Reviewerly/exHarmony huggingface dataset:
State-aligned trolling in Iran and the double-edged affordances of Instagram

Online harassment is increasingly applied as a form of information control to curb free speech and exert power in online public spheres. In recent years, states have appeared to be particularly invested in weaponizing information against dissidents in an attempt at dominating social and political discourses. Reports by prominent human rights institutions, as well as anecdotal evidence, indicate that Iran remains among the states with a track record of such actions. The scope of targeted cyber abuse varies by case. This study investigates the size and perpetrators of online violence, harassment, and abuse against critical members of the Iranian diaspora, including journalists, civil society activists, and artists, among many others. This study substantiates findings of qualitative interviews with a quantitative study of Instagram accounts of related individuals and explores the patterns and communities involved in disseminating hate speech in an attempt at manipulating public opinion and suppressing voices of dissidents.
Toward Privacy and Regulation in Blockchain-Based Cryptocurrencies

Privacy is supreme in cryptocurrencies since most users do not want to reveal their identities or the transaction amount in financial transactions. Nevertheless, achieving privacy in blockchain-based cryptocurrencies remains challenging since blockchain is by default a public ledger. For instance, Bitcoin provides builtin pseudonymity rather than true anonymity, which can be compromised by analyzing the transactions. Several solutions have been proposed to enhance the transaction privacy of Bitcoin. Unfortunately, full anonymity is not always desirable, because malicious users are able to conduct illegal transactions, such as money laundering and drug trading, under the cover of anonymity in cryptocurrencies. As a result, regulation in blockchain-based cryptocurrencies is very essential. In this article, we analyze the privacy issues in Bitcoin and investigate some existing privacy-enhancing techniques in blockchain- based cryptocurrencies as well as some privacy-focused altcoins. In addition, we review and compare some works dealing with regulation of cryptocurrencies. Finally, we propose two possible solutions from a top view to balance privacy and regulation of blockchain-based cryptocurrencies. One solution is based on decentralized group signature, in which a group manager is responsible for building a group and tracing the real payer of the group in a transaction. The other solution is based on verifiable encryption, in which a tracing manager is not actively involved in normal transactions but can trace suspicious transactions via an encrypted tag.
An information aware event-triggered scheme for particle filter based remote state estimation

The remote state estimation problem is considered for general non-Gaussian systems. The estimator runs particle filtering algorithm to track the non-Gaussian probability density function (PDF) of the target state. We are concerned with the reduction of sensor-to-estimator communication while maintaining acceptable estimation accuracy. For this purpose, a novel event-based transmission scheme is proposed where the Kullback–Leibler divergence is used to identify informative measurements. We develop a two-step approximation procedure to obtain a parametric form for the event generator function, thereby enabling each sensor to quantify the informativeness of its current measurement without running a copy of the estimator. Furthermore, a Monte Carlo method is proposed to evaluate the likelihood function of the set-valued measurements. Simulation results demonstrate the effectiveness of our scheme, especially when the predictive PDF of the measurement is strongly non-Gaussian.
Rover-IRL: Inverse Reinforcement Learning With Soft Value Iteration Networks for Planetary Rover Path Planning

Planetary rovers, such as those currently on Mars, face difficult path planning problems, both before landing during the mission planning stages as well as once on the ground. In this work, we present a new approach to these planning problems based on inverse reinforcement learning using deep convolutional networks and value iteration networks (VIN) as important internal structures. VIN are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. We propose a modification to the value iteration recurrence, referred to as the soft value iteration network (SVIN). SVIN is designed to produce more effective training gradients through the VIN. It relies on an internal soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action. We demonstrate the effectiveness of our proposed architecture in both a grid world dataset as well as a highly realistic synthetic dataset generated from currently deployed rover mission planning tools and real Mars imagery.
Active Perception for Foreground Segmentation: An RGB-D Data-Based Background Modeling Method

