Reviewerly/exHarmony huggingface dataset:
“Everything Has Changed, and Nothing Has Changed in Journalism”: Revisiting Journalistic Sourcing Practices and Verification Techniques during the 2011 Egyptian Revolution and Beyond

Using the Egyptian Revolution as a case study, this article studies journalistic sourcing and verification through in-depth interviews with journalists in the United Kingdom. While the coverage of the event in the British media was dominated by civic, unofficial sources, interviews conducted in 2014 revealed that journalists only included these if no other sources were available. In fact, journalists voiced concern with regards to verification of online sources, and rarely included these as direct, first-hand accounts. Follow-up interviews conducted in 2020 point to developments journalism practice has undergone since, particularly in relation to open-source content verification. Overall, the picture we paint of British journalists’ handling of content sourced from social media is one wedged between expressed enthusiasm and cautious scepticism.
An Optimized LightGBM Model for Fraud Detection

Abstract The rapid development of e-commerce and the growing popularity of credit cards have made online transactions smooth and convenient. However, large numbers of online transactions are also the targets of online credit card fraud, which aggregate to enormous losses annually. In response to this trend, many machine learning and deep learning methods have been proposed to solve this problem. Unfortunately, most models have been developed on small datasets and require tedious fine-tuning processes. In this paper, a LightGBM-based method for fraud detection is proposed. The dataset used for this study is the IEEE-CIS Fraud Detection dataset provided by Vesta Corporation, which includes over 1 million samples. Experiments have shown that the LightGBM-based method outperforms most classical methods based on Support Vector Machine, XGBoost, or Random Forest. Besides, effective feature engineering methods for feature selection and Bayesian fine-tuning for automatic hyperparameter searching are also proposed.
EfficientAutoGAN: Predicting the Rewards in Reinforcement-Based Neural Architecture Search for Generative Adversarial Networks

This article is inspired by human’s memory and recognition process to improve neural architecture search (NAS), which has shown novelty and significance in the design of generative adversarial networks (GANs), but the extremely enormous time consumption for searching GAN architectures based on reinforcement learning (RL) limits its applicability to a great extent. The main reason behind the challenge is that, the performance evaluation of subnetworks during the search process takes too much time. To solve this problem, we propose a new algorithm, EfficientAutoGAN, in which a graph convolution network (GCN) predictor is introduced to predict the performance of subnetworks instead of formally assessing or evaluating them. Experiments show that EfficientAutoGAN saves nearly half of the search time and at the same time, demonstrates comparable overall network performance to the state-of-the-art algorithm, AutoGAN, in the field of RL-based NAS for GAN.
Efficient and Scalable Electronic Health Record Management using Permissioned Blockchain Technology

Blockchain Technology has been relevant to the industry since the advent of Bitcoin in the year 2009. In recent years, it has been an important field of study and research as it has the potential to transform a wide range of industries. Recent studies reveal that Electronic Health Records (EHR) are susceptible to problems like Data Breaching, Interoperability and Information Asymmetry leading to poor accessibility of a patient's medical history. This paper focusses on the implementation of permissioned blockchain in EHR management by providing a decentralized, cost-efficient and a secure environment. The aim of our proposed architecture is to improve the scalability of Blockchain used in EHR to provide an improvised access to a patient's health record.
Deep Probabilistic Feature-Metric Tracking

Dense image alignment from RGB-D images remains a critical issue for real-world applications, especially under challenging lighting conditions and in a wide baseline setting. In this letter, we propose a new framework to learn a pixel-wise deep feature map and a deep feature-metric uncertainty map predicted by a Convolutional Neural Network (CNN), which together formulate a deep probabilistic feature-metric residual of the two-view constraint that can be minimised using Gauss-Newton in a coarse-to-fine optimisation framework. Furthermore, our network predicts a deep initial pose for faster and more reliable convergence. The optimisation steps are differentiable and unrolled to train in an end-to-end fashion. Due to its probabilistic essence, our approach can easily couple with other residuals, where we show a combination with ICP. Experimental results demonstrate state-of-the-art performances on the TUM RGB-D dataset and the 3D rigid object tracking dataset. We further demonstrate our method's robustness and convergence qualitatively.
Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter

The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works. ICI is the recently developed method of ellipsoidal intersection and is used to get the combined estimate of prior covariance. This method integrates the sample covariance estimate, which is unbiased but usually with high variance, together with a more structured but typically a biased target covariance through fusion gains. For verifying the performance of the proposed algorithm, analysis and simulations are performed. Through the simulations, the results are given to illustrate the consistency and accuracy of the proposed algorithm's estimation and target tracking performance.
Making the BKW Algorithm Practical for LWE

