Reviewerly/exHarmony huggingface dataset:
Forward-Secure Group Signatures from Lattices

Group signature is a fundamental cryptographic primitive, aiming to protect anonymity and ensure accountability of users. It allows group members to anonymously sign messages on behalf of the whole group, while incorporating a tracing mechanism to identify the signer of any suspected signature. Most of the existing group signature schemes, however, do not guarantee security once secret keys are exposed. To reduce potential damages caused by key exposure attacks, Song (ACMCCS 2001) put forward the concept of forward-secure group signature (FSGS), which prevents attackers from forging group signatures pertaining to past time periods even if a secret group signing key is revealed at the current time period. For the time being, however, all known secure FSGS schemes are based on number-theoretic assumptions, and are vulnerable against quantum computers. In this work, we construct the first lattice-based FSGS scheme. Our scheme is proven secure under the Short Integer Solution and Learning With Errors assumptions. At the heart of our construction is a scalable lattice-based key evolving mechanism, allowing users to periodically update their secret keys and to efficiently prove in zero-knowledge that key evolution process is done correctly. To realize this essential building block, we first employ the Bonsai tree structure by Cash et al. (EUROCRYPT 2010) to handle the key evolution process, and then develop Langlois et al.'s construction (PKC 2014) to design its supporting zero-knowledge protocol.
Exploring Attention Mechanism for Acoustic-based Classification of Speech Utterances into System-directed and Non-system-directed

Voice controlled virtual assistants (VAs) are now available in smartphones, cars, and standalone devices in homes. In most cases, the user needs to first "wake-up" the VA by saying a particular word/phrase every time he/she wants the VA to do something. Eliminating the need for saying the wake-up word for every interaction could improve the user experience. This would require the VA to have the capability of understanding whether the user is talking to it or not. In other words, the challenge is to distinguish between system-directed and non-system-directed speech utterances. In this paper, we present a number of neural network architectures for tackling this classification problem based on using only the acoustic signal. It is shown that a model comprised of convolutional, recurrent, and feed-forward layers can achieve an equal error rate (EER) of below 20% for this task. In addition, we investigate the use of an attention mechanism for helping the model to focus on the more important parts of the signal and to improve handling of variable length inputs sequences. The results show that the proposed attention mechanism significantly improves the model accuracy achieving an EER of 16.25% and 15.62% on two distinct realistic datasets.
Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

With an increasing worldwide need for energy and the ever-decreasing availability of energy resources, a wide range of interactive visualizations are being developed to allow people to use energy more efficiently by monitoring their consumption patterns and changing their energy usage behavior. For these visualizations to achieve their aim, they must not only target people’s energy saving objectives but also support the necessary factors that help people to change their underlying energy consumption behavior. In this paper, we survey several categories of existing interactive energy visualizations and through a number of selected examples in each case, identify possible potentials for supporting any user behavior changes. For this survey, we have used the behavior change model originally proposed by B. J. Fogg, which defines three factors of motivation, trigger and ability. Our survey has shown that most existing interactive visualizations target the motivation factor, with some supporting trigger or ability and only a few dealing with all the three factors of the behavior change model.
An Axiomatic Approach to Regularizing Neural Ranking Models

Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better relevance estimation functions. Neural ranking models typically contain many learnable parameters. The training of these models involves a search for appropriate parameter values based on large quantities of labeled examples. Intuitively, axioms that can guide the search for better traditional IR models should also help in better parameter estimation for machine learning based rankers. This work explores the use of IR axioms to augment the direct supervision from labeled data for training neural ranking models. We modify the documents in our dataset along the lines of well-known axioms during training and add a regularization loss based on the agreement between the ranking model and the axioms on which version of the document---the original or the perturbed---should be preferred. Our experiments show that the neural ranking model achieves faster convergence and better generalization with axiomatic regularization.
Nonrigid Image Registration Using Spatially Region-Weighted Correlation Ratio and GPU-Acceleration

