over time of a set of corporate bonds ratings. The precise limiting distribution is derived in a simple case with two states, one of which is absorbing which leads to a right-censored exponential scale mixture model. In that case, when centred by a function growing logarithmically in the sample size, the statistic has a limiting distribution of Gumbel extreme-value type rather than chi-squared.
Inference of time-ordered multibody interactions

We introduce time-ordered multibody interactions to describe complex systems manifesting temporal as well as multibody dependencies. First, we show how the dynamics of multivariate Markov chains can be decomposed in ensembles of time-ordered multibody interactions. Then, we present an algorithm to extract those interactions from data capturing the system-level dynamics of node states and a measure to characterize the complexity of interaction ensembles. Finally, we experimentally validate the robustness of our algorithm against statistical errors and its efficiency at inferring parsimonious interaction ensembles.
An Image Pre-Processing Improvement Procedure for Standardizing Multiple Colour Variations in the Astrocytoma Immunohistochemical Staining Images

Abstract Colour variations in the histopathological images can occur when the acquired images came from different laboratories. This issue happens may be due to different protocols between each laboratory. Besides that, the usage of dye or staining is also different since it arrived from different manufacturers. Hence, it will affect the performance of a system, especially for an automated image diagnosis system. If this issue is taken lightly, the whole process of image analysis can produce a false diagnosis outcome, and thus, it will affect the patient’s treatment options and endanger the patient’s life. This paper proposed an automated image pre-processing procedure for standardizing the colour variations in immunohistochemical staining images. The proposed procedure was done by modifying the reference image, which will be used later for the stain standardization process. Based on 50 astrocytoma histopathological images, the proposed procedure showed a promising result, where the quality of the output images has been enhanced and standardized.
Research and Implementation of Tire Tracking Algorithm Based on Multi-Feature Fusion

In order to improve highway utility, the national government is vigorously promoting automatic toll at highway entrances. Cameras, as indispensable facilities, have been installed on sides of toll lanes. When vehicles gradually drive through the video area, wheels are detected through target detection and tracking technologies. The number of axles is accordingly detected and it is a factor mattering toll for large trucks. This is turned out an essential methodology for axle counting. Multiple Object Tracking(MOT) is the key point for it, while most existing algorithms fail for the proximity of tire positions and the similarity of tire appearance. For the challenge of proximate positions, a relative position relation model is proposed. It defines adjacent tires as a group according to their proximity. Furthermore, Kalman filter is introduced to calibrate preliminary results by reducing matching range. To conquer the challenge in reference to similar appearance, factors in terms of relative position, background, texture and color of grouped tires are involved. These factors are synthetically integrated with weighted convolution features to improve association in tires. Based on the real video data collected from toll highway stations, it is tested and evaluated that the proposed algorithm can effectively improve the accuracy of tire tracking in adjacent positions and similar appearance.
Key Exchange and Management Schemes for Automotive Control Units

Today's vehicles are typically equipped with dozens of electronic control units that control one or more of the electrical systems or subsystems. Due to the rapid development towards autonomous driving, vehicle functionalities strongly depend on the communication between diverse control units (xCUs). Herein, tampering to manipulate e. g. the emission values of environmental protection systems (EPSs) to gain financial benefits poses a cyber risk to the various xCUs. To protect the communication channels from tampering/cyber-attacks, advanced key exchange and key management schemes are required.
The NMIA WebTimer: A Traceable Time Service For Checking Stopwatches and Timers

We have developed the WebTimer, an Internet service, accessible through a browser, for checking the operation of stopwatches and timers. We explain its design, operation and performance. A detailed uncertainty budget has been developed to assist the user. The WebTimer has been available as a paid subscription service since July 2020.
Psychological and pedagogical bases of visualization of educational information for younger schoolchildren

The question of the significance of the process of visualization of educational material by younger students is considered in the article. The concept of “visualization” is described. Types of visualization are characterized. The analysis of the visual perception of primary school students done. Methodical recommendations for the use of visualization with the aim of qualitative and informed perception of information by primary school students have been made.