Reviewerly/exHarmony huggingface dataset:
Bulletproofs+: Shorter Proofs for a Privacy-Enhanced Distributed Ledger

This paper presents a new short zero-knowledge argument for the range proof and arithmetic circuits without a trusted setup.In particular, it can achieve the shortest proof size of the proof system categories without a trusted setup.More specifically, when proving that a committed value is a positive integer less than 64 bits, except for negligible error in the 128-bit security parameter, the proof size is 576 bytes long, which is 85.7% the size of the previous shortest proof due to Bünz et al. (Bulletproofs, IEEE Security and Privacy 2018).Similarly, circuit satisfiability can be proven with less communication overhead.Nevertheless, computational overheads in both proof generation and verification are comparable with those of Bulletproofs.Bulletproofs is established as one of the important privacy-enhancing technologies for a distributed ledger due to its trustless feature and short proof size.In particular, it has been implemented and optimized in various programming languages for practical usage by independent entities since it was proposed.The essence of Bulletproofs is based on the logarithmic inner product argument with no zeroknowledge.This paper revisits Bulletproofs from the viewpoint of the first sublinear zero-knowledge argument for linear algebra due to Groth (CRYPTO 2009) and then propose Bulletproofs+, an improved variety of Bulletproofs.The main component is the zero-knowledge weighted inner product argument (zk-WIP) which enables to reduce both the range proof and the arithmetic circuit proof.It already has zeroknowledge properties, there is no additional information when reducing zk-WIP, and it incurs a minimal transmission cost during the reduction process.Note that zk-WIP has all characteristics of the inner product argument, such as an aggregating range proof and batch verification; thus, Bulletproofs+ is superior to Bulletproofs in all aspects.INDEX TERMS Zero-knowledge proofs, range proofs, arithmetic circuit. I. INTRODUCTIONAdistributed ledger is a database that is consensually shared and synchronized across multiple nodes without a trusted administrator.The blockchain is one type of distributed ledger, where the database consists of linked blocks, called chains, and cryptocurrency, such as Bitcoin [1], is a representative application of the blockchain.The benefit of aThe associate editor coordinating the review of this manuscript and approving it for publication was SK Hafizul Islam .distributed ledger is that it is immutable and that any independent observer can verify its validity without the aid of a trusted third party.The transparency of the natural realization of distributed ledgers often causes a data privacy issue since all information is public.For instance, all transaction details, including the sender, the receiver, and the amount transferred, are public in Bitcoin.Noninteractive zero-knowledge proofs (NIZKs) enable the data owner to generate proof to convince observers of the validity of the data without disclosing it.Range proofs are
Native Monkey Detection Using Deep Convolution Neural Network

We present development and analysis ofKumar, Pankaj deepShingala, Mitalee learning architectures for real life and real-time native Indian monkey (langur) detection. Our monkey detection work is motivated with the idea of developing an application which will assist farmers in protecting their crops from attack of native wild animals. Protecting destruction of crops from wild animals is very important for agrarian societies. We engineered pre-trained VGG19 convolution neural network (CNN) to detect live monkeys in images streamed in an image sequence generated from a USB webcam. We used transfer learning approach to custom train, YOLO, a much faster CNN architecture to detect monkeys. For training and analysis, we used the Google Open Images V4 dataset. We give details of how two significantly different architectures were customized for native monkey detection. The performance of the detectors developed is analysed in terms of time taken for detection and accuracy of detection. Custom trained architecture achieved high true positive rate of 97.19% and true negative rate of 95.32%, which are significantly higher and better than the pre-trained architecture. The developed system was successfully tried out on a 4GB RAM and Intel quadcore i3 laptop at Indroda National park.
How cute do I sound to you?: gender and age effects in the use and evaluation of Korean baby-talk register, Aegyo

This paper examines how segmental strategies of the baby-talk register in Korean, Aegyo, are used and evaluated through usage frequency analysis on Twitter and a cuteness rating experiment. The experimental results show that Korean speakers recognize different expressive strengths of segmental strategies of the Aegyo register and that the phonotactic naturalness of sound sequences as the outputs of segmental Aegyo strategies is a critical factor in evaluating the degree of cuteness of Aegyo expressions. The Aegyo variants involving sequences of an affricate and the following palatal glide that are natural on the phonotactic grounds in Korean got the highest cuteness scores. The pattern of usage frequencies on Twitter shows a positive correlation with the rating results. Since the function of the Aegyo register is to sound cute and young, we examine the gender and age effects in evaluating cuteness of Aegyo variants. The results of rating experiments also show that female and older speakers (the main users of Aegyo) are more sensitive to relative differences in expressive strengths between different segmental compared to male and younger speakers. Considering that use of Aegyo register is to set up a childlike persona of addressers to be taken care of by addressees in an intimate relationship, the gender and age effects could be understood as the motivated attention: female and older speakers have greater caretaking motivations.
Estimation of Matrix Variance-Covariance on Nonparametric Regression Spline Truncated for Longitudinal Data

