Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia

Illiteracy is one of the most serious issues in Indonesia. The government's ignorance of illiterate people makes the illiteracy rate quite high. It should be one of the government's targets for reducing illiteracy in order to reduce the number of illiterate people. Illiteracy rate in Indonesia itself has reached 34.55% in Papua province. One way to suppress illiteracy rate in Indonesia is by predicting illiterate figures for subsequent years. The data to be predicted is the data of illiterate figures of each province in Indonesia which is sourced from the Indonesian Central Bureau of Statistics from 2011 to 2017. The method used in the prediction is Backpropagation Neural Network. Data analysis was done with the help of matlab software R2011b (7.13). This study uses 5 architectures, 4-5-1, 4-6-1, 4-9-1, 4-14-1 and 4-18-1. From these 5 models the best network architecture is 4-14-1 with 91% accuracy and Mean Squared Error 0,00274166.
CDSP: A Solution for Privacy and Security of Multimedia Information Processing in Industrial Big Data and Internet of Things

With the widespread nature of wireless internet and internet of things, data have bloomed everywhere. Under the scenario of big data processing, privacy and security concerns become a very important consideration. This work focused on an approach to tackle the privacy and security issue of multimedia data/information in the internet of things domain. A solution based on Cryptographical Digital Signal Processor (CDSP), a Digital Signal Processor (DSP) based platform combined with dedicated instruction extension, has been proposed, to provide both programming flexibility and performance. We have evaluated CDSP, and the results show that the algorithms implemented on CDSP all have good performance. We have also taped out the platform designed for privacy and security concerns of multimedia transferring system based on CDSP. Using TSMC 55 nm technology, it could reach the speed of 360 MHz. Benefiting from its programmability, CDSP can be easily expanded to support more algorithms in this domain.
An Agents’ Model Using Ontologies and Web Services for Creating and Managing Virtual Enterprises

Currently, the economic competition has become very tough where successful companies find themselves obliged to develop their organizations and diversify their work strategies.Thereby, virtual enterprises concept imposes itself and overcomes geographical constraints by allowing companies to cooperate and address their shortcomings through exchanging their services and skills.The present paper proposes a virtual enterprise model based on the use of agents endorsed by the use of ontologies and web services.The coupling of these concepts allows reducing the high costs associated with the use of technology and improves flexibility and intelligence both in the negotiation process through the selection of the best partners, and in the managing process by ensuring the effective cooperation between partners.
Text Classification Based on Weighted Extreme Learning Machine

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed a great competence of the proposed WELM compared to the ELM.
Statistical Shape Clustering of Left Atrial Appendages

Fifteen percent of all strokes are caused by emboli formed in the left atrium (LA) in case of atrial fibrillation (AF). The most common site of thrombus formation is inside the left atrial appendage (LAA). The LAA is accounting for 70% to 90% of the thrombi formed in the LA in patients with non-valvular AF. Studies have shown there is a correlation between the LAA morphology and risk of ischemic stroke; Chicken Wing and Cauliflower LAA shapes are associated with lower and higher risk, respectively. These two LAA shape categories come from a popular classification in the medical domain, but it is subjective and based on qualitative shape parameters. In this paper, we describe a full framework for shape analysis and clustering of the LAA. Initially, we build a point distribution model to quantitatively describe the LAA shape variation based on 103 LAA surfaces segmented and reconstructed from multidetector computed tomography volumes. We are successfully able to determine point correspondence between LAA surfaces, by non-rigid volumetric registration of signed distance fields. To validate if LAA shapes are clustered, we employ an unsupervised clustering on the shape models parameters to estimate the natural number of clusters in our training set, where the number of shape clusters is estimated by validating the test log-likelihood of several Gaussian mixture models using two level cross-validation. We found that the LAAs surfaces basically formed two shape clusters broadly corresponding to the Chicken wing and non-Chicken Wing morphologies, which fits well with clinical knowledge.
A Modular and Optimized Toolbox for Side-Channel Analysis

The aim of this paper is to describe the characteristics of a toolbox developed to carry out a side-channel analysis of cryptosystems implemented in physical devices. The toolbox, called RamDPA and developed in C++, is flexible and modular and has been initially designed to carry out differential power analysis attacks. Different modules are used to enter text, keys, and hypothetical values related to the cryptographic algorithm under analysis, to control the digital oscilloscope in charge of the acquisition of power consumption traces, and to carry out the statistical operations to get the unknown key. An analysis of the different options to program the modules has been performed in order to optimize the toolbox in terms of memory consumption and execution time. Usually, the optimization of side-channel attacks comes from the selection of points-of-interest and re-sampling. Instead of that approach, the proposed toolbox optimizes DPA attacks from caching strategy and multithreading. Results are presented by applying RamDPA to extract an AES-128 key with the traces provided by the CHES2016 capture The Flag challenge.
A Cuckoo-based Workflow Scheduling Algorithm to Reduce Cost and Increase Load Balance in the Cloud Environment