Foreground moving object segmentation is a fundamental problem in many computer vision applications. As a solution for foreground segmentation, background modeling has been intensively studied over past years and many effective algorithms have been developed. However, accurate foreground segmentation is still a difficult problem. Currently, most of the algorithms work solely within the color space, in which the segmentation performance is prone to be degraded by a multitude of challenges, such as illumination changes, shadows, automatic camera adjustments, and color camouflage. RGB-D cameras are active visual sensors that provide depth measurements along with color images. We present in this paper an innovative background modeling method by using both the color and depth information from an RGB-D camera. The proposed method is evaluated using a public RGB-D data set. Various experiments confirm that our method is able to achieve superior performance compared with existing well-known methods.
Exploiting spatial relation for fine-grained image classification

Fine-Grained Image Classification (FGIC) aims to distinguish the images within a subordinate category. Recently, many FGIC methods have been proposed and huge progress has been made in the aspects of part detection and feature learning for FGIC. However, FGIC still remains a challenging task due to the large intra-class variance and small inter-class variance. To classify fine-grained images accurately, this paper proposes to exploit spatial relation to capture more discriminative details for FGIC. The proposed method contains two core modules: part selection module and representation module. The part selection module utilizes intrinsic spatial relation between object parts to select object part pairs with high discrimination power. The representation module exploits the interaction between object parts to describe the selected part pairs and construct a semantic image representation for FGIC. The proposed method is evaluated on CUB-200-2011 and FGVC-Aircraft datasets. Experimental results show that the classification accuracy of the proposed method can reach 85.5% on CUB-200-2011 and 86.9% on FGVC-Aircraft respectively, which exceed comparison methods obviously.
Spatial-Temporal Context-Aware Tracking

Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, most of the existing DCF trackers only consider the spatial features of the target and could hardly benefit from the inter-frame and historical information, which may degrade the tracking performance when occlusion and deformation occurs. To tackle the above-mentioned issues, in this letter, by introducing the temporal constrain into the DCF tracker, we advocate our spatial-temporal context-aware tracker. Through jointly modeling the spatial context and historical target information, our tracker could not only adapt the appearance change but also maintain a relatively stable filter due to the small target variation between inter-frames. Furthermore, we show that the proposed objective formula could be directly solved using the Alternating Direction Method of Multipliers (ADMM) technique with low computational cost. Experiments on the large-scale benchmark demonstrate that the proposed trackers perform favorably against other state-of-the-art methods.
Socio-technical systems cybersecurity framework

Purpose This paper aims to identify and appropriately respond to any socio-technical gaps within organisational information and cybersecurity practices. This culminates in the equal emphasis of both the social, technical and environmental factors affecting security practices. Design/methodology/approach The socio-technical systems theory was used to develop a conceptual process model for analysing organisational practices in terms of their social, technical and environmental influence. The conceptual process model was then applied to specifically analyse some selected information and cybersecurity frameworks. The outcome of this exercise culminated in the design of a socio-technical systems cybersecurity framework that can be applied to any new or existing information and cybersecurity solutions in the organisation. A framework parameter to help continuously monitor the mutual alignment of the social, technical and environmental dimensions of the socio-technical systems cybersecurity framework was also introduced. Findings The results indicate a positive application of the socio-technical systems theory to the information and cybersecurity domain. In particular, the application of the conceptual process model is able to successfully categorise the selected information and cybersecurity practices into either social, technical or environmental practices. However, the validation of the socio-technical systems cybersecurity framework requires time and continuous monitoring in a real-life environment. Practical implications This research is beneficial to chief security officers, risk managers, information technology managers, security professionals and academics. They will gain more knowledge and understanding about the need to highlight the equal importance of both the social, technical and environmental dimensions of information and cybersecurity. Further, the less emphasised dimension is posited to open an equal but mutual security vulnerability gap as the more emphasised dimension. Both dimensions must, therefore, equally and jointly be emphasised for optimal security performance in the organisation. Originality/value The application of socio-technical systems theory to the information and cybersecurity domain has not received much attention. In this regard, the research adds value to the information and cybersecurity studies where too much emphasis is placed on security software and hardware capabilities.
Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text

Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes.We propose a novel neural approach to model shortest dependency path (SDP) between target entities together with the sentence sequence for clinical relation extraction. Our neural network architecture consists of three modules: (1) sentence sequence representation module using bidirectional long short-term memory network (Bi-LSTM) to capture the features in the sentence sequence; (2) SDP representation module implementing the convolutional neural network (CNN) and Bi-LSTM network to capture the syntactic context for target entities using SDP information; and (3) classification module utilizing a fully-connected layer with Softmax function to classify the relation type between target entities.Using the 2010 i2b2/VA relation extraction dataset, we compared our approach with other baseline methods. Our experimental results show that the proposed approach achieved significant improvements over comparable existing methods, demonstrating the effectiveness of utilizing syntactic structures in deep learning-based relation extraction. The F-measure of our method reaches 74.34% which is 2.5% higher than the method without using syntactic features.We propose a new neural network architecture by modeling SDP along with sentence sequence to extract multi-relations from clinical text. Our experimental results show that the proposed approach significantly improve the performances on clinical notes, demonstrating the effectiveness of syntactic structures in deep learning-based relation extraction.
A Variable-Gain Finite-Time Convergent Recurrent Neural Network for Time-Variant Quadratic Programming With Unknown Noises Endured

A variable-gain finite-time convergent and noise-enduring zeroing neural network (VGFTNE-ZNN) is for the first time proposed for time-variant convex quadratic programming (QP). Differing from the existing finite-time convergent ZNNs with constant or variable design gains (i.e., CGFT-ZNN and VGFT-ZNN) that have limited noise-handling capabilities, the proposed VGFTNE-ZNN can endure additive noises by dynamically adjusting its design gains in finite time. Design gains of the unpolluted VGFTNE-ZNN are allowed to be constant when the QP problem is solved, whereas the design gain of the existing unpolluted VGFT-ZNN unrealistically increases to infinity when time evolves to infinity. Unlike existing polluted ZNNs with known noises involved, more practical unknown noises are successfully handled by the VGFTNE-ZNN. The finite-time convergence and noise-endurance properties of the VGFTNE-ZNN are mathematically proved based on the Lyapunov theory. Numerical verifications are comparatively performed with the superiorities of the VGFTNE-ZNN substantiated as compared with the existing CGFT-ZNN and VGFT-ZNN.
The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis.A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files.Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task.Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word.Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account.Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent.Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data.This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types.Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.
A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data

Deep convolutional neural networks (CNNs) have achieved state-of-the-art performances for multi-class segmentation of medical images. However, a common problem when dealing with large, high resolution 3D data is that the volumes input into the deep CNNs has to be either cropped or downsampled due to limited memory capacity of computing devices. These operations can lead to loss of resolution and class imbalance in the input data batches, thus downgrade the performances of segmentation algorithms. Inspired by the architecture of image super-resolution CNN (SRCNN), we propose a two-stage modified U-Net framework that simultaneously learns to detect a ROI within the full volume and to classify voxels without losing the original resolution. Experiments on a variety of multi-modal 3D cardiac images have demonstrated that this framework shows better segmentation performances than state-of-the-art Deep CNNs with trained with the same similarity metrics.
Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system

The measurement of solar energy data is a difficult task and rarely available even for those stations where measurement has already been done. Further, the PV power forecasting is an important element for smart energy management system. In the present scenario, utilities are developing the smart-grid application and PV power forecasting is an important key tool for a new paradigm. The forecasting of solar energy during clear sky-condition can be easily estimated using mathematical models; however, forecasting under the influence of hazy, cloudy, and foggy sky-conditions do not provide accuracy with these models. Therefore, an intelligent modelling techniques i.e. fuzzy logic, artificial neural network (ANN), and adaptive-neural-fuzzy-inference system (ANFIS) models are proposed based on sky-conditions namely clear/sunny sky, hazy sky, partially cloudy/foggy sky, and fully cloudy/foggy sky-conditions for forecasting global solar energy. To design the model, 15 years averaged datasets of meteorological parameters were used for distinct climate zones across India. Further, comparison of intelligent models has been carried out with regression models using statistical indicators. The proposed model has been implemented for short-term PV power forecasting under composite climatic conditions. Simulation results confirm that the ANFIS model provides supremacy for PV power forecast as compared to other models.
Unsupervised cross-modal retrieval via Multi-modal graph regularized Smooth Matrix Factorization Hashing

