Reviewerly/exHarmony huggingface dataset:
Generalized cryptanalysis of small CRT-exponent RSA

There have been several works for studying the security of CRT-RSA with small CRT exponents dp and dq by using lattice-based Coppersmith's method. Thus far, two attack scenarios have been mainly studied: (1) dq is small with unbalanced prime factors p≪q. (2) Both dp and dq are small for balanced p≈q. The best attacks for the both scenarios were proposed by Takayasu-Lu-Peng (Eurocrypt'17, Journal of Cryptology'19) and the attack conditions are much better than the other known attacks. Although the attacks have been very useful for studying the security of CRT-RSA, the structures of their proposed lattices are not well understood. In this paper, to further study the security of CRT-RSA, we first define a generalized attack scenario to unify the previous ones. Specifically, all p,q,dp, and dq can be of arbitrary sizes. Furthermore, we propose improved attacks in this paper when dp and/or p is sufficiently small. Technically, we construct a lattice whose basis vectors are chosen flexibly depending on the sizes of p,q,dp, and dq. Since the attack scenarios (1) and (2) are simpler than our general scenario, the previous Takayasu-Lu-Peng's lattices are simple special cases of ours. We are able to achieve the flexible lattice constructions by exploiting implicit but essential structures of Takayasu-Lu-Peng's lattices. We check the validity of our proposed attacks by computer experiments. We believe that the deeper understanding of the lattice structures will be useful for studying the security of CRT-RSA even in other scenarios.
A Supervised Framework for Review Spam Detection in the Persian Language

Sentiment analysis of online reviews has attracted an increasing attention from both academia and industry. Although online reviews are valuable sources of information for detecting public opinion towards different aspects of products, they may be written by spammers with different purposes. In order to detect such spam reviews, several methods have been proposed for English language but no study has been reported on Persian spam detection so far. In the current study, Persian reviews of cell-phones are investigated to find spam type 1 and type 2 which are fake reviews and reviews only written about brands, respectively. In the proposed framework a labeled dataset, SpamPer, is first created using a majority voting on the answers of 11 questions previously designed for spam detection by human annotators. Then several preprocessing steps for Persian language are performed to refine the training data. Finally review-based and metadata features are extracted. The obtained results on 3000 reviews of SpamPer shows that the highest accuracy is obtained using the decision tree with 0.78 F1-measure. Moreover, the results reveal that SVM for unbalanced data and decision tree for balanced data achieve better performance when they are trained on the combination of metadata and review-based features.
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams

Data stream mining has attracted much attention from scholars. In recent researches, ensemble classification has been wide aplied in concept drift detection; however, most of them regard classification accuracy as a criterion for judging whether concept drift happens or not. Information entropy is an important and effective method for measuring uncertainty. Based on the information entropy theory, a new algorithm using information entropy to evaluate a classification result is developed. It utilizes the methods of ensemble learning and the weight of each classifier is decided by the entropy of the result produced by an ensemble classifiers system. When the concept in data stream changes, the classifiers whose weight are below a predefined threshold will be abandoned to adapt to a new concept. In the experimental analysis, the proposed algorithm and six comparision algorithms are executed on six experimental data sets. The results show that the proposed method can not only handle concept drift effectively, but also have a better performance than the comparision algorithms.
Argumentation Theory for Mathematical Argument

To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been used to analyse mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport’s structured proofs.
Refined Lightweight Temporal Compression for Energy-Efficient Sensor Data Streaming

Lightweight Temporal Compression (LTC) is an energy-efficient lossy compression algorithm that maintains a memory usage and per-sample computational cost in O(1). The method provides a trade-off between compression ratio and accuracy using an error bound. In this paper, we present the Refined LTC (RLTC) algorithm, which uses a binning approach to widen the search space and increase the LTC’s compression ratio and reduce its dynamic energy consumption, which is characterized by CPU computations and radio transmissions, without compromising the error bound. The proposed RLTC algorithm adds negligible overhead to the memory usage and latency of LTC. Experimental results on an environmental sensor dataset have shown that the LTC’s compressed byte stream can be further reduced in size by up to 18%, while the dynamic energy consumption is reduced by 9.5% on average.
Process-Integrated State Estimation of Optical Systems With Macro–Micro Manipulators Based on Wavefront Filtering