Workflow scheduling is one of the important issues in implementing workflows in the cloud environment. Workflow scheduling means how to allocate workflow resources to tasks based on requirements and features of the tasks. The problem of workflow scheduling in cloud computing is a very important issue and is an NP problem. The relevant scheduling algorithms try to find optimal scheduling of tasks on the available processing resources in such a way some qualitative criteria when executing the entire workflow are satisfied. In this paper, we proposed a new scheduling algorithm for workflows in the cloud environment using Cuckoo Optimization Algorithm (COA). The aims of the proposed algorithm are reducing the processing and transmission costs as well as maintaining a desirable load balance among the processing resources. The proposed algorithm is implemented in MATLAB and its performance is compared with Cat Swarm Optimization (CSO). The results of the comparisons showed that the proposed algorithm is superior to CSO in discovering optimal solutions.
Approach for Intuitive and Touchless Interaction in the Operating Room

The consultation of medical images, 2D or 3D, has a crucial role for planned or ongoing surgical operations. During an intervention, this consultation induces a sterility loss for the surgeon due to the fact that the classical interaction devices are non-sterile. A solution to this problem would be to replace conventional devices by touchless interaction technologies, thereby enabling sterile interventions. In this paper, we present the conceptual development of an intuitive “gesture vocabulary” allowing the implementation of an effective touchless interactive system that is well adapted to the specificities of the surgical context. Our methodology and its implementation as well as our results are detailed. The suggested methodology and its implementation were both shown to be a valid approach to integrating this mean of interaction in the operating room.
The fluid limit of a random graph model for a shared ledger

Abstract A shared ledger is a record of transactions that can be updated by any member of a group of users. The notion of independent and consistent record-keeping in a shared ledger is important for blockchain and more generally for distributed ledger technologies. In this paper we analyze a stochastic model for the shared ledger known as the tangle, which was devised as the basis for the IOTA cryptocurrency. The model is a random directed acyclic graph, and its growth is described by a non-Markovian stochastic process. We first prove ergodicity of the stochastic process, and then derive a delay differential equation for the fluid model which describes the tangle at high arrival rate. We prove convergence in probability of the tangle process to the fluid model, and also prove global stability of the fluid model. The convergence proof relies on martingale techniques.
Saving Face: Playful Design for Social Engagement, in Public Smart City Spaces

Can social engagement and reflection be designed through social touch in today’s smart city’s public spaces? This paper explores ludic, playful design for shared engagement and reflection in public spaces through social touch. In two Artistic Social Labs (ASL), internationally presented in public spaces, a radically unfamiliar sensory synthesis is acquired, for which perception of ‘who sees and who is being seen, who touches and who is being touched’ is disrupted. Participants playfully ‘touch themselves and feel being touched, to connect with others on a screen’. On the basis of the findings in the ASLs, guidelines are proposed for orchestrating social engagement and reflection, through social touch as play.
A new construction of rotation symmetric bent functions with maximal algebraic degree

In this paper, for any even integer $ n = 2m\ge4 $, a new construction of $ n $-variable rotation symmetric bent function with maximal algebraic degree $ m $ is given as \begin{document}$ f(x_0,x_1\cdots,x_{n-1}) = \bigoplus\limits_{i = 0}^{m-1}(x_ix_{m+i})\oplus \bigoplus\limits_{i = 0}^{n-1}(x_ix_{i+1}\cdots x_{i+m-2} \overline{x_{i+m}} ), $\end{document} whose dual function is \begin{document}$ \widetilde{f}(x_0,x_1\cdots,x_{n-1}) = \bigoplus\limits_{i = 0}^{m-1}(x_ix_{m+i})\oplus \bigoplus\limits_{i = 0}^{n-1}(x_ix_{i+1}\cdots x_{i+m-2} \overline{x_{i+n-2}} ), $\end{document} where $ \overline{x_{i}} = x_{i}\oplus 1 $ and the subscript of $ x $ is modulo $ n $.
Improving Model Robustness with Transformation-Invariant Attacks.