The existing cross-modal hashing methods often encounter quantization loss which is caused by relaxing discrete hash codes in the process of cross-modal retrieval. To counter this problem, a Multi-modal graph regularized Smooth matrix Factorization Hashing (MSFH) approach is represented for unsupervised cross-modal retrieval. In the proposal framework, a smooth matrix generated by a control parameter is introduced into the matrix decomposition model, which can guarantee the sparsity of the dictionaries learned and the extracted common features at the same time, thus reducing the quantization loss in the hashing process. Furthermore, to preserve the topology of the original data, a multi-modal graph regularization term is drawn into the model, which consists of two parts. One is the intra-modal similarity graph which is used to preserve the geometric structure of each modality. The other is the inter-modal similarity graph reconstructed by the symmetric nonnegative matrix factorization, which is employed to soften the structure difference between modalities. The goal of MSFH is to learn unified hash-codes for multi-modal data in a shared latent semantic space in which the similarity of different modalities can be estimated effectively. And the corresponding experimental results on three benchmark data sets demonstrate the superiority of the proposed approach over several state-of-the-art cross-modality hashing approaches.
Blind image quality assessment via learnable attention-based pooling

Many recent algorithms based on convolutional neural network (CNN) for blind image quality assessment (BIQA) share a common two-stage structure, i.e., local quality measurement followed by global pooling. In this paper, we mainly focus on the pooling stage and propose an attention-based pooling network (APNet) for BIQA. The core idea is to introduce a learnable pooling that can model human visual attention in a data-driven manner. Specifically, the APNet is built by incorporating an attention module and allows for a joint learning of local quality and local weights. It can automatically learn to assign visual weights while generating quality estimations. Moreover, we further introduce a correlation constraint between the estimated local quality and attention weight in the network to regulate the training. The constraint penalizes the case in which the local quality estimation on a region attracting more attention differs a lot from the overall quality score. Experimental results on benchmark databases demonstrate that our APNet achieves state-of-the-art prediction accuracy. By yielding an attention weight map as by-product, our model gives a better interpretability on the learned pooling.
Who should be my teammates

We are building an intelligent agent to help teaming efforts. In this paper, we investigate the real-world use of such an agent to understand students deeply and help student team formation in a large university class involving about 200 students and 40 teams. Specifically, the agent interacted with each student in a text-based conversation at the beginning and end of the class. We show how the intelligent agent was able to elicit in-depth information from the students, infer the students' personality traits, and reveal the complex relationships between team personality compositions and team results. We also report on the students' behavior with and impression of the agent. We discuss the benefits and limitations of such an intelligent agent in helping team formation, and the design considerations for creating intelligent agents for aiding in teaming efforts.
Throughput/area optimised pipelined architecture for elliptic curve crypto processor

A pipelined architecture is proposed in this work to speed up the point multiplication in elliptic curve cryptography (ECC). This is achieved, at first; by pipelining the arithmetic unit to reduce the critical path delay. Second, by reducing the number of clock cycles (latency), which is achieved through careful scheduling of computations involved in point addition and point doubling. These two factors thus, help in reducing the time for one point multiplication computation. On the other hand, the small area overhead for this design gives a higher throughput/area ratio. Consequently, the proposed architecture is synthesised on different FPGAs to compare with the state-of-the-art. The synthesis results over GF(2m) show that the proposed design can work up to a frequency of 369, 357 and 337 MHz when implemented for m = 163, 233 and 283 bit key lengths, respectively, on Virtex-7 FPGA. The corresponding throughput/slice figures are 42.22, 12.37 and 9.45, which outperform existing implementations.