The Learning with Errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum-Kalai-Wasserman (BKW) algorithm. This paper presents new improvements for BKW-style algorithms for solving LWE instances. We target minimum concrete complexity and we introduce a new reduction step where we partially reduce the last position in an iteration and finish the reduction in the next iteration, allowing non-integer step sizes. We also introduce a new procedure in the secret recovery by mapping the problem to binary problems and applying the Fast Walsh Hadamard Transform. The complexity of the resulting algorithm compares favourably to all other previous approaches, including lattice sieving. We additionally show the steps of implementing the approach for large LWE problem instances. The core idea here is to overcome RAM limitations by using large file-based memory.
Analysis Techniques for Illicit Bitcoin Transactions

This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal, and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points.
WITHDRAWN: A novel method to enhance the performance evaluation of DBSCAN clustering algorithm using different distinguished metrics

The main task in data mining is to group data into meaningful clusters for which many clustering algorithms are used. Among various clustering algorithms the most effective one is DBSCAN algorithm which can be used for different application. This algorithm is noted as high quality density based method which has several advantages like identifying the arbitrarily shaped clusters, the number of clusters to be used need not be predefined, and it can identify the outliers and can ignore it before clustering. The two main input parameters used are Epsilon (Eps) and minimum number of points (MinPts) has great effect on clustering performance. Hence to solve this problem automatic selection of Eps and MinPts is done using K-distance graph method and neighbourhood calculation for each data point is speeded up using spatial access methods. The proposed new algorithm which makes use of spatial access method and k-distance graph method to increase the performance in terms of scalability and speeds up the execution process. The experimental results obtained clearly shows that by combining K-Distance tree method with DBSCAN algorithm is efficient in terms of all the metrics and can cluster both high and low dimensional data efficiently.
High spatial resolution Raman mapping of complex mineral assemblages: Application on phosphate mineral sequences in pegmatites

Abstract Phosphates are a group of complex minerals widespread on Earth that can crystallize in several environments. Changes in environmental conditions during crystallization are reflected in the formation of a sequence of phosphate minerals, termed paragenesis. Accordingly, analyses of textural and chemical properties of complex mineral assemblages are essential to reconstruct the history of rocks and solid materials. Raman mapping can detect those mineralogical changes in complex assemblages with high spatial resolution, successfully determining the paragenetic sequence. Specifically, Raman spectroscopy is more advantageous than conventional methods because it requires little sample preparation, it is nondestructive, and it is capable of identifying hydrated minerals and phases composed of light elements ( e.g ., H and Li). In this study, the parageneses of two phosphatic assemblages from the Buranga pegmatite, a P–Li–Nb–Ta‐rich magmatic rock, are reported in detail. The first case shows the replacement of primary F‐rich montebrasite [LiAl(PO 4 )(OH,F)] by secondary F‐poor montebrasite [LiAl(PO 4 )OH]. Mapping of the OH‐stretching peak position and width identifies the mineral phases, illustrates their spatial relations, and allows to estimate the F content distribution. The second case investigates a sequence of phosphate minerals in a complex assemblage (trolleite + rosemaryite ➔ lazulite–scorzalite ➔ samuelsonite ➔ wardite). The application of multivariate analysis methods to characterize complex assemblages is assessed, and the classical least squares method is identified as the best suited for this setting. An analysis of Raman images shows that Buranga samples experienced a progressive hydration process during cooling, followed by fluid‐assisted mineral reactions with remobilization of elements, such as Ca and Na.
Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios

Human eye movements while driving reveal that visual attention largely depends on the context in which it occurs. Furthermore, an autonomous vehicle which performs this function would be more reliable if its outputs were understandable. Capsule Networks have been presented as a great opportunity to explore new horizons in the Computer Vision field, due to their capability to structure and relate latent information. In this article, we present a hierarchical approach for the prediction of eye fixations in autonomous driving scenarios. Context-driven visual attention can be modeled by considering different conditions which, in turn, are represented as combinations of several spatio-temporal features. With the aim of learning these conditions, we have built an encoder-decoder network which merges visual features' information using a global-local definition of capsules. Two types of capsules are distinguished: representational capsules for features and discriminative capsules for conditions. The latter and the use of eye fixations recorded with wearable eye tracking glasses allow the model to learn both to predict contextual conditions and to estimate visual attention, by means of a multi-task loss function. Experiments show how our approach is able to express either frame-level (global) or pixel-wise (local) relationships between features and contextual conditions, allowing for interpretability while maintaining or improving the performance of black-box related systems in the literature. Indeed, our proposal offers an improvement of 29% in terms of information gain with respect to the best performance reported in the literature.
On Learning Semantic Representations for Large-Scale Abstract Sketches

