Reviewerly/exHarmony huggingface dataset:
Measurement of Dynamic Characteristics of Structures by the Speckle Interferometry on Single Speckle

- In this article the results associated with the use of speckle interferometry on a single speckle to determine the dynamic characteristics of mechanical constructions are presented. The optical scheme of the speckle interferometer on a single speckle is proposed, which makes it possible to simultaneously record the form and amplitude of the resonant vibrations of mechanical constructions. Theoretical and experimental results demonstrating the behavior of the output signals from the speckle interferometer on a single speckle in the study of the resonant vibrations are presented. The resonance method for measuring the logarithmic decrement of vibrations and other dynamic characteristics of a mechanical constructions using speckle interferometry on a single speckle is proposed.
Reconstruction probability-based anomaly detection using variational auto-encoders

Anomaly detection is a method of categorizing unexpected data points or events in a dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a variety of disciplines. We propose a technique for detecting anomalies based on the reconstruction probability of VAEs. The proposed method trains VAEs on three different datasets. The reconstruction probability is a much more principled and realistic anomaly score than the reconstruction error utilized by auto-encoders and other data compression methods because of the theoretical background and by including the concept of variability. The paper describes recent deep learning models for anomaly detection, as well as a comparison to other methodologies. Variational auto-encoders are trained on three different datasets, in an unsupervised setup to classify the anomalies, based on reconstruction probability. Further, the in-depth study of anomaly detection techniques is presented in this paper. The data are reconstructed using the VAEs generative characteristics to investigate the root cause of the anomalies.
DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection

To ensure the normal operation of the system, the enterprise's operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long-short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.
Yahoo Boys, Yahoo Parents? An Explorative and Qualitative Study of Parents’ Disposition Towards Children’s Involvement in Cybercrimes

Cybercrimes have been acknowledged as inflicting high emotional, financial, and psychological pains on individuals worldwide. While studies have focused on the prevalence, offenders' demography and antics, vulnerability, victims' losses, and other correlates of cyber-fraud, researchers know relatively little about the disposition of parents toward their children's engagement in cybercrimes. Therefore, a qualitative study was conducted with 78 parents of youths involved in different cybercrimes, and a thematic analysis of the narratives was carried out. Findings indicated participants' acceptance, support, and involvement in youths' perpetration of cybercrimes. Online scam perpetrators were not only able to influence their parents' participation in cybercrimes, but the parents also learned the neutralization techniques of their children in excusing their acceptance of deviant behavior. Findings raise doubts about the potency of parents controlling the deviant behaviors of their children and adopting a family-based approach to curbing the growth of cybercrimes in Nigeria. Approaches toward stemming the tide of cybercrimes should consider the family and social network of the perpetrators for corrective therapy. At the same time, cultural norms and values that emphasize positive parenting should be entrenched within the Nigerian community.
ClueDepth Grasp: Leveraging positional clues of depth for completing depth of transparent objects

Obtaining accurate depth information is key to robot grasping tasks. However, for transparent objects, RGB-D cameras have difficulty perceiving them owing to the objects' refraction and reflection properties. This property makes it difficult for humanoid robots to perceive and grasp everyday transparent objects. To remedy this, existing studies usually remove transparent object areas using a model that learns patterns from the remaining opaque areas so that depth estimations can be completed. Notably, this frequently leads to deviations from the ground truth. In this study, we propose a new depth completion method [i.e., ClueDepth Grasp (CDGrasp)] that works more effectively with transparent objects in RGB-D images. Specifically, we propose a ClueDepth module, which leverages the geometry method to filter-out refractive and reflective points while preserving the correct depths, consequently providing crucial positional clues for object location. To acquire sufficient features to complete the depth map, we design a DenseFormer network that integrates DenseNet to extract local features and swin-transformer blocks to obtain the required global information. Furthermore, to fully utilize the information obtained from multi-modal visual maps, we devise a Multi-Modal U-Net Module to capture multiscale features. Extensive experiments conducted on the ClearGrasp dataset show that our method achieves state-of-the-art performance in terms of accuracy and generalization of depth completion for transparent objects, and the successful employment of a humanoid robot grasping capability verifies the efficacy of our proposed method.
RESULTS OF THE SCIENTIFIC SEMINAR "THEORY AND PRACTICE OF APPLIED CULTURAL RESEARCH" ON 23TH SEPTEMBER 2022 (SIBERIAN FEDERAL UNIVERSITY, KRASNOYARSK)

