Reviewerly/exHarmony huggingface dataset:
STREETS: A Novel Camera Network Dataset for Traffic Flow

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body. Our dataset provides over 4 million still images across 2.5 months and one hundred web cameras in suburban Lake County, IL. We divide the cameras into two distinct communities described by directed graphs and count vehicles to track traffic statistics. Our goal is to give researchers a benchmark dataset for exploring the capabilities of inexpensive and non-invasive sensors like web cameras to understand complex traffic bodies in communities of any size. We present benchmarking tasks and baseline results for one such task to guide how future work may use our dataset.
Intrinsic Mitigation of the After-Gate Attack in Quantum Key Distribution through Fast-Gated Delayed Detection

Although quantum key distribution (QKD) promises information-theoretic security, several studies have been carried out showing how its security can be compromised by targeting the detectors in the system. This work demonstrates a measure to mitigate a special class of attack, the $a\phantom{\rule{0}{0ex}}f\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}g\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}e$ $a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}k$. By exploiting delayed detection events, which are usually considered detrimental for QKD, the authors show how an eavesdropper mounting such an attack can be unveiled.
Franck-Condon factors via compressive sensing

Probabilities of vibronic transitions in molecules are referred to as Franck-Condon factors (FCFs). Although several approaches for calculating FCFs have been developed, such calculations are still challenging. Recently it was shown that there exists a correspondence between the problem of calculating FCFs and boson sampling. However, if the output photon number distribution of boson sampling is sparse then it can be classically simulated. Exploiting these results, we develop a method to approximately reconstruct the distribution of FCFs of certain molecules. We demonstrate this method by applying it to formic acid and thymine at $0$ K. In our method, we first obtain the marginal photon number distributions for pairs of modes of a Gaussian state associated with the molecular transition. We then apply a compressive sensing method called polynomial-time matching pursuit to recover FCFs.
@Digital libraries: harnessing Twitter to build online communities

Purpose The purpose of this paper is to investigate the types of roles that Twitter played in digital libraries (DLs) and their relationships in building DL online communities. Design/methodology/approach A mixed method analysis of DLs’ tweets was conducted to identify the usage, roles of Twitter in DL communities and relationships among the roles. Twitter data from 15 different DLs for one year were extracted, and an open coding analysis was performed to identify types of Twitter roles. Pearson correlation coefficient was applied to examine the relationships among the roles based on word similarities. Findings The results present 15 types of Twitter roles representing five main categories identified from DL tweets, including information, promotion, related resources, social identity and social connection. Moreover, word similarities analysis identifies more strong relationships among the roles in four main categories (promotion, related resources, social identity and social connection) but less with roles in information. Research limitations/implications Characteristics of DL online communities are discussed and compared with physical library communities. Suggestions are proposed for how tweets can be improved to play more effective roles. To build a strong community, it is critical for digital librarians to engage with followers. Originality/value This study is a pioneering work that not only analyzes Twitter roles and their relationships in building DL online communities but also offers recommendations in terms of how to build a strong online community and improve Twitter use in DLs.
O-LoMST: An Online Anomaly Detection Approach And Its Application In A Hydropower Generation Plant

With the increasing availability of streaming data, industries nowadays are striving for an automated online anomaly detection algorithm that can analyze data stream and detect anomalous patterns in real time. Such an online algorithm should detect anomalies on the fly, without storing all, or a very long stretch of, the historical data. It should be able to update its control mechanism for anomaly detection upon receiving new data. Moreover, the algorithm must work in an unsupervised way; i.e., in the absence of class labeling information a priori. These fundamental requirements limit the application of traditional anomaly detection approaches in streaming scenarios. In this paper, we introduce an online anomaly detection method, based on an offline method recently developed. The prototypical offline method is one of the new approaches that specifically handle the issue of nonlinear manifold embedding in data spaces and use a minimum spanning tree to approximate and capture the manifold structures, leading to a much enhanced detection ability. The primary objective of this paper is to make the offline method applicable to streaming data and address the aforementioned unique online issues. We elaborate the steps of our proposed approach by applying it to a hydropower generation plant and demonstrating how it can contribute to automation in that context.
The Era of Digital Technology in Teaching and Learning in Nigeria Educational Institutions

