Reviewerly/exHarmony huggingface dataset:
Phishing email strategies: Understanding cybercriminals' strategies of crafting phishing emails

Abstract It is a known fact that cybercriminals often manipulate people to steal sensitive information. However, there has been a lack of research in investigating cybercriminals' strategies and the evolution of strategies when crafting phishing emails to entice people to perform a variety of malicious tasks such as clicking on fraudulent links. This study uses a combination of Natural Language Processing (NLP), topic modeling, and clustering techniques to analyze and evaluate the persuasive techniques/strategies, which cybercriminals use when crafting fraudulent emails. Our experimental results revealed the efficacy of using these techniques in understanding cybercriminals' mindset as well as how certain aspects of these techniques maintained consistency despite the evolution of the strategy from early Nigerian scams to more modern phishing emails. The findings of our study indicate that the most common technique/strategy used by cybercriminals is a combination of creating a sense of urgency while promising a reward. Moreover, despite the evolution of technology, cybercriminals are still effectively using the same persuasive techniques. Based on our findings, we provided several recommendations to improve anti‐phishing software and awareness and training programs.
Beware suppliers bearing gifts!: Analysing coverage of supply chain cyber security in critical national infrastructure sectorial and cross-sectorial frameworks

Threat actors are increasingly targeting extended supply chains and abusing client-supplier trust to conduct third-party compromise. Governments are concerned about targeted attacks against critical national infrastructures, where compromise can have significant adverse national consequences. In this paper we identify and review advice and guidance offered by authorities in the UK, US, and the EU regarding Cyber Supply Chain Risk Management (C-SCRM). We then conduct a review of sector specific guidance in the three regions for the chemical, energy, and water sectors. We assessed frameworks that each region’s sector offered organisations for C-SCRM suitability. Our results found a range of interpretations for “Supply Chain” that resulted in a diversity in the quantity and quality of advice offered by regional authorities, sectors, and their frameworks. This is exacerbated by the lack of a common taxonomy to support supply chain procurement and risk management that has led to limited coverage in most C-SCRM programs. Our results highlight the need for a taxonomy regarding C-SCRM and systematic guidance (both general and sector specific) to enable controls to be deployed to mitigate against supply chain risk. We provide an outline taxonomy based on our data analysis to promote further discussion and research.
ELM based two-handed dynamic Turkish Sign Language (TSL) word recognition

Hearing impaired individuals can easily overcome the barriers in communicating with other members of the society via computer technology. In this study, the recognition of dynamic words in Turkish Sign Language (TSL) with two hands was studied using the Leap Motion Controller (LMC) device. 50 dynamic words were determined considering the similarities and differences among themselves, and a dataset was created using 4 signers. For the system proposed in this paper, a comprehensive feature extraction process is executed. Applying feature selection algorithms and PCA, LDA and PCA+LDA dimension reduction methods to this dataset, new datasets with less dimension were obtained. For the first time, ELM architectures were operated as a classifier in a sign language recognition system. Recognition performance was tested with 5 different ELM networks and 2 classical classifiers and the results were compared. In addition, comparisons of classical and ELM based classifiers were presented. The 10-fold cross validation method was used to test the validity of the proposed system and the accuracy of the results obtained. Based on the results obtained by a comprehensive analysis, it was observed that the ML-KELM classifier maintains its performance rate and gives the highest performance rate. At the same time, it has been observed that ML-KELM classifier has a stable structure, which offers less user intervention.
Online Covariance Matrix Estimation in Stochastic Gradient Descent

The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge datasets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and randomness of the solutions has been rarely studied. This article aims at conducting statistical inference of SGD-based estimates in an online setting. In particular, we propose a fully online estimator for the covariance matrix of averaged SGD (ASGD) iterates only using the iterates from SGD. We formally establish our online estimator’s consistency and show that the convergence rate is comparable to offline counterparts. Based on the classic asymptotic normality results of ASGD, we construct asymptotically valid confidence intervals for model parameters. Upon receiving new observations, we can quickly update the covariance matrix estimate and the confidence intervals. This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.
Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1
Graph-based Multimodal Ranking Models for Multimodal Summarization

Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can cover both single-modal output summarization and multimodal output summarization. In our framework, we consider three different scenarios and propose the respective unsupervised graph-based multimodal summarization models without the requirement of any manually annotated document-summary pairs for training: (1) generic multimodal ranking, (2) modal-dominated multimodal ranking, and (3) non-redundant text-image multimodal ranking. Furthermore, an image-text similarity estimation model is introduced to measure the semantic similarity between image and text. Experiments show that our proposed models outperform the single-modal summarization methods on both automatic and human evaluation metrics. Besides, our models can also improve the single-modal summarization with the guidance of the multimedia information. This study can be applied as the benchmark for further study on multimodal summarization task.
Statistically significant tests of multiparticle quantum correlations based on randomized measurements

