Reviewerly/exHarmony huggingface dataset:
The Harmonization of Russian Criminal Legislation on Counteracting Cybercrime with the Legal Standards of the Council of Europe

Crimes that happen in the virtual environment created by digital technologies inflict considerable economic damage. Mercenary motives of criminals in the information society are giving rise to increasingly more and more sophisticated methods of abusing the trust of computer networks’ users. The harmonization of Russian legislation on counteracting cybercrimes with the legal standards of the Council of Europe is inevitable due to the trans-border character of crimes committed using information and telecommunication technologies, and to their high public danger. The methodology of researching cybercrime is based on the comparative analysis of Russian law enforcement practice on crimes in the sphere of computer information and the most progressive practices of counteracting cybercrime in the member states of the Council of Europe. The methods of inclusive observation and discursive analysis make it possible to identify latent delicts in contemporary information space. The analysis of criminal legislation and the practices of criminal behavior in cyberspace are aimed at improving the measures of counteracting the violations of fundamental human rights in the process of the digitization of economy, when fraud based on the abuse of network users’ trust becomes the most typical virtual crime. Internet users are interested in reporting the facts of offences in the information space on the condition that the state protects their fundamental freedoms. The creation of closed social networks by criminal organizations and the widening technical opportunities for extortion result in the creation of universal schemes that enrich criminals, who are not only interested in reducing the state’s influence on public relations, but are also trying to establish their dominance in the economic space of information society. The effective policy of law enforcement bodies on counteracting economic crimes in the global information space requires an international consensus regarding the development of public-private partnership in identifying cybercrimes and suppressing criminal practices connected with the use of information technologies.
Autonomous Reckless Driving Detection Using Deep Learning on Embedded GPUs

Reckless driving is dangerous, and must be monitored, detected, and law-enforced to assure road safety. For this purpose, this work presents an embedded system for monitoring and detecting reckless driving activities on the road autonomously in real-time. Using an embedded GPU (eGPU) platform, a camera, and a combination of light-weight deep learning models, we design a system that can identify abnormal vehicle motions on the road. Our system analyzes discrete per-frame images from vehicle detection algorithms, and creates a continuous trace of a vehicle's motion trajectory. While doing so, a virtual grid is generated on the road to obtain positions of vehicles with less overhead and accurately track a vehicle's movement even with low frame rate (5fps) videos. Vehicle's motion trajectory is then compared against the surrounding to identify abnormal behavior through driving activity classification, which can be provided to law enforcement personnel for final validation. The key challenge is the fundamental resource constraints of embedded platforms, and we design algorithms to overcome their limitations. Evaluation results show that our scheme can wellextract the horizontal and vertical movements of a vehicle (100% recall and 67% precision) and show the potential for truly autonomous reckless driving activity detection systems.
Rational points on cubic, quartic and sextic curves over finite fields

Let Fq denote the finite field with q elements. In this work, we use characters to give the number of rational points on suitable curves of low degree over Fq in terms of the number of rational points on elliptic curves. In the case where q is a prime number, we give a way to calculate these numbers. As a consequence of these results, we characterize maximal and minimal curves given by equations of the forms ax3+by3+cz3=0 and ax4+by4+cz4=0.
Mining Web Usage Profiles from Proxy Logs: User Identification

Personal web-surfing habits of individual users vary drastically. Thus, the selection and frequency of requested URLs can be viewed as an individual finger-print allowing to identify a user, which has many security and target marketing applications. In this paper, to model web usage, we derive web usage profiles for each user and employ instance-based classification methods to predict the most likely user in the monitored network. Thus, the proposed methods are scalable for large numbers of users. In our experimental evaluation, the accuracy of user identification is verified.
Investigating Root Causes of Authentication Failures Using a SAML and OIDC Observatory

Authentication is the most critical gatekeeper to the web applications that scientists use to carry out collaborative research. While authentication rarely fails, the impact of failures is huge, and root causes are not well understood. This paper analyzes the root causes of authentication failures from a production authentication system called CILogon, an ideal observatory to monitor authentication issues in a distributed identity federation. CILogon is used by 250+ identity providers and 150+ web applications while acting as a proxy to bridge different single sign-on protocols (OIDC and SAML). Our data on authentication is unique because it is: i) longitudinal (over thirty months), ii) realistic (8,000+ active users), and iii) large-scale (nearly three thousand failures out of 447,428 successful authentications). Our finding is surprising: OIDC has about double the failure rate compared to SAML, which contrasts with our prior belief that SAML is much more complex than OIDC. Our most impactful contribution is a fault tree of error types that quickly finds and mitigates the root cause of authentication errors.
(Im)material culture: towards an archaeology of cybercrime

