Reviewerly/exHarmony huggingface dataset:
The Online Behaviour and Victimization Study: The Development of an Experimental Research Instrument for Measuring and Explaining Online Behaviour and Cybercrime Victimization

The rise in cybercrime victimization underlines the need to understand how people behave online and how unsafe online behaviour may be related to victimization. Previous studies have often relied on self-reported behaviour or attitudes towards precautionary online behaviour. Studies that measured both actual online behaviour and explanatory factors in a large population are scarce. In this chapter, the research instrument of the online behaviour and victimization study is introduced. The chapter will outline the development of this instrument, which uses a population-based survey experiment. With this instrument, the actual behaviour of internet users can be measured. While filling out the survey respondents encounter (fictional) cyber-risk situations, allowing researchers to analyse how respondents deal with these situations. Moreover, based on theories and an extensive literature study that is shortly outlined in this chapter, measurements were incorporated in the study for numerous explanatory factors, including knowledge (awareness), opportunity and motivation. Finally, previous cybercrime victimization is measured, making it possible to research the association between actual online behaviour and online victimization.
Cooperative Monitoring of Malicious Activity in Stock Exchanges

Stock exchanges are marketplaces to buy and sell securities such as stocks, bonds and commodities. Due to their prominence, stock exchanges are prone to a variety of attacks which can be classified as external and internal attacks. Internal attacks aim to make profits by manipulation of trading processes e.g., Spoofing, Quote stuffing, Layering and others, which are the specific focus of this paper. Different types of proprietary fraudulent activity detectors are deployed by stock exchanges to analyze the time series data of trader’s activities or the activity of a particular stock to flag potentially malicious transactions while human analysts probe the flagged transactions further. The key issue faced here is that while the number of anomalous transactions identified can run into thousands or tens of thousands, the number of such transactions that can realistically be probed by human analysts would be a small fraction due to resource constraints. The issue therefore reduces to a dynamic resource allocation problem wherein alerts that represent the most malicious transactions need to be mapped to human analysts for further probing across different time intervals. To address this challenge, we encode the scenario as a Cooperative Target Observation (CTO) problem wherein the analysts (modeled as observers) perform a cooperative observation of alerts that represent potentially malicious activity (modeled as targets) and develop multiple solution approaches in order to identify malicious activity.
A note on extensions of multilinear maps defined on multilinear varieties

Abstract Let $G_1, \ldots , G_k$ be finite-dimensional vector spaces over a prime field $\mathbb {F}_p$ . A multilinear variety of codimension at most $d$ is a subset of $G_1 \times \cdots \times G_k$ defined as the zero set of $d$ forms, each of which is multilinear on some subset of the coordinates. A map $\phi$ defined on a multilinear variety $B$ is multilinear if for each coordinate $c$ and all choices of $x_i \in G_i$ , $i\not =c$ , the restriction map $y \mapsto \phi (x_1, \ldots , x_{c-1}, y, x_{c+1}, \ldots , x_k)$ is linear where defined. In this note, we show that a multilinear map defined on a multilinear variety of codimension at most $d$ coincides on a multilinear variety of codimension $O_{k}(d^{O_{k}(1)})$ with a multilinear map defined on the whole of $G_1\times \cdots \times G_k$ . Additionally, in the case of general finite fields, we deduce similar (but slightly weaker) results.
An Improved Interest Point Detection Approach Based on Combined Harris-SIFT Principles

Practical applications such as image registration, 3-D scene reconstruction, object detection, motion tracking, image classification and robot localization rely on the extraction of stable and well localized interest points. This paper proposes an improved interest point detection approach based on combined Harris-SIFT principles. Instead of direct application of Harris corner detector on the original image, the idea is to apply this conventional detector on difference-of-Gaussians (DoGs) of input image, extracted using SIFT principles as baseline. The applied scale-space configuration consists of single octave with varying levels of Gaussian blur. SIFT descriptor is utilized for robust feature description followed by feature matching using efficient nearest neighbor approach. The proposed approach minimizes the probability of poorly localized detections along the edges and also significantly improves the count of stable, well localized interest points. This subsequently increases the feature match rate which was found to be approximately double to that of the conventional Harris and popular SIFT approach, as per the obtained experimental results. Comparison with other state-of-the-art interest point detection approaches is also presented to highlight the improvement aspect.
Accelerating Force-directed Graph Layout with Processing-in-Memory Architecture