The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research. It has been shown that both neural networks and adversarial attacks against them can be sensitive to input transformations such as linear translation and rotation, and that human vision, which is robust against adversarial attacks, is invariant to natural input transformations. Based on these, this paper tests the hypothesis that model robustness can be further improved when it is adversarially trained against transformed attacks and transformation-invariant attacks. Experiments on MNIST, CIFAR-10, and restricted ImageNet show that while transformations of attacks alone do not affect robustness, transformation-invariant attacks can improve model robustness by 2.5\% on MNIST, 3.7\% on CIFAR-10, and 1.1\% on restricted ImageNet. We discuss the intuition behind this phenomenon.
Enhanced measurement-device-independent quantum key distribution in reference-frame-independent scenario

The protocol of reference-frame-independent measurement-device-independent quantum key distribution has many advantages compared with other existing quantum key distribution protocols, and thus has many interesting applications in the field of quantum information. In this paper, we propose a scheme on the reference-frame-independent measurement-device-independent quantum key distribution by implementing heralded single-photon sources, where we only need to modulate pump light into two intensities (vacuum and nonvacuum). Through numerical simulations, we demonstrate that our scheme can exhibit much longer transmission distance than the standard three-intensity decoy-state measurement-device-independent quantum key distribution under a reference-frame-independent scenario.
Robust Precise Dynamic Point Reconstruction From Multi-View

Reconstructing precise dynamic points with multiple camera systems (MCSs) is a pivotal work in many computer vision applications, such as motion capture. However, the deviation of 2-D position leads to frequent mismatch when searching for correspondence from multi-view. This paper puts forward a two-stage framework based on passive optical motion capture system to reconstruct precise dynamic points with MCSs. Our proposed method improves the performance of calibration and matching simultaneously. In the calibration stage, the extrinsic parameters of numerous cameras are calibrated synchronously via an L-shaped frame, where the position of four reference points is optimized with multiple geometric constraints. Bundle adjustment occurs after calibration. In the reconstruction stage, we propose a novel sparse multi-view matching method called cyclical voting, which includes multiple pairs of global voting and in-group voting. Point residual method is proposed to exclude outliers in matching groups further. The experiments show that our proposed method can decrease mismatching significantly and achieve commendable reconstruction results compared with Cortex (one of the most successful commercial motion analysis software).
A New Robust Deep Canonical Correlation Analysis Algorithm for Small Sample Problems

As a nonlinear feature learning method, deep canonical correlation analysis (DCCA) has got a great success in computer vision. Compared with kernel methods, deep neural networks can more easily process large amounts of training data and do not require referring to the training set at test time. However, in the real world, due to the noise disturbance and the limited number of training samples, within-set and between-set sample covariance matrices cannot usually be estimated accurately, which causes that the gradient direction deviates from the true one when training DCCA. It incorporates fractional-order within-set and between-set scatter matrices to reduce the deviations of sample covariance matrices for gradient direction correction. In addition, to make full use of convolutional network's feature extraction ability and fractional model in modifying neural network gradient directions, we further propose two novel convolutional network-based FDCCA methods, named convolutional neural network-based FDCCA (CNN-FDCCA) and two-convolutional neural networks based FDCCA (2CNNs-FDCCA), respectively. The experimental results on MNIST and RAVDNESS datasets show that FDCCA has better recognition rates than existing methods. The experiments on AT&T dataset show that CNN-FDCCA and 2CNNs-FDCCA have great robustness in processing images.
The Lemke–Howson Algorithm Solving Finite Non-Cooperative Three-Person Games in a Special Setting

We present a brief outline of an approximate method (3LP) proposed by E.G. Golshteyn for solving three-person games in mixed strategies. Similar to the 2LP-algorithm (which approximately solves bimatrix games), the solution procedure consists in the search of a global minimum of the so-called Nash function. By making an exhaustive search of the pairs of the initial strategies the algorithm 2LP (3LP) finds an exact solution of the game if the condition of reciprocal complementarity holds. The numerical experiments show that the 2LP-method successfully competes with the Lemkeâ€“Howson (LH) algorithm, which efficiently solves the bimatrix games. Unfortunately, the LH-algorithm cannot be applied to solve arbitrary three-person games. However, we have adapted the Lemkeâ€“Howson method to the solution of a special setting called the hexamatrix games. We have also conducted a thorough testing of the LH-algorithm to reveal its advantages and minor points as well.
Learning Weighted Forest and Similar Structure for Image Super Resolution