Cybercrime is ubiquitous. People now inhabit a digital environment comprising permanent risk, exponential threats, and multiple virtual/physical harms, forming a global community of malefactors and the criminally exploited. The purpose of this paper is two-fold. First, through an archaeological lens, to characterize the new materiality of cybercrime (including its artefacts and architecture alongside digital/virtual manifestations). And second, to explore the potential for new perspectives on cybercrime borne out of this archaeological approach. In short: what is the archaeology of cybercrime and can new understandings emerge from an archaeological perspective? In undertaking this research we also challenge the long-held presumption that non-physical traces cannot be studied archaeologically. It is our contention that they can.
Shockingly Simple:"KEYS" for Better AI for SE

As 2020 drew to a close, I was thinking about what lessons we have learned about software engineering (SE) for artificial intelligence (AI)-things that we can believe now but, in the last century, would have seemed somewhat shocking. One very surprising lesson, at least for me, is the success of the very complex and very simple. At the complex end, there is now much evidence for the value of deep learners for high-dimensional software engineering problems. For example, consider signal processing for autonomous cars. When reasoning over (say) 10,000 wavelets collected from a vision system, then deep learning can automate much of the engineering required to cover all those data.
Human Action Recognition Based on Multi-level Feature Fusion

Human action recognition has always been the focus of research in the field of computer vision. Different from the traditional human action recognition methods based on handcrafted features, the deep learning-based human action recognition methods can automatically learn features and hence have been widely concerned in recent years. However, with the increasing application of deep network in human action recognition, the problem of information loss cannot be ignored due to the deepening of convolutional layers, which will eventually affect the performance of recognition. To solve the abovementioned problem, we propose a deep learning-based action recognition method using a multi-level feature fusion mechanism which helps make full use of detailed features of middle layers of CNN network. Convolutional autoencoders are employed to reduce dimension of the middle-layer feature while retaining its representativeness. At the same time, a joint optimization module is designed to reduce the feature redundancy and achieve better recognition performance. Experimental results have shown the superiority of the proposed method, and the average accuracy of action recognition reaches 92.54%.
f2tag — Can Tags be Predicted Using Formulas?

The appropriate tagging of questions on Q&A networks like StackExchange is important for organizing the content of these platforms. Accordingly, recommender systems can help the users by suggesting relevant tags for their questions. The data used as input to these recommender systems can be manifold like headlines of the questions or their entire content. In this work, we explore whether mathematical formulas alone are sufficient for predicting tags of mathematical questions. To achieve this goal, we employ and compare well established methods on both textual and visual representations of formulas. To the best of our knowledge, we are the first to use this kind of limited content as input. Our results show that formulas are helpful for finding proper tags while visual representations outperform textual ones.
Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records

Electronic health records (EHRs) from type 2 diabetes (T2D) patients consist of longitudinally and sparsely measured health markers at clinical encounters. Our goal is to use such data to learn latent patterns that can inform patient's health status related to T2D while accounting for challenges in retrospectively collected EHRs. To handle challenges such as correlated longitudinal measurements, irregular and informative encounter times, and mixed marker types, we propose multivariate generalized linear models to learn latent patient subgroups. In our model, covariate effects were time-dependent and latent Gaussian processes were introduced to model between-marker correlations over time. Using inferred latent processes, we integrated the irregularly measured health markers of mixed types into composite scores and applied hierarchical clustering to learn latent subgroup structures among T2D patients. Application to an EHR dataset of T2D patients showed different trends of age, sex, and race effects on hypertension/high blood pressure, total cholesterol, glycated hemoglobin, high-density lipoprotein, and medications. The associations among these markers varied over time during the study window. Clustering results revealed four subgroups, each with distinct health status. The same patterns were further confirmed using new EHR records of the same cohort. We developed a novel latent model to integrate longitudinal health markers in EHRs and characterize patient latent heterogeneities. Analysis indicated that there were distinct subgroups of T2D patients, suggesting that effective healthcare managements for these patients should be performed separately for each subgroup.
Designing with Emerging Science

The emerging science of the “gut-brain axis” has been used as the basis for self-tracking technologies assuming that this connection can be used productively for better regulating mood, supporting digestive health, and avoiding disease. Taking this emerging science as a source of design inspiration, this paper presents a design research process to uncover opportunities for novel interaction design and generate alternative approaches to self-tracking. We explored how this emerging scientific knowledge might be experienced and used and what these design spaces might look like through designing a self-tracking probe and asking science communicators working with the gut-brain axis to live with that probe. Their reactions led to a set of exploratory interaction design briefs and a more refined research product that collectively articulate how design can engage with emerging science to inspire a new perspective on self-tracking practices—one of cultivation rather than control.
Artificial Intelligence in the Hospitality Sector

Hospitality is one of the most important sectors of the economy and offers employment to thousands of people. The recent advances in technology has seen that quite a few of the players in this industry have successfully deployed artificial intelligence, machine learning, and robotics. This chapter delves into the details of such deployment in the various processes in this sector and discusses the short-term, medium-term, and long-term impact of these technologies on all the major stakeholders of this industry. The author also looks at the cost benefit analysis of this technologies and concludes that most players sooner, rather than later would be forced by competition to strongly adopt them. The chapter also briefly discusses the changing roles of human employees in this scenario.
Dynamic Scheduling of an Autonomous PTZ Camera for Effective Surveillance

PTZ cameras can be an effective replacement for multiple camera networks with their pan-tilt-zoom capability. However, the state of the art scheduling method for the PTZ cameras focuses mainly on tracking, not on coverage. In this paper, we aim to maximize coverage as well as information gain, thus, leading to effective surveillance. Towards this goal, we define an information map that represents the sensitivity of a region. We propose a scheduling algorithm in which the camera visits those states more often that are likely to be more important than others, thus, maximizing information gain. A probabilistic framework is used to maximize information gain and coverage simultaneously. Currently, there are no existing datasets and methods to evaluate PTZ camera scheduling methods. We build a real multi-camera dataset and develop a performance measure for this purpose. Experimental results show that the proposed stochastic scheduling algorithm based on adaptive information gain probability is better than traditional as well as other variants proposed in the paper in terms of information gain as well as coverage.
Characterization of altered mafic and ultramafic rocks using portable XRF geochemistry and portable Vis-NIR spectrometry

The accurate characterization of mafic and ultramafic rocks is a challenging but necessary task given the spatial and genetic relationship of mineralization with specific lithologies (e.g. komatiite hosted nickel-sulfides preferentially associated with cumulate-rich ultramafic rocks). Rock classification is further complicated as most mafic and ultramafic rocks have undergone varying degrees of alteration. The accuracy and reproducibility of characterization can be significantly improved by using portable energy dispersive X-ray fluorescence (pXRF) chemical data with portable visible and near-infrared (pVis-NIR) mineralogical data. A new workflow using pXRF and pVis-NIR is presented and used to reliably characterize mafic and ultramafic rocks from the Yilgarn Craton, Western Australia. The workflow involves six steps: (1) Mitigate and identify compound processing and closure issues. For example, we used a pXRF with helium flush to reliably and rapidly measure light elements and mitigate closure, i.e. problems related to data failing to sum to 100%. (2) Identify and exclude geochemically heterogeneous samples. Heterogeneity may be unrelated to alteration and caused by veining or small-scale structure interleaving of different rock types. Geochemical heterogeneity was evaluated using skewness and kurtosis of SiO 2 data. (3) Relate rocks from similar magmatic, weathering and alteration events. This was achieved by interpreting data grouping of Vis-NIR ferric and ferrous iron data via a 852 nm/982 nm reflectance v. 651 nm/982 nm reflectance plot and the ferrous abundance index. Unrepresentative data were omitted. (4) Correct XRF iron data, and characterize lithology and alteration. Values ascribed to regions in the TAS (total alkali silica) diagram were used to approximate FeO and Fe 2 O 3 . Subsequently, geochemical indices (e.g. Mg#) were used to characterize the alteration box plot. (5) Characterize fractionation in detail. Fractionation variation diagrams were used to interpret fractionation, e.g. MgO v. Al 2 O 3 , Ca/Al v. Al 2 O 3 , Ni/Cr v. Ni/Ti, and MgO v. Cr. (6) Identify and quantify talc alteration and serpentinization. This included the use of a new alteration plot (Mg# v. 1410 nm RAD /Albedo) to estimate serpentinization and identify relationships between serpentine, carbonate, chlorite and talc abundances. The results and observations contained in this contribution have important implications for progressive technologies such as core logging platforms that are equipped with pXRF and pVis-NIR instruments.
Utilization of Hyperchaotic Environment and DNA Sequences for Digital Image Security

Propagation of digital images (or any other types of digital data) is always susceptible to intrusion and a risk factor is always associated with it. Various persons or organizations always try to get access of the transmitted data. Therefore, it is essential to design a mechanism to secure the precious data during transmission. It will be helpful in reducing various associated threats during data transmission and the total transmission process will be protected. In this article, a new image encryption method is proposed which is based on the hyperchatic environment and DNA computing. Obtained results are inspiring and proves the efficiency of the proposed method. Various statistical Experiments are performed to prove the power of the proposed image encryption method and the tolerance of the proposed method against various types of attacks can also be established. The method is strong enough and easy to implement.
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm

SAR images are affected by multiplicative noise that impairs their interpretations. In the last decades several methods for SAR denoising have been proposed and in the last years great attention has moved towards deep learning based solutions. Based on our last proposed convolutional neural network for SAR despeckling, here we exploit the effect of the complexity of the network. More precisely, once a dataset has been fixed, we carry out an analysis of the network performance with respect to the number of layers and numbers of features the network is composed of. Evaluation on simulated and real data are carried out. The results show that deeper networks better generalize on both simulated and real images.
Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide. This problem is addressed by the software development industry by software produ... | Find, read and cite all the research you need on Tech Science Press
SIMP-REAUTH: A Simple Multilevel Real User Remote Authentication Scheme for Mobile Cloud Computing

Remote authentication is commonly required for online transactions. Many computing activities are now done online through Cloud computing. Most of devices used to access data in the Cloud are low capacity mobile devices. The advancement of computing technology is also accompanied with increased cybercrimes. Thus, security system for securing user data online must meet up with the new wave of attacks. Many solutions for improved remote authentication have been proposed but the security of remote authentication with mobile devices is still facing challenges. This research proposes a Simple Multilevel Real User Remote Authentication Scheme (SIMP-REAUTH) for Mobile Cloud Computing to prevent misuse of authentication token. SIMP-REAUTH is designed to enable implementation of advance authentication on low memory, low capacity mobile devices for a more accurate real user identification. SIMP-REAUTH consists of three stages comprising the facial, voice, and password authentications. The authentication processes are carried out by separate modules. Failure of any of the stages will cause the system to deny the user access to data. The high capacity and memory consuming biometric processes were outsourced to the Cloud. Misuse of authentication information was prevented with liveness test challenges. The reports obtained from the test users and results of simulated attacks carried out using the testbench show that SIMP-REAUTH is well suited for mitigating authentication token misuse by identity thieves. The test results recorded 94% and 92% resistance against the facial and voice attacks tests, respectively.
AARef: Exploiting Authorship Identifiers of Micro-Messages with Refinement Blocks

With the rise of web-based social networking, a great many short texts/micro-messages are exchanged daily. Although short texts/micro-messages are a powerful and efficient way to communicate among individuals, their anonymity and short-length attributes give rise to a real challenge for Author Identifi-cation studies. In this paper, we tackle the Author Identification of short texts problem via Convolutional Neural Networks (CNNs). Specifically, we present a novel Multi-Channel CNN architecture that processes different features of text via word, character, and parts of speech (POS) embeddings. We examine the usefulness of different feature types and show that the combination of embeddings can capture different stylometric features. In addition, we add an identity mapping block in the convolutional layer to preserve the maximum amount of information from features. Extensive experiments with a variety number of authors and writing samples per author were conducted using our proposed architecture. Based on the experiments, our proposed method outperforms the state-of-the-art system on a large Twitter dataset.
Multi-scale spatialtemporal information deep fusion network with temporal pyramid mechanism for video action recognition

In the deep learning-based video action recognitio, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. This paper puts forward a network extracting video sequence semantic information based on deep integration of local Spatial-Temporal information. The network uses 2D Convolutional Neural Network (2DCNN) and Multi Spatial-Temporal scale 3D Convolutional Neural Network (MST_3DCNN) respectively to extract spatial information and motion information. Spatial information and motion information of the same time quantum receive 3D convolutional integration to generate the temporary Spatial-Temporal information of a certain moment. Then, the Spatial-Temporal information of multiple single moments enters Temporal Pyramid Net (TPN) to generate the local Spatial-Temporal information of multiple time scales. Finally, bidirectional recurrent neutral network is used to act on the Spatial-Temporal information of all parts so as to acquire the context information spanning the length of the entire video, which endows the network with video context information extraction capability. Through the experiments on the three video action recognitio common experimental data sets UCF101, UCF11, UCFSports, the Spatial-Temporal information deep fusion network proposed in this paper has a high correct recognition rate in the task of video action recognitio.