Abstract Regression analysis is a statistical analysis used to determine the pattern of relationships between predictor variables and response variables. There are two models estimation approaches in regression analysis, namely parametric regression and nonparametric regression. The parametric regression approach is used if the shape of the regression curve is known. In cases with unknown relationship patterns, the development is done using nonparametric regression. Nonparametric regression is a model estimation method which is based on an approach that is not bound by certain assumptions of the regression curve shape. One of the most commonly used nonparametric regression curve estimates is the spline approach. Spline is one of the nonparametric approaches that is often used and has a very good ability to handle data characters that are smooth as well as data whose behavior changes at certain sub-intervals. Theoretically, the spline approach is not only able to estimate nonparametric regression curves for cross section data, but can also be developed for longitudinal data. Longitudinal data is a combination of cross section data and time series data where the longitudinal data between subjects are mutually independent, but between observations are dependent. This study aims to find the estimation curves of spline truncated multivariable nonparametric regression for longitudinal data using the Weighted Least Square (WLS) method and to find the estimation of variance-covariance matrix using the Maximum Likelihood Estimate (MLE) method.
GPU-based fast clustering via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg"><mml:mi>K</mml:mi></mml:math>-Centres and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg"><mml:mi>k</mml:mi></mml:math>-NN mode seeking for geospatial industry applications

The emerging trends in data industry, particularly those related to the repeated processing of data streams, are pushing the limits of computer systems and processes. Among them, the near real-time clustering of geospatial location data is paradigmatic due to its scale, requirements and potential industrial applications. As a solution to deal with the large and continuous arrival of geospatial data, modern many-core (GPU-based) computer architectures are used for implementing fast and efficient clustering algorithms. This paper proposes GPU implementations of two different clustering algorithms — K-Centres and k-NN mode seeking — and compare them against their corresponding sequential implementations. Publicly available geospatial datasets have been used to exemplify the achieved performances using GPUs. Our main contribution is providing GPU implementations of the clustering algorithms that are feasible for near real-time problems. Results show speed-ups of up to 19 and 135 times, with the largest dataset, for K-Centres and k-NN mode seeking respectively. Important technical details of the sorting algorithms, required by the GPU implementation of k-NN mode seeking, are also highlighted.
A Two-step Procedure for Image Lossy Compression by ADCTC With a Desired Quality

Green computing has become a development trend in today's world. Here we consider how this tendency can be realized in image compression and transferring. A two-step method is proposed to better improve power consumption in image lossy compression and further transmission via communication channels. By analyzing the application of a two-step method of providing a desired image quality for Advanced DCT-based Coder (ADCTC), we prove that the two-step method can provide a larger compression ratio (and, thus, a smaller time for image transferring) alongside with good visual quality while simplifying and making faster the compression stage. Accuracy of providing the desired metric values for the two-step method characterized by mean square error has been improved by one-two orders compared to one-step approach. For the metric PSNR-HVS-M, the error for the two-step method does not exceed 1.5 dB.
Improved Whale Optimization Algorithm for Numerical Optimization

In this paper, an Improved Whale Optimization Algorithm which is intended towards the better optimization of the solutions under the category of meta-heuristic algorithms is proposed. Falling under the genre of nature-inspired algorithms, the Improved Whale Optimization delivers better results with comparatively better convergence techniques used. A detailed study and comparative analysis have been made between the principal and the modified algorithms, and a variety of fitness functions has been used to confirm the efficiency of the improved algorithm over the older version. The merits with nature-inspired algorithms include distributed computing, reusable components, network processes, mutations and crossovers leading to better results, randomness and stochasticity.
Motion Planning of Planar Closed Chains Based on Structural Sets

This paper investigates the motion planning problem of planar m-link (m ≥ 4) closed chains among point obstacles with extension to arbitrary convex 2-D obstacles.The configuration space (C-space) of closed chains is embedded into two copies of m-3 dimensional tori.Two structural sets, the C-boundaries and the C-obstacles, are analyzed based upon the C-spaces of recursively constructed lower-dimensional closed chains.They contain essential structural information about the connectivity of the collision-free portion (C-free) of the C-space.By approximating each workspace obstacle by a set of points on the boundary after dilation, its corresponding C-obstacle is guaranteed to be covered by the C-obstacle of the convex hull of the point set.This permits a resolution-complete roadmap algorithm that puts specific bias for sampling the structural sets.Several benchmark examples are presented that compare the performance between our algorithm and the traditional algorithms.Animation videos and source codes are also provided which demonstrate the effectiveness of our method for closed chains of up to 20 links.
SCR-Normalize: A novel trajectory planning method based on explicit quintic polynomial curves

