Reviewerly/exHarmony huggingface dataset:
Faithful entanglement distribution using quantum multiplexing in noisy channel

Abstract Quantum multiplexing provides us with a powerful approach that can distribute entanglement using only a single photonic pulse to interact with remote quantum memories. In common entanglement distribution protocols, the entanglement purification is often exploited if the distributed entanglement is degraded by the channel noise. In this protocol, we propose a faithful entanglement distribution protocol using quantum multiplexing of photon in a collective noise channel. By designing the encoding and decoding setup, our protocol can resist the collective noise. When the protocol is successful, the parties can obtain two pairs of maximally entangled states. When the protocol fails, the parties can still obtain one pair of maximally entangled state. Our protocol does not require the entanglement purification, so that it is more efficient. The above advantages enable our protocol to have important application potential in the future quantum communication field.
From Neuron Coverage to Steering Angle: Testing Autonomous Vehicles Effectively

A Deep Neural Network (DNN) based system, such as the one used for autonomous vehicle operations, is a "black box" of complex interactions resulting in a classification or prediction. An important question for any such system is how to increase the reliability of, and consequently the trust in, the underlying model. To this end, researchers have largely resorted to adapting existing testing techniques. For example, similar to statement or branch coverage in traditional software testing, neuron coverage has been hypothesized as an effective metric for assessing a test suite's strength toward uncovering failures and anomalies in the DNN. We investigate the use of realistic transformations to create new images for testing a trained autonomous vehicle DNN, and its impact on neuron coverage as well as the model output.
Computational Archives for Experimental Museology

This article addresses the potential for computational practises within archival and museological domains by charting a positional shift occurring in digital archives; from working with an object orientation and containment – to computing with a dimension orientation, segmentation, analytics and visualization. Computational museology allows us to conceive new trajectories that link all forms of cultural materiality: objects, knowledge systems, representation and participation. Following key definitions, the authors: outline state of the art in archival access and museological engagement through a series of examples; examine advances in the computational analysis of archives exploring forward-looking trends for massive digital cultural archives; and introduce an upcoming project, Narratives from the long tail: transforming access to audiovisual archives.
Image deep clustering based on local-topology embedding

Reasonable feature representation plays an important role in improving the performance of clustering algorithms. However, recent deep clustering studies only focusing on feature representation at the pixel level leads to feature representation with low discrimination. Our key insight is that considering local-topology information between images would help to get a highly discriminative representation, and therefore we design a replacement strategy to find local-topology representation of data, and propose a two-stage image deep clustering algorithm based on local-topology embedding called ITEC. Specifically, we take advantage of data augmentation technique to improve the generalization performance of the learning models; then local-topology representation of data is embedded into the representation of data itself, so as to better complete tasks of image clustering. Extensive experiments demonstrate that local-topology information effectively promotes the performance of deep clustering significantly.
Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

Abstract Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.
Intelligence Embedded Image Caption Generator using LSTM based RNN Model

Humans tend to extract information from everything they see be it living or non-living. This whole phenomenon motivated us to move in this direction and explore the field of computer vision and how this can be used with recurrent neural networks to generate captions from any image. By witnessing the recent increase in natural language processing-based applications; various other researchers have also worked on this concept and produced commendable results. Describing an image is not an easy task to implement, the structure and semantics of a sentence hold an important weight age in sentence formation. This paper approaches the problem of caption generation with an LSTM (Long-Short Term Memory) based RNN model and builds architecture based on the same to generate efficient and meaningful captions by training the dataset effectively. Flicker8k dataset is used to train our model and worked well. The accuracy of the model is evaluated based on standard evaluation metrics.
A UAV Global Planner to Improve Path Planning in Unstructured Environments

A good performance for path planning is essential to carry out real-world missions. In this paper, the path planning consists of a global planner, that finds the optimal path, and in a local planner, recalculates the path to avoid obstacles. The main focus is to improve the performance of local planning techniques decreasing the complexity. There are two main ways to do it: bidirectional algorithms, improving time, and global planners, improving time and completeness. Thus, we propose a global planner algorithm that generates auxiliary nodes, backtracking by the goal node. We perform a comparison among A*, Bi A*, Artificial Potential Field (APF), Bi APF, Rapid Exploring Random Tree (RRT), and Bi RRT with and without the global planner through statistical metrics of time, path length, CPU, and memory. The results show the advantages of using bidirectional algorithms and the proposed global planner. The bidirectional algorithms decrease the time to return to the trajectory and sometimes assist in the algorithm's completeness. The proposed global planner reduced the planning time by 91.6% and improved the completeness of all algorithms in an unstructured indoor environment.
Too few, too many, just right: Creating the necessary number of segments for large online customer populations