We consider statistical methods based on finite samples of locally randomized measurements in order to certify different degrees of multiparticle entanglement in intermediate-scale quantum systems. We first introduce hierarchies of multi-qubit criteria, satisfied by states which are separable with respect to partitions of different size, involving only second moments of the underlying probability distribution. Then, we analyze in detail the statistical error of the estimation in experiments and present several approaches for estimating the statistical significance based on large deviation bounds. The latter allows us to characterize the measurement resources required for the certification of multiparticle correlations, as well as to analyze given experimental data in detail.
Sign Language Alphabet Recognition Using Convolution Neural Network

Sign Language plays an indispensable role in the lives of people having speaking and hearing disabilities. Recognition of American Sign Language using Computer Vision is very challenging due to its increasing complexity and high intraclass variations. In this paper, convolutional neural networks (CNNs) are used to recognize the ASL Alphabets. This algorithm is useful to recognize it as a deep network, which is expected for the ASL alphabet classification task. Pre-Processing steps of the MNIST dataset are done in the first phase. After the first phase, different important features of pre-processed hand gesture image are computed. In the final phase, depending on the properties computed or calculated in the initial phases, the accuracy and AUC score of the network model with which it can recognize the sign language Alphabets were detected. The proposed CNN network has achieved an AUC score of 0.9981 and an accuracy of 0.9963.
OEC-RNN: Object-Oriented Delineation of Rooftops With Edges and Corners Using the Recurrent Neural Network From the Aerial Images

It is an important task to automatically and accurately map rooftops from very high resolution remote sensing images since buildings are very closely related to human activity. Two typical technologies are often utilized to accomplish the task, i.e., semantic segmentation and instance segmentation. The semantic segmentation is to independently allocate a label (e.g., “building” or not) to each pixel, resulting in blob-like segments. On the contrary, one might model the boundary of a rooftop as a polygon to improve the shape of the rooftop by encouraging vertices of polygon to adhere to the rooftop’s boundary. Following this line of work, we present a multitask learning approach to predict rooftop corners in a sequent way using the attention learned from where the boundaries are in a given image region. The approach simulates the process of manual delineation of rooftops’ outline in a given image, which can produce accurate boundaries of rooftops with sharp corners and straight lines between them. Specifically, the proposed method consists of three components, i.e., object detection, pixel-by-pixel classification of both edges and corners, and delineation of rooftops in a sequent manner using a convolutional recurrent neural network (RNN). It is called as object-oriented, edges and corners (OEC)-RNN in this article. Three image datasets of buildings are employed to validate the performance of the OEC-RNN, which are compared with state-of-the-art methods for instance segmentation. The experimental results show that the OEC-RNN achieves the best performance in terms of overlay, boundary adherence, and vertex location between ground-truth and predicted polygons.
Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones.
Entanglement in highly symmetric multipartite quantum states

A construction of genuinely entangled multipartite quantum states based on the group theory is presented. Analyzed states resemble the Dicke states, whereas the interactions occur only between specific subsystems related by the action of the selected group. The states constructed by this technique exhibit desired symmetry properties and form a natural resource for less-symmetric quantum informational tasks. We propose quantum circuits efficiently generating such states, which in general have smaller complexity than the circuits necessary to create fully symmetric entangled states. Exemplary five-qubit quantum states are simulated on quantum computers.
SCSGuard: Deep Scam Detection for Ethereum Smart Contracts

Smart contract is the building block of blockchain systems that enables automated peer-to-peer transactions and decentralized services. With the increasing popularity of smart contracts, blockchain systems, in particular Ethereum, have been the “paradise” of versatile fraud activities. In this work, we present SCSGuard, a novel deep learning scam detection framework that harnesses the automatically extractable bytecodes of smart contracts as their new features. We design a GRU network with attention mechanism learning from the N-gram bytecode patterns to determine whether a smart contract is fraudulent or not. Our framework is advantageous over the baseline algorithms in three aspects. Firstly, SCSGuard provides a unified solution to different scam genres, thus relieving the need for code analysis skills. Secondly, the inference of SCSGuard is faster than the code analysis by several orders of magnitudes. Thirdly, experimental results manifest that SCSGuard achieves high accuracy (0.92~0.94), precision (0.94~0.96) and recall (0.97~0.98) for both Ponzi and Honeypot scams under similar settings, and is potentially useful to detect new Phishing smart contracts.
Learning to restore light fields under low-light imaging