In the big data domain, the visualization of graph systems provides users more intuitive experiences, especially in the field of social networks, transportation systems, and even medical and biological domains. Processing-in-Memory (PIM) has been a popular choice for deploying emerging applications as a result of its high parallelism and low energy consumption. Furthermore, memory cells of PIM platforms can serve as both compute units and storage units, making PIM solutions able to efficiently support visualizing graphs at different scales. In this paper, we focus on using the PIM platform to accelerate the Force-directed Graph Layout (FdGL) algorithm, which is one of the most fundamental algorithms in the field of visualization. We fully explore the parallelism inside the FdGL algorithm and integrate an algorithm level optimization strategy into our PIM system. In addition, we use programmable instruction sets to achieve more flexibility in our PIM system. Our PIM architecture can achieve 8.07× speedup compared with a GPU platform of the same peak throughput. Compared with state-of-the-art CPU and GPU platforms, our PIM system can achieve an average of 13.33× and 2.14× performance speedup with 74.51× and 14.30× energy consumption reduction on six real world graphs.
Enhancing VTOL Multirotor Performance With a Passive Rotor Tilting Mechanism

This article discusses the benefits of introducing a simple passive mechanism to enable rotor tilting in Vertical Take-Off and Landing (VTOL) multirotor vehicles. Such a system is evaluated in relevant Urban Air Mobility (UAM) passenger transport scenarios such as hovering in wind conditions and overcoming rotor failures. While conventional parallel axis multirotors are underactuated systems, the proposed mechanism makes the vehicle fully actuated in SE(3), which implies independent cabin position and orientation control. An accurate vehicle simulator with realistic parameters is presented to compare in simulation the proposed architecture with a conventional underactuated VTOL vehicle that shares the same physical properties. In order to make fair comparisons, controllers are obtained solving an optimization problem in which the cost function of both systems is chosen to be equivalent. In particular, the control laws are Linear-Quadratic Regulators (LQR), which are derived by linearizing the systems around hover. It is shown through extensive simulation that the introduction of a passive rotor tilting mechanism based on universal joints improves performance metrics such as vehicle stability, power consumption, passenger comfort and position tracking precision in nominal flight conditions and it does not compromise vehicle safety in rotor failure situations.
IDSDL: a sensitive intrusion detection system based on deep learning

Abstract Device-free passive (DfP) intrusion detection system is a system that can detect moving entities without attaching any device to the entities. To achieve good performance, the existing algorithms require proper access point (AP) deployment. It limits the applying scenario of those algorithms. We propose an intrusion detection system based on deep learning (IDSDL) with finer-grained channel state information (CSI) to free the AP position. A CSI phase propagation components decomposition algorithm is applied to obtain blurred components of CSI phase on several paths as a more sensitive detection signal. Convolutional neuron network (CNN) of deep learning is used to enable the computer to learn and detect intrusion without extracting numerical features. We prototype IDSDL to verify its performance and the experimental results indicate that IDSDL is effective and reliable.
An Improved Error-Based Pruning Algorithm of Decision Trees on Large Data Sets

Decision tree is one of the models that are often used in classification. Pruning is necessary for decision tree in order to prevent overfitting. With the advent of the era of big data, there is one well-known folklore that, using decision tree pruning algorithms, growing trees with increasingly larger amounts of training data will result in larger tree size even when accuracy does not increase. This paper discusses issues in pruning a scalable classifier and presents the design of a new parallel algorithm based on error-based pruning. The improved error-based pruning (IEBP) uses k-fold cross validation and t-test to choose the optimal value for the certainty factor, which controls the pruning, instead of using the default one. Different experiments have been made to compare the behavior of the behavior of IEBP with the behavior of error-based pruning and reduced-error pruning. The Experimental results support the conclusion that varying the certainty factor allows significantly smaller trees to be obtained with no or minimal accuracy loss on large data sets.
Highlighting poor anonymity and security practice in the blockchain of Bitcoin

We highlight how Bitcoin addresses are reused in transactions (implicitly or explicitly), despite recommendations and best practice strongly advise to avoid this. We analyse the blockchain with the purpose to find and count highly reused Bitcoin addresses, starting from the birth of the protocol in 2009. In this study we also include hidden addresses: these addresses are not the explicit endpoints of a transaction, but they are somehow contained in it (e.g., in the script of a Pay to Script Hash transaction). Finally, we try to de-anonymise the top-100 reused addresses, grouping and linking them to their wallet owner. This work shows that such a misuse in reusing the address often corresponds to malicious activities in the bitcoin ecosystem. These results push towards a mandatory single usage, which mitigates dangerous consequences.
Blockchain &amp; Machine learning Based Secure Personal Medical Record Storage and Sharing Platform - DataBlock

