Reviewerly/exHarmony huggingface dataset:
Design of Secure Coding Challenges for Cybersecurity Education in the Industry

According to a recent survey with more than 4000 software developers, “less than half of developers can spot security holes”. As a result, software products present a low-security quality expressed by vulnerabilities that can be exploited by cyber-criminals. This lack of quality and security is particularly dangerous if the software which contains the vulnerabilities is deployed in critical infrastructures. Serious games, and in particular, Capture-the-Flag (CTF) events, have shown promising results in improving secure coding awareness of software developers in the industry. The challenges in the CTF event, to be useful, must be adequately designed to address the target group. This paper presents novel contributions by investigating which challenge types are adequate to improve software developers’ ability to write secure code in an industrial context. We propose 1) six challenge types usable in the industry context, and 2) a structure for the CTF challenges. Our investigation also presents results on 3) how to include hints and penalties into the cyber-security challenges. We evaluated our work through a survey with security experts. While our results show that “traditional” challenge types seem to be adequate, they also reveal a new class of challenges based on code entry and interaction with an automated coach.
Progress testing anytime and anywhere – Does a mobile-learning approach enhance the utility of a large-scale formative assessment tool?

The widespread use of mobile devices among students favors the use of mobile learning scenarios at universities. In this study, we explore whether a time- and location-independent variant of a formative progress test has an impact on the students' acceptance, its validity and reliability and if there is a difference in response processes between the two exam conditions.Students were randomly assigned to two groups of which one took the test free of local or temporal fixations, while the other group took the test at the local testing center under usual examination conditions. Beside the generated test data, such as test score, time-on-test, and semester status, students also evaluated the settings.While there was no significant effect on the test score between the two groups, students in the mobile group spent more time on the test and were more likely to use the help of books or online resources. The results of the evaluation show that the acceptability among students is increased by a mobile version of the formative progress test.The results suggest that the acceptance and motivation to participate in formative tests is enhanced by lifting local and temporal restrictions. The mobile version nonetheless does not have an impact on the students' performance.
Generating In-Between Images Through Learned Latent Space Representation Using Variational Autoencoders

Image interpolation is often implemented using one of two methods: optical flow or convolutional neural networks. These methods are typically pixel-based; they do not work well on objects between images far apart. Because they either rely on a simple frame average or pixel motion, they do not have the required knowledge of the semantic structure of the data. In this paper, we propose a method for image interpolation based on latent representations. We use a simple network structure based on a variational autoencoder and an adjustable hyperparameter that imposes the latent space distribution to generate accurate interpolation. To visualize the effects of the proposed approach, we evaluate a synthetic dataset. We demonstrate that our method outperforms both pixel-based methods and a conventional variational autoencoder, with particular improvements in nonsuccessive images.
Digital Literacy, Business Uncertainty & Economic Performance: An Empirical Study of Small Businesses in Sri Lanka

The study attempts to investigate empirically how Digital Literacy (DL) affects the Business Uncertainty and Economic Performance particularly Small Enterprises (SEs) in Sri Lankan. The data were collected from 110 owners of SEs, conducting face to face interviews and analysed using Partial Least Squares-Structural Equation Modelling. The results discovered that the different dimensions of DL have a negative impact on business uncertainty and positive effects on the economic performance of SEs. Thus, the study recognized that the DL highly affects the minimization of business uncertainty and thereby increases the economic performance of SEs in Sri Lanka. Further, the results confirmed that the business uncertainty has a strong significant mediate effect on the relationship between DL and the economic performance of SEs in Sri Lanka. The study strongly recommends SEs to strengthen the digital technologies which play a dominant role to enhance economic sustainability minimizing the behavioural and environmental uncertainty. Thus, the study provides useful insight into the role of DL on the economic performance of SEs minimizing business uncertainty in Sri Lanka from a novel perspective.
Examining the validity of Schatten- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -norm-based functionals as coherence measures

