Reviewerly/exHarmony huggingface dataset:
Curriculum Evaluation Based on HEW Method Under the Guidance of OBE Concept

OBE (Outcomes-Based Education) concept emphasizes learning achievement-oriented, and combining OBE concept to construct curriculum quality evaluation system is the basic activity of reverse design and forward implementation in OBE. The construction and evaluation of curriculum index system is a complex and systematic engineering. The HEW (Hierarchy Entropy Weight) Method evaluation method is designed by combining the hierarchical structure of AHP (Analytic Hierarchy Process) and EWM (Entropy Weight Method). A curriculum quality evaluation model suitable for the concept of OBE is constructed. Based on the three basic features of OBE concept, namely, reverse design, forward implementation and learning outcomes, an evaluation system with 27 indicators of 3 layers is constructed. EWM was used to convert experts’ opinions into evaluation weights of index. In the evaluation system, learning outcomes are critical index of curriculum evaluation, which includes goal achievement, achievement quality, and feedback. In the reverse design process, the level of teaching team, the rationality of teaching objectives and teaching organization are the most important concerns. In the forward implementation process, students’ attitude to learning is the most important factor. Under the student-centered concept, teacher needs to use various methods and means to stimulate students’ inner learning drive.
Knowledge Associated with a Question-Answer Pair

The article is devoted to the modeling and presentation of declarative knowledge, which exchanged by participants of a question-answer dialogue. The knowledge of the reactive (responding) agent of the dialogue is considered from the point of view of the classical epistemological understanding of knowledge. It is shown in what cases the epistemological formula “the subject $$R$$ knows that the proposition $$p^{\prime \prime}$$ is not complete and must be supplemented with a question for which the proposition $$p$$ is the true answer. Further, the article examines the logical connection between the question and the relevant answer in the context of the interrogative formula of the question, considered in erotetic logic. Declarative knowledge expressed by the structural component of the interrogative formula, which is called the subject of the question, is proposed to be modeled using linguistic-independent semantic entities in the categories of the Ternary Description Language. The use of the Ternary Description Language makes it possible to construct models of the subject of the question that do not depend on a specific natural language. The final part of the article describes a set of eight patterns that can be used to represent declarative knowledge associated with a question-answer pair.
The Relevance of a Systematic Approach to the Use of Information Technologies in the Educational Process

The object of research in this work is modern information technologies and software, which is advisable to use for solving different tasks in the educational process. The authors focus on the positive aspects of the use of information technologies and consider them an integral part of the educational process. Digital competencies gained by students in the process of computer-based learning are necessary for their professional activities. The better students’ information technology skills are developed, the more competitive they will be in the labor market. The article presents an overview of information technologies and software that are successfully used in teaching students. The paper provides examples of the use of modern information technologies to solve the problems of information retrieval, data analysis, and visualization. It is established that information technologies and software should be applied, considering the continuity of the educational material, as well as the interdisciplinary nature of the educational process. The novelty of the research lies in the proposal of a systematic approach to the use of information technologies in the educational process. Under the systematic approach, information technologies should ensure not only the continuity of educational material but also the continuity of each other.
Are External Auditors Capable of Dealing with Cybersecurity Risks?

Cyber risk presents a significant threat to financial reporting systems and capital markets, regulatory authorities expect external auditors to obtain the competence necessary to deal with cyber risk. As an exploratory study, we aim to address the urgent question that whether current external financial auditors can deal with cybersecurity-related tasks, as well as what drives their performance in these emerging tasks related to cybersecurity. Based on the survey data of external auditors from accounting firms located in Shanghai, China, we found that these auditors did not consistently understand fundamental concepts related to cyber risk. Using partial least squares based structural equation modeling, results indicate that the personality trait of openness to experiences positively, while operating stress negatively impact auditor performance in cybersecurity. Auditors’ risk attitudes did not show significant influence on their cybersecurity performance. These findings can be used as a new source for regulators, researchers, and practitioners in their efforts of identifying audit quality drivers in the changing environments.
Shedding Light on Monopoly: Temporal Analysis of Drug Trades