In this paper, we focus on learning semantic representations for large-scale highly abstract sketches that were produced by the practical sketch-based application rather than the excessively well dawn sketches obtained by crowd-sourcing. We propose a dual-branch CNN-RNN network architecture to represent sketches, which simultaneously encodes both the static and temporal patterns of sketch strokes. Based on this architecture, we further explore learning the sketch-oriented semantic representations in two practical settings, i.e., hashing retrieval and zero-shot recognition on million-scale highly abstract sketches produced by practical online interactions. Specifically, we use our dual-branch architecture as a universal representation framework to design two sketch-specific deep models: (i) We propose a deep hashing model for sketch retrieval, where a novel hashing loss is specifically designed to further accommodate both the abstract and messy traits of sketches. (ii) We propose a deep embedding model for sketch zero-shot recognition, via collecting a large-scale edge-map dataset and proposing to extract a set of semantic vectors from edge-maps as the semantic knowledge for sketch zero-shot domain alignment. Both deep models are evaluated by comprehensive experiments on million-scale abstract sketches produced by a global online game QuickDraw and outperform state-of-the-art competitors.
Compiler-Assisted Semantic-Aware Encryption for Efficient and Secure Serverless Computing

Serverless computing like Function-as-a-Service (FaaS) is attractive for IoT service providers, liberating the providers from server maintenance. Since a data processing function is executed on the cloud instead of a dedicated server in the FaaS platform, the service users send their private data in their IoT devices to the third-party cloud, taking privacy leakage risks. Homomorphic encryption (HE) can preserve the privacy by enabling encrypted data processing on the cloud, but using HE for every data item incurs large computation and communication overheads. This work proposes SelectiveCrypt, a compiler-assisted semantic-aware encryption scheme that applies different cryptographic primitives depending on the operations on each data item. SelectiveCrypt homomorphically encrypts data items if arithmetic operations are applied to the data, while SelectiveCrypt encrypts data items with a symmetric key if the data are stored in the cloud without any arithmetic operation. The SelectiveCrypt framework consists of a compiler and its runtime system. The SelectiveCrypt compiler statically analyzes the data processing, determines an appropriate cryptographic primitive for each data item, and automatically transforms arithmetic operations into the homomorphic computation. The SelectiveCrypt runtime encrypts and decrypts the data items according to the static analysis result. This work evaluates the prototype SelectiveCrypt framework with five benchmarks that reflect real-world IoT scenarios. The evaluation results show that the SelectiveCrypt framework successfully reduces response time and communication overhead by 1.59 times and 9.61 times, respectively, compared with a HE scheme.
Is Deep Learning ready to satisfy Industry needs?

The impact that Artificial Intelligence is having in modern society is undeniable. Many companies are now using AI to improve the throughput and automate their processes. But the challenge is that Artificial Intelligence is both a source of enthusiasm and skepticism for industries. The manuscript points out the main causes of skepticism giving at the same time some possible technical solutions to exploit at the best the potentialities of AI even in those conditions in which the data are imbalanced and the object classes are not well separated. This work also emphasizes the delicate relationship between artificial intelligence, researchers, and industries, and tries to give an overview of a possible trade-off between the two parties. The document ends up proposing an ‘interpretable learning’ approach that can be exploited as a common language between the two players. The desirable practice would be to make AI explainable, provable, and easily understandable by the companies.
Implementation of a multi-agent multi-criteria negotiation protocol for self-sustainable smart grids

Smart grids, IoT and big data are the drivers of change in the energy sector. The presented research builds on these concepts by establishing a cooperative model that supports autonomous, self-sustainable and self-organised energy communities. It is based on a multi-agent and multi-criteria negotiation protocol in which consumers are involved on one side, while suppliers take part on the other side. The model maximises utility functions and gains of both sides according to the changing real-time demand and supply of electricity. The negotiation support system aims to automate, aid and structure the decision-making process in the energy sector by the means of monotonic concession protocol and autonomous agents. In addition, machine learning is applied to find patterns in big data pertaining to energy consumption, while blockchain is introduced to reliably store distributed data and to manage contractual relationships between actors in the market. The system hence consists of several interconnected modules that deal with data, knowledge, intelligence, decision-making, negotiation and communication.
The Information System Security Governance Tasks in Small and Medium Enterprises