The influence of digital technology in society has made it critical for all students in the 21st century to become literate with the use of digital tools. One way to understand the evolving role of digital technologies in education in Nigeria is to review the literature and see how the use of digital technologies in the classroom are documented. The development and proliferation of the internet have contributed to a revolution in teaching and learning; it has also provided new opportunities for delivering instruction through various media. Despite all the plentiful opportunities of learning through digital technology and improving access to education in the country at all levels, there are, however, some setbacks. In this chapter, some success stories were presented with some benefits and setbacks. Even though many educational institutions from elementary levels to universities levels are using digital devices and applications for teaching and learning purposes, the tools may fail in many places due to the domination of traditional pedagogical approaches and methods.
Deep Learning with Maxout Activations for Visual Recognition and Verification

Visual recognition is one of the most active research topics in computer vision due to its potential applications in self-driving cars, healthcare, social media, manufacturing, etc. For image classification tasks, deep convolutional neural networks have achieved state-of-the-art results, and many activation functions have been proposed to enhance the classification performance of these networks. We explore the performance of multiple maxout activation variants on image classification, facial recognition and verification tasks using convolutional neural networks. Our experiments compare rectified linear unit, leaky rectified linear unit, scaled exponential linear unit, and hyperbolic tangent to four maxout variants. Throughout the experiments, we find that maxout networks train relatively slower than networks comprised of traditional activation functions. We found that on average, across all datasets, rectified linear units perform better than any maxout activation when the number of convolutional filters is increased six times.
UnclearBallot: Automated Ballot Image Manipulation

As paper ballots and post-election audits gain increased adoption in the United States, election technology vendors are offering products that allow jurisdictions to review ballot images—digital scans produced by optical-scan voting machines—in their post-election audit procedures. Jurisdictions including the state of Maryland rely on such image audits as an alternative to inspecting the physical paper ballots. We show that image audits can be reliably defeated by an attacker who can run malicious code on the voting machines or election management system. Using computer vision techniques, we develop an algorithm that automatically and seamlessly manipulates ballot images, moving voters’ marks so that they appear to be votes for the attacker’s preferred candidate. Our implementation is compatible with many widely used ballot styles, and we show that it is effective using a large corpus of ballot images from a real election. We also show that the attack can be delivered in the form of a malicious Windows scanner driver, which we test with a scanner that has been certified for use in vote tabulation by the U.S. Election Assistance Commission. These results demonstrate that post-election audits must inspect physical ballots, not merely ballot images, if they are to strongly defend against computer-based attacks on widely used voting systems.
Pengembangan Modul Quick Math Berbasis Mobile Learning sebagai Penunjang Pembelajaran Matematika di SMA

[Title: Development of Quick Math Module Based on Mobile Learning as a Support for Mathematics Learning in High School]. Learning resources is a very important component of the learning process. One of the learning resources that can be utilized independently is the module. The purpose of this research is to develop the Quick Math module and evaluate the feasibility of the module. The development model used is the ADDIE model namely analysis, design, development, implementation and evaluation. The subject in this study was 15 students consisting of low, moderate and high-performance level and 6 teachers at SMA Negeri 2 Kota Serang. The instruments used to collect data in this research consist of expert validation questionnaire (content and media), Teacher and student response questionnaire. Based on data analysis results, obtained the score of the content material about 86.4% with a very feasible category, media experts about 88% with very feasible category, teacher response of 92% with very good category and student response of 88.39% with the very good category. Based on the data analysis it can be concluded that the Quick Math module is developed feasible to be used as a learning resource in accordance with the demands of the 2013 curriculum. This app makes it easy for students to access flexible learning resources wherever and whenever.
Learning Image Relations with Contrast Association Networks

Inferring the relations between two images is an important class of tasks in computer vision. Examples of such tasks include computing optical flow and stereo disparity. We treat the relation inference tasks as a machine learning problem and tackle it with neural networks. A key to the problem is learning a representation of relations. We propose a new neural network module, contrast association unit (CAU), which explicitly models the relations between two sets of input variables. Due to the non-negativity of the weights in CAU, we adopt a multiplicative update algorithm for learning these weights. Experiments show that neural networks with CAUs are more effective in learning five fundamental image transformations than conventional neural networks.
Mako-no-te