Objective: Nonrigid image registration with high accuracy and efficiency remains a challenging task for medical image analysis. In this paper, we present the spatially region-weighted correlation ratio (SRWCR) as a novel similarity measure to improve the registration performance. Methods: SRWCR is rigorously deduced from a three-dimension joint probability density function combining the intensity channels with an extra spatial information channel. SRWCR estimates the optimal functional dependence between the intensities for each spatial bin, in which the spatial distribution modeled by a cubic B-spline function is used to differentiate the contribution of voxels. We also analytically derive the gradient of SRWCR with respect to the transformation parameters and optimize it using a quasi-Newton approach. Furthermore, we propose a GPUbased parallel mechanism to accelerate the computation of SRWCR and its derivatives. Results: The experiments on synthetic images, public four-dimensional thoracic computed tomography (CT) dataset, retinal optical coherence tomography data, and clinical CT and positron emission tomography images confirm that SRWCR significantly outperforms some state-of-the-art techniques such as spatially encoded mutual information and Robust PaTch-based cOrrelation Ration. Conclusion: This study demonstrates the advantages of SRWCR in tackling the practical difficulties due to distinct intensity changes, serious speckle noise, or different imaging modalities. Significance: The proposed registration framework might be more reliable to correct the nonrigid deformations and more potential for clinical applications.
Lattice-based group signatures: Achieving full dynamicity (and deniability) with ease

Lattice-based group signature is an active research topic in recent years. Since the pioneering work by Gordon et al. (2010) [20], eight other schemes have been proposed, providing various improvements in terms of security, efficiency and functionality. However, most of the existing constructions work only in the static setting where the group population is fixed at the setup phase. The only two exceptions are the schemes by Langlois et al. (2014) [27] that handles user revocations (but new users cannot join), and by Libert et al. (2016) [28], [29] which addresses the orthogonal problem of dynamic user enrollments (but users cannot be revoked). In this work, we provide the first lattice-based group signature that offers full dynamicity (i.e., users have the flexibility in joining and leaving the group), and thus, resolve a prominent open problem posed by previous works. Moreover, we achieve this non-trivial feat in a relatively simple manner. Starting with Libert et al.'s fully static construction (2016) [30] - which is arguably the most efficient lattice-based group signature to date, we introduce simple-but-insightful tweaks that allow to upgrade it directly into the fully dynamic setting. More startlingly, our scheme even produces slightly shorter signatures than the former, thanks to an adaptation of a technique proposed by Ling et al. (2013) [35], allowing to prove inequalities in zero-knowledge. The scheme satisfies the strong security requirements of Bootle et al.'s model (2016) [7], under the Short Integer Solution (SIS) and the Learning With Errors (LWE) assumptions. Furthermore, we demonstrate how to equip the obtained group signature scheme with the deniability functionality in a simple way. This attractive functionality, put forward by Ishida et al. (2016) [22], enables the tracing authority to provide an evidence that a given user is not the owner of a signature in question. In the process, we design a zero-knowledge protocol for proving that a given LWE ciphertext does not decrypt to a particular message.
NetTailor: Tuning the Architecture, Not Just the Weights

Real-world applications of object recognition often require the solution of multiple tasks in a single platform. Under the standard paradigm of network fine-tuning, an entirely new CNN is learned per task, and the final network size is independent of task complexity. This is wasteful, since simple tasks require smaller networks than more complex tasks, and limits the number of tasks that can be solved simultaneously. To address these problems, we propose a transfer learning procedure, denoted NetTailor, in which layers of a pre-trained CNN are used as universal blocks that can be combined with small task-specific layers to generate new networks. Besides minimizing classification error, the new network is trained to mimic the internal activations of a strong unconstrained CNN, and minimize its complexity by the combination of 1) a soft-attention mechanism over blocks and 2) complexity regularization constraints. In this way, NetTailor can adapt the network architecture, not just its weights, to the target task. Experiments show that networks adapted to simple tasks, such as character or traffic sign recognition, become significantly smaller than those adapted to hard tasks, such as fine-grained recognition. More importantly, due to the modular nature of the procedure, this reduction in network complexity is achieved without compromise of either parameter sharing across tasks, or classification accuracy.
Effective Image Retrieval via Multilinear Multi-Index Fusion

