Reviewerly/exHarmony huggingface dataset:
Generation of Secure and Reliable Honeywords, Preventing False Detection

Breach in password databases has been a frequent phenomena in the software industry. Often these breaches go undetected for years. Sometimes, even the companies involved are not aware of the breach. Even after they are detected, publicizing such attacks might not always be in the best interest of the companies. This calls for a strong breach detection mechanism. Juels et al. (in ACM-CCS 2013) suggest a method called ‘Honeywords’, for detecting password database breaches. Their idea is to generate multiple fake passwords, called honeywords and store them along with the real password. Any login attempt with honeywords is identified as a compromise of the password database, since legitimate users are not expected to know the honeywords corresponding to their passwords. The key components of their idea are (i) generation of honeywords, (ii) typo-safety measures for preventing false alarms, (iii) alarm policy upon detection, and (iv) testing robustness of the system against various attacks. In this work, we analyze the limitations of existing honeyword generation techniques. We propose a new attack model called ‘Multiple System Intersection attack considering Input’. We show that the ‘Paired Distance Protocol’ proposed by Chakraborty et al., is not secure in this attack model. We also propose new and more practical honeyword generation techniques and call them the ‘evolving-password model’, the ‘user-profile model’, and the ‘append-secret model’. These techniques achieve ‘approximate flatness’, implying that the honeywords generated using these techniques are indistinguishable from passwords with high probability. Our proposed techniques overcome most of the risks and limitations associated with existing techniques. We prove flatness of our ‘evolving-password model’ technique through experimental analysis. We provide a comparison of our proposed models with the existing ones under various attack models to justify our claims.
Cloud architecture for plant phenotyping research

Summary Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.
Omnibus outlier detection in sensor networks using windowed locality sensitive hashing

Wireless Sensor Networks (WSNs) have become an integral part of cutting edge technological paradigms such as the Internet-of-Things (IoT) which incorporates a variety of smart application scenarios. WSNs include tiny sensors (motes), with constrained hardware capabilities and limited power supply that can collaboratively function in an unsupervised manner for a long period of time. Their purpose is to continuously monitor quantities of interest and provide answers to application queries. Sensor data streams are inherently spatiotemporal in nature, both because mote measurements form multidimensional time series and due to the spatial reference on the data based on the realm sensed by a mote. Motes are designed to be inexpensive, and thus sensory hardware is prone to temporary or permanent failures yielding faulty measurements. Such measurements may unpredictably forge a query answer, while truthful but abnormal mote samples may indicate undergoing phenomena. Therefore, outlier detection in sensor networks is of utmost importance. With limited power supply and communication being by far the main culprit in energy drain, outlier detection techniques in WSNs should achieve appropriate balance between reducing communication and providing real-time, continuously updated outlier reports. Prior works employ probabilistic or best effort approaches to accomplish the task, which either unpredictably compromise outlier detection accuracy or fail to explicitly tune the amount of communicated data. In this work, we introduce an omnibus outlier detection solution over spatiotemporally referenced sensor data that is capable of: (a) directly trading communication reduction for outlier detection quality with predictable accuracy guarantees, (b) accommodating both uni- and multi-dimensional outlier definitions, (c) operating under various streaming window models and (d) incorporating a wide variety of similarity measures to judge outliers.
Synthesis of Constraints for Mathematical Programming With One-Class Genetic Programming

Mathematical programming (MP) models are common in optimization of real-world processes. Models are usually built by optimization experts in an iterative manner: an imperfect model is continuously improved until it approximates the reality well-enough and meets all technical requirements (e.g., linearity). To facilitate this task, we propose a genetic one-class constraint synthesis method (GOCCS). Given a set of exemplary states of normal operation of a business process, GOCCS synthesizes constraints in linear programming or nonlinear programming form. The synthesized constraints can be then paired with an arbitrary objective function and supplied to an off-the-shelf solver to find optimal parameters of the process. We assess GOCCS on three families of MP benchmarks and conclude promising results. We also apply it to a real-world process of wine production and optimize that process.
Privacy-Preserving Location-Based Services Query Scheme Against Quantum Attacks

