Reviewerly/exHarmony huggingface dataset:
SoK: Techniques for Verifiable Mix Nets

Since David Chaum introduced the idea of mix nets 40 years ago, they have become widely used building blocks for privacy-preserving protocols.Several important applications, such as secure e-voting, require that the employed mix net be verifiable.In the literature, numerous techniques have been proposed to make mix nets verifiable.Some of them have also been employed in politically binding elections.Verifiable mix nets differ in many aspects, including their precise verifiability levels, possible trust assumptions, and required cryptographic primitives; unfortunately, these differences are often opaque, making comparison painful.To shed light on this intransparent state of affairs, we provide the following contributions.For each verifiability technique proposed to date, we first precisely describe how the underlying basic mix net is to be extended and which (additional) cryptographic primitives are required, and then study its verifiability level, including possible trust assumptions, within one generic and expressive verifiability framework.Based on our uniform treatment, we are able to transparently compare all known verifiability techniques for mix nets, including their advantages and limitations.Altogether, our work offers a detailed and expressive reference point for the design, employment, and comparison of verifiable mix nets.
Learning through WhatsApp: students’ beliefs, L2 pragmatic development and interpersonal relationships

By using small focus group discussions, the present study aims to gain insight into students' perceptions about the impact of WhatsApp use in their learning process. More specifically, this study presents a cross-cultural analysis of British and Spanish university students' personal beliefs about the effectiveness of using WhatsApp to complete group tasks outside the classroom. In so doing, this research represents a step forward in technology-enhanced instruction insofar as it not only incorporates the voices of learners themselves, but also contrasts students' beliefs with their group interactions on WhatsApp. The exploration of these WhatsApp interactions makes it possible to argue that the use of WhatsApp does not seem to create a natural context for non-native language users to produce contextualise relevant utterances while interacting with native English speakers. Furthermore, the results show that the lack of pragmatic competence on the part of the participants prevents them from communicating effectively. These pragmatic failures, in turn, have a negative impact on their interpersonal relationships and make them develop negative opinions towards the use of WhatsApp as a learning tool.
Determinants and convergence of government effectiveness in Africa and Asia

Purpose The purpose of this paper is to examine the determinants and convergence of government effectiveness in African and Asian countries. Design/methodology/approach The study utilizes data from 100 countries in Africa and Asia from 2002 to 2018. The panel-corrected standard error regression is used for the regression analysis, while both beta-convergence and sigma-convergence among the countries are tested. Findings Both beta-convergence and sigma-convergence exist among African and Asian countries. Asia performs better than Africa across all indicators except for press freedom, and voice and accountability. Corruption perception index, government size, voice and accountability, regulatory quality and economic wealth have a significant positive effect on government effectiveness. Press freedom negatively impacts on government effectiveness, suggesting that freedom is necessary but not sufficient if there are political actors whose actions undermine freedom. Similarly, the political constraint index, as reflected by checks and balances are necessary but not sufficient to enhance government effectiveness, especially in Asia. Practical implications The results reveal that for press freedom and political checks and balances to enhance government effectiveness, there is a need for a different and holistic approach. The results are relevant for policymakers, public sector practitioners and academics. Originality/value This study utilizes a new dataset and is premier in exploring the convergence of government effectiveness among African and Asian countries.
Notes on Communication and Computation in Secure Distributed Matrix Multiplication

We consider the problem of secure distributed matrix multiplication in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. In this paper, we answer the following question: Is it beneficial to offload the computations if security is a concern? We answer this question in the affirmative by showing that by adjusting the parameters in a polynomial code we can obtain a trade-off between the user's and the servers' computational time. Indeed, we show that if the computational time complexity of an operation in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is at most Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> and the computational time complexity of multiplying two n × n matrices is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ω</sup> Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) then, by optimizing the trade-off, the user together with the servers can compute the multiplication in O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> -6/ω+1 Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) time. We also show that if the user is only concerned in optimizing the download rate, a common assumption in the literature, then the problem can be converted into a simple private information retrieval problem by means of a scheme we call Private Oracle Querying. However, this comes at large upload and computational costs for both the user and the servers.
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing

Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap between long-range and short-range dependencies, which largely reduces the models' flexibility in application to diverse spatial scales and relationships in complicated natural scene images. To fill such a gap, we develop a Middle-Range (MR) branch to capture middle-range dependencies by restricting self-attention into local patches. Also, we observe that the spatial regions which have large correlations with others can be emphasized to exploit long-range dependencies more accurately, and thus propose a Reweighed Long-Range (RLR) branch. Based on the proposed MR and RLR branches, we build an Omni-Range Dependencies Network (ORDNet) which can effectively capture short-, middle- and long-range dependencies. Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images. Extensive experiments show that our proposed ORDNet outperforms previous state-of-the-art methods on three scene parsing benchmarks including PASCAL Context, COCO Stuff and ADE20K, demonstrating the superiority of capturing omni-range dependencies in deep models for scene parsing task.
Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.
IoT Event Classification Based on Network Traffic

The Internet of Things (IoT) consists of sensors and actuators that facilitate many aspects of our daily life. Compared to typical computing devices such as laptops and smartphones, these devices have a very limited set of functionalities and states. Researchers have shown that it is possible to infer the device type from its network traffic. In this paper, we show that an external observer that sniffs the network traffic of an IoT device can further classify device events and hence infer user actions by employing machine learning classifiers. We evaluate and compare the performance of ten machine learning algorithms in classifying 128 device events from 39 different devices. We analyze the impact of the user interaction through LAN and WAN as well as controllers such as Alexa voice assistant on the correct classification of device actions. We also inspect whether the region from which the device is impacts the performance of classifiers as researchers have shown that differing privacy restrictions lead to different external communications.
LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition

In this work, we proposed a novel method, called Local Modified Zernike Moment per unit Mass (LMZMPM), for face recognition, which is invariant to illumination, scaling, noise, in-plane rotation, and translation, along with other orthogonal and inherent properties of the Zernike Moments (ZMs). The proposed LMZMPM is computed for each pixel in a neighborhood of size 3 × 3 , and then considers the complex tuple that contains both the phase and magnitude coefficients of LMZMPM as the extracted features. As it contains both the phase and the magnitude components of the complex feature, it has more information about the image and thus preserves both the edge and structural information. We also propose a hybrid similarity measure, combining the Jaccard Similarity with the L1 distance, and applied to the extracted feature set for classification. The feasibility of the proposed LMZMPM technique on varying illumination has been evaluated on the CMU-PIE and the extended Yale B databases with an average Rank-1 Recognition (R1R) accuracy of 99.8% and 98.66% respectively. To assess the reliability of the method with variations in noise, rotation, scaling, and translation, we evaluate it on the AR database and obtain an average R1R higher than that of recent state-of-the-art methods. The proposed method shows a very high recognition rate on Heterogeneous Face Recognition as well, with 100% on CUFS, and 98.80% on CASIA-HFB.
An occlusion-resistant circle detector using inscribed triangles

Circle detection is a critical issue in pattern recognition and image analysis. Conventional geometry-based methods such as tangent or symmetry are sensitive to noise or occlusion. Area computation is more robust against noise, because it avoids differential calculations. Inspired by this characteristic, we present a novel method for fast circle detection using inscribed triangles. The proposed algorithm, which is robust to noise and resistant to occlusion, first extracts circular arcs by approximating line segments and identifying inflection points and sharp corners. To speed up the computation, irrelevant segments are filtered out through the triangle inequality. Arcs that belong to the same circle are then combined according to the position constraint and the inscribed triangle constraint. The circle parameters are further estimated by inscribed triangles based upon the Theil-Sen estimator and linear error refinement without the dependence of least-square fitting but still with the equivalent accuracy. Finally, candidate circles are verified to prune false positives through an inlier ratio rule, which jointly considers both distance and angle deviations. Extensive experiments are conducted on synthetic images including overlapping circles, and real images from four diverse datasets (three publicly available and one we built). Results are compared with those of representative state-of-the-art methods, and the proposed method is demonstrated to embraces several advantages: resistant to occlusion, more robust to noise, and better performance and efficiency.
Dewarping Document Image by Displacement Flow Estimation with Fully Convolutional Network

As camera-based documents are increasingly used, the rectification of distorted document images becomes a need to improve the recognition performance. In this paper, we propose a novel framework for both rectifying distorted document image and removing background finely, by estimating pixel-wise displacements using a fully convolutional network (FCN). The document image is rectified by transformation according to the displacements of pixels. The FCN is trained by regressing displacements of synthesized distorted documents, and to control the smoothness of displacements, we propose a Local Smooth Constraint (LSC) in regularization. Our approach is easy to implement and consumes moderate computing resource. Experiments proved that our approach can dewarp document images effectively under various geometric distortions, and has achieved the state-of-the-art performance in terms of local details and overall effect.
Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval

Technical Q&#x0026;A sites (e.g., Stack Overflow (SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&#x0026;A sites and information retrieval approaches (e.g., word embedding-based) have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user&#x2019;s preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed in a query is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, we propose an interactive query refinement approach for question retrieval, named <i>Chatbot4QR</i>, which can assist users in recognizing and clarifying technical details missed in queries and thus retrieve more relevant questions for users. Chatbot4QR automatically detects missing technical details in a query and generates several clarification questions (CQs) to interact with the user to capture their overlooked technical details. To ensure the accuracy of CQs, we design a heuristic-based approach for CQ generation after building two kinds of technical knowledge bases: a manually categorized result of 1,841 technical tags in SO and the multiple version-frequency information of the tags. We develop a Chatbot4QR prototype that uses 1.88 million SO questions as the repository for question retrieval. To evaluate Chatbot4QR, we conduct six user studies with 25 participants on 50 experimental queries. The results are as follows. (1) On average 60.8 percent of the CQs generated for a query are useful for helping the participants recognize missing technical details. (2) Chatbot4QR can rapidly respond to the participants after receiving a query within approximately 1.3 seconds. (3) The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70 percent of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6 percent in terms of two measurements: Pre<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq1-3016006.gif"/></alternatives></inline-formula>k and NDCG<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq2-3016006.gif"/></alternatives></inline-formula>k. (4) For 48-88 percent of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines (e.g., the SO search engine and Google) using the original queries.
Open Source FaaS Performance Aspects

FaaS is a novel, but highly emerging service model in cloud computing. The importance of FaaS can be seen as public service providers have their own FaaS infrastructures and also the open source community makes efforts to implement FaaS initiatives.In this paper, we show the performance differences of the Python3, Node.js and Golang runtimes of OpenFaaS, Kubeless, Fission, and Knative platforms. We also show how the supported auto-scaling algorithms of the examined FaaS systems influence the performance of the function runtimes. Finally, we propose solutions to increase the performance of the Python3 runtime of OpenFaaS and Kubeless.
10 Tbit/s QAM Quantum Noise Stream Cipher Coherent Transmission Over 160 Km

We describe in detail our recent demonstration of a 10 Tbit/s secure physical layer transmission that we achieved by using digital coherent QAM quantum noise stream cipher (QNSC) and injection-locked WDM techniques. We used an FPGA-based transmitter and receiver to demonstrate a 165 channel polarization-multiplexed WDM 5 Gbaud 128 QAM/QNSC (70 Gbit/s) on-line transmission over 160 km with a spectral efficiency of 6 bit/s/Hz. In the present system, the original 128 QAM data were encrypted in a 1024 × 1024 QAM format using basis information. The encrypted signal was then masked by a large ASE noise, which reduced the detection “success” probability for an eavesdropper to 0.13% for each symbol. Furthermore, the multiplicity of the original QAM data and the seed keys used to generate the basis information were arbitrarily changed with time, which makes the decryption much more difficult.
An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images

Ship target classification plays an important role in tasks such as maritime traffic control, maritime target tracking, and military reconnaissance.The complex ocean environment often causes obscuration of the ship targets, thus resulting in low accuracy of the obscured targets.This article presents a novel target classification algorithm-improved InceptionV3 and center loss convolution neural network (IICL-CNN)-based on the well-established inception network to improve the accuracy of obscured targets.This algorithm features a new objective function, which is designed to learn common features of both the clear samples and the obscured samples and, in the meantime, reduce the intraclass distance among the obscured samples.Experiments were performed on an optical remote sensing image dataset which consisted of 48 000 ship images in nine categories.The proposed method demonstrated superior performance on the obscured ship targets compared to the original InceptionV3 model.On average, the accuracy was 4.23%, 5.98%, and 17.48% higher on the ship targets that were occluded by levels of 30%, 50%, and 70%, respectively.Our experimental results showed that the proposed IICL-CNN could effectively improve the accuracy of the ship targets at various occlusion levels.
Privacy-preserving Navigation Supporting Similar Queries in Vehicular Networks

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this paper, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.
Mouse dynamics based user recognition using deep learning

Abstract Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.