Reviewerly/exHarmony huggingface dataset:
Universally Composable Secure Computation with Corrupted Tokens

We introduce the corrupted token model. This model generalizes the tamper-proof token model proposed by Katz (EUROCRYPT '07) relaxing the trust assumption on the honest behavior of tokens. Our model is motivated by the real-world practice of outsourcing hardware production to possibly corrupted manufacturers. We capture the malicious behavior of token manufacturers by allowing the adversary to corrupt the tokens of honest players at the time of their creation. We show that under minimal complexity assumptions, i.e., the existence of one-way functions, it is possible to UC-securely realize (a variant of) the tamper-proof token functionality of Katz in the corrupted token model with n stateless tokens assuming that the adversary corrupts at most $$n-1$$ of them (for any $$n>0$$ ). We apply this result to existing multi-party protocols in Katz's model to achieve UC-secure MPC in the corrupted token model assuming only the existence of one-way functions. Finally, we show how to obtain the above results using tokens of small size that take only short inputs. The technique in this result can also be used to improve the assumption of UC-secure hardware obfuscation recently proposed by Nayak et al. (NDSS '17). While their construction requires the existence of collision-resistant hash functions, we can obtain the same result from only one-way functions. Moreover using our main result we can improve the trust assumption on the tokens as well.
Sluice

short-paper Share on Sluice: Network-Wide Data Plane Programming Authors: Vikas Natesh New York University New York UniversityView Profile , Pravein Govindan Kannan National University of Singapore National University of SingaporeView Profile , Anirudh Sivaraman New York University New York UniversityView Profile , Ravi Netravali University of California, Los Angeles University of California, Los AngelesView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 156–158https://doi.org/10.1145/3342280.3342343Published:19 August 2019Publication History 2citation256DownloadsMetricsTotal Citations2Total Downloads256Last 12 Months29Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Hash‐balanced binary tree–based public auditing in vehicular edge computing and networks

Summary In recent years, cloud storage technology is developed maturely, so data owners could deposit data to relieve the storage pressure. In vehicular communication networks, vehicles tend to use low‐latency networks to transfer the valid road information and vehicle information generated by smart sensors in real time. Vehicular edge computing (VEC) networks satisfy the low‐latency requirements of the vehicular communication networks. However, vehicles have no space to store a copy of the data locally like using a local storage service. Vehicles, as users of cloud storage, pay attention to the integrity of the data especially. Unfortunately, many existing auditing schemes for integrity are not fully applicable to the VEC paradigm, because of significant differences in security assumptions. In this paper, a dynamic public audit protocol is proposed to the VEC paradigm. First, a new threat model is defined to formalize noncollusive and collusive attacks according to VEC servers and cloud server providers. Second, we propose a novel auditing protocol based on hash‐balanced binary tree (HBBT) and describe the auditing process and dynamic operations. Third, security analysis and experimental analysis are completed to demonstrate the safety and effectiveness of the proposed scheme.
Facial Biometric Template Post-processing by Factorization

Facial recognition systems have reached a relatively mature stage nowadays. In a typical facial recognition system, an input facial image is converted into a biometric template that is matched against stored templates in the database to have a final decision. This paper addresses a post-processing step applied to the biometric template in order to improve the overall system's recognition accuracy. The biometric template is built upon the well known Local Binary Pattern (LBP) method that is considered one of the state-of-the-art approaches for feature extraction in the field. As novel step, we have included a postprocessing module the template that decomposes the template into factors via Non-negative Matrix Factorization (NMF). By so doing we keep only the discriminative information while disregarding irrelevant information. By projecting the initial template into the NMF factors, we showed that the accuracy is increased compared to the case where this step is skipped. The method is applied to a challenging facial database, namely, the extended Yale Face Database B, faces acquired under difficult illumination conditions.
Corpus Assembly as Text Data Integration from Digital Libraries and the Web

We here explore a new corpus construction workflow which exploits the inherent potential of the growing number of Digital Libraries worldwide and the ever-expanding Internet Archive. Rather than building corpora from scratch (which typically consumes a huge amount of resources), we search the Web for fragments of relevant digitized contents scattered across the world, check their digitization quality, select those digital versions with highest quality, and finally assemble from those pieces an integrated corpus with a maximum coverage of the targeted resource. As a use case within the framework of Digital Humanities, we illustrate this approach for the Allgemeine Literatur-Zeitung (General Literature Gazette, ALZ) published from 1785 to 1849, which is considered as one of the most important text collections from the Romantic Age in Germany. With lots of incomplete and overlapping fragments physically scattered over many Web sites, we started to assemble these fragments, to bind these pieces together using a homogeneous format, and thus constructed the first (almost) complete corpus of ALZ, now accessible (in XML format obeying to TEI standards) as a whole for in-depth scientific investigations.
A Component-Based Approach to Feature Modelling