Location-based service (LBS) provides more and more conveniences to people. However, it also brings potential threats of offending users' privacy. How to protect users' privacy in LBS schemes has aroused increasing research interests in recent years. Most of the existing privacy-preserving LBS schemes are based on the hardness of traditional number-theoretic problems such as the integer factorization or the discrete logarithm problems. However, with the development of large scale quantum computers, these traditional problems can be easily solved by Shor's algorithms, hence the security of these LBS schemes is greatly threatened. In this paper, we solve this problem by constructing a privacy-preserving LBS scheme against quantum attacks from an LWE-based key-homomorphic pseudorandom functions (PRF). In our scheme, due to the key-homomorphic property of the PRF, an LBS user only has to compute one PRF value of the target location and the remaining computation is outsourced to a cloud server, which releases the user from heavy computation burden. In addition, by dividing the key encrypting LBS data into two parts and assigning the two parts to the cloud sever and each user respectively, our scheme avoids the threats of key abuse and information leaking of LBS data. Moreover, we use this PRF to realize an authenticated protocol, which protects the communications between the LBS users and the cloud server. We stress that the security of our scheme is based only on the security of the LWE-based key-homomorphic PRF, hence our scheme is the first LBS scheme secure against quantum attacks.
Dynamic Priority-Based Resource Provisioning for Video Transcoding With Heterogeneous QoS

Video transcoding is widely adopted in online video services to transcode videos into multiple representations for dynamic adaptive bitrate streaming. This solution may consume significant resources and incur intolerable processing delays. Meanwhile, different videos have different quality-of-service (QoS) requirements for transcoding. Delay-sensitive videos must be transcoded within a strict deadline, whereas delay-tolerant videos are not required to be transcoded immediately. Some intelligent policies are required for provisioning the right amount of resources in the transcoding system to meet the heterogeneous QoS requirements, especially under dynamic workloads. To this end, we develop a robust dynamic priority-based resource provisioning scheme for video transcoding. We adopt the preemptive resume priority discipline to design a multiple-priority transcoding mechanism. The system performs the transcoding for delay-tolerant videos by utilizing idle resources for improving resource utilization while not affecting the transcoding for delay-sensitive videos. We adopt the model predictive control framework to design an online algorithm for dynamic resource provisioning to accommodate time-varying workloads by predicting future workloads. To seek performance robustness against prediction noise, we improve the performance of our online algorithm via robust design. The experimental results demonstrate that our proposed method can satisfy the heterogeneous QoS requirements while significantly reducing computing resource consumption.
A parallelized disjunctive query based searchable encryption scheme for big data

Searchable Encryption (SE) allows a client to search over large amounts of encrypted data outsourced to the Cloud. Although, this helps to maintain the confidentiality of the outsourced data but achieving privacy is a difficult and resource intensive task. With the increase in the query effectiveness, i.e., by shifting from single keyword SE to multi-keyword SE there is a notable drop in the efficiency. This motivates to make use of the advances in the multi-core architectures and multiple threads where the search can be delegated across different threads to perform search in a parallel fashion. The proposed scheme is based on probabilistic trapdoors that are formed by making use of the property of modular inverses. The use of probabilistic trapdoors helps resist distinguishability attacks. The rigorous security analysis helps us to appreciate the advantage of having a probabilistic trapdoor. Furthermore, to validate the performance of the proposed scheme, it is implemented and deployed onto the British Telecommunication's Public Cloud offering and tested over a real speech corpus. The implementation is also extended to anticipate the performance gain by using the multi-core architecture that helps to maintain the lightweight property of the scheme.
Comparative analysis of integer wavelet transforms in reversible data hiding using threshold based histogram modification