Dark marketplaces pioneered a new way to monetise illicit goods. A unique combination of technologies (such as overlay networks, end-to-end encryption, and cryptocurrencies) guarantees anonymity for dark marketplace users (including operators, vendors, and buyers). We have developed a tool for monitoring and investigating dark marketplaces (namely, collecting and processing evidence directly from their websites), which we used for real-time detection of various illicit activities. This paper presents a well-described and structured dataset that contains high-frequency web-scraped information from Monopoly Market (one of the most popular dark marketplaces in 2021). The evaluation demonstrates how high-resolution temporal analysis can reveal mission-critical information about the frequency of trades, vendor activities, and the drug market.
Automating the Flow of Data Between Digital Forensic Tools Using Apache NiFi

In digital forensics, sources of digital evidence range from computer disk drives, memories, mobile phones, network dumps, and all kinds of IoT devices, etc. Therefore, different tools are required for digital evidence collection and analysis from various sources. Even though each tool works automatically, data from one tool to another often need to be prepared manually. This paper introduces a NiFi-based solution that enables automatically moving data between digital forensic tools, reducing manual work in practice. A DataFlow designed in NiFi can monitor and fetch the input data, pre-processing the data and run digital forensic tools for data analytics. Besides, NiFi can also be used for remote evidence acquisition and data sharing between law enforcement agencies (LEAs). This paper also presents a couple of use cases of using NiFi for digital evidence processing: they are 1) file carving, 2) NSRL (National Software Reference Library) hash lookup, 3) categorising files by MIME type, and 4) IoT logs parsing.
Crime and Incident Watch for Smart Cities: A Sensor-Based Approach

Beyond the many advantages which Smart City brings, the issue of security and privacy remain very important concerns. As things get more interconnected, cyber orchestrated crimes such as cyberterrorism may become more prevalent. For example, an autonomous vehicle or drone may be used to commit acts of terrorism, such as driving or flying into a crowd. Responses to such incidents need to be swift and likewise smart. Law enforcement needs to be equipped with the necessary tools to respond speedily and even automatically, equally taking advantage of the Internet of Things (IoT). We propose a Sensor-Based Crime and Incident Watch for Smart Cities (SBCI-Watch), leveraging Human Activity Recognition (HAR). SBCI-Watch may be used to automatically detect and possibly report occurrences of public disturbances (likely caused by acts of terrorism or similar crimes) to foster swift response. Slightly similar reports exist, but they are focused on different topics, such as helping law enforcement to pick suitable officers to respond to crime scenes based on proximity, but without attempting to automatically detect crimes. We are using the sensor-based approach; it is less privacy intrusive compared to the vision-based method, which is dominant in the public surveillance area but also more privacy intrusive and more expensive. To the best of our knowledge, our work is the first to address the problem in this fashion, and the results illustrate the viability of the SBCI-Watch.
Can Image Watermarking Efficiently Protect Deep-Learning-Based Image Classifiers? – A Preliminary Security Analysis of an IP-Protecting Method

Being widely adopted by an increasingly rich array of classification tasks in different industries, image classifiers based on deep neural networks (DNNs) have successfully helped boost business efficiency and reduce costs. To protect the intellectual property (IP) of DNN classifiers, a blind-watermarking-based technique that opens “backdoors” through image steganography has been proposed. However, it is yet to explore whether this approach can effectively protect DNN models under practical settings where malicious attacks may be launched against it. In this paper, we study the feasibility and effectiveness of this previously proposed blind-watermarking-based DNN classifier protection technique from the security perspective (Our code is available at https://github.com/ByGary/Security-of-IP-Protection-Frameworks .). We first show that, IP protection offered by the original algorithm, when trained with $$256\,\times \,256$$ images, can easily be evaded due to obvious visibility issue. Adapting the original approach by replacing its steganalyzer with watermark extraction algorithm and revising the overall training strategy, we are able to mitigate the visibility issue. Furthermore, we evaluate our improved approaches under three simple yet practical attacks, i.e., evasion attacks, spoofing attacks, and robustness attacks. Our evaluation results reveal that further security enhancements are indispensable for the practical applications of the examined blind-watermarking-based DNN image classifier protection scheme, providing a set of guidelines and precautions to facilitate improved protection of intellectual property of DNN classifiers.
The Lightweight Botnet Detection Model Based on the Improved UNet