To date, the assembly of optical systems is still not fully automated. Automated assembly can be facilitated by having an optical simulation at hand, which, in turn, requires knowledge about the current state of the optical system. Due to the strict demands on the positioning tolerances, the uncertainties of the positioning system play an important role and lead to non-negligible deviations from the nominal poses. Therefore, the actual poses of the optical components need to be estimated in order to correct misaligned components. Furthermore, it is beneficial to develop methods that utilize the dedicated primary sensor and to avoid additional external sensors. In this letter, we employ a macro-micro manipulator for moving optical components and utilize filtering methods for realizing an in-process state estimation. For this, the uncertainty of the positioning system, as well as sensor noise, need to be identified, which lay the groundwork for methods such as the extended Kalman filter, iterated extended Kalman filter, unscented Kalman filter, or particle filter. In this letter, we compare these methods in simulation with current nonlinear approaches from the literature with respect to the estimation error. Experimental verification is carried out by a macro-micro manipulator comprised of a Cartesian piezo-driven 3-DOF positioning system attached to a 6-DOF industrial robot. With the proposed filtering approach and macro-micro manipulator, the pose of a bi-convex lens is estimated via a wavefront sensor.
Reconciling Multiple Social Networks Effectively and Efficiently: An Embedding Approach

Recently, reconciling social networks, identifying the accounts belonging to the same individual across social networks, receives significant attention from both academic and industry. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these limitations, we rethink this problem and, for the first time, robustly and comprehensively reconcile multiple social networks. In this paper, we propose two frameworks, MASTER and MASTER+, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In MASTER, we first design a novel Constrained Dual Embedding model, simultaneously embedding and reconciling multiple social networks, to formulate this problem into a unified optimization. To address this optimization, we then design an effective NS-Alternating algorithm and prove it converges to KKT points. To further speed up MASTER, we propose a scalable framework, namely MASTER+. The core idea is to group accounts into clusters and then perform MASTER in each cluster in parallel. Specifically, we design an efficient Augmented Pre-Embedding model and Balance-aware Fuzzy Clustering algorithm for the high efficiency and the high accuracy. Extensive experiments demonstrate that both MASTER and MASTER+ outperform the state-of-the-art approaches. Moreover, MASTER+ inherits the effectiveness of MASTER and enjoys higher efficiency.
Simulated autonomous driving in a realistic driving environment using deep reinforcement learning and a deterministic finite state machine

In the field of autonomous driving, the system controlling the vehicle can be seen as an agent acting in a complex environment and thus naturally fits into the modern framework of reinforcement learning. However, learning to drive can be a challenging task and current results are often restricted to simplified driving environments. To advance the field, we present a method to adaptively restrict the action space of the agent according to its current driving situation and show that it can be used to swiftly learn to drive in a realistic environment based on the deep Q-learning algorithm.
Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines

There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation of spike train sequences and introduce a new liquid state machine (LSM) network architecture and a new forward orthogonal regression algorithm to learn an input-output signal mapping or to decode the brain activity. The proposed algorithm uses precise spike timing to select the presynaptic neurons relevant to each learning task. We show that using precise spike timing to train the LSM and selecting the readout presynaptic neurons leads to a significant increase in performance on binary classification tasks, in decoding neural activity from multielectrode array recordings, as well as in a speech recognition task, compared with what is achieved using the standard architecture and training methods.
An Efficient Web Traffic Defence Against Timing-Analysis Attacks

We introduce a new class of lower overhead tunnel that is resistant to traffic analysis. The tunnel opportunistically reduces the number of dummy packets transmitted during busy times when many flows are simultaneously active while maintaining well-defined privacy properties. We find that the dummy packet overhead is typically less than 20% on lightly loaded links and falls to zero as the traffic load increases, i.e., the tunnel is capacity achieving. The additional latency incurred is less than 100 ms. We build an experimental prototype of the tunnel and carry out an extensive performance evaluation that demonstrates its effectiveness under a range of network conditions and real Web page fetches.
A Comprehensive Analysis of Image Forensics Techniques: Challenges and Future Direction