In the secured data communication scenario, image based Reversible Data Hiding (RDH) at transform domain is receiving its due popularity now. In research works so far, evaluation of transforms in RDH is very minor compared to that of image coding. Since both the areas deal primarily with integers, the present study takes an appreciable step forward by investigating the utilization of integer-to-integer wavelet transforms that have reversibility, which helped in developing high capacity and robust RDH algorithms. The proposed investigation tends to bridge gaps in the area of data hiding with the available coding transforms. In this work, integer wavelet transforms like Haar, 5/3, 2/6, 9/7-M, 2/10, 5/11-C, 5/11-A, 6/14, 13/7-T, 13/7-C and 9/7-F are evaluated using a generalized threshold-based histogram shifting technique. This work compares the performance of all integer wavelet transforms and other state of the art techniques with respect to their embedding capacity and image visual quality. This analysis leads to a better understanding of the relationship between the embedding capacity and the stego image quality whenever different wavelets were utilized.
Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.
Service Outsourcing in F2C Architecture with Attribute-based Anonymous Access Control and Bounded Service Number

F2C (fog-to-cloud) enables service providers to rent the low-cost cloud/fog resources to publish their services, and the fog nodes, which are deployed at the edge, can provide short-latency service to users. However, new security threats come along with this new computing paradigm, where the access control and trusted payment are concerned in this work. We propose a privacy-preserving authentication scheme. By integrating k-times anonymous authentication (k-TAA) and attribute-based access control, in our proposed scheme, service providers can autonomously determine a fine-grained access policy and the maximal access times for authorized users. Thus, users who satisfy the access policy can receive benefits of this service for certain number of times without leaking any private information. Our authentication phase has a low latency because it is offloaded to the fog as what the service does. This paper presents a lightweight and trusted billing mechanism using Merkle Hash Tree (MHT), which can detect the cloud's service forgery with high probability, without costing too much of service provider's bandwidth and computation. Rigorous security analysis proves that the proposed scheme is secure against malicious users, fogs, and cloud, and the experimental results show the significant performance advantage on both the delay reduction and service providers' cost saving.
Weakly well-composed cell complexes over nD pictures

In previous work we proposed a combinatorial algorithm to “locally repair” the cubical complex Q(I) that is canonically associated with a given 3D picture I. The algorithm constructs a 3D polyhedral complex P(I) which is homotopy equivalent to Q(I) and whose boundary surface is a 2D manifold. A polyhedral complex satisfying these properties is called well-composed. In the present paper we extend these results to higher dimensions. We prove that for a given n-dimensional picture the obtained cell complex is well-composed in a weaker sense but is still homotopy equivalent to the initial cubical complex.
Experts and likely to be closed discussions in question and answer communities: An analytical overview

How do important members of online Question & Answer communities (who we call experts) behave? And how do they influence the discussions in which they take part? This work reports on an investigation into these questions, which we answer through analyses exploring metrics, machine learning classifiers, and recommendations. We report on several findings: the degree of expertise correlates to behavioral patterns, whereby experts would rarely ask for help, and instead, predominantly provide help to other community members; the inclusion of an expert results in longer discussions. We propose a metric (the weighted sum), which enables us to better quantify the reputations of expert members of the community. We describe the use of four machine learning classifiers for the identification of both expert users and the most significant conversations within these communities. We propose a novel approach for a recommendation system, which utilizes semantic annotations to identify topical experts and to ascertain their respective area of specialism. We foresee the suitability of our expertise-finding methods and findings to support Learning Analytics, and in scenarios where users may apply lessons learnt from our results to improve their status in a community. Our findings can also inform systems for recommending experts and discussions.
Private Anonymous Data Access

