Reviewerly/exHarmony huggingface dataset:
Accountable Proxy Re-Encryption for Secure Data Sharing

Proxy re-encryption (PRE) provides a promising solution for encrypted data sharing in public cloud. When data owner Alice is going to share her encrypted data with data consumer Bob, Alice generates a re-encryption key and sends it to the cloud server (proxy); by using it, the proxy can transform Alice's ciphertexts into Bob's without learning anything about the underlying plaintexts. Despite that existing PRE schemes can prevent the proxy from recovering Alice's secret key by collusion attacks with Bob, due to the inherent functionality of PRE, it is inevitable that the proxy and Bob together are capable to gain and distribute Alices decryption capabilities. Even worse, the malicious proxy can deny that it has leaked the decryption capabilities and has very little risk of getting caught. To tackle this problem, we introduce the concept of Accountable Proxy Re-Encryption (APRE), whereby if the proxy is accused to abuse the re-encryption key for distributing Alice's decryption capability, a judge algorithm can decide whether it is innocent or not. We then present a non-interactive APRE scheme and prove its CPA security and accountability under DBDH assumption in the standard model. Finally, we show how to extend it to a CCA secure one.
3G structure for image caption generation

It is a big challenge of computer vision to make machine automatically describe the content of an image with a natural language sentence. Previous works have made great progress on this task, but they only use the global or local image feature, which may lose some important subtle or global information of an image. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. The model mainly has three gated structures. (1) Gate for the global image feature, which can adaptively decide when and how much the global image feature should be imported into the sentence generator. (2) The gated recurrent neural network (RNN) is used as the sentence generator. (3) The gated feedback method for stacking RNN is employed to increase the capability of nonlinearity fitting. More specially, the global and local image features are combined together in this paper, which makes full use of the image information. The global image feature is controlled by the first gate and the local image feature is selected by the attention mechanism. With the latter two gates, the relationship between image and text can be well explored, which improves the performance of the language part as well as the multi-modal embedding part. Experimental results show that our proposed method outperforms the state-of-the-art for image caption generation.
Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection

Night-time vehicle detection is essential in building intelligent transportation systems (ITS) for road safety. Most of current night-time vehicle detection approaches focus on one or two classes of vehicles. In this paper, we present a novel multiclass vehicle detection system based on tensor decomposition and object proposal. Commonly used features such as histogram of oriented gradients and local binary pattern often produce useless image blocks (regions), which can result in unsatisfactory detection performance. Thus, we select blocks via feature ranking after tensor decomposition and only extract features from these selected blocks. To generate windows that contain all vehicles, we propose a novel object-proposal approach based on a state-of-the-art object-proposal method, local features, and image region similarity. The three terms are summed with learned weights to compute the reliability score of each proposal. A bio-inspired image enhancement method is used to enhance the brightness and contrast of input images. We have built a Hong Kong night-time multiclass vehicle dataset for evaluation. Our proposed vehicle detection approach can successfully detect four types of vehicles: 1) car; 2) taxi; 3) bus; and 4) minibus. Occluded vehicles and vehicles in the rain can also be detected. Our proposed method obtains 95.82% detection rate at 0.05 false positives per image, and it outperforms several state-of-the-art night-time vehicle detection approaches.
Automatic brain labeling via multi-atlas guided fully convolutional networks

Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.
Automated framework for classification and selection of software design patterns

Though, Unified Modeling Language (UML), Ontology, and Text categorization approaches have been used to automate the classification and selection of design pattern(s). However, there are certain issues such as time and effort for formal specification of new patterns, system context-awareness, and lack of knowledge which needs to be addressed. We propose a framework (i.e. Three-phase method) to discuss these issues, which can aid novice developers to organize and select the correct design pattern(s) for a given design problem in a systematic way. Subsequently, we propose an evaluation model to gauge the efficacy of the proposed framework via certain unsupervised learning techniques. We performed three case studies to describe the working procedure of the proposed framework in the context of three widely used design pattern catalogs and 103 design problems. We find the significant results of Fuzzy c-means and Partition Around Medoids (PAM) as compared to other unsupervised learning techniques. The promising results encourage the applicability of the proposed framework in terms of design patterns organization and selection with respect to a given design problem.
Higher inductive types in cubical computational type theory

Homotopy type theory proposes higher inductive types (HITs) as a means of defining and reasoning about inductively-generated objects with higher-dimensional structure. As with the univalence axiom, however, homotopy type theory does not specify the computational behavior of HITs. Computational interpretations have now been provided for univalence and specific HITs by way of cubical type theories, which use a judgmental infrastructure of dimension variables. We extend the cartesian cubical computational type theory introduced by Angiuli et al. with a schema for indexed cubical inductive types (CITs), an adaptation of higher inductive types to the cubical setting. In doing so, we isolate the canonical values of a cubical inductive type and prove a canonicity theorem with respect to these values.
Exploiting social and local contexts propagation for inducing Chinese microblog-specific sentiment lexicons