Background: Image forensics deal with the problem of authentication of pictures or their origins. There are two types of forensics techniques namely active and passive. Passive forgery is also known as blind forensics technique. In passive forgery, copy-move (cloning) image forensics is most common forgery technique. In this approach, an object or region of a picture is copied and positioned somewhere else in the same image. The active method used watermarking to solve picture genuineness problem. It has limitations like human involvement or particularly equipped cameras. To overwhelm these limitations, numerous passive authentication approaches have been developed. Moreover, both approaches do not require any prior information about the picture. Objective: The prime objective of this survey is to provide an inclusive summary as well as recent advancement, challenges and future direction in image forensics. In today’s digital era, digital pictures and videos are having a great impact on our life as well as society, as they became an important source of information. Though earlier it was very difficult to doctor the picture, nowadays digital pictures can be doctored easily with the help of editing tools and the internet. These practices make pictures as well as videos genuineness deceptive. Conclusion: This paper presents the current state-of-the-art of passive (cloning) image forensics techniques, challenges and future direction of this research domain. Furthermore, the major open issues in developing a robust cloning image forensics detector with their performance are discussed. Lastly, the available benchmark datasets are also discussed.
Quantum Versus Classical Simultaneity in Communication Complexity

This paper addresses two problems in the context of two-party communication complexity of functions. First, it concludes the line of research which can be viewed as demonstrating qualitative advantage of quantum communication in the three most common communication “layouts”: two-way interactive communication, one-way communication and simultaneous message passing (SMP). I demonstrate a functional (problem c̅E̅q̅T̅, whose communication complexity is O (log n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in the quantum version of the SMP and Ω̃ (√n) in the classical (randomized) version of SMP. Second, this paper contributes to understanding the power of the weakest commonly studied regime of quantum communication-SMP with quantum messages and without shared randomness (the latter restriction can be viewed as a somewhat artificial way of making the quantum model “as weak as possible”). Our function c̅E̅q̅T̅ has an efficient solution in this regime as well, which means that even lacking shared randomness, quantum SMP can be exponentially stronger than its classical counterpart with shared randomness.
Multiple target, multiple type filtering in the RFS framework

A Multiple Target, Multiple Type Filtering (MTMTF) algorithm is developed using Random Finite Set (RFS) theory. First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple types of targets, each with distinct detection properties, to develop a multiple target, multiple type filtering, N-type PHD filter, where N≥2, for handling confusions among target types. In this approach, we assume that there will be confusions between detections, i.e. clutter arises not just from background false positives, but also from target confusions. Then, under the assumptions of Gaussianity and linearity, we extend the Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter. Furthermore, we analyze the results from simulations to track sixteen targets of four different types using a four-type (quad) GM-PHD filter as a typical example and compare it with four independent GM-PHD filters using the Optimal Subpattern Assignment (OSPA) metric. This shows the improved performance of our strategy that accounts for target confusions by efficiently discriminating them.1
Towards a Uniform Architecture for the Efficient Implementation of 2D and 3D Deconvolutional Neural Networks on FPGAs

Three-dimensional deconvolution is widely used in many computer vision applications. However, most previous works have only focused on accelerating 2D deconvolutional neural networks (DCNNs) on FPGAs, while the acceleration of 3D DCNNs has not been studied in depth as they have higher computational complexity and sparsity than 2D DCNNs. In this paper, we focus on the acceleration of both 2D and 3D DCNNs on FPGAs by proposing efficient schemes for mapping 2D and 3D DCNNs on a uniform architecture. By implementing our design on the Xilinx VC709 platform for four real-life 2D and 3D DCNNs, we can achieve up to 3.0 TOPS with high hardware efficiency. Comparisons with CPU and GPU solutions demonstrate that we can achieve an improvement of up to 63.3 × in throughput relative to a CPU solution and an improvement of up to 8.3 × in energy efficiency compared to a GPU solution.
Transitioning to ebooks: Using Interaction Theory as a Lens To Characterize General Chemistry Students’ Use of Course Resources

This 21-item survey administered at the end of first-semester General Chemistry asked students to estimate their average weekly use of course resources and rate how helpful they find various textbook and online homework features of their current textbook. During data collection, the General Chemistry textbook changed from a print version to one that is primarily electronic. A comparison of previous results of a print textbook semester to a semester using an ebook provided insight into how the equivalent features are utilized in different mediums. In the first three semesters’ worth of data analyzed, General Chemistry I students report relying on their textbook for approximately 5 h of studying per week, regardless of textbook medium; when the ebook was adopted, the homework resource was more frequently used. A comparison of equivalent textbook features between formats revealed that the helpfulness ratings of reading outline and animations increased significantly, whereas worked-examples and end-of-chapter questions decreased significantly. In addition to mathemagenic features, ebook features that help students stay on-task or visualize reactions are significantly highly rated.
Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems

This paper considers approximate smoothing for discretely observed non-linear stochastic differential equations. The problem is tackled by developing methods for linearising stochastic differential equations with respect to an arbitrary Gaussian process. Two methods are developed based on (1) taking the limit of statistical linear regression of the discretised process and (2) minimising an upper bound to a cost functional. Their difference is manifested in the diffusion of the approximate processes. This in turn gives novel derivations of pre-existing Gaussian smoothers when Method 1 is used and a new class of Gaussian smoothers when Method 2 is used. Furthermore, based on the aforementioned development the iterative Gaussian smoothers in discrete-time are generalised to the continuous-time setting by iteratively re-linearising the stochastic differential equation with respect to the current Gaussian process approximation to the smoothed process. The method is verified in two challenging tracking problems, a reentry problem and a radar tracked coordinated turn model with state dependent diffusion. The results show that the method has competitive estimation accuracy with state-of-the-art smoothers.
Depth perceptual quality assessment for symmetrically and asymmetrically distorted stereoscopic 3D videos

Depth perception is one of the most important features provided by stereoscopic 3D videos, which is also the major difference to 2D videos. Its quality, called depth perceptual quality or depth quality, is one of the key factors that directly affects the quality of experience of 3D videos. Since 3D video shall be compressed, transmitted and reconstructed in 3D video system, distortion introduced from compression and processing affects the video quality as well as the depth perceptual quality. However, understandings of how the distortions affect the depth perception and how to evaluate the depth perceptual quality of 3D video remain limited. In this paper, subjective experiments are firstly conducted for investigating the compression and processing distortions’ impacts on the perceived depth quality of 3D videos. The subjective experiments over the datasets show that the loss of video details can cause the degradation of the depth perceptual quality in monocular and binocular perspectives. In addition, the relationships between video quality and depth quality for both symmetric and asymmetric distorted 3D stereoscopic videos are analyzed. Both the subjective video quality and depth quality scores are released and available for public. Secondly, an objective depth quality assessment algorithm is proposed for measuring the depth perceptual quality degradation of symmetrically and asymmetrically distorted stereoscopic 3D videos. The experimental results demonstrate that the proposed metric has better consistency with the subjective scores of human vision system compared with the state-of-the-art schemes.
On Cyclic Codes of Composite Length and the Minimum Distance II

In this paper, we provide two complementary results for cyclic codes of composite length. First, we give a general construction of cyclic codes of length nr from cyclic codes of length n and a lower bound on the minimum distance. Numerical data show that many cyclic codes of composite length with the best parameters can be obtained in this way. Second, in the other direction, we show that for cyclic codes of length n with a primitive n-th root of unity as a non-zero, the minimum distance is roughly bounded by the square-free part of n. This means that we shall not expect good cyclic codes of length n in general if, for example, the length n is divisible by a high power of a prime.
Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking

A number of vision-based algorithms designed to detect and track unmanned aerial vehicles (UAVs) from on board a second UAV have been researched, implemented, and experimentally validated over the last decade. However, the successful methods have tended to rely on characteristics such as color or shape, meaning they require the target UAV to have particular markings or geometries. This paper uses the Viola–Jones cascade classifier, a computer vision algorithm originally designed to detect human faces in video streams, and demonstrates its capability for detecting and tracking an arbitrary type of UAV with excellent performance in either indoor or outdoor environments and with a variety of backgrounds. The Viola–Jones algorithm is applied to two specific quadrotor UAV models, the Solo from 3D Robotics and the AR.Drone 2.0 from Parrot. Experimental testing demonstrates that the resulting system achieves very good detection and tracking performance in real time on each UAV type for both indoor and outdoor flight tests.
Knowing Where to Look? Analysis on Attention of Visual Question Answering System

Attention mechanisms have been widely used in Visual Question Answering (VQA) solutions due to their capacity to model deep cross-domain interactions. Analyzing attention maps offers us a perspective to find out limitations of current VQA systems and an opportunity to further improve them. In this paper, we select two state-of-the-art VQA approaches with attention mechanisms to study their robustness and disadvantages by visualizing and analyzing their estimated attention maps. We find that both methods are sensitive to features, and simultaneously, they perform badly for counting and multi-object related questions. We believe that the findings and analytical method will help researchers identify crucial challenges on the way to improve their own VQA systems.