Multi-index fusion has demonstrated impressive performances in the retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via a neighbor structure, ignoring the high-order information among different visual representations. In this paper, we propose a new multi-index fusion scheme for image retrieval. By formulating this procedure as a multilinear-based optimization problem, the complementary information hidden in different indexes can be explored more thoroughly. Specifically, we first build our multiple indexes from various visual representations. Then, a so-called index-specific functional matrix, which aims to propagate similarities, is introduced to update the original index. The functional matrices are then optimized in a unified tensor space to achieve a refinement, such that the relevant images can be pushed closer. The optimization problem can be efficiently solved by the augmented Lagrangian method with a theoretical convergence guarantee. Unlike the traditional multi-index fusion scheme, our approach embeds the multi-index subspace structure into the new indexes with sparse constraint and, thus, it has little additional memory consumption in the online query stage. Experimental evaluation on three benchmark datasets reveals that the proposed approach achieves state-of-the-art performance, that is, N-score 3.94 on UKBench, mAP 94.1% on Holiday, and 62.39% on Market-1501.
Are Donation Badges Appealing?: A Case Study of Developer Responses to Eclipse Bug Reports

Eclipse, an open source software project, acknowledges its donors by presenting donation badges. However, the effect of this strategy is unknown. We found that donation badges are appealing for donors and organizers because of their practical, rewarding, and inexpensive effects.
Generalization in Metric Learning: Should the Embedding Layer Be Embedding Layer?

This work studies deep metric learning under small to medium scale as we believe that better generalization could be a contributing factor to the improvement of previous fine-grained image retrieval methods; it should be considered when designing future techniques. In particular, we investigate using other layers in a deep metric learning system (besides the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study, we suggest a new regularization practice where one can add or choose a more optimal layer for feature extraction. State-of-the-art performance is demonstrated on 3 fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011, and Stanford Online Product.
Frequent Pattern Mining on Time and Location Aware Air Quality Data

With the advent of big data era, enormous volumes of data are generated every second.Varied data processing algorithms and architectures have been proposed in the past to achieve better execution of data mining algorithms.One such algorithm is extracting most frequently occurring patterns from the transactional database.Dependency of transactions on time and location further makes frequent itemset mining task more complex.The present work targets to identify and extract the frequent patterns from such time and location-aware transactional data.Primarily, the spatio-temporal dependency of air quality data is leveraged to find out frequently co-occurring pollutants over several locations of Delhi, the capital city of India.Varied approaches have been proposed in the past to extract frequent patterns efficiently, but this work suggests a generalized approach that can be applied to any numeric spatio-temporal transactional data, including air quality data.Furthermore, a comprehensive description of the algorithm along with a sample running example on air quality dataset is shown in this work.A detailed experimental evaluation is carried out on the synthetically generated datasets, benchmark datasets, and real world datasets.Furthermore, a comparison with spatio-temporal apriori as well as the other state-of-the-art non-apriori-based algorithms is shown.Results suggest that the proposed algorithm outperformed the existing approaches in terms of execution time of algorithm and memory resources.
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions

We consider the paradigm of a black box AI system that makes life-critical decisions. We propose an “arguing machines” framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task. We show that disagreement between the two systems, without any knowledge of underlying system design or operation, is sufficient to improve the accuracy of the overall system given human supervision over disagreements. We demonstrate this system in two applications: (1) image classification and (2) large-scale real-world semi-autonomous driving. For the first application, we apply this framework to image classification achieving a reduction from 8.0% to 2.8% top-5 error on ImageNet. For the second application, we apply this framework to Tesla Autopilot and demonstrate the ability to predict 90.4% of system disengagements that were labeled by human as challenging.
Discrete-time quantum walks and graph structures

We formulate three current models of discrete-time quantum walks in a combinatorial way. These walks are shown to be closely related to rotation systems and 1-factorizations of graphs. For two of the models, we compute the traces and total entropies of the average mixing matrices for some cubic graphs. The trace captures how likely a quantum walk is to revisit the state it started with, and the total entropy measures how close the limiting distribution is to uniform. Our numerical results indicate three relations between quantum walks and graph structures: for the first model, rotation systems with higher genera give lower traces and higher entropies, and for the second model, the symmetric 1-factorizations always give the highest trace.
Matched Filters for Noisy Induced Subgraph Detection