How do we interact with mechanical systems such as PC, smartphone and so on? Users command something to the system, and the system always accepts and reacts it. That kind of relationship can be named "leader-follower" relationship. On the other hand, thinking about a scene that you have a walk in a park with your partner, there are neither a definite leader nor a follower. You and your partner co-adjust the walking direction and pace by considering each other's intention and feeling from implicit cues. Such relationship has "intersubjectivity" that people share their feeling and intention with others. This study is motivated to build "intersubjective relationship" between human and robot. In this paper, we propose the concept of a side-by-side walking robot "Mako-no-te".
South African bot behaviour post the July 2018 Twitter account cull

World wide, there is a concerning use of social media to sway public opinion through the use of disinformation campaigns. Elections, political decisions such as the Brexit referendum and topics such as vaccination have all been targets of what has come to be known as computational propaganda. Twitter maintainers finally acted on the problem and in July 2018 they suspended millions of fake accounts. Automated accounts, known as bots, are substantially easier to spot and analyse when they are newly created and in the “egg” stage. The culling of accounts therefore provided the perfect opportunity to look for common behaviour. This paper provides an account of the common behaviour seen on a sample of high profile South African focused Twitter accounts in the days following the action by Twitter maintainers to remove abusive accounts.
Automatic Attack Graph Generation for Industrial Controlled Systems

Industrial Controlled Systems (ICSs) are prone to cyber-attacks exploiting weaknesses in their units. This chapter illustrates through an example of a pressurized water Nuclear Power Plant (NPP) control system how these vulnerabilities may be exploited by an attacker compromising the system. The control system is described by Architecture Analysis and Design Language (AADL), and then checked with a security property via JKind checker. The resulted Lustre file is later fed to an Attack Scenarios Generation and Filtration Tool (ASGFT). This tool automatically generates all possible attack scenarios resulting in overall plant disruption. The union of these attack scenarios is visualized by a Visualizer.exe, a Graphical User Interface (GUI) built employing C#.
Understanding Blogs through the Lens of Readers’ Comments

In order to keep their audience engaged, authors need to make sure that the blogs or articles they write cater to the taste of their audience and are understood by them. With the rapid proliferation of online blogging websites, the participation of readers by expressing their opinions and reviews has also increased in the form of comments on the blogs. These comments are valuable source for the authors to understand how their audience are perceiving their blogs. We believe that associating comments to the specific part of the blog they refer to will help authorin getting insights about parts of the blog which are being discussed and the questions or concerns that readers have about those parts. Moreover, categorizing these comments will further aid the author in imbibing the comments. In this work, we describe a method to associate comments to the specific parts of the blog and introduce a hierarchical way to categorize the comments as Suggestion, Agreement, Disagreement or Question.
SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.
Distributing hash families with few rows

Column replacement techniques for creating covering arrays rely on the construction of perfect and distributing hash families with few rows, having as many columns as possible for a specified number of symbols. To construct distributing hash families in which the number of rows is less than the strength, we examine a method due to Blackburn and extend it in three ways. First, the method is generalized from homogeneous hash families (in which every row has the same number of symbols) to heterogeneous ones. Second, the extension treats distributing hash families, in which only separation into a prescribed number of parts is required, rather than perfect hash families, in which columns must be completely separated. Third, the requirements on one of the main ingredients are relaxed to permit the use of a large class of distributing hash families, which we call fractal. Constructions for fractal perfect and distributing hash families are given, and applications to the construction of perfect hash families of large strength are developed.
Empirical Bayesian Mixture Models for Medical Image Translation

Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation of brain scans to handle missing data, the model allows for predicting entirely missing modalities from one, or a few, MR contrasts. Furthermore, the model can be trained on a fairly small number of subjects. The proposed model is validated on three clinically relevant scenarios. Results appear promising and show that a principled, probabilistic model of the relationship between multi-channel signal intensities can be used to infer missing modalities – both MR contrasts and CT images.
Using Recent Advances in Contextual Word Embeddings to Improve the Quantitative Ethnography Workflow

