Reviewerly/exHarmony huggingface dataset:
Model Selection for Generalized Zero-Shot Learning

In the problem of generalized zero-shot learning, the datapoints from unknown classes are not available during training. The main challenge for generalized zero-shot learning is the unbalanced data distribution which makes it hard for the classifier to distinguish if a given testing sample comes from a seen or unseen class. However, using Generative Adversarial Network (GAN) to generate auxiliary datapoints by the semantic embeddings of unseen classes alleviates the above problem. Current approaches combine the auxiliary datapoints and original training data to train the generalized zero-shot learning model and obtain state-of-the-art results. Inspired by such models, we propose to feed the generated data via a model selection mechanism. Specifically, we leverage two sources of datapoints (observed and auxiliary) to train some classifier to recognize which test datapoints come from seen and which from unseen classes. This way, generalized zero-shot learning can be divided into two disjoint classification tasks, thus reducing the negative influence of the unbalanced data distribution. Our evaluations on four publicly available datasets for generalized zero-shot learning show that our model obtains state-of-the-art results.
A probabilistic multivariate copula-based technique for faulty node diagnosis in wireless sensor networks

Wireless sensor networks (WSNs) find extensive applications in various sensitive domains such as tracking, monitoring, environmental data collection and border surveillance. In these cases, the collected data are considered as a critical resource and used to detect any anomalies or abnormal behavior, providing information about an occurring event or a node failure. An outlier detection process must be set up to ensure the proper functioning of the monitoring system. The existing approaches are limited by assumptions on a specific distribution or a predefined data range of the collected data. Often these assumptions do not hold in practice, the data distribution is not known or determining reliable upper and lower bounds for the set of data is not possible. To overcome this, we propose a new copula-based probabilistic multivariate outlier detection method for faulty node detection in wireless sensor networks (WSNs). The joint probability density function of the copula is constructed considering dependency among the captured n−sensed measures without making any assumptions on the distribution of the collected data. The samples having probabilities violating a predetermined control limit are classified to be faulty. The performance of the proposed technique is observed to be better than the existing statistical methods.
Tamper detection of social media images using quality artifacts and texture features

Maliciously edited images, also referred to as tampered or forged images, have become ubiquitous and challenging in recent times with the advent of social media platforms. Image splicing is a kind of image tampering where regions of images are copied and pasted into another image. Image tampering detection is a method for the authenticity verification of images produced as evidence in the court of law and forms a sub problem in image forensics. The aim of our work is to detect and locate the boundaries of image splicing, without a-priori knowledge of the reference image. We propose two methods, of which the first uses classification of texture patterns along with the standard deviations of block discrete cosine transformation of textures. In the second method, we combine the first with image quality artifacts incurred due to image tampering along with the entropy of histograms to get an integrated method. We use a support vector machine classifier for training and testing these approaches. Our methods are tested on two publicly available tamper detection datasets and the results are compared with seven other existing works. It is observed that our integrated method gives best results. We have also tested the proposed methods on a social media dataset and the results are encouraging.
Creative AI Through Evolutionary Computation

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques are also well positioned to take advantage of large-scale parallel computing resources, making creative AI through evolutionary computation the likely “next deep learning”.
Kernel Protection Against Just-In-Time Code Reuse

The abundance of memory corruption and disclosure vulnerabilities in kernel code necessitates the deployment of hardening techniques to prevent privilege escalation attacks. As stricter memory isolation mechanisms between the kernel and user space become commonplace, attackers increasingly rely on code reuse techniques to exploit kernel vulnerabilities. Contrary to similar attacks in more restrictive settings, as in web browsers, in kernel exploitation, non-privileged local adversaries have great flexibility in abusing memory disclosure vulnerabilities to dynamically discover, or infer, the location of code snippets in order to construct code-reuse payloads. Recent studies have shown that the coupling of code diversification with the enforcement of a “read XOR execute” (R ∧ X) memory safety policy is an effective defense against the exploitation of userland software, but so far this approach has not been applied for the protection of the kernel itself. In this article, we fill this gap by presenting kR ∧ X: a kernel-hardening scheme based on execute-only memory and code diversification. We study a previously unexplored point in the design space, where a hypervisor or a super-privileged component is not required. Implemented mostly as a set of GCC plugins, kR ∧ X is readily applicable to x86 Linux kernels (both 32b and 64b) and can benefit from hardware support (segmentation on x86, MPX on x86-64) to optimize performance. In full protection mode, kR ∧ X incurs a low runtime overhead of 4.04%, which drops to 2.32% when MPX is available, and 1.32% when memory segmentation is in use.
Coherent <i>H</i> <sup>∞</sup> control for linear quantum passive systems with model uncertainties