The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding the smaller adjacency matrix and applying graph matching methods to align it to the larger network. The centering and padding schemes can be incorporated into any algorithm that matches using adjacency matrices. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks. However, there are currently no efficient algorithms for solving this problem. To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to Drosophila and human connectomes that this approach can achieve good performance.
A convolutional feature map-based deep network targeted towards traffic detection and classification

Vehicle detection and classification is an important task for street surveillance and scene perception for robot navigation or autonomous vehicles. This research work focuses on traffic detection for real time applications using three components. The first component includes designing convolutional feature map-based classifier based on multimodal optical flow features. The second component is to utilize an effective adaptive learning rate technique to deal with saddle points; and to propose an average covariance matrix based pre-conditioning approach. The third component is to separately train multimodal model using blur data which caters blur effect of real time data. Extensive experimental results with different learning rates, architectures are reported using benchmark datasets such as Apollo, KITTI, Cityscapes, Berkeley, Caltech, PASCAL VOC and self created. Experimental results demonstrate that in comparison to fully connected network based classifier, Network on Convolutional (NoC) feature map classifier provided approximately 10% hike in classification accuracy without data per-processing, and almost 18% improvement with pre-processed data. The blur model enhances accuracy by almost 15% on blurred data as compared to normal RGB data. Moreover, multimodal features provide 12% and 2% higher accuracy while using standard classifiers and NoC classifiers respectively.
Blockchain-based Multi-Robot Path Planning

The blockchain is a secure and trustworthy distributed transaction management system that is being extensively researched and developed for various applications and use cases. This study introduces a novel distributed control system using the Blockchain. A multi-robot path planning application is developed and deployed to benchmark a blockchain platform, the Hyperledger Fabric. Blockchain technology has the reputation of being sufficiently dilatory that it is inappropriate for time-sensitive applications. This research demonstrates how the enterprise-grade blockchain solutions overcome this shortcoming and investigates their potential for enabling secure and trusted distributed control systems for IoT.
Multi-view dynamic facial action unit detection

We propose a novel convolutional neural network approach to address the fine-grained recognition problem of multi-view dynamic facial action unit detection. We leverage recent gains in large-scale object recognition by formulating the task of predicting the presence or absence of a specific action unit in a still image of a human face as holistic classification. We then explore the design space of our approach by considering both shared and independent representations for separate action units, and also different CNN architectures for combining color and motion information. We then move to the novel setup of the FERA 2017 Challenge, in which we propose a multi-view extension of our approach that operates by first predicting the viewpoint from which the video was taken, and then evaluating an ensemble of action unit detectors that were trained for that specific viewpoint. Our approach is holistic, efficient, and modular, since new action units can be easily included in the overall system. Our approach significantly outperforms the baseline of the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric. Additionally, it compares favorably against the winner of the FERA 2017 Challenge.
Blind Batch Encryption-Based Protocol for Secure and Privacy-Preserving Medical Services in Smart Connected Health

Smart connected health is a vital Internet of Things (IoT) application proposed to improve the efficiency of healthcare systems and reduce healthcare costs. However, there are many complex security and privacy-preserving concerns in medical services. Blind batch encryption provides a solution for the tradeoff between privacy and security. We propose an efficient blind batch encryption scheme from the CDH assumption, which can be proven to be suitably secure and blind. Based on this scheme, we design a protocol for secure and privacy-preserving medical services in smart connected health. We analyze our protocol in the context of six typical attacks and implement the prototype over the Intel Edison platform. The experiments show that our protocol is efficient for resource-limited devices and “cheap” communication protocols.
Causal Inference by String Diagram Surgery

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior dependencies. We represent the effect of such an intervention as an endofunctor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We showcase this technique on a well-known example, predicting the causal effect of smoking on cancer in the presence of a confounding common cause. We then conclude by showing that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature.