Reviewerly/exHarmony huggingface dataset:
Awareness, Intention, (In)Action: Individuals’ Reactions to Data Breaches

Data breaches are prevalent. We provide novel insights into individuals’ awareness, perception, and responses to breaches that affect them through two online surveys: a main survey ( n = 413) in which we presented participants with up to three breaches that affected them, and a follow-up survey ( n = 108) in which we investigated whether the main study participants followed through with their intentions to act. Overall, 73% of participants were affected by at least one breach, but participants were unaware of 74% of breaches affecting them. Although some reported intention to take action, most participants believed the breach would not impact them. We also found a sizable intention-behavior gap. Participants did not follow through with their intention when they were apathetic about breaches, considered potential costs, forgot, or felt resigned about taking action. Our findings suggest that breached organizations should be held accountable for more proactively informing and protecting affected consumers.
Foreword to the special section on best papers of the Eurographics 2022 Education Papers Program

• This special section includes papers on computer graphics education. • These papers are the best papers from the Eurographics 2022 Education Papers Program. • Specific topics include ray-tracing, the Vulkan API, and virtual reality.
Pharos: A Transparent and Steerable Visualization Recommendation System

We propose Pharos, a novel visualization recommendation system that makes use of several provenance data sources and multi-perspective overviews to boost the transparency and steerability of the recommendation engine. Pharos helps a user understand recommendation contexts along with the user’s analysis progress in three complementary overviews. Pharos also serves categorized and scalable recommendations, either expanding or narrowing a user’s analysis scope. Based on provenance data and explicit user annotations (i.e., bookmarked or excluded visualizations), Pharos dynamically updates a recommendation list. According to the provided context, a user can steer the recommendation direction by filtering recommended candidates and rearranging them via the weight controller of the similarity measure on the recommendation engine. We showed how Pharos helped users understand and steer visualization recommendations through two comparative user studies.
Project "Schedule Is Different" Why? and What Should Be Done About It?

The portion of projects in the industrial activity is growing.One major cause are the rapid changes of product and models which force more frequent conduct of development projects.In the short history of project management as a self stand discipline, it is well known that many projects fail to wholly meet their planned objectives.A major reason to this reality is that projects last time.Often, very long time.This results in knowledge deficiencies and high levels of uncertainties.To improve projects' performances, substantial efforts are invested in project control, which is aimed at assuring that the project meets its planned objectives within budget and no later than a specified deadline.Accordingly, schedule is a prime concern in project control.It had been observed, however, that 'schedule is different' in the sense that it is known that schedule (performance) indicators of methods for project control fail to provide good information.Here it is proposed that the cause for this shortcoming is the fundamental difference between costs and duration in projects.The duration of the project is determined by only a subset of its activities as oppose to the project's cost, which is the sum of the costs of its components.This is the notion of criticality: the critical path, or critical chain -project's duration is determined by its critical activities.A direct consequence of this distinction is that the analysis and evaluation of control data at the project level can be misleading.This issue is discussed and demonstrated.Moreover, schedule and schedule control provide foremost input to the coordination of the project's operation -the synchronization between the various resources which are required for each activity.Hence, in this aspect, too, the relevant information should be developed and provided at the activity level.In response, it is argued that project control data should be processed, analyzed and evaluated at the activity level.A framework to implement this approach is proposed and its benefits are discussed.
Microsoft 365 Apps for Library Statistics

The role and responsibilities of medical librarians are not always clear to leadership and stakeholders. Providing clear and meaningful data demonstrates the value of medical librarians. Securing funding to purchase a data tracking system on the market can be challenging if not impossible for librarians with limited budgets. The objective of this project was to build a data tracking system using Microsoft 365 applications provided by the institution for no additional cost. As an industry standard, many institutions have site-wide subscriptions to a suite of Microsoft tools including productivity, collaboration and cloud services. This includes Microsoft 365, with over 20 applications available. The authors reviewed individual applications and conducted trials before selecting three, namely Microsoft Forms, Excel, and Power BI, to work together effectively to input, analyze, and report library statistics. The final data tracking system integrates within Teams for easy sharing within the institution without additional funding.
Efficient but Effective Perceptual Quality Model of Screen Content Image