This text presents a summary of the work of the educational, scientific and methodological seminar "Theories and Practices of Applied Cultural Research" (September 23, 2022, Krasnoyarsk, Siberian Federal University). The seminar was devoted to the presentation of topical art studies in accordance with memorable dates for the world of culture and art: 1) Art studies of works of fine art for the anniversaries of the artists Marc Chagall, Pavel Korin, Ivan Aivazovsky, Vladimir Borovikovsky; 2) Research of animation art for the 140th anniversary of the birth of Vladislav Starevich; 3) Current research on works of Russian cinematography dedicated to the 70th anniversary of the birth of Karen Shakhnazarov; 4) Studies in the history of theatrical art, for the 160th anniversary of the birth of Maurice Maeterlinck; 5) The art of book illustration in Alexander Koshkin’s works.
Design and Development Approach for an Interactive Virtual Museum with Haptic Glove Technology

With a rise in the use Virtual Reality (VR) applications in museums and exhibition displays, digital heritage has still shown limitations in what a visitor can experience from the intersection of technology and history. Traditionally, interpretative narrative within the museum has been communicated through text panels offering limited context from a largely connoisseurly perspective. In addition to these interactive digital resources are often data base orientated via touch screen technologies with no multisensory immersion. This paper addresses the digital changes in these educational landscapes and the way it is being handled to co-create digital tools for exhibitions to educate and entertain museum visitors. This paper explores the use of haptic technology in conjunction with virtual reality to facilitate multi-faceted modes of interpretation, that offer novel access to an artefact's history from a range of perspectives. It also provides evidence of increased visitor engagement with a ceramic display through these immersive methods to communicate a narrative. This research bridges the gap between history and technology to offer an immersive experience of visiting a museum virtually and providing an intimate one-one experience to interact with artefacts and learn about history. As its focus, this research digitally reconstructs a collection of East-Asian ceramics bequeathed by Ernest Thornhill in 1944 to North Staffordshire Technical College (Now known as Staffordshire University). The digital prototype was developed to replicate the museum environment without the restrictions to access artefacts and handle them. This experience offers visual insights to contextualise the history of a ceramic to be utlised as an education tool to enhance learning within a museum setting. Evidence showed a significantly positive response to this prototype in museum and gallery settings, responses revealed these methods of interaction did assist in learning about ceramics, with a distinct majority of participants confirming these installations would encourage future visits, shaping the possibilities of how history can be combined with technology to create new and innovative solutions to learn about an artefact.
Interactive fault localization for Python with CharmFL

We present a plug-in called “CharmFL” for the PyCharm IDE. It employs Spectrum-based Fault Localization to automatically analyze Python programs and produces a ranked list of potentially faulty program elements (i.e., statements, functions, etc.). Our tool offers advanced features, e.g., it enables the users to give their feedback on the suspicious elements to help re-rank them, thus improving the fault localization process. The tool utilizes contextual information about program elements complementary to the spectrum data. The users can explore function call graphs during a failed test. Thus they can investigate the data flow traces of any failed test case or construct a causal inference model for the location of the fault. The tool has been used with a set of experimental use cases.
Identifying security-related requirements in regulatory documents based on cross-project classification

Security is getting substantial focus in many industries, especially safety-critical ones. When new regulations and standards which can run to hundreds of pages are introduced, it is necessary to identify the requirements in those documents which have an impact on security. Additionally, it is necessary to revisit the requirements of existing systems and identify the security related ones. We investigate the feasibility of using a classifier for security-related requirements trained on requirement specifications available online. We base our investigation on 15 requirement documents, randomly selected and partially pre-labelled, with a total of 3,880 requirements. To validate the model, we run a cross-project prediction on the data where each specification constitutes a group. We also test the model on three different United Nations (UN) regulations from the automotive domain with different magnitudes of security relevance. Our results indicate the feasibility of training a model from a heterogeneous data set including specifications from multiple domains and in different styles. Additionally, we show the ability of such a classifier to identify security requirements in real-life regulations and discuss scenarios in which such a classification becomes useful to practitioners.
Guidelines for GUI testing maintenance: a linter for test smell detection