Information Technology (IT) has become the backbone of Small and Medium Enterprises (SME) since it is crucial to their everyday activities and development. Such dependency on IT also provides certain risks [1], therefore there is a need for Enterprise Governance of IT (EGIT) and Information System security implementation. Many frameworks, including one of the most popular - COBIT, provides the structure and the best practice for such implementation. EGIT implementation often requires plenty of IT resources which SME lack, thus some recommendations and guidance are needed. The research is addressing the lack of support for the information security implementation of the COBIT framework in Small and Medium Enterprises (SME).
FreezeNet: Full Performance by Reduced Storage Costs

Pruning generates sparse networks by setting parameters to zero. In this work we improve one-shot pruning methods, applied before training, without adding any additional storage costs while preserving the sparse gradient computations. The main difference to pruning is that we do not sparsify the network’s weights but learn just a few key parameters and keep the other ones fixed at their random initialized value. This mechanism is called freezing the parameters. Those frozen weights can be stored efficiently with a single 32bit random seed number. The parameters to be frozen are determined one-shot by a single for- and backward pass applied before training starts. We call the introduced method FreezeNet. In our experiments we show that FreezeNets achieve good results, especially for extreme freezing rates. Freezing weights preserves the gradient flow throughout the network and consequently, FreezeNets train better and have an increased capacity compared to their pruned counterparts. On the classification tasks MNIST and CIFAR-10/100 we outperform SNIP, in this setting the best reported one-shot pruning method, applied before training. On MNIST, FreezeNet achieves \(99.2\%\) performance of the baseline LeNet-5-Caffe architecture, while compressing the number of trained and stored parameters by a factor of \(\times 157\).
Distributed Model Training Based on Data Parallelism in Edge Computing-Enabled Elastic Optical Networks

The emergence of edge computing provides an effective solution to execute distributed model training (DMT). The deployment of training data among edge nodes affects the training efficiency and network resource usage. This letter aims for the efficient provisioning of DMT services by optimizing the partition and distribution of training data in edge computing-enabled optical networks. An integer linear programming (ILP) model and a data parallelism deployment algorithm (DPDA) are proposed to solve this problem. The performance of the proposed approaches is evaluated through simulation. Simulation results show that the proposed algorithm can deploy more DMT services compared with benchmark.
Translating Morphologically Rich Indian Languages under Zero-Resource Conditions

This work presents an in-depth analysis of machine translations of morphologically-rich Indo-Aryan and Dravidian languages under zero-resource conditions. It focuses on Zero-Shot Systems for these languages and leverages transfer-learning by exploiting target-side monolingual corpora and parallel translations from other languages. These systems are compared with direct translations using the BLEU and TER metrics. Further, Zero-Shot Systems are used as pre-trained models for fine-tuning with real human-generated data taken in different proportions that range from 100 sentences to the entire training set. Performances of the Indo-Aryan and Dravidian languages are compared with a focus on their morphological complexity. The systems with a Dravidian source language performed much better and reached very near to the level of direct translations. This is observed likely due to morphological richness and complexity in the language, which in turn provided more room for transfer-learning in this case. A comparative analysis based on language families has been done. These systems were fine-tuned further, which in turn outperformed direct translations with just 500 parallel sentences for a Dravidian source language. However, systems with an Indo-Aryan source language showed similar performance after getting fine-tuned with 10,000 sentences.
Regularizing Meta-learning via Gradient Dropout

With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization. However, meta-learning models are prone to overfitting when there are no sufficient training tasks for the meta-learners to generalize. Although existing approaches such as Dropout are widely used to address the overfitting problem, these methods are typically designed for regularizing models of a single task in supervised training. In this paper, we introduce a simple yet effective method to alleviate the risk of overfitting for gradient-based meta-learning. Specifically, during the gradient-based adaptation stage, we randomly drop the gradient in the inner-loop optimization of each parameter in deep neural networks, such that the augmented gradients improve generalization to new tasks. We present a general form of the proposed gradient dropout regularization and show that this term can be sampled from either the Bernoulli or Gaussian distribution. To validate the proposed method, we conduct extensive experiments and analysis on numerous computer vision tasks, demonstrating that the gradient dropout regularization mitigates the overfitting problem and improves the performance upon various gradient-based meta-learning frameworks.