It has been asked by different authors whether the two classes of Schatten-$p$-norm-based functionals $C_p(\rho)=\min_{\sigma\in\mathcal{I}}||\rho-\sigma||_p$ and $ \tilde{C}_p(\rho)= \|\rho-\Delta\rho\|_{p}$ with $p\geq 1$ are valid coherence measures under incoherent operations, strictly incoherent operations, and genuinely incoherent operations, respectively, where $\mathcal{I}$ is the set of incoherent states and $\Delta\rho$ is the diagonal part of density operator $\rho$. Of these questions, all we know is that $C_p(\rho)$ is not a valid coherence measure under incoherent operations and strictly incoherent operations, but all other aspects remain open. In this paper, we prove that (1) $\tilde{C}_1(\rho)$ is a valid coherence measure under both strictly incoherent operations and genuinely incoherent operations but not a valid coherence measure under incoherent operations, (2) $C_1(\rho)$ is not a valid coherence measure even under genuinely incoherent operations, and (3) neither ${C}_{p>1}(\rho)$ nor $\tilde{C}_{p>1}(\rho)$ is a valid coherence measure under any of the three sets of operations. This paper not only provides a thorough examination on the validity of taking $C_p(\rho)$ and $\tilde{C}_p(\rho)$ as coherence measures, but also finds an example that fulfills the monotonicity under strictly incoherent operations but violates it under incoherent operations.
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

In this paper, we propose a novel stochastic gradient estimator -- ProbAbilistic Gradient Estimator (PAGE) -- for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p_t$ or reuses the previous gradient with a small adjustment, at a much lower computational cost, with probability $1-p_t$. We give a simple formula for the optimal choice of $p_t$. Moreover, we prove the first tight lower bound $Ω(n+\frac{\sqrt{n}}{ε^2})$ for nonconvex finite-sum problems, which also leads to a tight lower bound $Ω(b+\frac{\sqrt{b}}{ε^2})$ for nonconvex online problems, where $b:= \min\{\frac{σ^2}{ε^2}, n\}$. Then, we show that PAGE obtains the optimal convergence results $O(n+\frac{\sqrt{n}}{ε^2})$ (finite-sum) and $O(b+\frac{\sqrt{b}}{ε^2})$ (online) matching our lower bounds for both nonconvex finite-sum and online problems. Besides, we also show that for nonconvex functions satisfying the Polyak-Łojasiewicz (PL) condition, PAGE can automatically switch to a faster linear convergence rate $O(\cdot\log \frac{1}ε)$. Finally, we conduct several deep learning experiments (e.g., LeNet, VGG, ResNet) on real datasets in PyTorch showing that PAGE not only converges much faster than SGD in training but also achieves the higher test accuracy, validating the optimal theoretical results and confirming the practical superiority of PAGE.
Adversarial Attacks and Defenses

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection. These adversarial examples are intentionally crafted instances, either appearing in the train or test phase, which can fool the DNN models to make severe mistakes. Therefore, people are dedicated to devising more robust models to resist adversarial examples, but usually they are broken by new stronger attacks. This arms-race between adversarial attacks and defenses has been drawn increasing attention in recent years. In this tutorial, we provide a comprehensive overview on the frontiers and advances of adversarial attacks and their countermeasures. In particular, we give a detailed introduction of different types of attacks under different scenarios, including evasion and poisoning attacks, white-box and black box attacks. We will also discuss how the defending strategies develop to compete against these attacks, and how new attacks come out to break these defenses. Moreover, we will discuss the story of adversarial attacks and defenses in other data domains, especially in graph structured data. Then, we introduce DeepRobust, a Pytorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. Finally, we summarize the tutorial with discussions on open issues and challenges about adversarial attacks and defenses. Via our tutorial, our audience can grip the main idea and key approaches of the game between adversarial attacks and defenses.
Hybrid quantum–classical optimization with cardinality constraints and applications to finance

In this work we develop tools to address combinatorial optimization problems with a cardinality constraint, in which only a subset of variables end up having nonzero values.Firstly, we introduce a new heuristic pruning method that iteratively discards variables through a hybrid quantum-classical optimization step.Secondly, we analyse the use of soft constraints in the form of "chemical potentials" to control the number of non-zero variables.We illustrate the power of both techniques using the problem of index tracking, which aims to mimicking the performance of a financial index with a balanced subset of assets.We also compare the performance of different state-of-the-art quantum variational optimization algorithms in our pruning method.
EXPLORATION OF IOCG MINERALIZATIONS USING INTEGRATION OF SPACE-BORNE REMOTE SENSING DATA WITH AIRBORNE GEOPHYSICAL DATA