Light Field (LF) images have the unique advantage of recording scenes from multiple viewpoints, which provides many applications, such as refocusing and depth estimation. However, low-light conditions can severely influence these applications. In this paper, we propose a two-stage deep learning framework for the LF restoration under low-light imaging. First, there is a multi-to-one (MTO) network, which restores each view separately by utilizing multiple auxiliary views. All the views share the same feature extractor, with an efficient spatial-channel attention mechanism to extract more informative features. A channel-attention feature fusion (CAFF) module is designed to selectively fuse more useful complementary information from the auxiliary views, with a learnable global scalar to adjust the importance of the auxiliary features. Then, the outputs of the MTO network are further enhanced by an (all-to-all) ATA network, which uses spatial and angular residual blocks to process all the views synchronously for fully encoding the spatial-angular information. Extensive experiments have been conducted to demonstrate the superior performance and robustness of our method, i.e., it can restore the luminance, spatial details and angular geometries of the LF images under various light levels effectively.
On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy

It is well-known that general secure multi-party computation can in principle be applied to implement differentially private mechanisms over distributed data with utility matching the curator (a.k.a. central) model. In this paper we study the power of protocols running on top of a much weaker primitive: A non-interactive anonymous channel, known as the shuffle model in the differential privacy literature. Such protocols are implementable in a scalable way using known cryptographic methods and are known to enable non-interactive, differentially private protocols with error much smaller than what is possible in the local model. We study fundamental counting problems in the shuffle model and obtain tight, up to polylogarithmic factors, bounds on the error and communication in several settings. For the classic problem of frequency estimation for n users and a domain of size B, we obtain: For the selection problem on a domain of size $$B$$ , we prove: A key ingredient in our lower bound proofs is a lower bound on the error of locally-private frequency estimation in the low-privacy (a.k.a. high $$\varepsilon $$ ) regime. For this we develop new tools to improve the results of Duchi et al. (FOCS 2013; JASA 2018) and Bassily & Smith (STOC 2015), whose techniques only gave tight bounds in the high-privacy setting.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding

The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU).As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource scenarios.We introduce XSID, a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.We study two setups which differ by type and language coverage of the pre-trained embeddings.Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification. 1
Mexican sign language segmentation using color based neuronal networks to detect the individual skin color

In recent years, the development of algorithms that assist in communicate with deaf people is an important challenge. The development of automatic systems to translate sign language is a current research topic. However, this involves several processes that range from video capture, pre-processing to identification or classification of the signal. The development of systems capable of extracting discriminative features that enhance the power of generalization of a classifier is even a very challenging problem. The meaning of a sign is the combination of the hand movement, hand shape, and the point of contact of the hand in the body. This paper presents a method to detect and translate hand gestures. First, we obtain 15 frames per word, obtaining 3 regions of interest (hands and face) from which we obtain geometric features. Finally we use several classifier techniques and present the experimental results.
Learning Connected Attentions for Convolutional Neural Networks

While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full advantage of the attention mechanism. In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure. To achieve this, we interconnect adjacent attention blocks, making information flow among attention blocks possible. With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning. Our DCANet is generic. It is not limited to a specific attention module or base network architecture. Experimental results on ImageNet and MS COCO benchmarks show that DCANet consistently outperforms the state-of-the-art attention modules with a minimal additional computational overhead in all test cases. The code is available at: https://github.com/13952522076/DCANet.
Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks

This work addresses weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class Activation Maps (CAMs) using a random-walk mechanism in order to arrive at complete pseudo labels for training a semantic segmentation network in a fully-supervised manner. However, the feed-forward nature of the random walk imposes no regularization on the quality of the resulting complete pseudo labels. To overcome this issue, we propose a Graph Convolutional Network (GCN)-based feature propagation framework. We formulate the generation of complete pseudo labels as a semi-supervised learning task and learn a 2-layer GCN separately for every training image by back-propagating a Laplacian and an entropy regularization loss. Experimental results on the PASCAL VOC 2012 dataset confirm the superiority of our scheme to several state-of-the-art baselines. Our code is available at https: //github.com/Xavier-Pan/WSGCN.
Spatial Attention-Based Non-Reference Perceptual Quality Prediction Network for Omnidirectional Images

Due to the strong correlation between visual attention and perceptual quality, many methods attempt to use human saliency information for image quality assessment. Although this mechanism can get good performance, the networks require human saliency labels, which is not easily accessible for omnidirectional images (ODI). To alleviate this issue, we propose a spatial attention-based perceptual quality prediction network for non-reference quality assessment on ODIs (SAP-net). Without any human saliency labels, our network can adaptively estimate human perceptual quality on impaired ODIs through a self-attention manner, which significantly promotes the prediction performance of quality scores. Moreover, our method greatly reduces the computational complexity in quality assessment task on ODIs. Extensive experiments validate that our network outperforms 9 state-of-the-art methods for quality assessment on ODIs. The dataset and code have been available on https://github.com/yanglixiaoshen/SAP-Net.