We develop a framework to reduce the number of customer segments to the smallest quantity without losing essential information of the underlying population in the electronic marketplace. As a use case of this approach, we create personas for these segments to enhance customer understanding. We use (a) matrix factorization to identify customer behaviors and construct customer segments, (b) statistical heuristics to collapse into meaningful segments, and (c) automation to enrich by generating a persona profile for each segment. We evaluate our approach in a case study using more than 21 million online flight bookings of a major airline company resulting in a 57.5% decrease from 1194 to 507 segments, thereby reducing segment noise. Three customer mega-segments emerge: Behaviorally same – Demographically different, Behaviorally different - Demographically same, and Behaviorally and Demographically different. As one of the first efforts at the essential task of customer segmentation reduction for large customer populations, findings have implications for organizations desiring to employ segmentation and/or personas for enhanced customer understanding.
Cooking Cryptographers: Secure Multiparty Computation Based on Balls and Bags

Imagine two cryptographers wishing to securely compute the AND value of their secret input bits. They are in the kitchen, where all they have are three saucepans with Borscht soup and some kinds of ingredients. Interestingly, by secretly putting ingredients in the saucepans depending on their inputs, they can find only the AND value from the taste of the cooked Borscht. Because cooking for secure computations is not so convenient, let us regard ingredients and saucepans (with Borscht soup) as balls and bags, respectively, which are easy to handle and also familiar tools for learning Probability in high school. Then, our problem is generalized as: Can we realize secure multiparty computations (MPCs) with balls and bags?There are techniques to realize MPCs with everyday objects, such as physical cards, coins, and a PEZ dispenser. We encode the input bits with such objects and securely compute some predetermined function using them. In this paper, we present a novel technique based on the physical properties of balls and bags. That is, our challenges are how to utilize an interesting feature that the balls become disordered immediately after they are put into a bag, namely they are “automatically shuffled”. We give the first framework of MPCs using balls and bags (namely, ball-based cryptography), and propose secure AND computation and general MPCs. Our protocols are realizations of usable security which helps people with understanding the principles of MPCs as well as solving social problems in daily life.
Power Side Channel Attacks: Where Are We Standing?

Side channel attacks are a serious threat to integrated circuits. They are hardly detectable and use inherent information leaked by the hardware to infer sensitive information like secret keys. Over the last ten years, numerous side channel attacks have been examined, exploring various forms of leakage channels such as time, power, electromagnetic field, photon emission, and acoustic. Among them, power side channel attacks are the most popular ones. Developing an appropriate counter-measure against such attacks requires a deep understanding of these attacks. This paper presents a study of the most popular power attacks such as differential power attack and correlation power attack and discusses the latest countermeasures in this domain and their shortcomings.
Review of Privacy Preservation with Blockchain Technology in the Context of Smart Cities

Blockchain technology has credit for empowering smart cities. Due to the generation of huge amounts of data for direct transactions between devices, blockchain technology, by definition, must be used in this type of context. Although, due to the use of large amounts of multidimensional data, exists a risk that users’ privacy may be violated. This privacy issue turns out to be of paramount importance, especially with new privacy regulations, like the General Data Protection Regulation (GDPR), that legalizes the requirement to protect the privacy of personal information. This work contributes with a review of the current state of the art in the context of blockchain-based smart cities and how it is managed the privacy-preserving issue.
Combating Adversarial Network Topology Inference by Proactive Topology Obfuscation

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We evaluate ProTO under different evaluation scenarios. Experimental results show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.
An Optimal Higher Order Likelihood Distribution Based Approach for Strong Edge and High Contrast Restoration

The existing studies are generally focused on the pixel intensity or noise variance, and not much attention is given to describe the actual distribution. Some modifications have been proposed over the years in conventional parametric estimation techniques such as maximum likelihood estimation (MLE) and Bayesian estimation (BE). These methods have better properties to provide visually pleasing results for any imagery. In this paper, we use the likelihood estimate from the perspective that as the distribution order of likelihood increases, the overall effect of parameters under consideration improves within the defined confidence interval. In this paper, we use two parameters of interest, i.e., contrast and edge information. The proposed idea uses prior information w.r.t. particular parameter of interest, which is derived from likelihood estimate. The prior estimation has also been used to compute the reliability of the point estimations under a confidence interval. The proposed idea has desirable properties such that its optimization improves the overall image reconstruction.
How different rewards tend to influence employee non-compliance with information security policies

