Reviewerly/exHarmony huggingface dataset:
Information security policy noncompliance: An integrative social influence model

Abstract Despite the significant advancements made in understanding the factors that drive employees' compliance and noncompliance behaviours with information security policy (ISP), less is known about how different factors interact to impact such behaviours. Having been drawn on the social information processing theory, this research develops an integrative model that investigates how ethical work climate, beliefs, and neutralization interact to jointly explain ISP noncompliance. The model is tested via a survey of a broad cross section of employees. Neutralization, perceived cost of compliance, and perceived cost of noncompliance are found to significantly impact ISP noncompliance. Egoistic, benevolent, and principled climates are found to differentially influence neutralization and individuals' cognitive beliefs about the cost and benefit of ISP compliance versus noncompliance. Neutralization appears to be a more important moderator of the belief‐noncompliance relationship than the principled climate.
Understanding the Role of Alternatives in Data Analysis Practices

Data workers are people who perform data analysis activities as a part of their daily work but do not formally identify as data scientists. They come from various domains and often need to explore diverse sets of hypotheses and theories, a variety of data sources, algorithms, methods, tools, and visual designs. Taken together, we call these alternatives. To better understand and characterize the role of alternatives in their analyses, we conducted semi-structured interviews with 12 data workers with different types of expertise. We conducted four types of analyses to understand 1) why data workers explore alternatives; 2) the different notions of alternatives and how they fit into the sensemaking process; 3) the high-level processes around alternatives; and 4) their strategies to generate, explore, and manage those alternatives. We find that participants' diverse levels of domain and computational expertise, experience with different tools, and collaboration within their broader context play an important role in how they explore these alternatives. These findings call out the need for more attention towards a deeper understanding of alternatives and the need for better tools to facilitate the exploration, interpretation, and management of alternatives. Drawing upon these analyses and findings, we present a framework based on participants' 1) degree of attention, 2) abstraction level, and 3) analytic processes. We show how this framework can help understand how data workers consider such alternatives in their analyses and how tool designers might create tools to better support them.
An Adaptive and Parameter-Free Recurrent Neural Structure for Wireless Channel Prediction

Predicting channel state information (CSI) is a fundamental element in wireless communications systems. An accurate CSI estimation and prediction is critical to the system performance. This paper introduces a recurrent neural network (RNN) based approach for real-time prediction in real-world non-stationary channels. It uses the recent history data for online training, followed by prediction employing the trained model, in order to adapt to the changing channel and obtain a more accurate CSI prediction compared to conventional methods. Furthermore, the proposed method needs no additional knowledge, such as the internal properties of the channel itself, or the external features that affect the channel propagation, greatly facilitating its use in practical systems. Simulation results show that the proposed adaptive and parameter free recurrent neural structure (APF-RNS) outperforms the existing methods under a dynamically changing non-stationary environment. Therefore, the proposed online training based RNN approach is a promising method for channel prediction in wireless communications.
Adversarial Examples for Automatic Speech Recognition: Attacks and Countermeasures

Speech is a common and effective approach for communication between humans and modern mobile devices such as smartphones or home hubs. The remarkable advances in computing and networking have popularized automatic speech recognition (ASR) systems, which can interpret received speech signals on mobile devices and enable us to remotely control and interact with those devices. Despite promising development, audio adversarial examples, a new kind of attack on advanced ASR systems, are found to be extremely effective in imitating human speech while fooling mobile devices to produce incorrect commands. In this article, we provide a systematic survey of audio adversarial examples in the literature. We first present an overview of the architecture of ASR systems and outline the basic attack philosophy. Followed by a brief introduction of the state-of-the-art solutions to audio adversarial examples, a comprehensive comparison is presented. Finally, after discussing existing countermeasures to defend ASR, we highlight several promising future research directions and challenges on constructing more robust and practical audio adversarial examples.
A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

Chicken Swarm Optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with Teaching–Learning-based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs

Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation.Weakly-supervised semantic parsers are trained on utterancedenotation pairs treating programs as latent.The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples.Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs.We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables.In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial "abstract program" and (2) refining it while modeling structured alignments with differential dynamic programming.We obtain state-of-the-art performance on the WIK-ITABLEQUESTIONS and WIKISQL datasets.When compared to a standard attention baseline, we observe that the proposed structuredalignment mechanism is highly beneficial.
Syntax-aware Multilingual Semantic Role Labeling

Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation.However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment.Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation.Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively.We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement.Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages.Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction

Distance supervision is widely used in relation extraction tasks, particularly when large-scale manual annotations are virtually impossible to conduct.Although Distantly Supervised Relation Extraction (DSRE) benefits from automatic labelling, it suffers from serious mislabelling issues, i.e. some or all of the instances for an entity pair (head and tail entities) do not express the labelled relation.In this paper, we propose a novel model that employs a collaborative curriculum learning framework to reduce the effects of mislabelled data.Specifically, we firstly propose an internal self-attention mechanism between the convolution operations in convolutional neural networks (CNNs) to learn a better sentence representation from the noisy inputs.Then we define two sentence selection models as two relation extractors in order to collaboratively learn and regularise each other under a curriculum scheme to alleviate noisy effects, where the curriculum could be constructed by conflicts or small loss.Finally, experiments are conducted on a widely-used public dataset and the results indicate that the proposed model significantly outperforms baselines including the state-of-the-art in terms of P@N and PR curve metrics, thus evidencing its capability of reducing noisy effects for DSRE.
A Survey on Reversible Watermarking for Multimedia Content: A Robustness Overview

In recent years, the growth of the Internet along with multimedia data delivery, have caused the misuse and even alteration of the original digital content. As a security mechanism, digital watermarking has been used to protect digital content, traditionally in copyright protection applications, and more recently digital watermarking has been used in content enhancement applications. Regardless of the application, digital watermarking schemes always cause some level of signal degradation. There are however, certain applications as in the military or medical fields, where loss of information cannot be tolerated during the reconstruction of host signals, therefore conventional watermarking schemes do not satisfy the quality requirements. In order to reconstruct signals without loss of data, reversible watermarking is employed. Recent research in reversible watermarking has focused on either, the improvement of embedding capacity or the reduction of perceptual impact of the schemes, but scarcely exploring the robustness of reversible schemes. In this survey, we provide a deep review of reversible watermarking schemes and propose a new classification outline based on the robustness point of view. Finally, some future directions for reversible watermarking research are presented and open problems in robust reversible watermarking are outlined.
Accountable privacy preserving attribute based framework for authenticated encrypted access in clouds

In this paper, we propose an accountable privacy preserving attribute-based framework, called Ins-PAbAC, that combines attribute based encryption and attribute based signature techniques for securely sharing outsourced data contents via public cloud servers. The proposed framework presents several advantages. First, it provides an encrypted access control feature, enforced at the data owner’s side, while providing the desired expressiveness of access control policies. Second, Ins-PAbAC preserves users’ privacy, relying on an anonymous authentication mechanism, derived from a privacy preserving attribute based signature scheme that hides the users’ identifying information. Furthermore, our proposal introduces an accountable attribute based signature that enables an inspection authority to reveal the identity of the anonymously-authenticated user if needed. Third, Ins-PAbAC is provably secure, as it is resistant to both curious cloud providers and malicious users adversaries. Finally, experimental results, built upon OpenStack Swift testbed, point out the applicability of the proposed scheme in real world scenarios.
Target Discrimination Based on Weakly Supervised Learning for High-Resolution SAR Images in Complex Scenes

To design a highly automatic and practical discrimination method for high-resolution synthetic aperture radar (SAR) images in complex scenes, a novel target discrimination framework based on weakly supervised learning (WSL) of the mid-level features is proposed in this article. First, we extract the dense SAR scale-invariant feature transform (SAR-SIFT) features of the candidate regions obtained from the detected SAR images. Then, the dense SAR-SIFT descriptors are transformed into richer mid-level features by coding and pooling. Finally, the mid-level features are input into a WSL-based target discrimination method, where the training set is initially selected by the unsupervised latent Dirichlet allocation (LDA) and iteratively updated by the linear support vector machine (SVM) discriminator. In the proposed method, only the image-level annotations (weak labels), which indicate whether the images containing the targets of interest or not, are required. By introducing WSL, the manual annotations of target regions from SAR images can be avoided, which is generally expensive in complex scenes and may tend to be less accurate and unreliable for the occluded or camouflaged targets. The comprehensive and specific experiments on the measured SAR data have demonstrated the effectiveness of the proposed method in benchmarking with the supervised learning-based linear SVM and linear support vector data description (SVDD) discriminators.
Data Mining and Information Retrieval in the 21st century: A bibliographic review

Data Mining and Information Retrieval is an emerging interdisciplinary discipline dealing with Information Retrieval and Data Mining techniques. It has undergone rapid development with the advances in mathematics, statistics, information science, and computer science. In this paper, we present an empirical analysis of publication metadata obtained from 6 top-tier journals and 9 conferences for the first 16 years of the 21st Century, and evaluate the dynamic characteristics of Data Mining and Information Retrieval. We find a steady growth both in terms of productivity and impact, evidenced by the unabated number of publications/citations over the period of study. We note that the modality for co-operation in this field is changing from independent to collaborative. Furthermore, according to the citation pattern, the field is becoming open-minded as illustrated by a gradual decline of self-citation rates, which was dropped to 10% in 2015, nearly three times lower than what it was in 2000. Finally, we explore the inner structure relying on the topics evolution from the aspects of popular keywords/topics identification and evolution. Overall, this study provides insights of Data Mining and Information Retrieval behind its demonstrated growth in the recent past, with the ultimate goal of revealing its potential of driving scientific innovation in the future.
Adaptive latent similarity learning for multi-view clustering

