Reviewerly/exHarmony huggingface dataset:
Lessons Learned from SunDEW: A Self Defense Environment for Web Applications

Machine learning has started to be deployed in fields such as healthcare and finance, which involves dealing with a lot of sensitive data.This propelled the need for and growth of privacy-preserving machine learning (PPML).We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks.Our 4PC protocol tolerating at most one malicious corruption is practically more efficient than Gordon et al. (ASIACRYPT 2018) as the 4th party in our protocol is not active in the online phase, except input sharing and output reconstruction stages.Concretely, we reduce the online communication as compared to them by 1 ring element.We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds.Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning, where the data is secretly shared among the servers.Also, we propose conversions especially relevant to privacy-preserving machine learning.With the privilege of having an extra honest party, we outperform the current state-of-the-art ABY3 (for three parties), in terms of both rounds as well as communication complexity.The highlights of our framework include using minimal number of expensive circuits overall as compared to ABY3.This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase.Our B2A conversion has an improvement of 7× in rounds and 18× in the communication complexity.In addition to these, all of the special conversions for machine learning, e.g.Secure Comparison, achieve constant round complexity.The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3.All the protocols are implemented over a 64-bit ring in both LAN and WAN settings.Our improvements go up to 187× for the training phase and 158× for the prediction phase when observed over LAN and WAN.
Instructional Design in Online Education: a Systemic Approach

Online education is becoming more and more valid, but as a different modality from the face-to-face teaching-learning process, it has special characteristics that must be considered. Online education is much more than uploading material to a repository and using it in a linear manner. Electronic online education platforms, seen as an integral system, offer a large number of technological resources that must be used according to the educational model that is being applied. To achieve good performance, an online education model must be based on a harmonious architecture of the educational, administrative, legal and infrastructure aspects of ICT, that is, an integral model. The educational models created based on the prevailing pedagogical models - behavioral, constructivist, cognitive and connectivist - must be implemented through an instructional design, aligned with the pedagogical objectives and learning strategies, based on the proper use of the technological resources of The electronic platform. Instructional design models, such as ADDIE, ASSURE, Dick and Carey, and others, applied to online education, should take advantage of the resources of the technology platforms and the characteristics of each. These models, as a guide to instructional design processes, can also be enriched with other methodological processes, such as DevOps, which through continuous deliveries enrich and keep the educational content updated. This article proposes strategies for applying the technological resources of online learning platforms, aligned with the instructional design corresponding to the different pedagogical models.
A Standard Driven Software Architecture for Fully Autonomous Vehicles

The development of self-driving vehicles is often regarded as adding a layer of intelligence on top of classic vehicle platforms.However, the amount of software needed to reach autonomy will exceed the software deployed for operation of normal vehicles.As complexity increases, the demand for proper structure also increases.Moreover, the shift from open, deterministic components to more opaque, probabilistic software components raises new challenges for system designers.In this paper we introduce a functional software architecture for fully autonomous vehicles aimed to standardise and ease the development process.Existing literature presents past experiments with autonomous driving or implementations specific to limited domains (e.g.winning a competition).The architectural solutions are often an after-math of building or evolving an autonomous vehicle and not the result of a clear software development life-cycle.A major issue of this approach is that requirements cannot be traced with respect to functional components and several components group most functionality.Therefore, it is often difficult to adopt the proposals.In this paper we take a prescriptive approach starting with requirements from a widely adopted automotive standard.We follow a clear software engineering process, specific to the automotive industry.During the design process, we make extensive use of robotic architectures -which seem to be often ignored by automotive software engineers -to support standard driven requirements.
Quantum weak coin flipping with a single photon

Weak coin flipping is among the fundamental cryptographic primitives which ensure the security of modern communication networks. It allows two mistrustful parties to remotely agree on a random bit when they favor opposite outcomes. Unlike other two-party computations, one can achieve information-theoretic security using quantum mechanics only: both parties are prevented from biasing the flip with probability higher than $1/2+\ensuremath{\epsilon}$, where $\ensuremath{\epsilon}$ is arbitrarily low. Classically, the dishonest party can always cheat with probability 1 unless computational assumptions are used. Despite its importance, no physical implementation has been proposed for quantum weak coin flipping. Here, we present a practical protocol that requires a single photon and linear optics only. We show that it is fair and balanced even when threshold single-photon detectors are used, and reaches a bias as low as $\ensuremath{\epsilon}=1/\sqrt{2}\ensuremath{-}1/2\ensuremath{\approx}0.207$. We further show that the protocol may display a quantum advantage over a few-hundred meters with state-of-the-art technology.
Applications of Structural Equivalence to Subgraph Isomorphism on Multichannel Multigraphs