The qualitative content analysis process has traditionally been reliant on human researchers to read and code data, with limited use of automation. However, recent advances in Natural Language Processing (NLP) offer new techniques to improve the reliability and usefulness of content analysis, especially in the area of quantitative ethnography. In this paper we propose a new qualitative content analysis workflow that utilizes techniques such as contextual word embeddings and semantic search. Each of the design principles that inform this workflow are outlined and potential NLP solutions are discussed. This is followed by the description of a new prototype, currently in development, that implements elements of the workflow. The paper concludes with an outline of two proposed research studies to evaluate the effectiveness of the workflow and prototype as well as directions for future research.
ICT-competence of university lecturers: diagnostics and development

The purpose of the study. This article is devoted to reveal the directions of development of lecturers’ information and communication technology (ICT) competences. The level of lecturers’ ICT-competence, feedback from students about efficiency of ICT using at the University, structure of lecturers’ ICT-competence and communicative competence correlation, lecturers’ demands in ICT-competence training were investigated. Methods. Lecturers’ survey (questionnaire “ICT-using by University lecturers” by O. Chuvgunova), students’ survey (questionnaire “ICT at University: students’ feedback” by O. Chuvgunova), psychological test (“Communicative skills test” by L. Michelson), math statistics methods. Sample was formed from lecturers (N = 102) and students (N = 198) of St. Petersburg University. Results. Results show that lecturers have high marks of base level and technological level of ICT competence. Lecturers have ICT literacy and can use ICT as a tool for communication with students. But professional level of the most part of participants’ ICT competence is not developed well enough. Hence, some lecturers cannot create and implement new instruments, methods into educational process through ICT. The feedback from undergraduates demonstrates that students appreciate highly using ICT at their faculties. Respondents think that ICT improves the effectiveness of learning process. At the same time, it is important for students to take into account their individual characteristics and the optimal balance between virtual and “live” communication. Study revealed positive correlation between indicators of ICT competence and communicative competence. Lecturers with high communicative competence have high level of ICT competence and high self-rating of their own ICT competence. Lecturers are interested in ICT-competence training. Mostly, they use different courses and self-education. Most lecturers do not have a plan of their ICT-competence development. Conclusion. The study found out such directions of lecturers’ ICT-competence development, as: – using ICT as a resource of improvement of educational process, development of students’ intelligence and creativity; – lecturers should take into account students’ individual features and students’ feedback through ICT using; – enhancement of lecturers’ communicative competence as a necessary skill of interactive ICT using; – efficiency increase of lecturers’ ICT competence training through permanent educational environment and consulting by ICT specialists.
Software Framework for Fast Image Retrieval

In the paper we describe a system for retrieving images by their features. The software components are built around a multi-layer architecture. We propose also an idea of finding similar images by the CEDD descriptor andvector similarity. We test the proposed method on the Pascal VOC dataset. We published the sources of the system in a public repository.
CJEU rules that copyright protection for designs only requires sufficient originality

Court of Justice of the European Union, Cofemel – Sociedade de Vestuário SA v G-Star Raw CV, C-683/17, EU:C:2019:721, 12 September 2019 (‘Cofemel’) In its Cofemel decision, the Court of Justice of the European Union (CJEU) has ruled that, for designs to be protected by copyright under Directive 2001/29 (the ‘InfoSoc Directive’), what is required is just their sufficient originality; no other conditions can be imposed. As Advocate General (AG) Szpunar noted in his Opinion (EU:C:2019:363), despite the fact that the protection of works of applied art is as old as IP, it is not something that has been devoid of issues. Systems of sui generis protection have appeared over time, though never with an exclusive character, so that cumulation of protection has remained possible. With particular regard to copyright, the conflict with the principles of free competition has meant that the former has been often dependent on the design displaying a high level of artistic value. Theories like the Italian scindibilità and German Stufentheorie are examples in this sense. Other countries, however, have adopted a different approach: for instance, France has traditionally followed the theory of unity of art. In all this, at the EU level uncertainties regarding the correct approach to copyright protection for designs have been exacerbated further by CJEU decisions like Infopaq International A/S v Danske Dagblades Forening, C-5/08, EU:C:2009:465 (‘Infopaq’) and its progeny, notably Flos SpA v Semeraro Casa e Famiglia SpA, C-168/09, EU:C2011: 29 (‘Flos’).