This paper presents a framework for generating explicit quintic polynomial curves as the trajectory of autonomous vehicles. The method is called SCR-Normalize and is founded on two novel ideas. The first concept is to rotate the coordinate reference, regarding the boundary conditions, in order to reach a special coordinate reference called Secondary Coordinate Reference (SCR). In the SCR, the explicit quintic polynomial curve has short length and low curvature values (i.e. the curve is not wiggly). The second concept is to normalize the trajectory in order to speed up the framework. Two kinds of problems are considered to be solved in this paper: (a) generating a length optimal trajectory for arbitrary boundary conditions subject to a curvature constraint; and (b) path smoothing. For case (a), for the lane change manoeuvre, the problem is solved explicitly. In addition, the familiar expression for the optimal interval of the lane change manoeuvre is analytically proved for the first time. For arbitrary swerving manoeuvres, two algorithms are presented and their performance is compared with each other and with two other algorithms. Similarly, for case (b), an algorithm is presented and its performance is compared with three other methods. Evaluating the algorithms in the examples, and comparing the results with other methods illustrates the efficiency of the SCR-Normalize framework to generate optimal trajectories in real time.
Investigating the Relationship of Mental Immersion and Physiological Measures during Cloud Gaming

The ultimate goal of designing game applications is to evoke a state of mental immersion in human users. Recently, advancing and very promising cloud gaming services catch high interest of the research community and industry. Cloud gaming services reduce computational costs of a client by outsourcing the game logic and rendering to a remote server. Consequently, the degree to which a game runs smoothly and enables uninterrupted interaction depends on impairments of the network connection between client and server. Furthermore, the visibility of such impairments may be constrained by properties of the visual display at the client-side. The present paper investigates the impact of common network impairments (bit rate, delay, packet loss) and screen size (small, medium, large) on mental immersion. In addition to traditional subjective assessment using the Immersive Experience Questionnaire (IEQ), also less intrusive, continuous physiological methods are employed (electrocardiography, ECG; electro-dermal activity, EDA). Participants engaged in playing an action platform computer game under different combinations of network impairments and screen sizes. Results revealed a small main effect of screen size on gaming Quality of Experience and real-world dissociation ratings between the small and medium screen size. Effects of network impairments on all IEQ scales were significant and also manifested as increased heart rate variability for packet loss. Besides, positive correlations were found between IEQ scales and heart rate variability. These findings suggest that network impairments influencing gameplay interaction might be of higher importance for immersive experience than varying screen size. Heart rate variability shows promise as a useful ECG measure in future studies on gaming immersion.
Fusion Method of Primary Surveillance Radar Data and IFF systems Data

In this article, based on the Bayesian approach, the Primary Surveillance Radar (PSR) and Identification Friend or Foe (IFF) systems data fusion model is proposed and investigated for the case, where the uncertainty volume of PSR and IFF systems are the same, as well as for the case, where the uncertainty volume of IFF systems significantly exceeds the PSR uncertainty volume and several air object are in the IFF systems uncertainty volume. It is shown that for the second case, the identification process has three phases: the detection and measurement of air object (AO) coordinates, AO selection and AO binding to target designation, which includes fusing the AO coordinates determined by the PSR and IFF systems, followed by identification of the detected AO. Each of the phases can be random in nature and can be described by numerical parameters: the probability of measuring the trait by which it will be selected; the probability of selection, which characterizes the ability of IFF systems to classify AOs as "Friend" and "Foe" according to a measured score; the likelihood of correctly linking the "Friend and Foe" score to target designation.
Optimal sampling in unbiased active learning

A common belief in unbiased active learning is that, in order to capture the most informative instances, the sampling probabilities should be proportional to the uncertainty of the class labels. We argue that this produces suboptimal predictions and present sampling schemes for unbiased pool-based active learning that minimise the actual prediction error, and demonstrate a better predictive performance than competing methods on a number of benchmark datasets. In contrast, both probabilistic and deterministic uncertainty sampling performed worse than simple random sampling on some of the datasets.
Revisiting the Hardness of Binary Error LWE