Abstract. Nowadays, remote sensing technologies are playing a significant role in mineral potential mapping. To optimize the exploration approach along with a cost-effective way, narrow down the target areas for a more detailed study for mineral exploration using suitable data selection and accurate data processing approaches are crucial. To establish optimum procedures by integrating space-borne remote sensing data with other earth sciences data (e.g., airborne magnetic and electromagnetic) for exploration of Iron Oxide Copper Gold (IOCG) mineralization is the objective of this study. Further, the project focus is to test the effectiveness of Copernicus Sentinel-2 data in mineral potential mapping from the high Arctic region. Thus, Inglefield Land from northwest Greenland has been chosen as a study area to evaluate the developed approach. The altered minerals, including irons and clays, were mapped utilizing Sentinel-2 data through band ratio and principal component analysis (PCA) methods. Lineaments of the study area were extracted from Sentinel-2 data using directional filters. Self-Organizing Maps (SOM) and Support Vector Machines (SVM) were used for classification and analysing the available data. Further, various thematic maps (e.g., geological, geophysical, geochemical) were prepared from the study area. Finally, a mineral prospectively map was generated by integrating the above mentioned information using the Fuzzy Analytic Hierarchy Process (FAHP). The prepared potential map for IOCG mineralization using the above approach of Inglefield Land shows a good agreement with the previous geological field studies.
Pengembangan Multimedia Interaktif Macromedia Flash Berbasis K-13 Sebagai Inovasi Pembelajaran Tematik Untuk Siswa Sekolah Dasar

Currently at the elementary school level has used Curriculum 2013 (K-13) which emphasizes thematic learning by studying various fields of science simultaneously in a series of themes. But the problem is there is no learning material which is packaged in the form of interactive multimedia that is able to link K-13 thematic learning in elementary school.&#x0D; The aim of this research is to produce interactive multimedia products based on K-13 as a thematic learning innovation in valid primary schools.&#x0D; The type of this research is a research and development (R&amp;D) by adapting the ADDIE development model. It is said to adapt because this research process stops at the development stage in terms of product validity. Product validity based on graphic design experts score 87 with valid criteria. Meanwhile, according to Sciences experts this product got a score of 86 with valid criteria, for Mathematic experts it got a score of 86, Indonesian material experts got a score of 90, and Civics material received a score of 89 with valid information. In the expert assessment of Sports education received a score of 93 with a very valid category. From the multimedia linguistic point of view, the evaluation is very valid and appropriate to cognitive development level of children with a validity value of 92. Thus the conclusion of this study is that interactive K-13 based multimedia products are said to be valid and feasible to use.
Speech Pseudonymisation Assessment Using Voice Similarity Matrices

The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications. These are essential since speech signals convey a wealth of rich, personal and potentially sensitive information. Anonymisation, the focus of the recent VoicePrivacy initiative, is one strategy to protect speaker identity information. Pseudonymisation solutions aim not only to mask the speaker identity and preserve the linguistic content, quality and naturalness, as is the goal of anonymisation, but also to preserve voice distinctiveness. Existing metrics for the assessment of anonymisation are ill-suited and those for the assessment of pseudonymisation are completely lacking. Based upon voice similarity matrices, this paper proposes the first intuitive visualisation of pseudonymisation performance for speech signals and two novel metrics for objective assessment. They reflect the two, key pseudonymisation requirements of de-identification and voice distinctiveness.
Tackling Over-Smoothing for General Graph Convolutional Networks

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification. The main cause of this lies in over-smoothing. The over-smoothing issue drives the output of GCN towards a space that contains limited distinguished information among nodes, leading to poor expressivity. Several works on refining the architecture of deep GCN have been proposed, but it is still unknown in theory whether or not these refinements are able to relieve over-smoothing. In this paper, we first theoretically analyze how general GCNs act with the increase in depth, including generic GCN, GCN with bias, ResGCN, and APPNP. We find that all these models are characterized by a universal process: all nodes converging to a cuboid. Upon this theorem, we propose DropEdge to alleviate over-smoothing by randomly removing a certain number of edges at each training epoch. Theoretically, DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by dimension collapse. Experimental evaluations on simulated dataset have visualized the difference in over-smoothing between different GCNs. Moreover, extensive experiments on several real benchmarks support that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs.
One-shot detection limits of quantum illumination with discrete signals