Most existing clustering methods employ the original multi-view data as input to learn the similarity matrix which characterizes the underlying cluster structure shared by multiple views. This reduces the flexibility of multi-view clustering methods due to the fact that multi-view data usually contains noise or the variation between multi-view data points, which should belong to the same cluster, is larger than the variation between data points belonging to different clusters. To address these problems, we propose a novel multi-view clustering model, namely adaptive latent similarity learning (ALSL) for multi-view clustering. ALSL employs the adaptively learned graph, which characterizes the relationship between clusters, as the new input to learn the latent data representation and integrates the latent similarity representation learning, manifold learning and spectral clustering into a unified framework. With the complementarity of multiple views, the latent similarity representation characterizes the underlying cluster structure shared by multiple views. Our model is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have demonstrated the superiority of the proposed method.
A New Trajectory-Planning Beetle Swarm Optimization Algorithm for Trajectory Planning of Robot Manipulators

Based on a heuristic optimization algorithm, this paper proposes a new algorithm named trajectory-planning beetle swarm optimization (TPBSO) algorithm for solving trajectory planning of robots, especially robot manipulators.Firstly, two specific manipulator trajectory planning problems are presented as the practical application of the algorithm, which are point-to-point planning and fixed-geometric-path planning.Then, in order to verify the effectiveness of the algorithm, this paper develops a control model and conducts numerical experiments on two planning tasks.Moreover, it compares with existing algorithms to show the superiority of our proposed algorithm.Finally, the results of numerical comparisons show that algorithm has a relatively faster computational speed and better control performance without increasing computational complexity.
TC-Net for iSBIR

Sketch has been employed as an effective communication tool to express the abstract and intuitive meaning of object. While content-based sketch recognition has been studied for several decades, the instance-level Sketch Based Image Retrieval (iSBIR) task has attracted significant research attention recently. In many previous iSBIR works -- TripletSN, and DSSA, edge maps were employed as intermediate representations in bridging the cross-domain discrepancy between photos and sketches. However, it is nontrivial to efficiently train and effectively use the edge maps in an iSBIR system. Particularly, we find that such an edge map based iSBIR system has several major limitations. First, the system has to be pre-trained on a significant amount of edge maps, either from large-scale sketch datasets, e.g., TU-Berlin~\citeeitz2012hdhso, or converted from other large-scale image datasets, e.g., ImageNet-1K\citedeng2009imagenet dataset. Second, the performance of such an iSBIR system is very sensitive to the quality of edge maps. Third and empirically, the multi-cropping strategy is essentially very important in improving the performance of previous iSBIR systems. To address these limitations, this paper advocates an end-to-end iSBIR system without using the edge maps. Specifically, we present a Triplet Classification Network (TC-Net) for iSBIR which is composed of two major components: triplet Siamese network, and auxiliary classification loss. Our TC-Net can break the limitations existed in previous works. Extensive experiments on several datasets validate the efficacy of the proposed network and system.
CogniVal: A Framework for Cognitive Word Embedding Evaluation

An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain.However, most, if not all, previous works only focus on small datasets and a single modality.In this paper, we present the first multimodal framework for evaluating English word representations based on cognitive lexical semantics.Six types of word embeddings are evaluated by fitting them to 15 datasets of eyetracking, EEG and fMRI signals recorded during language processing.To achieve a global score over all evaluation hypotheses, we apply statistical significance testing accounting for the multiple comparisons problem.This framework is easily extensible and available to include other intrinsic and extrinsic evaluation methods.We find strong correlations in the results between cognitive datasets, across recording modalities and to their performance on extrinsic NLP tasks.
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task

This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge.We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence.We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence selection and as the entailment classifier.We report a FEVER score of 68.46% on the blind test set.
Fading channel estimation for free-space continuous-variable secure quantum communication

Abstract We investigate estimation of fluctuating channels and its effect on security of continuous-variable quantum key distribution. We propose a novel estimation scheme which is based on the clusterization of the estimated transmittance data. We show that uncertainty about whether the transmittance is fixed or not results in a lower key rate. However, if the total number of measurements is large, one can obtain using our method a key rate similar to the non-fluctuating channel even for highly fluctuating channels. We also verify our theoretical assumptions using experimental data from an atmospheric quantum channel. Our method is therefore promising for secure quantum communication over strongly fluctuating turbulent atmospheric channels.
Learning Disentangled Representations via Mutual Information Estimation

In this paper, we investigate the problem of learning disentangled representations. Given a pair of images sharing some attributes, we aim to create a low-dimensional representation which is split into two parts: a shared representation that captures the common information between the images and an exclusive representation that contains the specific information of each image. To address this issue, we propose a model based on mutual information estimation without relying on image reconstruction or image generation. Mutual information maximization is performed to capture the attributes of data in the shared and exclusive representations while we minimize the mutual information between the shared and exclusive representation to enforce representation disentanglement. We show that these representations are useful to perform downstream tasks such as image classification and image retrieval based on the shared or exclusive component. Moreover, classification results show that our model outperforms the state-of-the-art models based on VAE/GAN approaches in representation disentanglement.