Sentiment lexicons including opinion words, sentiment phrases, and idioms with sentiment polarities play an important role in sentiment analysis tasks. Apart from explicit sentiment features, extracting implicit sentiment features is a challenging research issue. The sentiment expression is very domain-specific, and constructing a general sentiment lexicon that is suitable for all domains is hard or even impossible. In this paper, we propose a novel sentiment unit context propagation framework to extract Chinese microblog-specific explicit and implicit sentiment features. In the process of the selection of seed sentiment units, we select the seed sentiment units that have a large standard degree of centrality with other units, and mark these units with sentiment labels using general sentiment lexicons and manual calibrations. To realize sentiment label propagation from a small amount of labeled sentiment units to unlabeled ones, we exploit local contexts, topic features, and so`cial relationships among users in microblog social networks. After that, the sentiment scores of units are calculated using unit context sentiment propagation. Experiments on two real-world microblog data sets demonstrate that our method can generate microblog-specific sentiment lexicons effectively. Furthermore, the sentiment classification accuracies significantly outperform state-of-the-art baselines.
Distortion Design for Secure Adaptive 3-D Mesh Steganography

We propose a novel technique for steganography on 3-D meshes so as to resist steganalysis. The majority of existing methods modulate vertex coordinates to embed messages in a nonadaptive way. We take account of complexity of local regions as joint distortion of a triple unit (vertice) and coding method such as syndrome trellis codes to adaptively embed messages, which owns stronger security with respect to existing steganalysis. Key to the distortion is a novel formulation of adaptive steganography, which relies on some effective steganalytic features such as variation of vertex normal. We provide quantitative and qualitative comparisons of our method with several baselines against steganalytic features LFS64, LFS76, and ensemble classifiers, and show that it outperforms the current state of the art. Meanwhile, we proposed an attacking method on steganography proposed by Chao et al. (2009) with a high detection rate.
A Coalitional Cyber-Insurance Framework for a Common Platform

Despite the benefits of cyber-insurance, organizations are reluctant to enroll in such policies mainly because of their limitation and high price. On the other hand, insurers are confronting the adverse selection and moral hazard problems as monitoring and distinguishing insureds' cybersecurity posture are highly complicated. Considering the organizations' security interdependency and their demand for cyber-insurance, we study the design of coalitional insurance mechanisms with the goal of covering the adverse selection, moral hazard, and motivating players for cybersecurity investment and information sharing. To this end, we propose a synergistic insurance framework, where organizations collaboratively insure a common platform instead of themselves. We present three models for insuring a common platform. In the first model, organizations act as both insurer and insured to distribute the risk in the coalition. In the second model, the system provides rewards to crowdfund the insurance. Finally, in the third model, we investigate the outsourcing of a common platform insurance. Furthermore, we discuss how our proposed mechanisms for such framework satisfy the budget balanced, ex ante individual rationality, and incentive compatibility properties. We study how such a system can improve the social welfare by leveraging cyber-insurance as a motivation for organizations to cooperate on the cybersecurity investment and information sharing.
Semantically-enhanced rule-based diagnostics for industrial Internet of Things: The SDRL language and case study for Siemens trains and turbines

An Industrial Internet of Things (IoT) is a network of intelligent industrial equipment such as trains and power generating turbines that collect and share large amounts of data. These data are either generated by various sensors deployed in the equipment or captures equipment specific information such as configurations, history of use, and manufacturer. Diagnostics of the industrial IoT is critical to minimise the maintenance cost and downtime of its equipment. It is common that industry today employs rule-based diagnostic systems for this purpose. Rules are typically used to process signals from sensors installed in equipment by filtering, aggregating, and combining sequences of time-stamped measurements recorded by the sensors. Such rules are often data-dependent in the sense that they rely on specific characteristics of individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers especially when the rules are applied in industrial IoT scenarios. In this work we propose an approach to address these problems by relying on the well-known Ontology-Based Data Access approach: we propose to use ontologies to mediate the sensor signals and the rules. To this end, we propose a semantic rule language, SDRL, where signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system and evaluated it. For evaluation, we developed a use case of rail systems as well as power generating turbines at Siemens and conducted experiments to demonstrate both usability and efficiency of our solution.
Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity.We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points.We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing.In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities.In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.
Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning

Traditional collaborative representation based classification (CRC) method usually faces the challenge of data uncertainty hence results in poor performance, especially in the presence of appearance variations in pose, expression and illumination. To overcome this issue, this paper presents a CRC-based face classification method by jointly using block weighted LBP and analysis dictionary learning. To this end, we first design a block weighted LBP histogram algorithm to form a set of local histogram-based feature vectors instead of using raw images. By this means we are able to effectively decrease data redundancy and uncertainty derived from image noises and appearance variations. Second, we adopt an analysis dictionary learning model as the projection transform to construct an analysis subspace, in which a new sample is characterized with the improved sparsity of its reconstruction coefficient vector. The crucial role of the analysis dictionary learning method in CRC is revealed by its capacity of the collaborative representation in an analytic coefficient space. Extensive experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method.
Performance Modeling of Moving Target Defenses with Reconfiguration Limits

Moving Target Defense (MTD) has recently emerged as a game changer in the security landscape due to its proven potential to introduce asymmetric uncertainty that gives the defender a tactical advantage over the attacker. Many different MTD techniques have been developed, but, despite the huge progress made in this area, critical gaps still exist with respect to the problem of studying and quantifying the cost and benefits of deploying MTDs. In fact, all existing techniques address a very narrow set of attack vectors, and, due to the lack of shared metrics, it is difficult to quantify and compare multiple techniques. Building on our preliminary work in this field, we propose a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for maximizing a utility function that captures the tradeoffs between security and performance. The proposed model generalizes our previous model and can be applied to a wider range of MTDs and operational scenarios to improve availability and performance by imposing limits on the maximum number of resources that can be in the process of being reconfigured. The analytic results are validated by simulation and experimentation, confirming the accuracy of our model.
Multi-scale semantic image inpainting with residual learning and GAN

Image inpainting aims to fill the corrupt area semantically and recover the semantic and detailed information; however, concurrent methods suffer from convergence crash and arbitrarily paste the inference of the missing area into the corrupt context. In this paper, we study a combination of an encoder–decoder generator for image semantic inpainting and a multi-layer convolutional net for image seamless fusion, which is capable of restoring image effectively and seamlessly. Specifically, the encoder–decoder generator learns and extracts the latent compressed representations of missing areas from the context of a corrupt image, and further predicts a semantically correct estimation of the missing area based on the latent representations. The consecutive convolutional net smooths the discrepancy between the original image context and the estimation and seamlessly merges predictions and original images. The skip connections between the encoder and the decoder bridge the backward propagation of gradients, therefore boost the learning ability of the generator and stabilize the convergence of reconstruction loss. The performance and superiority of our method are illustrated and demonstrated on the real-world dataset qualitatively and quantitatively, and the experiments manifest acceptable semantic inpainting results, which significantly illustrates the effectiveness of our model.
Bayesian Zero-Inflated Negative Binomial Regression Based on Pólya-Gamma Mixtures

Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of latent variables that are represented as scale mixtures of normals, where the precision terms follow independent Pólya-Gamma distributions. Conditional on the latent variables, inference proceeds via straightforward Gibbs sampling. For fixed-effects models, our approach is comparable to existing methods. However, our model can accommodate more complex data structures, including multivariate and spatiotemporal data, settings in which current approaches often fail due to computational challenges. Using simulation studies, we highlight key features of the method and compare its performance to other estimation procedures. We apply the approach to a spatiotemporal analysis examining the number of annual inpatient admissions among United States veterans with type 2 diabetes.
Registration-Based Encryption from Standard Assumptions

The notion of Registration-Based Encryption (RBE) was recently introduced by Garg, Hajiabadi, Mahmoody, and Rahimi [TCC'18] with the goal of removing the private-key generator (PKG) from IBE. Specifically, RBE allows encrypting to identities using a (compact) master public key, like how IBE is used, with the benefit that the PKG is substituted with a weaker entity called "key curator" who has no knowledge of any secret keys. Here individuals generate their secret keys on their own and then publicly register their identities and their corresponding public keys to the key curator. Finally, individuals obtain "rare" decryption-key updates from the key curator as the population grows. In their work, they gave a construction of RBE schemes based on the combination of indistinguishability obfuscation and somewhere statistically binding hash functions. However, they left open the problem of constructing RBE schemes based on standard assumptions. In this work, we resolve the above problem and construct RBE schemes based on standard assumptions (e.g., CDH or LWE). Furthermore, we show a new application of RBE in a novel context. In particular, we show that anonymous variants of RBE (which we also construct under standard assumptions) can be used for realizing abstracts forms of anonymous messaging tasks in simple scenarios in which the parties communicate by writing messages on a shared board in a synchronized way.