GUI Test suites suffer from high fragility, in fact modifications or redesigns of the user interface are commonly frequent and often invalidate the tests. This leads, for both DOM- and visual-based techniques, to frequent need for careful maintenance of test suites, which can be expensive and time-consuming. The goal of this work is to present a set of guidelines to write cleaner and more robust test code, reducing the cost of maintenance and producing more understandable code. Based on the provided recommendations, a static test suite analyzer and code linter has been developed. An ad-hoc grey literature research was conducted on the state of the practice, by performing a semi-systematic literature review. Authors' experience was coded into a set of recommendations, by applying the grounded theory methodology. Based on these results, we developed a linter in the form of a plugin for Visual Studio Code, implementing 17 of the provided guidelines. The plugin highlights test smells in the Java and Javascript languages. Finally, we conducted a preliminary validation of the tool against test suites from real GitHub projects. The preliminary evaluation, meant to be an attempt of application of the plugin to real test suites, detected three main smells, namely the usage of global variables, the lack of adoption of the Page Object design pattern, and the usage of fragile locator such as the XPath.
Neural language models for code quality identification

Neural Language Models for code have lead to interesting applications such as code completion and bug fix generation. Another type of code related application is the identification of code quality issues such as repetitive code and unnatural code. Neural language models contain implicit knowledge about such aspects. We propose a framework to detect code quality issues using neural language models. To handle repository-specific conventions, we use local or repository-specific models. The models are successful in detecting real-world code quality issues with low false positive rate.
On the application of machine learning models to assess and predict software reusability

Software reuse has proven to be an effective strategy for developers to significantly increase software quality, reduce costs and increase the effectiveness of software development. Research in software reuse typically addresses two main hurdles: reduce the time and effort required to identify reusable candidates, and avoid selecting low-quality software components that may lead to higher cost of development (i.e., solving bugs, errors, refactoring). Inherently, human judgment falls short in the aspect of reliability and effectiveness. Hence this paper investigates the applicability of Machine Learning (ML) algorithms in assessing software reuse. We collected more than 32k open-source projects and employed GitHub fork as the ground truth to its reuse. We developed ML classification pipelines based on both internal and external software metrics to perform software reuse prediction. Our best-performing ML classification model achieved an accuracy of 86%, outperforming existing research in prediction performance and data coverage. Subsequently, we leverage our results by identifying key software characteristics that make software highly reusable. Our results show that size-related metrics (i.e., number of setters, methods, attributes) are the most impactful in contributing to the reuse of the software.
Coordination and explanation of reconfigurations in self-adaptive high-performance systems

Self-adaptive systems that modify their architecture to achieve different Service Level Objectives (SLOs) are becoming well-established parts of a company's portfolio. Current systems however do a poor job of coordinating the underlying rules and explaining the system's behavior to developers. This negatively impacts a system's performance due to behaving in unintended and hard-to-comprehend ways.
Augmenting model-based systems engineering with knowledge

This article presents a general approach for the integration of Knowledge Bases into Model-Based Systems Engineering tools. In existing tools, domain-specific modeling languages are well supported. However when it comes to enforcing design constraints, existing approaches are verbose, it is difficult to be complete and consistent, and the reuse of knowledge is only possible in a limited way (mainly through model libraries). Furthermore, current tools usually lack or have limited capability to detect semantic errors, ability to evaluate the models with respect to formal expert knowledge, and the ability to understand what is being designed. Our work addresses these limitations through the semantic annotation of UML models in Papyrus (an MBSE Tool), to attach domain-specific semantics to the models. This integration enables not only reasoning capabilities over the annotated models, but the models can be shared with semantic-compatible tools and stakeholders. Moreover, the models can reuse and integrate knowledge generated outside the tooling environment. The approach's feasibility is demonstrated through an implementation that defines a technology stack, with emphasis on the mapping of UML elements and its counterparts in the ontology. We address the coherence and preservation of the semantics throughout the transformation process, which enable the formalization of constraints coming from the UML's system design. Finally, we illustrate the reasoning capabilities by evaluating expert knowledge via SPARQL queries and SWRL rules.
Transferring knowledge between topics in systematic reviews

In the medical domain, a systematic review (SR) is a well-structured process aimed to review all available literature on a research question. This is however a laborious task, both in terms of money and time. As such, the automation of a SR with the aid of technology has received interest in several research communities, among which the Information Retrieval community. In this work, we experiment on the possibility of leveraging previously conducted systematic reviews to train a classifier/ranker which is later applied to a new SR. We also investigate on the possibility of pre-training Deep Learning models and eventually tuning them in an Active Learning process. Our results show that the pre-training of these models deliver a good zero-shot (i.e., with no fine-tuning) ranking, achieving an improvement of 79% for the MAP metric, with respect to a standard classifier trained on few in-domain documents. However, the pre-trained deep learning algorithms fail to deliver consistent results when continuously trained in an Active Learning scenario: our analysis shows that using smaller sized models and employing adapter modules might enable an effective active learning training.
Doc-Chain: A University Document Verification Blockchain

In today's digital world there are a lot of issues in verifying the legitimacy of documents that an individual provides such as School/College mark sheets, Course certificates, etc. Companies are concerned whether a certificate provided to them is legit or forged. Verifying these documents manually or by offline visits is a tedious procedure and takes days to complete. The use of physical documents is also inconvenient and inefficient in today's fast paced digital world. In this paper, to counter this forgery issue a platform using blockchain technology is suggested. Blockchain provides immutability, once the document is stored in the blockchain it cannot be changed. An overview of the working and implementation of our platform and the functionalities provided are presented.
A Run a Day Won't Keep the Hacker Away

Fitness tracking social networks such as Strava allow users to record sports activities and share them publicly. Sharing encourages peer interaction but also constitutes a risk, because an activity's start or finish may inadvertently reveal privacy-sensitive locations such as a home or workplace. To mitigate this risk, networks introduced endpoint privacy zones (EPZs), which hide track portions around protected locations. In this paper, we show that EPZ implementations of major services remain vulnerable to inference attacks that significantly reduce the effective anonymity provided by the EPZ, and even reveal the protected location. Our attack leverages distance information leaked in activity metadata, street grid data, and the locations of the entry points into the EPZ. This yields a constrained search space where we use regression analysis to predict protected locations. Our evaluation on 1.4 million Strava activities shows that our attack discovers the protected location for up to 85% of EPZs. Larger EPZs reduce the performance of our attack, while geographically dispersed activities in sparser street grids yield better performance. We propose six countermeasures, that, however, come with a usability trade-off, and responsibly disclosed our findings and countermeasures to the major networks.
Phishing URL Detection

Many cyberattacks start with disseminating phishing URLs. When clicking these phishing URLs, the victim's private information is leaked to the attacker. There have been proposed several machine learning methods to detect phishing URLs. However, it still remains under-explored to detect phishing URLs with evasion, i.e., phishing URLs that pretend to be benign by manipulating patterns. In many cases, the attacker i) reuses prepared phishing web pages because making a completely brand-new set costs non-trivial expenses, ii) prefers hosting companies that do not require private information and are cheaper than others, iii) prefers shared hosting for cost efficiency, and iv) sometimes uses benign domains, IP addresses, and URL string patterns to evade existing detection methods. Inspired by those behavioral characteristics, we present a network-based inference method to accurately detect phishing URLs camouflaged with legitimate patterns, i.e., robust to evasion. In the network approach, a phishing URL will be still identified as phishy even after evasion unless a majority of its neighbors in the network are evaded at the same time. Our method consistently shows better detection performance throughout various experimental tests than state-of-the-art methods, e.g., F-1 of 0.891 for our method vs. 0.840 for the best feature-based method.
STD-Yolov5: a ship-type detection model based on improved Yolov5

Ship visible images detection based on computer vision plays an important role in the field of intelligent ship. To increase the model speed, accuracy, and reduce the parameters of the model to facilitate the deployment on hardware devices in practical applications, this study proposed a new model named STD-Yolov5. Firstly, the attention mechanism module of ECA was embedded in backbone to enhance the feature extraction capability of the network. Secondly, GAFPN was designed to reduce the parameters and GFLOPs. Thirdly, to solve the problem of ship-type false detection and missing detection, this paper presented a new receptive field amplification module named GSPP. Finally, replaced the GIoU bounding box regression loss function with a simpler generalisation of α-GIoU to improve the accuracy of the model. Compared to Yolov5, the mAP@.5:.95 of STD-Yolov5 increased by 1.2%, the parameters decreased by 24.85%, and the GFLOPs decreased by 14.46%.