The coherent robust control problem for a class of linear quantum passive systems with model uncertainties is considered in this study, where both the plant and the controller are described by quantum stochastic differential equations (QSDEs). In the framework of language, the model uncertainties are translated into the uncertainties in the Hamiltonian, the coupling operators, and the scattering matrices. Based on the quantum bounded real lemma, the robust controller design of the original uncertain plant is transformed into the controller design of a new scaled system without model uncertainties. Further, the latter is transformed into the solving problem of a couple of Riccati equations and therefore the numerical solutions of the coefficient matrices of QSDEs associated with the controller dynamics are obtained. Finally, numerical simulations on an optical system coupled to three optical channels are performed to verify the effectiveness of the method proposed in this study.
Applying Anomaly Pattern Score for Outlier Detection

Outlier detection is an important sub-field of data mining and studied intensively by researchers in the past decades. For neighborhood-based outlier detection methods like KNN and LOF, different settings in the number of neighbors (indicated by a parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ) would greatly affect the model’s performance. Thereby, there are some recent studies which focus on identifying the optimal value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> by analyzing the global or local structure of the dataset. But, we argue that neighborhood-based outlier detection model could obtain an improvement in performance without parameter tuning. In this paper, from a novel angle of view, we adopt a uniform sampling strategy to generate a series of local proximity graphs and propose a new adaptive outlier detection model named anomaly pattern score which does not rely on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> tuning. In addition, the theoretical analysis of the effectiveness of the proposed model is conducted as well. The extensive experiments on both synthetic and real-world datasets show that the proposed model outperforms the state-of-the-art algorithms on most datasets.
A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle

This paper presents a method of particle filter localization for autonomous vehicles, based on two-dimensional (2D) laser sensor measurements and road features. To navigate an urban environment, an autonomous vehicle should be able to estimate its location with a reasonable accuracy. By detecting road features such as curbs and road markings, a grid-based feature map is constructed using 2D laser range finder measurements. Then, a particle filter is employed to accurately estimate the position of the autonomous vehicle. Finally, the performance of the proposed method is verified and compared to accurate Differential Global Positioning Systems (DGPS) data through real road driving experiments.
Double CNOT attack on “Quantum key distribution with limited classical Bob”

This paper points out a security loophole in the Quantum key distribution with limited classical Bob [Int. J. Quantum Inf. 11(01) (2013) 1350005]. With the loophole, an eavesdropper can perform the double CNOT attack to reveal about [Formula: see text]-bits out of an [Formula: see text]-bit key without being detected by the protocol.
Efficient convNets for fast traffic sign recognition

While deep convolutional networks gain overwhelming accuracy for computer vision, they are also well-known for their high computation costs and memory demands. Given limited resources, they are difficult to apply. As a consequence, it is beneficial to investigate small, lightweight, accurate deep convolutional neural networks (ConvNets) that are better suited for resource-limited electronic devices. This study presents qNet and sqNet, two small and efficient ConvNets for fast traffic sign recognition using uniform macro-architecture and depth-wise separable convolution. The qNet is designed with fewer parameters for even better accuracy. It possesses only 0.29M parameters (0.6 of one of the smallest models), while achieving a better accuracy of 99.4% on the German Traffic Sign Recognition Benchmark (GTSRB). The resulting sqNet possesses only 0.045M parameters (almost 0.1 of one of the smallest models) and 7.01M multiply-add computations (reducing computations to 30% of one of the smallest models), while keeping an accuracy of 99% on the benchmark. The experimental results on the GTSRB demonstrate that authors’ networks are more efficient in using parameters and computations.
Efficient Window Aggregation with General Stream Slicing.

Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, and minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. Violating the assumptions of a technique can deem it unusable or drastically reduce its performance. In this paper, we present the first general stream slicing technique for window aggregation. General stream slicing automatically adapts to workload characteristics to improve performance without sacrificing its general applicability. As a prerequisite, we identify workload characteristics which affect the performance and applicability of aggregation techniques. Our experiments show that general stream slicing outperforms alternative concepts by up to one order of magnitude.
An association rule based approach to reducing visual clutter in parallel sets

Although Parallel Sets, a popular categorical data visualization technique, intuitively reveals the frequency based relationships in details, a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations. Association rule mining is a popular approach to discovering relationships among categorical variables. It could complement Parallel Sets to group ribbons in a meaningful way. However, it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset. In this paper, we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome. The system not only helps users interpret association rules intuitively, but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization. The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.
Demonstration of Refinity

This demo presents Refinity - an interactive holographic signage for the new retail shopping experience. In our demo, we show a concept of futuristic shopping experience with a tangible 3D mid-air interface that allows customers to directly select and explore realistic virtual products using autostereoscopic 3D display combined with mid-air haptics and finger tracker. We also present an example of in-store shopping scenario for natural interactions with 3D. This shopping experience will engage users in producing a memorable in-store experience with the merging of digital and physical interactions.
Domain-invariant representation learning using an unsupervised domain adversarial adaptation deep neural network

Domain adaptation is proposed to improve the recognition performance of the domain shift or the dataset bias. The domain shift is a very common problem, which is caused by diverse factors, such as data capturing angles, illumination, and image quality existing in the natural scene image understanding. Since the domain shift leads to the feature distribution discrepancy, some solutions have been proposed to alleviate the distribution discrepancy by mapping feature spaces between source and target domains, so as to ensure the transferable features can be learned by the deep networks during the end-to-end training for the classification tasks. However, it is still a big challenge to address the domain shift when the distribution spaces are not clearly separated. Inspired by the adversarial idea, we propose a novel unified deep neural network architecture named the unsupervised domain adversarial adaptation deep neural network. It addresses the domain adaptation problem by learning domain-invariant features through mitigating the feature discriminative ability in the domain classification task alternatively by alleviating the feature distribution discrepancy in the main classification task. Therefore, in our proposed unified deep network, we integrate two main modules. One is an auxiliary task module for the domain classifier, which is trained to make sure the learned features are domain-invariant under the adversarial optimization strategy by minimizing the domain discriminative ability. The other is the module at task-specific layers to enhance the learning of the transferable features with the less distribution discrepancy by adding multiple maximum mean discrepancy constraints to map the features to reproducing kernel Hilbert spaces. The experimental results show that the features learned by our proposed unified deep neural network perform better than the features learned by previous cross-domain neural networks on classification tasks. Our proposed approach achieves the state-of-the-art performance on three cross-domain datasets: Office-31 (different capturing angles, illumination, and image quality), Office-Caltech (modified from Office-31) and a combined cross-domain digit dataset, including MNIST, USPS and SVHN (different style digits in each dataset).
Enhancing DLV for Large-Scale Reasoning

Several real-world applications of made evident the need for efficiently handling multiple queries and reasoning tasks over large-sized knowledge bases. In this paper we present some recent enhancements in the ASP system for enabling reasoning over large-scale domains. In particular, we developed both an optimized implementation, sensibly reducing memory consumption, and a server-like behaviour to support efficiently multiple-query scenarios. The high potential of for large-scale reasoning is outlined by the results of an experiment on data-intensive benchmarks. The applicability of the system in real-world scenarios is demonstrated employing as reasoning service to query, in natural language, the large DBpedia knowledge base. The relevance and the high potential industrial value of this research is also confirmed by the direct interest of a major international industrial player, which has stimulated and partially supported this work.
Deep Clustering and Block Hashing Network for Face Image Retrieval