This paper presents an approach to feature modelling based on the use of modelling constructs from the component-based software development domain. The proposed models allow establishing feature hierarchies, making a clear distinction between the features themselves and their realisations or variants. Furthermore, they enable the definition of complex dependency relationships between the different feature realisations, making it possible to define variable configurations associated with these dependencies. Finally, the approach allows the modelling of product configurations as a set of interconnected and configured feature realisations. The proposal is illustrated with an example based on the on-board satellite software applications domain.
An RNN-based Learnable Extended Kalman Filter Design and Application

The paper investigates the trajectory tracking problem of a flight vehicle performing complex maneuvers. A learnable Extended Kalman Filtering (L-EKF) method is proposed. First, two recurrent neural networks, named Input Modification Network and Gain Modification Network, are designed to identify the model inaccuracy and compensate for the estimation error of the EKF. Then, the L-EKF algorithm is proposed by embedding the two networks into the EKF algorithm. Then, the proposed L-EKF method is applied to a trajectory tracking problem of a gliding vehicle with complex maneuvers. The simulation results show that the proposed method has a higher estimation accuracy and better dynamic performance than the EKF and Adaptive EKF method.
Color Marker Detection with WebGL for Mobile Augmented Reality Systems

This paper presents a real-time tracking method based on a color marker detection. The goal is to develop an algorithm that can find the marker in the input image with preference to speed, precision and flexibility. The paper firstly presents related works in the field of marker detection and their advantages and limitations are summarized. The main advantage of the new proposed solution is that it can be easily parallelized and therefore be implemented on GPU which offers a massive performance for this kind of problems. The designed solution is implemented in WebGL as a two-step algorithm which outputs the image coordinates of the center of the detected marker. Using WebGL technology allows the algorithm to be easily used on all platforms including mobile phones. Testing proves that this implementation of the proposed solution is robust and fast enough for real-time video processing, for example in augmented reality applications. There are also discussed possible improvements in the paper.
RT-ADI: Fast Real-Time Video Representation for Multi-view Human Fall Detection

Falling accidents are potential risks that significantly threaten the elders' health, which leads to severe injuries, such as hip fractures, broken bones or head injury. Nowadays, modern surveillance technologies have been adopted to prevent elders from falling. In this paper, we propose a multi-view human fall detection system with a fast video representation method called RT-ADI to extract video temporal features with low time consumption. We also design a multi-branch convolutional neural network to merge computed dynamic images from multiple cameras installed. Furthermore, we have evaluated our approach on Multiple camera fall dataset. Our system exhibits low time consumption and high accuracy with almost no miss-classification.
Fast Adaptive Decision-Based Mean Filter for Removing Salt-and-Pepper Noise in Images

This paper presents a very simple and extremely fast approach to remove salt-and-pepper noise from digital images. Initially, the pixels are roughly divided into two classes, noisy pixels and non-noisy pixels based on the intensity values. Then, the second stage is to eliminate the noise from the images. In this stage, only the noise pixels are processed and the noise-free pixels remain unchanged. This method takes four non-noisy pixels in four directions around noisy pixel and predicts the intensity value of the noisy candidate. This proposed method is suitable to be implemented in consumer electronics products such as digital televisions or digital cameras. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality.
Querying Knowledge Graphs with Natural Languages

With the unprecedented proliferation of knowledge graphs, how to process query evaluation over them becomes increasingly important. On knowledge graphs, queries are typically evaluated with graph pattern matching, i.e., given a pattern query Q and a knowledge graph G, it is to find the set M(Q, G) of matches of Q in G, where matching is defined with subgraph isomorphism. However querying big knowledge graphs brings us challenges: (1) queries are often issued with natural languages, hence can not be evaluated directly; (2) query evaluation is very costly and match results are often difficult to inspect. In light of these, this paper studies the problem of querying knowledge graphs with natural languages. (1) We extend pattern queries by designating a node $$u_o$$ as "query focus", and revise the matching semantic based on the extension. (2) We develop techniques to understand natural language queries, and generate pattern queries with "query focus". (3) We develop efficient techniques to identify top-k matches of "query focus". (4) We experimentally verify that our techniques for query understanding perform well, and our query algorithm is able to find diversified top-k matches efficiently.
Analysis and Simulation of Extracting Radar Modulation Signal Based on CIC Filter