Data is the most important part of machine learning. In the bioinformatics field, the sensitivity of the data is high and due to that, the accessibility of the data for a secondary purpose (e.g.: research) consists of many legal and ethical issues. Due to that in many bioinformatics research collecting the data consume more time than the development phase. There are some researches done to solve the legal and ethical issues by anonymizing the data using encryption, de-identification and perturbation of potentially identifiable attributes. But for some extend those solutions restricted the data breach but on the other hand, anonymized data not performed well during the analysis and mining tasks and some researches done to generate fake data like the real data sets. But those researches not full fill the requirements because of the generated data more restricted to the knowledge of the training data. The evolution of Blockchain provided a secure and trusted way to transfer valuable assets between two unknown parties. This research used Blockchain technology to store and share personal medical data to data scientists. And that will help them to build more accurate and efficient models. It also proposed a machine learning model to predict the authenticity and validity of the personal data based on domain knowledge and the validator's trust percentage.
AT-ROP: Using static analysis and binary patch technology to defend against ROP attacks based on return instruction

Return-Oriented Programming (ROP) is one of the most common techniques to exploit software vulnerabilities. Although many solutions to defend against ROP attacks have been proposed, they still have various drawbacks, such as requiring additional information (source code, debug symbols, etc.), increasing program running cost, and causing program instability. In this paper, we propose a method: using static analysis and binary patch technology to defend against ROP attacks based on return instruction. According to this method, we implemented the AT- ROP tool in a Linux 64-bit system environment. Compared to existing tools, it clears the parameter registers when the function returns. As a result, it makes the binary to defend against ROP attacks based on return instruction without having to obtain the source code of the binary. We use the binary challenges in the CTF competition and the binary programs commonly used in the Linux environment to experiment. It turns out that AT-ROP can make the binary program have the ability to defend against ROP attacks based on return instruction with a small increase in the size of the binary program and without affecting its normal execution.
Informatisation of Project Activities Performed by Innovation Clusters of the Kamsky Agglomeration in Russia

The paper considers informatisation of project management processes at enterprises and in clusters of Kamsky agglomeration in Russia. The role of business process reengineering in the strategy of Kamsky agglomeration clusters towards high-tech production is investigated. The results of the reengineering project implemented by enterprises of the Machine-Building Cluster in order to solve the problem of import substitution are presented. The paper studies the tools of formation of a unified information space in the Machine-Building Cluster of Kamsky agglomeration, including formation of the spatial infrastructure, use of CALS-technologies and CASE-means. The results of implementation in the unified information space of the KAMAZ truck production reengineering project based on Enterprise Information Management concepts are described.
Comparing Classical ML Models with Quantum ML Models with Parametrized Circuits for Sentiment Analysis Task

Abstract This paper studies the performance of classical and quantum machine learning models for sentiment analysis task. Here, popular machine learning algorithms viz support vector machine (SVM), gradient boosting (GB) and random forest (RF) are compared with variational quantum classifier (VQC) using two sets of parameterized circuits viz EfficientSU2 and RealAmplitudes. For experimenting with VQC, IBM Quantum Experience and IBM Qiskit were used while for classical machine learning models, scikit-learn was used. It was found that the performance of the VQC was slightly better than popular machine learning algorithms. For our experiments, we have used popular restaurant sentiment analysis dataset. The extracted features from this dataset and then after applying PCA reduced the feature set into 5 features. Quantum ML models were trained using 100 epochs and 150 epochs. Overall, four Quantum ML models were trained and three Classical ML models were trained. The performance of the trained models was evaluated using standard evaluation measures viz, Accuracy, Precision, Recall, F-Score etc. In all the cases EfficientSU2 based model with 100 Epochs performed better than all other models. Efficient SU2 model with 100 epochs produced an accuracy of 74.5% and an F-Score of 0.7605 which were highest across all the trained models.
Model of formation of professional activity safety culture of future occupational safety and health engineers

The purpose of the article is to consider the model of the formation of the safety culture of professional activity in future occupational safety and health engineers. The urgency of the implementation of the stated research within the Concept of sustainable development has been substantiated. The study establishes the structure of the model represented by five blocks: prospective-target, theoretical-methodological, content-technological, diagnostic-resultant. The prospective-target block defines the goals and objectives of the research; the theoretical-methodological block provides for a concept, methodological approaches, principles, factors, pedagogical conditions that contribute to the effective formation of the culture of safety of professional activity; content-technological is expressed by a complex of didactic units that include content components, forms, methods and technologies of teaching; the diagnostic-resultant block contains diagnostic tools for the formation of the safety culture of professional activity among future engineers on labor protection. The conclusion is made about the dynamism and balance of the components of the proposed model, aimed at the personal growth of future occupational safety and health engineers and the development of their creative potential, most significantly focused on the prevention of the risks and harm caused by occupational hazards.
Locality-Promoting Representation Learning

