Reviewerly/exHarmony huggingface dataset:
Connecting the Dots: Privacy Leakage via Write-Access Patterns to the Main Memory

Data-dependent access patterns of an application to an untrusted storage system are notorious for leaking sensitive information about the user's data. Previous research has shown how an adversary capable of monitoring both read and write requests issued to the memory can correlate them with the application to learn its sensitive data. However, information leakage through only the write access patterns is less obvious and not well studied in the current literature. In this work, we demonstrate an actual attack on power-side-channel resistant Montgomery's ladder based modular exponentiation algorithm commonly used in public key cryptography. We infer the complete 512-bit secret exponent in ~ 3.5 minutes by virtue of just the write access patterns of the algorithm to the main memory. In order to learn the victim algorithm's write access patterns under realistic settings, we exploit a compromised DMA device to take frequent snapshots of the application's address space, and then run a simple differential analysis on these snapshots to find the write access sequence. The attack has been shown on an Intel Core(TM) i7-4790 3.60GHz processor based system. We further discuss a possible attack on McEliece public-key cryptosystem that also exploits the write-access patterns to learn the secret key.
Iterative Collaborative Filtering for Sparse Matrix Estimation

Matrix estimation or completion has served as a canonical mathematical model for recommendation systems. More recently, it has emerged as a fundamental building block for data analysis as a first step to denoise the observations and predict missing values. Since the dawn of e-commerce, similarity-based collaborative filtering has been used as a heuristic for matrix etimation. At its core, it encodes typical human behavior: you ask your friends to recommend what you may like or dislike. Algorithmically, friends are similar “rows” or “columns” of the underlying matrix. The traditional heuristic for computing similarities between rows has costly requirements on the density of observed entries. In “Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Jennifer T. Chayes, Devavrat Shah, and Christina Lee Yu, the authors introduce an algorithm that computes similarities in sparse datasets by comparing expanded local neighborhoods in the associated data graph: in effect, you ask friends of your friends to recommend what you may like or dislike. This work provides bounds on the max entry-wise error of their estimate for low rank and approximately low rank matrices, which is stronger than the aggregate mean squared error bounds found in classical works. The algorithm is also interpretable, scalable, and amenable to distributed implementation.
Statistical Inference for the Population Landscape via Moment-Adjusted Stochastic Gradients

Summary Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint informs us only how well the solution is approximated numerically but overlooks the sampling nature of the data. In contrast, recognizing the randomness in the data, statisticians are keen to provide uncertainty quantification, or confidence, for the solution obtained by using iterative optimization methods. The paper makes progress along this direction by introducing moment-adjusted stochastic gradient descent: a new stochastic optimization method for statistical inference. We establish non-asymptotic theory that characterizes the statistical distribution for certain iterative methods with optimization guarantees. On the statistical front, the theory allows for model misspecification, with very mild conditions on the data. For optimization, the theory is flexible for both convex and non-convex cases. Remarkably, the moment adjusting idea motivated from ‘error standardization’ in statistics achieves a similar effect to acceleration in first-order optimization methods that are used to fit generalized linear models. We also demonstrate this acceleration effect in the non-convex setting through numerical experiments.
A Subspace Learning Approach to Multishot Person Reidentification

This paper addresses the challenging problem of multishot person reidentification (Re-ID) in real world uncontrolled surveillance systems. A key issue is how to effectively represent and process the multiple data with various appearance information due to the variations of pose, occlusions, and viewpoints. To this end, this paper develops a novel subspace learning approach, which pursues regularized low-rank and sparse representation for multishot person Re-ID. For the images of a person crossing a certain camera, we assume that the appearances of those subset images with similar viewpoints against a camera draw from the same low-rank subspace, and all the images of a person under a camera lie on a union of low-rank subspaces. Based on this assumption, we propose to learn a nonnegative low-rank and sparse graph to represent the person images. Moreover, the recurring pattern prior is integrated into our model to refine the affinities among images. Extensive experiments on four public benchmark datasets yield impressive performance by improving 22.9% on imagery library for intelligent detection systems video re identification (iLIDS-VID), 42.4% on person RE-ID (PRID) dataset 2011, 39.7% and 30.6% on speech, audio, image, and video technology-SoftBio camera 3/8 and camera 5/8, respectively, and 1.6% on motion analysis and re identification set compared to the state-of-the-art methods.
Determining the enabling factors for implementing cloud data governance in the Saudi public sector by structural equation modelling

Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Data management solutions on their own are becoming very expensive and unable to cope with the reality of everlasting data complexity. Forward-thinking organisations believe that the only way to solve the data problem will be the implementation of effective data governance. Attempts to govern data failed before, as they were driven by information technology (IT), and affected by rigid processes and fragmented activities carried out on a system-by-system basis. Until very recently, governance remained mostly informal, with very ambiguous and generic regulations in silos around specific enterprise repositories, lacking structure and the wider support of the organisation. Despite its highly recognised importance, the area of data governance is still underdeveloped and under-researched. In the cloud computing context, the cloud brings new issues of data governance, where the loss of governance is one of the top risks of cloud computing. Thus, before adopting the cloud, the organisations should develop a cloud data governance programme. It is important, therefore, to understand the enabling factors for successful implementation of cloud data governance in organisations. However, as every organisation has its own constraints and requirements, the phrase ‘no one size fits all’ applies in this case. This study focuses on the case of the public sector in the Kingdom of Saudi Arabia. Therefore, the aim of this research is to identify the enabling factors in adopting and implementing cloud data governance in the Saudi public sector. The study’s sample covered the largest and most important Saudi public sector organisations, with questionnaires distributed to relevant employees. The results of the study were based on 206 respondents, and structural equation modelling (SEM) was used to evaluate these results.
Smart contracts software metrics: A first study

Smart contracts (SC) are software programs that reside and run over a blockchain. The code can be written in different languages with the common purpose of implementing various kinds of transactions onto the hosting blockchain. They are ruled by the blockchain infrastructure with the intent to automatically implement the typical conditions of traditional contracts. Programs must satisfy context-dependent constraints which are quite different from traditional software code. In particular, since the bytecode is uploaded in the hosting blockchain, the size, computational resources, interaction between different parts of the program are all limited. This is true even if the specific programming languages implement more or less the same constructs as that of traditional languages: there is not the same freedom as in normal software development. The working hypothesis used in this article is that Smart Contract specific constraints should be captured by specific software metrics (that may differ from traditional software metrics). We tested this hypothesis on 85K Smart Contracts written in Solidity and uploaded on the Ethereum blockchain. We analyzed Smart Contracts from two repositories "Etherscan" and "Smart Corpus" and we computed the statistics of a set of software metrics related to Smart Contracts and compared them to the metrics extracted from more traditional software projects. Our results show that generally, Smart Contract metrics have more restricted ranges than the corresponding metrics in traditional software systems. Some of the stylized facts, like power law in the tail of the distribution of some metrics, are only approximate but the lines of code follow a log-normal distribution which reminds us of the same behaviour already found in traditional software systems.
DNN Feature Map Compression Using Learned Representation over GF(2)

In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architectures derived from modified SqueezeNet and MobileNetV2 to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.
A forward secure signcryption scheme with ciphertext authentication for e-payment systems using conic curve cryptography

Signcryption is an authenticated encryption technique that concurrently establishes message confidentiality, authenticity, integrity and non-repudiation. In this paper, we propose an efficient signcryption scheme, based on the hardness of RSA assumption and discrete logarithm problem on conic curves over a ring Zn. The protocol ensures forward secrecy, in case the sender’s secret keys are exposed and supports ciphertext authentication by an external entity, without full decryption. The protocol remains secure, as long as, either one of the hardness assumptions hold. The scheme is implemented over conic curves, which facilitates effective message encoding and decoding, as well as, efficient point operations and inverses. Conic-based RSA assumption offers resistance to low public key and low private key exponent attacks, prevalent in the original RSA cryptosystem. The proposed protocol is used to design a Business to Customer (B2C) e-commerce system, with security against replay attacks, man-in-the-middle attacks, impersonation attacks, server spoofing and double spending. The protocol is validated using automated cryptographic verification tool ProVerif.
Detection of local bonding failure damage by Digital Image Correlation technique

Detectability of bonding failure damage for GFRP(Glass-Fiber Reinforced Plastic) composite under in-plane and out-of-plane loads was investigated through the DIC(Digital Image Correlation) strain and deflection measurement. The damages were introduced to the tensile specimen and trailing edge of 100 kW wind turbine blade which were subject to in-plane and out-of-plane load, respectively. DIC measurements were performed over the tensile specimen surface and the blade surface, including the damage, respectively, with a 3D DIC system(ARAMIS 5M). Longitudinal and transverse strain profiles for the in-plane tensile specimen were determined, and the deflection profile and deflection, longitudinal and chordwise strain profile for the wind turbine blade were obtained. From these measurements, it was found that the dissimilarities, like local concentration and gradient, in the directional strain profiles were corresponded to the presence of the damage. Therefore, the bonding failure damages in the tensile specimen and wind turbine blade could be detected from the deflection and directional strain profile. Besides, in this study, sensitivity of these profiles to the applied loads was examined to determine an optimal load level for DIC measurement.
Learning How Design Choices Impact Gameplay Behavior

Designers structure a game to provide a desired range of player behaviors: the play space of a game. Any given game is one instance from a design space of alternatives. Navigating a design space to achieve a designer's goals requires knowledge of how design choices shape the play space in a game. We present algorithms to automatically measure play patterns using statistical models that predict how design choices alter player behavior. We present Monte Carlo tree search as a way to sample behaviors from a play space, action metrics to automate play space measurement, and predictive modeling techniques to model design spaces. We demonstrate these techniques in two simplified, perfect information, adversarial game domains based on Scrabble and Hearthstone showing their use for automated design space modeling.
A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture.
Optimized Contract-Based Model for Resource Allocation in Federated Geo-Distributed Clouds