Botnet detection tasks in many network devices require deployment of a large number of detection models. Deep learning-based Botnet detection models are big and resource-intensive. Besides, the UNet is primarily used for two-dimensional inputs but with higher complexity. This paper presents a One-Dimensional UNet (1D-UNet) based on one-dimensional feature vectors generated to design a lightweight detection engine. Second, we propose a One-Dimensional Lightweight UNet (1DL-UNet) by combining the 1D-UNet with depthwise separable convolution to reduce the model’s complexity. Finally, we reduce the number of packets for Botnet detection based on our observation that the first packet with an effective payload in a network session plays the most important role in detection. The experiments show that the 1DL-UNet outperforms other models with 99.66% accuracy and is 12 times smaller than one-dimensional MobileNet. Meanwhile, the designed 1DL-UNet is 4 times smaller than the 1D-UNet. Furthermore, it is observed that 4 packets are enough to achieve satisfactory Botnet detection while only 1 packet with effective payload is possible with 99.26% accuracy in the 1DL-UNet.
A MILP Formulation and a Metaheuristic Approach for the Scheduling of Drone Landings and Payload Changes on an Automatic Platform

We present a mixed-integer linear programming formulation and a metaheuristic approach based on direct search to schedule landings and payload changes of a set of unmanned aerial vehicles that cooperate to achieve given mission objectives. In more detail, such vehicles require landing on an automatic platform able to rapidly substitute batteries and switch the payload they are currently carrying with another one, if required by the mission at hand. Preliminary numerical results are presented to show the effectiveness of the metaheuristic algorithm as a compromise between accuracy of suboptimal solutions and computational effort.
DevOps in Robotics: Challenges and Practices

DevOps, which refers to a set of practices for streamlining the development and operations of software companies, is becoming increasingly popular as businesses strive to adopt a loosely coupled architecture that supports frequent software delivery. As a result, DevOps is also gaining traction in other domains and involved architectures, including robotics, though research in this area is still lacking. To address this gap, this paper investigates how to adapt key DevOps principles from the domain of software engineering to the domain of robotics. In order to demonstrate the feasibility of this in practice, an industrial robotics case study is conducted. The results indicate that the adoption of these principles is also beneficial for robotic software architectures, though general DevOps approaches may require some adaptation to match the existing infrastructure.
Image Restoration Using a Coupled Reaction-Diffusion Equations

This work addresses In the present paper, we propose a a novel denoising model based on nonlinear-reaction-diffusion system. This model is driven by using the image decomposition strategy of $$H^{-1}$$ norm given by Guo et al. [1]. This choice is motivated by the success of the decomposition strategy in preserving and illustrating the small features in the textured image. Our results include a rigorous discretization of the proposed model, and numerical results are provided with numerous comparisons to show the performance of the proposed approach.
Towards Uncertainty Reduction Tactics for Behavior Adaptation

An autonomous system must continuously adapt its behavior to its context in order to fulfill its goals in dynamic environments. Obtaining information about the context, however, often leads to partial knowledge, only, with a high degree of uncertainty. Enabling the systems to actively reduce these uncertainties at run-time by performing additional actions, such as changing a mobile robot’s position to improve the perception with additional perspectives, can increase the systems’ performance. However, incorporating these techniques by adapting behavior plans is not trivial as the potential benefit of such so-called tactics highly depends on the specific context. In this paper, we present an analysis of the performance improvement that can theoretically be achieved with uncertainty reduction tactics. Furthermore, we describe a modeling methodology based on probabilistic data types that makes it possible to estimate the suitability of a tactic in a situation. This methodology is the first step towards enabling autonomous systems to use uncertainty reduction in practice and to plan behavior with more optimal performance.
A Taxonomy for Design Decisions in Software Architecture Documentation