We consider a scenario where a server holds a huge database that it wants to make accessible to a large group of clients. After an initial setup phase, clients should be able to read arbitrary locations in the database while maintaining privacy (the server does not learn which locations are being read) and anonymity (the server does not learn which client is performing each read). This should hold even if the server colludes with a subset of the clients. Moreover, the run-time of both the server and the client during each read operation should be low, ideally only poly-logarithmic in the size of the database and the number of clients. We call this notion Private Anonymous Data Access (PANDA). PANDA simultaneously combines aspects of Private Information Retrieval (PIR) and Oblivious RAM (ORAM). PIR has no initial setup, and allows anybody to privately and anonymously access a public database, but the server's run-time is linear in the data size. On the other hand, ORAM achieves poly-logarithmic server run-time, but requires an initial setup after which only a single client with a secret key can access the database. The goal of PANDA is to get the best of both worlds: allow many clients to privately and anonymously access the database as in PIR, while having an efficient server as in ORAM. In this work, we construct bounded-collusion PANDA schemes, where the efficiency scales linearly with a bound on the number of corrupted clients that can collude with the server, but is otherwise poly-logarithmic in the data size and the total number of clients. Our solution relies on standard assumptions, namely the existence of fully homomorphic encryption, and combines techniques from both PIR and ORAM. We also extend PANDA to settings where clients can write to the database.
Robustness of Interdependent Random Geometric Networks

We propose an interdependent random geometric graph (RGG) model for interdependent networks.Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks.We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears.In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist.We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99% confidence intervals for the percolation thresholds by simulation.Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks.
Repetition Estimation

Visual repetition is ubiquitous in our world. It appears in human activity (sports, cooking), animal behavior (a bee’s waggle dance), natural phenomena (leaves in the wind) and in urban environments (flashing lights). Estimating visual repetition from realistic video is challenging as periodic motion is rarely perfectly static and stationary. To better deal with realistic video, we elevate the static and stationary assumptions often made by existing work. Our spatiotemporal filtering approach, established on the theory of periodic motion, effectively handles a wide variety of appearances and requires no learning. Starting from motion in 3D we derive three periodic motion types by decomposition of the motion field into its fundamental components. In addition, three temporal motion continuities emerge from the field’s temporal dynamics. For the 2D perception of 3D motion we consider the viewpoint relative to the motion; what follows are 18 cases of recurrent motion perception. To estimate repetition under all circumstances, our theory implies constructing a mixture of differential motion maps: $$\mathbf {F}$$, $${\varvec{\nabla }}\mathbf {F}$$, $${\varvec{\nabla }}{\varvec{\cdot }} \mathbf {F}$$ and $${\varvec{\nabla }}{\varvec{\times }} \mathbf {F}$$. We temporally convolve the motion maps with wavelet filters to estimate repetitive dynamics. Our method is able to spatially segment repetitive motion directly from the temporal filter responses densely computed over the motion maps. For experimental verification of our claims, we use our novel dataset for repetition estimation, better-reflecting reality with non-static and non-stationary repetitive motion. On the task of repetition counting, we obtain favorable results compared to a deep learning alternative.
Quantum speedup at zero temperature via coherent catalysis

It is known that secondary nonstoquastic drivers may offer speedups or catalysis in some models of adiabatic quantum computation accompanying the more typical transverse field driver. Their combined intent is to raze potential barriers to zero during adiabatic evolution from a false vacuum to a true minimum; first-order phase transitions are softened into second-order transitions. We move beyond mean-field analysis to a fully quantum model of a spin ensemble undergoing adiabatic evolution in which the spins are mapped to a variable mass particle in a continuous one-dimensional potential. We demonstrate that the necessary criterion for enhanced mobility or ``speedup'' across potential barriers is actually a quantum form of the Rayleigh criterion. Quantum catalysis is exhibited in models where previously thought not possible, when barriers cannot be eliminated. For the 3-spin model with a secondary antiferromagnetic driver, catalyzed time complexity scales between linear and quadratic with the number of qubits. As a corollary, we identify a useful resonance criterion for quantum phase transition that differs from the classical one, but converges on it, in the thermodynamic limit.