Binary error LWE is the particular case of the learning with errors (LWE) problem in which errors are chosen in $$\{0,1\}$$ . It has various cryptographic applications, and in particular, has been used to construct efficient encryption schemes for use in constrained devices. Arora and Ge showed that the problem can be solved in polynomial time given a number of samples quadratic in the dimension n. On the other hand, the problem is known to be as hard as standard LWE given only slightly more than n samples. In this paper, we first examine more generally how the hardness of the problem varies with the number of available samples. Under standard heuristics on the Arora–Ge polynomial system, we show that, for any $$\epsilon >0$$ , binary error LWE can be solved in polynomial time $$n^{O(1/\epsilon )}$$ given $$\epsilon \cdot n^{2}$$ samples. Similarly, it can be solved in subexponential time $$2^{\tilde{O}(n^{1-\alpha })}$$ given $$n^{1+\alpha }$$ samples, for $$0<\alpha <1$$ . As a second contribution, we also generalize the binary error LWE to problem the case of a non-uniform error probability, and analyze the hardness of the non-uniform binary error LWE with respect to the error rate and the number of available samples. We show that, for any error rate $$0< p < 1$$ , non-uniform binary error LWE is also as hard as worst-case lattice problems provided that the number of samples is suitably restricted. This is a generalization of Micciancio and Peikert’s hardness proof for uniform binary error LWE. Furthermore, we also discuss attacks on the problem when the number of available samples is linear but significantly larger than n, and show that for sufficiently low error rates, subexponential or even polynomial time attacks are possible.
Different Points of View: Impact of 3D Point Cloud Reduction on QoE of Rendered Images

Modern photogrammetric methods as well as laser measurement systems make it easy to collect large 3D point clouds that sample objects or environments. As the recorded point clouds can be used to render computer-generated images and models, they are of particular interest in the domains of geographical and architectural engineering, as well as for computer graphics (e.g., games or virtual reality). However, point clouds have a huge storage demand, thus, point clouds shall be reduced by removing some of the points. This will inevitably also reduce the Quality of Experience (QoE) of media, which is rendered from the reduced point clouds. In this work, the impact of two different reduction methods on the QoE of rendered images is investigated from two point of views, i.e., based on ratings from both naive crowdworkers as well as point cloud experts.
Improving Energy Efficiency and Minimizing Service-Level Agreement Violation in Mobile Cloud Computing Environment

In mobile cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, which requiring large-scale cloud data centers. The mobile cloud computing enables customers to outsource the large and complex tasks to the cloud data centers for the execution and result generations. To provide resources such as CPU, RAM, memory, and bandwidth cloud data centers host incoming tasks, due to the large data centers that provide the basic resources for the host operations, and it consumes a huge amount of energy, which results in very high operational costs. In order to improve the energy efficiency and SLA violation of a cloud data center, energy-aware methods for overloaded host detection and VM selection from an overloaded host are needed. Cloud computing research community felt the need to provide or improve the energy-efficient solutions that reduce the impact of the issues. In this paper, we have shown many existing algorithm for energy efficiency and SLA violation reduction.
Coordinated Air-Ground Vehicle Routing with Timing Constraints

We consider a coordinated routing problem involving an aerial and a ground vehicle for surveillance and reconnaissance mission. We are given a set of targets from which data has to be collected using a capacity-constrained aerial vehicle i.e., the aerial vehicle can hold data collected from at most a pre-specified number of targets. The aerial vehicle has to periodically upload the collected data to the ground vehicle by performing a rendezvous with the ground vehicle. This rendezvous can serve other additional purposes like recharging etc. The ground vehicle is assumed to travel with a nominal speed and the aerial vehicle is assumed to have a nominal speed range. This paper aims to compute a feasible path for the aerial vehicle that visits all the targets, identify rendezvous points for the two vehicles, compute a path for the ground vehicle through the rendezvous points so that the total travel cost of both the vehicles is a minimum. A novel technique to enforce constraints that model a feasible rendezvous for both the vehicles using their respective speed ranges is proposed. A variable neighborhood search algorithm is developed to compute near optimal paths for both the vehicles that satisfy these constraints. Finally, computational experiments that corroborate the effectiveness of the proposed algorithm are also presented.
Automated Scoring of Clinical Expressive Language Evaluation Tasks

Many clinical assessment instruments used to diagnose language impairments in children include a task in which the subject must formulate a sentence to describe an image using a specific target word.Because producing sentences in this way requires the speaker to integrate syntactic and semantic knowledge in a complex manner, responses are typically evaluated on several different dimensions of appropriateness yielding a single composite score for each response.In this paper, we present a dataset consisting of non-clinically elicited responses for three related sentence formulation tasks, and we propose an approach for automatically evaluating their appropriateness.Using neural machine translation, we generate correct-incorrect sentence pairs to serve as synthetic data in order to increase the amount and diversity of training data for our scoring model.Our scoring model uses transfer learning to facilitate automatic sentence appropriateness evaluation.We further compare custom word embeddings with pre-trained contextualized embeddings serving as features for our scoring model.We find that transfer learning improves scoring accuracy, particularly when using pre-trained contextualized embeddings.