A software system is the result of all design decisions that were made during development and maintenance. Documentation, such as software architecture documentation, captures a variety of different design decisions. Classifying the kinds of design decisions facilitates various downstream tasks by enabling more targeted analyses. In this paper, we propose a taxonomy for design decisions in software architecture documentation to primarily support consistency checking. Existing taxonomies about design decisions have different purposes and do not fit well because they are too coarse. We take an iterative approach, starting with an initial taxonomy based on literature and considerations regarding consistency checking. Then, we mine open-source repositories to extract 17 software architecture documentations that we use to refine the taxonomy. We evaluate the resulting taxonomy with regard to purpose, structure, and application. Additionally, we explore the automatic identification and classification of design decisions in software architecture documentation according to the taxonomy. We apply different machine learning techniques, such as Logistic Regression, Decision Trees, Random Forests, and BERT to the 17 software architecture documentations. The evaluation yields a $$F_1$$ -score of up to 92.1% for identifying design decisions and a $$F_1$$ -score of up to 55.2% for the classification of the kind of design decision.
Blockchain-Based Architecture of Immutable Document Repository

The paper presents architecture of blockchain-based immutable document repository, called Durable Media. The system was designed to ensure long-term document storage while enabling access to the documents by various users and verification of document’s authenticity. The system is used by banks, e-commerce companies and thousands of their clients in Poland.
Pengembangan Media Pembelajaran IPS Pada Buku Tema 1 ASEAN Untuk Siswa Kelas VI SD

Social Sciences is a subject studied by students since the elementary school level which of course requires learning media as an aid in the teaching and learning process. The background of this research is the limited use of learning media in social studies subjects causes students to experience boredom because the learning process is less fun. Social studies learning is still dominated by teachers which results in a lack of interaction between students when participating in the learning process. The purpose of the research is to develop learning media that will be applied to learning as social studies learning media for ASEAN countries in grade VI SD. In this study, using the type or approach of library research with library data collection methods, reading and recording and processing research materials from journals, books, articles, magazines, websites with various types of publications that are relevant to the development of learning media for ASEAN Class. VI SD. The results of the study show that the learning media Smart Sticker Spin Wheel on the material of ASEAN countries in social studies learning is very suitable to be used in the social studies learning process in class VI SD.
Computational Models of Evolution and Development

This chapter provides the fundamentals of computational models related to biological evolution and development. It starts with an introduction to evolutionary algorithms for emulating natural evolution in computer systems, including representations, genetic operators, parameter self-adaptation, and their application to single- and multi-objective optimization. This is followed by a presentation of computational models for gene regulatory networks describing the dynamics of the gene expression process, which plays an essential role in biological evolution and development. Then, models for simulating morphological and neural development governed by gene regulatory networks and cellular interactions are given, providing the foundation models for evolving neural and morphological development. Finally, computational models of activity-dependent neural plasticity, including the BCM rule and spike-timing-dependent plasticity rule are given.
Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning

Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy.
A Multi-Feature Fusion-Based Automatic Detection Method for High-Severity Defects

It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects.
Mitigating Position Bias in Hotels Recommender Systems

Nowadays, search ranking and recommendation systems rely on a lot of data to train machine learning models such as Learning-to-Rank (LTR) models to rank results for a given query, and implicit user feedback (e.g. click data) have become the dominant source of data collection due to its abundance and low cost, especially for major Internet companies. However, a drawback of this data collection approach is the data could be highly biased, and one of the most significant biases is the position bias, where users are biased towards clicking on higher ranked results. In this work, we will investigate the marginal importance of properly mitigating the position bias in an online test environment in Tripadvisor Hotels recommender systems. We propose an empirically effective method of mitigating the position bias that fully leverages the user action data. We take advantage of the fact that when a user clicks a result, they have almost certainly observed all the results above, and the propensities of the results below the clicked result will be estimated by a simple but effective position bias model. The online A/B test results show that this method leads to an improved recommendation model.