Image super resolution (SR) based on example learning is a very effective approach to achieve high resolution (HR) image from image input of low resolution (LR). The most popular method, however, depends on either the external training dataset or the internal similar structure, which limits the quality of image reconstruction. In the paper, we present a novel SR algorithm by learning weighted random forest and non-local similar structures. The initial HR image patches are obtained from a weighted forest model, which is established by calculating the approximate fitting error of the leaf nodes. The K-means clustering algorithm is exploited to get a non-local similar structure inside the initial HR image patches. In addition, a low rank constraint is imposed on the HR image patches in each cluster. We further apply the similar structure model to establish an effective regularization prior under a reconstruction-based SR framework. Comparing with current typical SR algorithms, the results of comprehensive experiments implemented on three publicly datasets show that peak signal-to-noise ratio (PSNR) has been effectively promoted by the presented SR approach, and a better visual effect has been realized.
Beyond Big Data Capitalism, Towards Dialectical Digital Modernity : Reflections on David Chandler’s Chapter

Our logo, an open laptop and an open book forming a W, was intended as a succinct comment and a visual representation of our mission.For UWP the most signficant development in the last year has been the addition of three new journal titles: the first, Anthropocenes -Human, Inhuman, Posthuman (p.34) an interdisciplinary title of great range tackling some of the big questions of our age including climate change, species extinction and latterly Covid-19.Likewise we are delighted to welcome the Journal of Deliberative Democracy (p.32).As populism surges across the world, the need for democratic legitimacy and real engagement continues to grow.JDD's August 2020 relaunch with UWP highlights key debates in participative democracy and public deliberation and considers how new insights might assist politics grapple with mounting challenges.We also look forward, later in the year to the first issue of Active Travel Studies (p.31).Healthier and more environmentally conscious transport is the focus of the journal's parent research body, the Active Travel Academy at the University of Westminster.Also during this period two of our existing journals Silk Road (p.36) and Westminster Papers in Communication and Culture (p.38) are now presented in a new research environment that of ScienceOpen.We welcome ScienceOpen and other new channels assisting readers in discovering our publications.UWP book titles remain available via
Leakage-Resilient Certificate-Based Signature Resistant to Side-Channel Attacks

Certificate-based cryptography is an attractive public-key setting, and it not only simplifies certificate management in the traditional public-key cryptography but also eliminates the key escrow problem inherent in the identity-based cryptography. Recently, leakage-resilient cryptography resistant to side-channel attacks has received significant attention from cryptographic researchers. By side-channel attacks, adversaries could obtain partial information of secret and private keys involved in cryptographic algorithms by perceiving execution time or energy consumptions of each algorithm invocation. The certificate-based signature (CBS) is a class of important public-key signature. Up to date, there exists no leakage-resilient CBS (LR-CBS) scheme resistant to side-channel attacks. In this paper, the first LR-CBS scheme is proposed and it possesses overall unbounded leakage property, namely, it permits adversaries to continuously obtain partial information of secret or private keys involved in the associated algorithm invocations. The security analysis is given to prove that the proposed LR-CBS scheme is existential unforgeability against adaptive chosen-message attacks for adversaries in the generic bilinear group model.
UNDERSTANDING AND FOSTERING INTERACTION IN THREADED DISCUSSION

This study (N=2,826 postings from 92 participants) examines the phenomenon of interactivity in asynchronous computer-mediated communication (ACMC), also known as threaded discussion, in the context of master’s level Teaching English as a Second Language (MATESL) and Teaching English as a Foreign Language (MATEFL) courses. The study, which is grounded in a group of interrelated pragmatic, learning community, and pedagogical theories, attempts to determine when and under what conditions interactivity, here defined as a response to a previous posting, occurs. We focus on conditions that are present in interactive threaded discussions, those with low rates of serial monologuism and high rates of participant uptake. Taking interactivity as the dependent variable, we test a number of properties of individual ACMC postings to determine their relationships to interactivity. These variables include biographical properties of the writers (gender and first language (L1), role in the course) and a group of individual ACM posting properties, such the content of the posting (course related, phatic, both), whether or not the posting is interactive, the length of the posting, its intended audience, and whether or not the posting contains indicators of social presence (use of social speech, humor, naming, and more), face-threatening speech acts, and direct questions.Data used in the study were collected from ACMC, part of a web-based graduate introduction to second language acquisition and research methods courses. Participants in the courses were from various L1 backgrounds, including American English, Polish, Korean, and Arabic. Among our findings is that while social presence markers do not predict interactivity, there does seem to be some relationship between indicators of social presence and the quality of interaction.