Purpose To help reduce the increasing number of information security breaches that are caused by insiders, past research has examined employee non-compliance with information security policy. However, existent studies have observed mixed results, which suggest that an interaction is likely to exist among the variables that explain employee non-compliance. In an effort to provide evidence for this possibility, this paper aims to better explain why employees routinely engage in non-compliant behaviors by examining the direct and interactive effects of employees’ perceived costs and rewards of compliance and non-compliance on their routinized non-compliant behaviors. Design/methodology/approach Based on rational choice theory, this study used 16 hypothetical scenarios in an experimental survey, collecting data from 326 respondents and analyzing them via structural equation modeling and a four-way factorial experiment. Findings The results suggest that routinized non-compliance of employees is more strongly influenced by the rewards than the costs they perceive in their non-compliance. Further, employees’ routinized non-compliance behavior was found to be positively influenced by an interactive effect of perceived rewards of compliance when their perceptions of their non-compliance costs and rewards were both high and low. Originality/value This paper’s key contribution is to suggest that non-compliance behavior is influenced by direct and interactive effects of perceived rewards of compliance and non-compliance.
Contextual similarity-based multi-level second-order attention network for semi-supervised few-shot learning

In this paper, we tackle the few-shot learning problem in a semi-supervised setting where a limited number of labeled data-points and a number of low-cost unlabeled samples are assumed to be available. In particular, some of the unlabeled samples share the same label space with the support set, referring to as known samples, while some of them are from distracter classes, referring to as unknown ones. The keys are how to learn a powerful representation and how to pick and label unlabeled known instances to construct discriminative classifiers. We address both issues by learning multi-level second-order attention representation followed by a contextual similarity. We first develop a novel trainable multi-level second-order attention network(MSAN) to adaptively learn more powerful feature representation by using second-order feature statistics. Our proposed MSAN is able to better represent the samples while the parameter is not increased. Then we introduce a contextual measure that considers not only the pair-wise relationship but also the task-specific condition, to calculate the similarity between unlabeled samples and each support class, thus to label and pick the quasi-known samples. The hypothesis is that the known unlabeled sample should not only be strongly similar to one particular class, but also be significantly dissimilar to other classes. With the combination of support set and pseudo-labeled set, we train an episodic linear classifier for each episode and the parameters of multi-level second-order attention network are updated by minimizing the loss of the query set. Extensive experiments on four popular benchmarks (Omniglot, miniImageNet, tieredImageNet, and CUB-200-2011) demonstrate that our simple yet effective approach can achieve competitive accuracy compared to the state-of-the-art methods.
KidSpell: Making a difference in spellchecking for children

Children's ability to spell effectively is a major barrier to using search engines successfully. While search engines make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users (i.e., adults) and have proven inadequate for children. The specific target of children for this research are those with early literacy skills (whose are typically ages 6–12). The aim of this work is twofold: first, to address the types of spelling errors children make by researching, developing, and evaluating algorithms to generate and rank candidate English spelling suggestions for children; and second, to improve children's user experience when using our proposed spellchecker by involving them in the design process through participatory design and evaluating the impact of interactive elements on children's spellchecking behaviors. The outcomes of our studies and assessments result in a phonetic-based spelling correction model (KidSpell) that can more accurately correction children's spelling errors than existing state-of-the-art models. Further, we learned that visual and audio cues have a positive impact on children's ability to find their intended word from a list of spelling suggestions.
A sparse negative binomial mixture model for clustering RNA-seq count data

Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse $K$-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with a Gaussian assumption. In this article, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.
Learning Aligned Image-Text Representations Using Graph Attentive Relational Network

Image-text matching aims to measure the similarities between images and textual descriptions, which has made great progress recently. The key to this cross-modal matching task is to build the latent semantic alignment between visual objects and words. Due to the widespread variations of sentence structures, it is very difficult to learn the latent semantic alignment using only global cross-modal features. Many previous methods attempt to learn the aligned image-text representations by the attention mechanism but generally ignore the relationships within textual descriptions which determine whether the words belong to the same visual object. In this paper, we propose a graph attentive relational network (GARN) to learn the aligned image-text representations by modeling the relationships between noun phrases in a text for the identity-aware image-text matching. In the GARN, we first decompose images and texts into regions and noun phrases, respectively. Then a skip graph neural network (skip-GNN) is proposed to learn effective textual representations which are a mixture of textual features and relational features. Finally, a graph attention network is further proposed to obtain the probabilities that the noun phrases belong to the image regions by modeling the relationships between noun phrases. We perform extensive experiments on the CUHK Person Description dataset (CUHK-PEDES), Caltech-UCSD Birds dataset (CUB), Oxford-102 Flowers dataset and Flickr30K dataset to verify the effectiveness of each component in our model. Experimental results show that our approach achieves the state-of-the-art results on these four benchmark datasets.
Convergence rates of deep ReLU networks for multiclass classification

For classification problems, trained deep neural networks return probabilities of class memberships. In this work we study convergence of the learned probabilities to the true conditional class probabilities. More specifically we consider sparse deep ReLU network reconstructions minimizing cross-entropy loss in the multiclass classification setup. Interesting phenomena occur when the class membership probabilities are close to zero. Convergence rates are derived that depend on the near-zero behaviour via a margin-type condition.