Screen contents are generated by computer, like text, animation or graphics. Existing image quality assessment (IQA) models of screen content image(SCI) still cannot make a good compromise between complexity and effectiveness. To tackle this problem, we propose an efficient but effective IQA model for SCI, called multi-features similarity deviation(MFSD). To ensure the low complexity of the model, we adopt conventional technology path (i.e. feature extraction, similarity measurement and feature pooling) instead of deep learning method. To ensure the effectiveness of the model, we enhance the feature measurement of gradient magnitude (SCI contains abundant sharp edges, causing the structure information sensitive for human eyes), considering luminance and chrominance (to make our method complete and robust). Compared with existing SC-IQA models, MFSD can averagely achieve the best effectiveness with low computing complexity.
A NEW APPROACH TO THE DEVELOPMENT OF MULTIDIMENSIONAL CRYPTOGRAPHY ALGORITHMS

Purpose of work is the reduction in the size of the public key of public-key algorithms of multivariate cryptography based on the computational difficulty of solving systems of many power equations with many unknowns. Research method is use of non-linear mappings defined as exponentiation operations in finite extended fields GF(qm) represented in the form of finite algebras. The latter makes it possible to perform the exponentiation operation in the field GF(qm) by calculating the values of power polynomials over the field GF(q), which define a hardly reversible nonlinear mapping of the vector space over GF(q) with a secret trapdoor. Due to the use of nonlinear mappings of this type, it is possible to specify a public key in multidimensional cryptography algorithms in the form of a nonlinear mapping implemented as a calculation of the values of a set of polynomials of the third and sixth degree. At the same time, due to the use of masking linear mappings that do not lead to an increase in the number of terms in polynomials, the size of the public key is reduced in comparison with known analogue algorithms, in which the public key is represented by a set of polynomials of the second and third degrees. The proposed approach potentially expands the areas of practical application of post-quantum algorithms for public encryption and electronic digital signature, related to multidimensional cryptography, by significantly reducing the size of the public key. Results of the study are the main provisions of a new approach to the development of algorithms of multidimensional cryptography are formulated. Hardly invertible nonlinear mappings with a secret trapdoor are proposed in the form of exponentiation operations to the second and third powers in finite extended fields GF(qm), represented in a form of a finite algebra. A rationale is given for specifying a public key in a form that includes a superposition of two non-linear mappings performed as a calculation of a set of second and third degree polynomials defined over GF(q). Techniques for implementing mappings of this type are proposed and specific options for specifying the fields GF(qm) in the form of finite algebras are considered. An estimate of the size of the public key in the algorithms developed within the framework of the new approach is made. at a given security level.. Practical relevance includes the developed main provisions of a new method for constructing multidimensional cryptography algorithms based on the computational difficulty of solving systems of many power equations with many unknowns and related to post-quantum cryptoschemes. The proposed approach expands the areas of practical application of post-quantum algorithms of this type by significantly reducing the size of the public key, which provides the prerequisites for improving performance and reducing technical resources for their implementation
THE SOCIAL SIGNIFICANCE OF INTERNET VOCABULARY IN CHINESE LANGUAGE

Nowadays the concept of «Internet vocabulary or Internet language» is widely used in modern electronic information space. Internet vocabulary can also be called the language of electronic communication, the language of computers and the Internet. This concept is directly related to the use of words, language culture and the quality of language use of Internet users. Chinese is one of the ten main languages widely used on the Internet in modern society. The article provides comprehensive answers to questions such as how Internet vocabulary, which is a branch of sociolinguistics, has developed in Chinese society in the last ten years, what is its impact on language learners, and what are the conditions of Internet use in China. At the same time, the theoretical principles of sociolinguistics are also discussed, the history of the development of the Internet vocabulary in the Chinese language, the ways of its creation and the ways of word formation are also considered. Many examples of Chinese Internet slang, jargon, and abbreviations commonly used in Internet language or electronic communications are given by the author. In the conclusion of the article, after researching the Internet vocabulary in Chinese, it is pointed out that the Internet vocabulary will be of great benefit to language learners in the future, and it will be very effective in Chinese teaching in Kazakhstan universities.
Entanglement of particles versus entanglement of fields: Independent quantum resources