Structural Equivalence refers to the ability to exchange two vertices in a graph without changing the structure of the graph. We provide basic definitions and properties applicable to the subgraph isomorphism problem. We show examples of structural equivalence that reduce the size of the search tree for subgraph isomorphism counting and enumeration, applied to multichannel networks.
Mining Frequent Patterns in Data Using Apriori and Eclat: A Comparison of the Algorithm Performance and Association Rule Generation

This paper aims to compare Apriori and Eclat algorithms for association rules mining by applying them on a real-world dataset. In addition to considering performance efficiency of the algorithms, the research takes into consideration the distribution of the support, as well as the number of rules generated by Apriori and Eclat.
Does virtual currency development harm financial stocks’ value? Comparing Taiwan and China markets

This research incorporated virtual currency development factors into the capital asset pricing model (CAPM) and interpreted the effects of bitcoin and fin-tech on the capital market through a deduction of the portfolio theory and innovation diffusion theory (IDT) on changes to financial stocks’ value. This paper examined a total of 67,166 panel data of financial stocks in the two emerging markets of Taiwan and China between July 2016 and April 2019, presenting the following significant findings. (1) Financial stocks in Taiwan’s market are more greatly shocked by the bitcoin and interaction effect between bitcoin and fin-tech than those in China’s market. (2) Even after changing proxy variable or autocorrelation and heteroskedasticity are considered, the asymmetric shocks on financial stocks in Taiwan’s market are still great. (3) The effects of the two variables of bitcoin and interaction with fin-tech on financial stocks are consistently important as the three-factor CAPM model. (4) Transmitted by the changes in currency supply and demand as well as exchange rate volatilities, the spillover effects of virtual currencies and financial innovation indirectly change the currency multiplier of the home country, investors’ sensibility to interest rates, and balance of import and export trades and may eventually impact the gross outputs and inflation of individual economies.
Interactive game “Kahoot!” as the media of students’ vocabulary assessment

This research aims to investigate students’ perspectives on using the interactive game “Kahoot!” as the media of vocabulary test at one of the public senior high schools in Yogyakarta Indonesia. It was qualitative research with five voluntarily participating students as the participants. An in-depth semi-structured interview was applied as the data collection technique. The theme-based analysis was used in this study. The finding shows that first, Kahoot! is more practical for a vocabulary test. Second, the game can attract the students’ attention to the test. Third, it helps the students to be more active in the classroom and the last, the game makes the students more enthusiastic in doing the test. However, this research also found some negative sides of using Kahoot!; first, the students become less confident with their score, especially when they get lower scores than other students because the scores were displayed on the leader board in Kahoot! application; second, some students had a lack of knowledge in using the application; and the last, the time was limited. This study implies that the use of Kahoot! in the EFL classroom can be an alternative for teachers to teach vocabulary and provide fun learning activities for the students.
Method for conducting <i>in situ</i> high-temperature digital image correlation with simultaneous synchrotron measurements under thermomechanical conditions

This work presents a novel method of obtaining in situ strain measurements at high temperature by simultaneous digital image correlation (DIC), which provides the total strain on the specimen surface, and synchrotron x-ray diffraction (XRD), which provides lattice strains of crystalline materials. DIC at high temperature requires specialized techniques to overcome the effects of increased blackbody radiation that would otherwise overexpose the images. The technique presented herein is unique in that it can be used with a sample enclosed in an infrared heater, remotely and simultaneously with synchrotron XRD measurements. The heater included a window for camera access, and the light of the heater lamps is used as illumination. High-temperature paint is used to apply a random speckle pattern to the sample to allow the tracking of displacements and the calculation of the DIC strains. An inexpensive blue theatrical gel filter is used to block interfering visible and infrared light at high temperatures. This technique successfully produces properly exposed images at 870 °C and is expected to perform similarly at higher temperatures. The average strains measured by DIC were validated by an analytical calculation of the theoretical strain. Simultaneous DIC and XRD strain measurements of Inconel 718 (IN718) tensile test specimens were performed under thermal and mechanical loads and evaluated. This approach uses the fact that with DIC, the total strain is measured, including plastic strain, while with XRD, only elastic strain is captured. The observed differences were discussed with respect to the effective deformation mechanisms.
TKRM: A Formal Knowledge Representation Method for Typhoon Events

Typhoon events can cause serious environmental damage and economic losses. Understanding the development of typhoon events will provide valuable knowledge for disaster prevention and mitigation. In the age of big data, the sharp contrast between the sudden increase of mass information and the lack of a knowledge appreciation mechanism appears. There is an urgent need to promote the transformation of information services to knowledge services in the field of hazard management. Knowledge representation, as a strategy for symbolizing and formalizing knowledge, affects knowledge acquisition, storage, management, and application, and is the basis and prerequisite for the implementation of knowledge services. Based on the evolution law of typhoon events and human cognitive habits, a formal knowledge representation method for typhoon events (TKRM) is proposed in this paper. First, by analyzing the evolution characteristics of typhoon events, the TKRM framework with three layers consisting of “event–process–state” was constructed, which was used to describe the knowledge composition and relationship of the different granularity of typhoon events. Second, the formal representation of the TKRM framework was formed by using a finite state machine (FSM) as a reference, taking time and location as the basic conditions, and extending the hierarchical and parallel representation mechanism. Finally, the rationality and practical value of the TKRM were verified using a case study.
Homomorphic Encryption-Based Robust Reversible Watermarking for 3D Model

Robust reversible watermarking in an encrypted domain is a technique that preserves privacy and protects copyright for multimedia transmission in the cloud. In general, most models of buildings and medical organs are constructed by three-dimensional (3D) models. A 3D model shared through the internet can be easily modified by an unauthorized user, and in order to protect the security of 3D models, a robust reversible 3D models watermarking method based on homomorphic encryption is necessary. In the proposed method, a 3D model is divided into non-overlapping patches, and the vertex in each patch is encrypted by using the Paillier cryptosystem. On the cloud side, in order to utilize addition and multiplication homomorphism of the Paillier cryptosystem, three direction values of each patch are computed for constructing the corresponding histogram, which is shifted to embed watermark. For obtaining watermarking robustness, the robust interval is designed in the process of histogram shifting. The watermark can be extracted from the symmetrical direction histogram, and the original encrypted model can be restored by histogram shifting. Moreover, the process of watermark embedding and extraction are symmetric. Experimental results show that compared with the existing watermarking methods in encrypted 3D models, the quality of the decrypted model is improved. Moreover, the proposed method is robust to common attacks, such as translation, scaling, and Gaussian noise.
QoE-Sensitive Economic Pricing Model for Wireless Multimedia Communications Using Stackelberg Game

With the advances in wireless multimedia communication technologies and the rise in end- user experience expectations, meeting the Quality of Experience (QoE) requirements of the mobile multimedia user has become challenging for content providers and wireless carriers. In this research, we chalk up a QoE-sensitive multimedia data pricing economic model, by pricing the quality of the data rather than the quantity of the data. Utility maximization problem between the content provider (sells multimedia content), wireless carrier (provides transmission service) and the mobile user (requests multimedia data) is modeled using the proposed framework. We then formulate the aforementioned problem as a two-stage Stackelberg game and derive the Nash Equilibrium using backward induction method. Simulation study show that each player can obtain optimal strategy where the Stackelberg equilibrium exists stably. Finally, the proposed smart pricing mechanism was tested against the traditional uniform pricing scheme and the results indicate that better utilities can be achieved by leveraging the proposed scheme.
ANALYSIS OF TWO-STEP APPROACH FOR COMPRESSING TEXTURE IMAGES WITH DESIRED QUALITY

considered. Quality is mainly characterized by the peak signal-to-noise ratio (PSNR) but visual quality metrics are briefly studied as well. Potentially, a two-step approach can be used to carry out a compression with providing the desired quality in a quite simple way and with a reduced compression time. However, the two-step approach can run into problems for PSNR metric under conditions that a required PSNR is quite small (about 30 dB). These problems mainly deal with the accuracy of providing a desired quality at the second step. The paper analyzes the reasons why this happens. For this purpose, a set of nine test images of different complexity is analyzed first. Then, the use of the two-step approach is studied for a wide set of complex structure texture test images. The corresponding test experiments are carried out for several values of the desired PSNR. The obtained results show that the two-step approach has limitations in the cases when complex texture images have to be compressed with providing relatively low values of the desired PSNR. The main reason is that the rate-distortion dependence is nonlinear while linear approximation is applied at the second step. To get around the aforementioned shortcomings, a simple but efficient solution is proposed based on the performed analysis. It is shown that, due to the proposed modification, the application range of the two-step method of lossy compression has become considerably wider and it covers PSNR values that are commonly required in practice. The experiments are performed for a typical image encoder AGU based on discrete cosine transform (DCT) but it can be expected that the proposed approach is applicable for other DCT-based image compression techniques.
Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution

With the help of deep convolutional neural networks, a vast majority of single image superresolution (SISR) methods have been developed, and achieved promising performance.However, these methods suffer from over-smoothness in textured regions due to utilizing a single-resolution network to reconstruct both the low-frequency and high-frequency information simultaneously.To overcome this problem, we propose a Multi-resolution space-Attended Residual Dense Network (MARDN) to separate lowfrequency and high-frequency information for reconstructing high-quality super-resolved images.Specifically, we start from a low-resolution sub-network, and add low-to-high resolution sub-networks step by step in several stages.These sub-networks with different depth and resolution are utilized to produce feature maps of different frequencies in parallel.For instance, the high-resolution sub-network with fewer stages is applied to local high-frequency textured information extraction, while the low-resolution one with more stages is devoted to generating global low-frequency information.Furthermore, the fusion block with channelwise sub-network attention is proposed for adaptively fusing the feature maps from different sub-networks instead of applying concatenation and 1 × 1 convolution.A series of ablation investigations and model analyses validate the effectiveness and efficiency of our MARDN.Extensive experiments on benchmark datasets demonstrate the superiority of the proposed MARDN against the state-of-the-art methods.Our super-resolution results and the source code can be downloaded from https://github.com/Periter/MARDN.
Practicality and Effectiveness Test of Graphic Design Learning Media Based on Android

&lt;p class="0abstract"&gt;&lt;strong&gt;— &lt;/strong&gt;Based on preliminary observations on the implementation of Graphic Design learning, it is indicated that it is only limited to using printed module media, lecture methods, guided practice and not yet using media with android technology. This study aims to: (1) Generate a practical learning media for Graphic Design based on Android Technology. (2) Producing Learning Media Graphic Design based on Effective Android Technology. This research is a Research and Development. This study uses a study design development with 4D development model that consists of four main stages, namely, Define (definition), Design (design), Develop (development), and Disseminate (deployment). Based on the practicality test, the practicality test value was 85%. Effectiveness Test of learning media for Android-based Graphic Design shows effective results , this is also shown with a significant level of 0,000 &amp;lt;0.05, which means that there are significant differences between Student Learning Outcomes before being given an Android-based media ( Pretest ) and learning outcomes students after being given an Android-based media ( Posttest ) in the Informatics Engineering Education Study Program, Faculty of Engineering, State University of Padangs.&lt;/p&gt;
Using Class-Specific Feature Selection for Cancer Detection with Gene Expression Profile Data of Platelets

A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, small samples, and collinear data. The strategy of one-against-all (OVA) was employed to decompose the multi-classification problem into a series of binary classification problems. The elastic net was used to select class-specific features for the binary classification problems, and the probabilistic support vector machine was used to make the outputs of the binary classifiers with class-specific features comparable. Simulation data and gene expression profile data were intended to verify the effectiveness of the proposed method. Results indicate that the proposed method can automatically select class-specific features and obtain better performance of classification than that of the conventional multi-class classification methods, which are mainly based on global feature selection methods. This study indicates the proposed method is suitable for general multi-classification problems featured with high-dimension, small samples, and collinear data.
An Analysis on Digital Literacy Level of Faculty of Sports Science Students

The study aims to examine the digital literacy levels of students in the faculty of sports sciences. In the study group; there are 394 students (105 female and 289 male) studying in the departments of Physical Education and Sports Teaching, Sports Management, Coaching and Recreation of the Faculty of Sport Sciences at Atatürk University in the 2018-2019 academic year. "Digital Literacy Scale (DLS) is a scale, which was developed by Ng (2012) and translated into Turkish by Hamutoğlu, Güngören, Uyanık, and Erdoğan (2017) and it is composed of 17 items and 4 factors (attitude, technique, cognitive and social). Nonparametric tests (Kruskal Wallis, Mann-Whitney U) were used to analyse the data of the study. According to the findings; digital literacy levels of the participating students differ significantly in social sub-dimension in respect of gender variation. In the social sub-dimension of digital literacy scale, digital literacy levels of male students were higher than female students.