Abstract To detect a stealth target, one may directly probe it with a single photon and analyze the reflected signals. The efficiency of such conventional detection scheme can potentially be enhanced by quantum illumination, where entanglement is exploited to break the classical limits. The question is what is the optimal signal state for achieving the detection limit? Here, we address this question in a general discrete model, and derive a complete set of analytic solutions. For one-shot detection, the parameter space can be classified into three distinct regions, in the form of a “phase diagram” for both conventional and quantum illumination. Interestingly, whenever the reflectivity of the target is less than some critical value, all received signals become useless, which is true even if entangled resources are employed. However, there does exist a region where quantum illumination can provide advantages over conventional illumination; there, the optimal signal state is an entangled state with an entanglement spectrum inversely proportional to the spectrum of the environmental noise state and is, surprisingly, independent of the occurrence probability and the reflectivity of the object. The entanglement of the ideal probe state increases with the entropy of the environment; it becomes more entangled as the temperature of the environment increases. Finally, we show that the performance advantage cannot be fully characterized by any measure of quantum correlation, unless the environment is a complete mixed state.
Novel discrete chaotic system via fractal transformation and its DSP implementation

Designing a discrete chaotic system via fractal transformation has become a new method for engineering applications. This method generates new discrete chaotic system through external mechanisms, instead of the traditional way of internal mechanisms. The way of building novel discrete chaotic system is enriched by fractal and mathematical operation. Taking one-dimensional ICMIC map and two-dimensional Hénon map as the seed maps, dynamics of the generated chaotic map is analyzed by bifurcations, complexity and spectrum distribution characteristics. The results show that the new discrete chaotic map has the advantages in complexity and distribution in the parameter space. Finally, the digital circuit of fractal chaotic system is implemented based on DSP technique. The feasibility of the circuit is verified. Therefore, it has good application prospects in secure communication.
Where Do Pedestrians Look When Crossing? A State of the Art of the Eye-Tracking Studies

It has been widely shown in the literature that analysing eye movements and positions can provide useful information for a better understanding of human perception and cognition. The eye-tracking technology, as a process of measuring where people look, has established itself as a widespread means of studying visual information processing in several domains, including in the study of human walking. Street-crossing can be defined as a particular form of walking. Indeed, several elements have to be considered in the decision-making process, such as the distance headway, traffic density, vehicle speed, etc. It is also a very risky aspect of walking as pedestrians are considered one of the most vulnerable road users. In this article, we present an up-to-date comprehensive review of existing eye-tracking experiments in the literature, from the pedestrian's point of view, with a view to study the effects of both internal (e.g., age) and external (e.g., road environment) factors on pedestrians' road crossing gaze behaviour. Furthermore, the current gaps in the literature are then discussed in order to open up some future perspectives in the field, such as the forthcoming introduction of automated vehicles on the roads.
StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures

Information visualization research has developed powerful systems that enable users to author custom data visualizations without textual programming. These systems can support graphics-driven practices by bridging lazy data-binding mechanisms with vector-graphics editing tools. Yet, despite their expressive power, visualization authoring systems often assume that users want to generate visual representations that they already have in mind rather than explore designs. They also impose a data-to-graphics workflow, where binding data dimensions to graphical properties is a necessary step for generating visualization layouts. In this paper, we introduce StructGraphics, an approach for creating data-agnostic and fully reusable visualization designs. StructGraphics enables designers to construct visualization designs by drawing graphics on a canvas and then structuring their visual properties without relying on a concrete dataset or data schema. In StructGraphics, tabular data structures are derived directly from the structure of the graphics. Later, designers can link these structures with real datasets through a spreadsheet user interface. StructGraphics supports the design and reuse of complex data visualizations by combining graphical property sharing, by-example design specification, and persistent layout constraints. We demonstrate the power of the approach through a gallery of visualization examples and reflect on its strengths and limitations in interaction with graphic designers and data visualization experts.
ProbMinHash – A Class of Locality-Sensitive Hash Algorithms for the (Probability) Jaccard Similarity

The probability Jaccard similarity was recently proposed as a natural generalization of the Jaccard similarity to measure the proximity of sets whose elements are associated with relative frequencies or probabilities. In combination with a hash algorithm that maps those weighted sets to compact signatures which allow fast estimation of pairwise similarities, it constitutes a valuable method for big data applications such as near-duplicate detection, nearest neighbor search, or clustering. This paper introduces a class of one-pass locality-sensitive hash algorithms that are orders of magnitude faster than the original approach. The performance gain is achieved by calculating signature components not independently, but collectively. Four different algorithms are proposed based on this idea. Two of them are statistically equivalent to the original approach and can be used as drop-in replacements. The other two may even improve the estimation error by introducing statistical dependence between signature components. Moreover, the presented techniques can be specialized for the conventional Jaccard similarity, resulting in highly efficient algorithms that outperform traditional minwise hashing and that are able to compete with the state of the art.