Aimed at the difficulty of extracting weak information such as projectile movement and precession during flight., this paper analyzes the characteristics of extracting the periodic motion information of the projectile from the original data, and designs the resampling and filtering methods based on the characteristics and measured data of a certain type of continuous wave radar system. This method achieves the purpose of non-destructive extraction of low frequency weak signals.
Black Hole and k-Means Hybrid Clustering Algorithm

In recent advancements, to tackle with NP problems, many heuristic approaches have been developed. A lot of meta-heuristic methods are inspired from nature. In this paper, Black Hole optimization approach for data clustering has been hybridized with k-Means to get improved results. As compared to Black Hole optimization approach for data clustering, where the initial population is initialized randomly, in the proposed method, a part of the population is initialized with some of the better results from k-Means clustering algorithm and rest of the population is initialized randomly.
Perception of Motion-Adaptive Color Images Displayed by a High-Speed DMD Projector

Recent progress of high-speed projectors using DMD (Digital Micromirror Device) has enabled low-latency motion adaptability of displayed images, which is a key challenge in achieving projection-based dynamic interaction systems. This paper presents evaluation of different approaches in achieving fast motion adaptability with DMD projectors through a subjective image evaluation experiment and a discrimination experiment. The results suggest that the approach proposed by the authors, which updates the image position for every binary frame instead of for every video frame, applied to 60-fps video input offers perceptual image quality comparable with the quality offered by 500-fps projection.
Multi-Objective Evolutionary Metric Learning for Image Retrieval Using Convolutional Neural Network Features

For an image retrieval system, the metric to measure the similarity between target images and queries greatly affects its retrieval performance. However, most of the existing metrics are based on single distance metric, which has been shown lack of robustness for different kinds of queries. In this work, we view the metric learning task as an optimization problem for a robust combination of existing metrics, where the objective function is data-driven rather than analytical. Our contribution is two-fold. Firstly, considering the robustness of image retrieval systems, we formulate the optimization problem as a multiobjective optimization with both the average and worst retrieval performance (precision) for different kinds of queries as objectives. Secondly, we apply a popular multi-objective evolutionary algorithm, NSGA-II, to search the optimal combined metric. With the experiment on two different datasets for image retrieval, we find that the proposed algorithm can find combined metrics with better and more robust retrieval performance than other existing single metrics.
A Non-Deterministic Method to Construct Ensemble-Based Classifiers to Protect Decision Support Systems Against Adversarial Images

In recent years, Deep Learning has presented impressive performance when solving complex image classification and recognition tasks in decision support systems. Nonetheless, studies have demonstrated Deep Learning models are susceptible to attacks conducted with adversarial images, i.e. images containing subtle perturbations in order to induce models to misclassification. The main existing countermeasures against adversarial images have shown ineficiency, permitting attackers to map their internal operation more easily. Therefore, this work aims to evaluate a defense method called MultiMagNet which randomly incorporates at runtime multiple defense components, implemented as autoencoders, in order to introduce an expanded form of non-determinism behavior for hindering evasions of adversarial nature. Experiments on CIFAR-10 dataset showed MultiMagNet was able to detect images generated by different attack algorithms.
Augmenting Action Model Learning by Non-Geometric Features

Learning from demonstration is a powerful tool for teaching manipulation actions to a robot. It is, however, an unsolved problem how to consider knowledge about the world and action-induced reactions such as forces imposed onto the gripper or measured liquid levels during pouring without explicit and case dependent programming. In this paper, we present a novel approach to include such knowledge directly in form of measured features. To this end, we use action demonstrations together with external features to learn a motion encoded by a dynamic system in a Gaussian Mixture Model (GMM) representation. Accordingly, during action imitation, the system is able to couple the geometric trajectory of the motion to measured features in the scene. We demonstrate the feasibility of our approach with a broad range of external features in real-world robot experiments including a drinking, a handover and a pouring task.
Fantasy Gaming and Virtual Heritage

Virtual worlds are increasingly being developed to provide a navigable space for museum collections. The Virtual Sydney Rocks (VSR) [3] is one such virtual world that has been constructed as an authentic representation of the area of first European settlement in Australia 230 years ago - from a time just before its settlement until the present day. We constructed two versions of a game for children in order to motivate interaction with, and learning of, the historical content of the VSR. One of these game versions contained a number of fantasy design elements with the idea that their inclusion would motivate children to engage with VSR content in a pleasurable way. A preliminary study with a small number of school-aged children was not able to show that the inclusion of fantasy elements affected either the comprehension of historical facts or the quality of moral judgements made by the children. This study did, however, provide some evidence that interactivity with the relevant parts of the virtual world may have affected the retention of historical facts.
An enhanced path planning method for unmanned surface vehicle based on JPS+ and goalbounding algorithm

Abstract The path planning issue in complex environments remains an challenging problem in the field of unmanned surface vehicle (USV) navigation due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard). Aiming at generating a safe path for the USV within tractable time, this paper proposes an enhanced path planning method via integrating JPS+ algorithm and the goalbounding algorithm. Firstly, principles of the JPS+ and goalbounding algorithms are respectively analysed and investigated in this study. Subsequently, the application platform of the proposed path planning approach for USV is established via combining the YimaEnc electronic chart with WinForm dialog. Finally, the performance of the proposed method is validated through some numerical simulations against some other well-known path planning methods. The simulation results confirm that the proposed method performs superior to the artificial field method and A* algorithm in terms of the computation time and the path safety. Besides, the superiority of the proposed method over the artificial field method and A* algorithm is more significant in the long-distance path planning scenarios as far as the computation time is concerned.
An intrinsic evaluation of the Waterloo spam rankings of the ClueWeb09 and ClueWeb12 datasets

The ClueWeb09 dataset and its successor, the ClueWeb12 dataset, are two of the largest collections of Web pages released by Text REtrieval Conference (TREC). The ClueWeb datasets were used in various tracks of TREC ran through 2009 to 2017. For every year, approximately 50 new queries are released and a pool of Web pages are judged against these queries by human assessors as relevant, non-relevant or spam. In this article, a ground truth for binary classification (spam vs non-spam) is constructed from Web pages that are judged as spam or relevant under the assumption that a Web page judged as relevant for any query cannot be spam. Based on this ground truth, we evaluate classification performances of the Waterloo spam rankings (Fusion, Britney, GroupX and UK2006), which have been traditionally used to identify and filter spam pages in retrieval systems. The experimental results in terms of the universal binary classification evaluation measures suggest that the Fusion (with threshold = 11%) is the best for the ClueWeb09 dataset. Analysis of the frequency distributions of relevant/spam documents over spam scores reveals that the GroupX is the most powerful at identifying relevant documents, whereas the Fusion is the most powerful at identifying spam documents. It is also confirmed that the effectiveness of the Fusion spam ranking of the ClueWeb12 dataset is not as good as that of the ClueWeb09.
Human Identification Recognition in Surveillance Videos

Although face recognition has achieved good performance in attendance marking system, people need to cooperate with the camera. Human identification recognition performs poorly in surveillance videos because of the deformed and occluded conditions. In this paper, we propose a new method to recognize human identification using global and local structural information. Firstly, we combine pedestrian detection and tracking with face recognition, in order to improve the recognition performance of occlusion and deformed faces. Secondly, we propose a selective recognition algorithm based on pedestrian trajectory, which is used to identify the pedestrian walking towards the webcam. Finally, we conduct experiments on our own human identification dataset, which contains 93 challenging video sequences captured in the corridor. Our method achieves better performance than methods only using face information.
Benchmark-Based Cost Analysis of Auto Scaling Web Applications in the Cloud

Applications executed in the cloud can exploit its elasticity features, varying dynamically the amount of leased resources so as to adapt to load variations and to guarantee good quality of service. As auto scaling has severe implications on execution costs, making optimal scaling choices is of paramount importance. This paper presents an analysis method based on off-line benchmarking that allows to define scaling policies to be used by auto-scalers. The indexes obtained by benchmarking multiple deployment configurations can be used on-line, to scale the application making a trade-off between cost and user-perceived performance.
On selected issues of Boolean function application in symmetric key cryptography against side channel attacks

Abstract Boolean function plays a prominent role in building strong cryptography algorithm especially in symmetric block cipher. However, there may be some issues and conflicting criteria which prevent such a Boolean function to resist the algorithm to all known attacks. Within this work, the nonlinearity and correlation immunity issues of Boolean function is discussed followed by complementary counter measure against side channel attack especially differential power attack (DPA)