This work investigates questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regularization schemes violate this principle. Thus, we introduce Locality-promoting Regularization, which yields accuracy gains across multiple architectures and datasets. We also show theoretically that the empirical finding could be explained by maximizing feature cohesion under the assumption of spatial locality.
Organized Financial Cybercrime: Criminal Cooperation, Logistic Bottlenecks, and Money Flows

This chapter provides an overview of what we know about organized forms of cybercrime executed with a financial goal. First, criminal cooperation is covered. We discuss recent insights into the structure, composition, and mechanisms of origin and growth. Second, bottlenecks in the criminal business process and criminal money flows are described. Every criminal business process entails logistical bottlenecks: logistical problems that must be resolved to ensure the successful execution of criminal activities. One major bottleneck is safely spending illegally obtained money without drawing the attention of the authorities. After all, when it comes to financial cybercrime, the goal of criminals is gaining financial benefits. Finally, in the last section of this chapter, several overarching conclusions about organized forms of cybercrime are presented.
Randomly Wired Network Based on RoBERTa and Dialog History Attention for Response Selection

Recently, chatbot research for conversation has been actively conducted in the field of natural language processing. Among the dialogue systems, a retrieval-based system has emerged as an important one. Because, it showed better performance than the response generation models and it actually seemed applicable. The response selection task is used to develop a retrieval-based system. Thus, we did research to improve the performance of retrieval-based dialogue systems for response selection. We used Google's bidirectional encoder representations from transformers (BERT) natural-language processor with the robustly optimized BERT pre-training model (RoBERTa) as the base model to improve performance. The Recall@N of our model was improved by 1.7–4.2% using two methods: First, instead of using a simple feed-forward network at the end of the model, we employed a randomly wired neural network that contained multiple wiring paths, thus achieving better performance than that of feed-forward networks. The experimental results of this study demonstrated that feed-forward networks can be replaced by randomly wired neural networks. Second, we calculated the correlation between dialog history (i.e., context) and the last utterance of the context, reflecting this correlation as an attention network that yielded an answer prediction. Using first and second method achieved higher performance than using only the first method in dialog response selection tasks.
Feature Selection Using Equilibrium Optimizer

To tackle the problem of the feature selection, the newly introduced algorithm of equilibrium optimization is featured in this paper. The algorithm is inspired from dynamic mass balance inside a control volume. Each agent (particle) updates concentration with best solutions obtained so far leading to equilibrium state, i.e., optimum result. To find effectiveness of the proposed algorithm, it was tested on 13 benchmark datasets from UCI machine learning repository from different domains.
Type-Aware Open Information Extraction via Graph Augmentation Model

Open information extraction (IE) can support knowledge graph enrichment. Open IE systems are capable of extracting relational tuples from texts without the need for a pre-specified vocabulary. There have been more researches on open IE in English than in Chinese, and most of them rely on word segmentation and syntactic analysis tools, which have a great influence on the results. Besides, the lack of annotated Chinese corpus also makes it difficult to classify triples in a supervised manner. To address the problems, we propose an unsupervised Chinese open IE model, named graph augmentation model (GAM). It first uses the knowledge graph to obtain linked entities and types of entities, where the linked entities can benefit the word segmentation accuracy and the entity types can help obtain the domain and range of relations for knowledge graph schema completion. Then it uses manually set rules to obtain candidate triples and uses a designed graph-based algorithm to iteratively calculate the importance and accuracy of triples. Experiments demonstrate that our method outperforms existing baseline methods. Specifically, GAM is proved to effectively extract domain and range of relations that other methods cannot. GAM achieves high accuracy of triples above a certain threshold, and the triples obtained show benefits in enriching a knowledge graph without the need for data annotation.
A Deep Learning Module Design for Workspace Identification in Manufacturing Industry

In this paper, in order to solve various problems occurring in the workspace, a deep learning-based workspace identification module was designed, and the performance was analyzed through an experiment on the recognition accuracy according to the configuration of the training dataset and the number of training. The data model of the designed deep learning module is ResNetl8, and after setting up three dataset strategies, a dataset using five types of workspaces of the manufacturing industry was selected. In terms of the average top 5 and all training, strategy 2 was 81.2% and 76.4%, respectively, confirming that it was the best among the 3 strategies. In the future, after upgrading the designed module, it is planned to implement a module with real-time workspace identification performance level of practical use in a mobile environment with an image input device installed.