This paper presents a new hashing method to learn the compact binary codes for implementing a large-scale face image retrieval system. Since it is very difficult to deal with the inter-class similarities (similar appearance between different persons) and intra-class variations (same person with different pose, facial expressions, illuminations) in face-related problems, we propose a new deep clustering and block hashing (DCBH) approach to alleviate these issues. The network we adopt for the feature extraction is the VGG, where we design a new loss function to learn the robust and mulit-scale facial features for addressing the above-stated problems. Specifically, we design a center-clustering loss term to minimize the distance between the image descriptors belonging to the same class. Besides, the classification errors of the image descriptors and the learned binary codes are minimized to learn the discriminative binary codes. In addition, we introduce a block hashing layer for reducing the redundancy among hash codes and the number of parameters simultaneously without loss of similarity. Extensive experiments on two large scale face image datasets demonstrate that our proposed method outperforms the state-of-the-art face image retrieval methods.
Neural Lexicons for Slot Tagging in Spoken Language Understanding

We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12%. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models.
Revealing the Conceptual Schemas of RDF Datasets

RDF-based datasets, thanks to their semantic richness, variety and fine granularity, are increasingly used by both researchers and business communities. However, these datasets suffer a lack of completeness as the content evolves continuously and data contributors are loosely constrained by the vocabularies and schemes related to the data sources. Conceptual schemas have long been recognized as a key mechanism for understanding and dealing with complex real-world systems. In the context of the Web of Data and user-generated content, the conceptual schema is implicit. In fact, each data contributor has an implicit personal model that is not known by the other contributors. Consequently, revealing a meaningful conceptual schema is a challenging task that should take into account the data and the intended usage. In this paper, we propose a completeness-based approach for revealing conceptual schemas of RDF data. We combine quality evaluation and data mining approaches to find a conceptual schema for a dataset, this model meets user expectations regarding data completeness constraints. To achieve that, we propose LOD-CM; a web-based completeness demonstrator for linked datasets.
Side-Channel Masking with Pseudo-Random Generator

High-order masking countermeasures against side-channel attacks usually require plenty of randomness during their execution. For security against t probes, the classical ISW countermeasure requires $$\mathcal{O}(t^2 s)$$ random bits, where s is the circuit size. However running a True Random Number Generator (TRNG) can be costly in practice and become a bottleneck on embedded devices. In [IKL+13] the authors introduced the notion of robust pseudo-random number generator (PRG), which must remain secure even against an adversary who can probe at most t wires. They showed that when embedding a robust PRG within a private circuit, the number of random bits can be reduced to $$\mathcal{\tilde{O}}(t^{4})$$ , that is independent of the circuit size s (up to a logarithmic factor). Using bipartite expander graphs, this can be further reduced to $$\mathcal{\tilde{O}}(t^{3+\varepsilon })$$ ; however the resulting construction is impractical. In this paper we describe a construction where the number of random bits is only $$\mathcal{\tilde{O}}(t^2)$$ for security against t probes, without expander graphs; moreover the running time of each pseudo-random generation goes down from $$\mathcal{\tilde{O}}(t^{4})$$ to $$\mathcal{\tilde{O}}(t)$$ . Our technique consists in using multiple independent PRGs instead of a single one. We show that for ISW circuits, the robustness property of the PRG is not required anymore, which leads to simple and efficient constructions. For example, for AES we only need 48 bytes of randomness to get second-order security ( $$t=2$$ ), instead of 2880 in the original Rivain-Prouff countermeasure. As a first feasibility result, we have implemented our countermeasure on an ARM-based embedded device with a relatively slow TRNG, and obtained a $$50\%$$ speed-up compared to Rivain-Prouff.
Multiparty Non-Interactive Key Exchange and More From Isogenies on Elliptic Curves

Abstract We describe a framework for constructing an efficient non-interactive key exchange (NIKE) protocol for n parties for any n ≥ 2. Our approach is based on the problem of computing isogenies between isogenous elliptic curves, which is believed to be difficult. We do not obtain a working protocol because of a missing step that is currently an open mathematical problem. What we need to complete our protocol is an efficient algorithm that takes as input an abelian variety presented as a product of isogenous elliptic curves, and outputs an isomorphism invariant of the abelian variety. Our framework builds a cryptographic invariant map , which is a new primitive closely related to a cryptographic multilinear map, but whose range does not necessarily have a group structure. Nevertheless, we show that a cryptographic invariant map can be used to build several cryptographic primitives, including NIKE, that were previously constructed from multilinear maps and indistinguishability obfuscation.