Nature allows one to explore a manifold of remarkable quantum effects. Most prominently, quantum entanglement can be observed in many-particle systems, between multiple quantized fields, and in hybrid combinations thereof. This diversity, however, also leads to contradicting conclusions about what truly constitutes entanglement in any given physical scenario. By explicitly allowing various perspectives, we rigorously consider different notions of entanglement in the context of first and second quantization. By providing instructive examples, we show that particle entanglement and field entanglement are actually distinct phenomena that can occur and be observed independently of each other. This conclusion not only affects our fundamental understanding but has direct implications for quantum technology which can harness those independent forms of entanglement in practical scenarios.
Imagery-based post-flooding infrastructure damages assessment

Coastal flooding events have caused many issues to infrastructure including bridges and highways. How to assess the flooding level and infrastructure damages in a low-cost, rapid, and accurate approach is critical to the infrastructure performance recovery. Due to the limited access to infrastructure during the post-flooding events, it is very challenging to evaluate infrastructure conditions closely. With the help of small unmanned aerial vehicles and onboard cameras, it provides the possibility to inspect the infrastructure conditions from images captured by drones remotely. With the additional help of image processing algorithms, it can help capture the infrastructure conditions and flooding levels from the imageries automatically with post-processing analysis. In this paper, we apply several different image processing algorithms to assess the infrastructure conditions by segmenting the flooding zone from the infrastructure. The performance of these algorithms in assessing infrastructure conditions is compared based on different factors with previously taken airborne imageries of infrastructure and flooding events. The performance of image processing is summarized and future work of assessing the infrastructure post-flooding damages is discussed.
Quantum grid states and hybrid graphs

Using the signed Laplacian matrix, and weighted and hybrid graphs, we present additional ways to interpret graphs as grid states. Hybrid graphs offer the most general interpretation. Existing graphical methods that characterize entanglement properties of grid states are adapted to these interpretations. These additional classes of grid states are shown to exhibit rich entanglement properties, including bound entanglement. Further, we introduce graphical techniques to construct bound-entangled states in a modular fashion. We also extend the grid states model to hypergraphs. Our work, on one hand, opens up possibilities for constructing additional families of mixed quantum states in the grid state model. On the other hand, it can serve as an instrument for investigating entanglement problems from a graph theory perspective.
ContestNotiﬁer: A Web Application for Tracking Programming Competitions

Programming has become a vital skill for securing a job in the Information Technology field, as it is used to assess a student's problem-solving abilities.Students with technical backgrounds are encouraged to practice their programming skills on various platforms.Various websites like HackerEarth, HackerRank, LeetCode, etc., conduct coding challenges and hackathons for students and working professionals.However, many participants miss ongoing challenges, which has led to the need for a platform that can notify them about upcoming competitions.The project being developed aims to address this issue and also help students keep track of their performance to identify areas for improvement [1][4].This new web app provides customized notifications for coding competitions and hackathons, tailored to each user's individual interests and preferences.The technologies used in this application stack include ReactJS for the front-end, and ExpressJS, ReactJS, and Python for the backendThe application provides several features such as generating a list of programming contests based on filters, receiving notification alerts an hour before a contest starts, sharing contests with friends and fellow contestants, and allowing users to see their profile data from each platform.Users can also set notification priorities based on the contest platforms [1].
Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness.
Research on New Electric Power Data Center Based on DC Power Supply and Distribution Technology

In view of the problems of large energy consumption, low efficiency, high cost and the urgent need to improve the reliability, efficiency and flexibility of power supply methods in most traditional power data centers, this paper proposes a new research scheme of power data centers based on DC power supply and distribution technology. Firstly, the defects of traditional power data center and the deficiencies of power supply mode are studied and analyzed, and then a new construction scheme of power data center using DC microgrid, containerized data center and energy-saving air conditioning system is proposed. Finally, the feasibility and superiority of this scheme are proved through the calculation of pue value and the analysis of application and prospect.
Context-aware Relation Classification based on Deep Learning

Modern information supports carry heterogeneous data, in such large quantities that the traditional means of processing become obsolete and inefficient to meet today's needs. In addition to the quantity of data, the unstructured nature of this data requires new intelligent, efficient and automated processing techniques. In order to produce automatic systems capable of managing this data and extracting relevant knowledge from it, a number of problems must be solved, including the extraction and classification of relations from textual data. While the extraction of relations is mainly based on syntactic aspects of the text, the classification requires a semantic approach. Such existing relation classification systems deal only with few pre-defined types. These systems don't take into account the context, thus reducing the relevance of this classification. In this paper, we propose a simplified definition of what is context and, based on this definition, we propose an approach to classify relations according to their types while taking into account this context. The system, allowing to obtain a degree of “contextualization” of relations, has been tested on the SemEval-2010 Task-8, New York Times corpora and a contextual dataset, named WikiContext, that we have built for this purpose. The results show that our system outperforms the state-of-the-art relation classification systems, thus demonstrating the relevance of taking context into account in this classification process.
Data-Driven Unsupervised Semantic Network Algorithm for Large Language Model Examination/Exploration

Large Language Models (LLMs) now possess the capability of intricate and complex text generation regarding a multitude of topics, and are additionally trained on extensive \& diverse amounts of data. In this paper, we propose and develop a data-driven algorithm that generates a semantic network in order to identify the associations between different topics and demographics as well as expose potential biases. We demonstrate how the exploration of these models and the linkages of concepts within them can reveal associations between ideas. Our approach has implications to better understand how generative pre-trained (GPT-3) models capture social attitudes.
Cryptocurrency in the Darknet: sustainability of the current national legislation

Purpose The purpose of the study is to show that divergent perceptions among regulators, the regulated and the associated regulatory bodies across multiple jurisdictions regarding the nature and functionality of cryptocurrencies hamper the development of a more comprehensive and coherent regulatory framework in curbing crimes and other related risks associated with cryptocurrencies. Design/methodology/approach The study has used a descriptive doctrinal legal research method to investigate and understand the insights of existing laws and regulations in four selected jurisdictions concerning cryptocurrencies and how these laws could be further improved and developed to reduce crypto-related crimes. Furthermore, the study has also used a comparative research method to conceptualize the contours of the new legal discourse emerging from cryptocurrencies to adopt and implement a sound regulatory framework. Findings The study illustrated that divergent regulatory treatment among different jurisdictions might suffocate novel digital innovations such as cryptocurrency. These fragmented regulatory approaches by various jurisdictions question the sustainability of the present national legislation adopted to regulate cryptocurrencies. Looking into other jurisdictional developments in regulating cryptocurrencies, it is apparent that a concerted regulatory approach is needed to minimize the abuse of this innovation. Research limitations/implications The study has implications for regulators and policymakers to review the current regulatory framework for regulating cryptocurrencies to prevent regulatory arbitrage. The divergent legislative measures concerning cryptocurrency among different jurisdictions question the sustainability of these legislative initiatives, considering the evolving and borderless nature of cryptocurrency. Therefore, this paper will help regulators to consider the present legislative gaps in establishing a common global regulatory approach in the crypto sphere. Originality/value The study contributes to the existing body of literature by examining the regulatory frameworks of four jurisdictions, namely, the USA, Canada, China and the EU, related to cryptocurrencies, with a discussion on the development of cryptocurrencies-related laws among these four jurisdictions and their sustainability in curbing crimes in the Darknet.
Supplemental Material: Cenozoic slip along the southern Sierra Nevada normal fault, California (USA): A long-lived stable western boundary of the Basin and Range

&lt;p&gt; Figure 1 is interactive. Maps, labels, and data sets for this figure are organized in a series of layers that may be viewed separately or in combination using the capabilities of the Acrobat (PDF) layering function (click “Layers” icon along vertical bar on left side of window for display of available layers, or turn layers on or off by clicking the box that encompasses the layer label located within the gray box in the lower right corner of the map).&lt;/p&gt; &lt;p&gt;Figure 7 is interactive. Graph, labels, and data sets for this figure are organized in a series of layers that may be viewed separately or in combination using the capabilities of the Acrobat (PDF) layering function (click “Layers” icon along vertical bar on left side of window for display of available layers, and/or tturn layers on or off by clicking the box that encompasses the layer label located within the gray box beneath the plot). &lt;/p&gt; &lt;p&gt; &lt;/p&gt;