In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go model continues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources towards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies in local resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model.
FingerAuth: 3D magnetic finger motion pattern based implicit authentication for mobile devices

Smart devices, as the most widely used platforms for the mobile cyber–physical system (CPS) applications, such as smart home and health care systems, are becoming the prime targets of various attackers for users' considerable private and confidential data in them. To fight against side channel attacks aiming to obtain credentials, e.g., passwords, during the process of user authentication, touch pattern based implicit authentication has been proposed. However, such a defensive technique fails to obtain an entire pattern of user operation by deriving user operation data via a touch-enabled screen. Considering that user operations, including on-screen and in-air finger movements, are performed in three-dimensional (3D) space, we propose a novel 3D magnetic finger motion pattern based implicit authentication technique, referred to as FingerAuth. To use FingerAuth, a user operates on her mobile device, e.g., texting a message and browsing websites, with a magnetic ring on the finger she uses. With the help of a built-in three-axis magnetometer on the mobile device, we can derive the 3D magnetic finger motion pattern as a human behavioral feature for implicitly authenticating the user. By using machine learning techniques, a robust 3D magnetic finger motion pattern detection model can be constructed. Two rounds of usability tests are conducted for the evaluation of FingerAuth. In the initial usability test targeting a given group of smart device users, we test the uniqueness of the proposed trait in typing scenario, achieving high average accuracy of 96.38%, low average false acceptance rate (FAR) of 4.06%, and false rejection rate (FRR) of 3.18%. In the second user usability test, we further evaluate the permanence of 3D finger motion pattern in multiple user–device interaction scenarios. There is an interim of two-week period between the training data collection phase and the testing data collection phase. The results of the high accuracy of over 80%, as well as the FAR and FRR of below 15%, indicate the applicability of FingerAuth.
Privacy Protection in Interactive Content Based Image Retrieval

Privacy protection in Content Based Image Retrieval (CBIR) is a new research topic in cyber security and privacy. The state-of-art CBIR systems usually adopt interactive mechanism, namely relevance feedback, to enhance the retrieval precision. How to protect the user's privacy in such Relevance Feedback based CBIR (RF-CBIR) is a challenge problem. In this paper, we investigate this problem and propose a new Private Relevance Feedback CBIR (PRF-CBIR) scheme. PRF-CBIR can leverage the performance gain of relevance feedback and preserve the user's search intention at the same time. The new PRF-CBIR consists of three stages: 1) private query; 2) private feedback; 3) local retrieval. Private query performs the initial query with a privacy controllable feature vector; private feedback constructs the feedback image set by introducing confusing classes following the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -anonymity principle; local retrieval finally re-ranks the images in the user side. Privacy analysis shows that PRF-CBIR fulfills the privacy requirements. The experiments carried out on the real-world image collection confirm the effectiveness of the proposed PRF-CBIR scheme.
T-generable indistinguishability operators and their use for feature selection and classification

T-generable indistinguishability operators are operators E that can be expressed in the form E=T(Eμ1,Eμ2,...,Eμm), where T is a t-norm and Eμ is the fuzzy relation generated by the fuzzy subset μ. In this paper we analyse their relation with powers with respect to the t-norm T and with quasi-arithmetic means. For non-strict continuous Archimedean t-norms they are completely characterised as generable by crisp equivalence relations. These fuzzy relations are used to define a method, called JADE, useful for feature selection and classification tasks. JADE is based on minimising the distance between two indistinguishability measures: the one given by weighting the attribute-values describing the domain objects and the other one given by the correct classification taken as an equivalence relation. The preliminary experiments we carried out with JADE are promising concerning the accuracy in solving classification tasks. We also report some issues of the method that could be improved in the future.
Filters for graph-based keyword spotting in historical handwritten documents

The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions. Keyword Spotting (KWS), that allows to retrieve arbitrary query words from documents, has been proposed as alternative. In the present paper, we make use of graphs for representing word images. The actual keyword spotting is thus based on matching a query graph with all documents graphs. However, even with relative fast approximation algorithms the shear amount of matchings might limit the practical application of this approach. For this reason we present two novel filters with linear time complexity that allow to substantially reduce the number of graph matchings actually required. In particular, these filters estimate a graph dissimilarity between a query graph and all document graphs based on their node and edge distribution in a polar coordinate system. Eventually, all graphs from the document with distributions that differ to heavily from the query’s node/edge distribution are eliminated. In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected.
Sustainable Computing Based Deep Learning Framework for Writing Research Manuscripts

Writing research manuscripts is always a tough task at the eleventh hour. Often researchers do not find time to rewrite the manuscript to satisfaction, which is not quantifiable though. This paper proposes a sustainable computing based deep learning framework for iterated accumulation of ideas while writing research manuscripts. The framework suggests Deep Author Topic Models (DATM) where every author of the manuscript is modeled. For this, we have assumed time based sustainable computing as a measure of evaluation for research manuscript effectiveness. Using respective DATM, the region contributed by every author in the manuscriptis analyzed and fine-tuned semantically such that the manuscript is made to perfection in least time.