Reviewerly/exHarmony huggingface dataset:
Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine

In this paper, a novel extreme-learning-machine (ELM)-based robust adaptive integral terminal sliding mode (AITSM) control strategy is developed for the precise tracking control of a steer-by-wire (SBW) system with uncertain dynamics. The proposed control not only ensures the finite-time error convergence but also effectively estimates the lumped uncertainty via a single-hidden layer feedforward network (SLFN) with ELM. Different from conventional ELM using least square optimization approach, the ELM in this work is designed to adaptively estimate the lumped uncertainty from the perspective of global stability of the closed-loop system. The stability of the closed-loop control system is proved in Lyapunov sense. Simulations are carried out to demonstrate the superior control performance of the proposed control.
The limits of the imaginary: Challenges to intervening in future speculations of memory, data, and algorithms

Over 3 years, researchers, artists, and activists collaborated on eight public engagement experiments in five countries. All focused on building critical consciousness about digital futures. The interventions worked: Once participants broke through the seamlessness of interface surfaces, they immediately thought more critically about how digital platforms actually operate. Yet even as participants reached into these black boxes, they did not imagine alternatives. This article offers a critical theory reading of the theme of inevitability, using the concept of discursive closure, whereby we can see how particular values and (infra)structures are naturalized, neutralized, and legitimated, closing off discussion of alternatives that might counter current hegemonic power. This article highlights the importance of considering iterative formats for speculative interventions, whereby facilitators can focus not only on imagining new or different digital futures, but find creative ways to identify and help overcome the current resignation to the inevitable.
Hybrid Classroom

In the end of December 2019, there was a reported of novel coronavirus in Wuhan, China. Later, this virus named COVID-19 became one of a major outbreak of the world. Since the virus can be spread through the air, the social distancing is essential. Many reports indicated that this will make the world change into a new normal. Working or learning from home by using information and communication technologies will become a new standard of the future. This paper proposed the ideas of the King Mongkut's University of Technology Thonburi to design and implement the hybrid classroom for teaching and conducting university's business after the pandemic crisis. The hybrid classroom concept aims to reducing the number of involved people in each activity by offloading some group of people to online from their home. The hybrid room has also been designed to allow online and physical attendees interact during operating sessions. The conclusion of this paper describes the problems and obstacles that arise during the implementation of prototype rooms, as well as the solutions and future direction of this work.
Learning robust affinity graph representation for multi-view clustering

Recently, an increasingly pervasive trend in real-word applications is that the data are collected from multiple sources or represented by multiple views. Owing to the powerful ability of affinity graph in capturing the structural relationships among samples, constructing a robust and meaningful affinity graph has been extensively studied, especially in spectral clustering tasks. However, conventional spectral clustering extended to multi-view scenarios cannot obtain the satisfactory performance due to the presence of noise and the heterogeneity among different views. In this paper, we propose a robust affinity graph learning framework to deal with this issue. First, we employ an improved feature selection algorithm that integrates the advantages of hypergraph embedding and sparse regression to select significant features such that more robust graph Laplacian matrices for various views on this basis can be constructed. Second, we model hypergraph Laplacians as points on a Grassmann manifold and propose a Consistent Affinity Graph Learning (CAGL) algorithm to fuse all views. CAGL aims to learn a latent common affinity matrix shared by all Laplacian matrices by taking both the clustering quality evaluation criterion and the view consistency loss into account. We also develop an alternating descent algorithm to optimize the objective function of CAGL. Experiments on five publicly available datasets demonstrate that our proposed method obtains promising results compared with state-of-the-art methods.
Attention-Based Modality-Gated Networks for Image-Text Sentiment Analysis

Sentiment analysis of social multimedia data has attracted extensive research interest and has been applied to many tasks, such as election prediction and products evaluation. Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multimodal data. Different modalities usually have information that is complementary. Thus, it is necessary to learn the overall sentiment by combining the visual content with text description. In this article, we propose a novel method—Attention-Based Modality-Gated Networks (AMGN)—to exploit the correlation between the modalities of images and texts and extract the discriminative features for multimodal sentiment analysis. Specifically, a visual-semantic attention model is proposed to learn attended visual features for each word. To effectively combine the sentiment information on the two modalities of image and text, a modality-gated LSTM is proposed to learn the multimodal features by adaptively selecting the modality that presents stronger sentiment information. Then a semantic self-attention model is proposed to automatically focus on the discriminative features for sentiment classification. Extensive experiments have been conducted on both manually annotated and machine weakly labeled datasets. The results demonstrate the superiority of our approach through comparison with state-of-the-art models.
Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance

The multi-fold growth of the social media user-base fuelled a substantial increase in the amount of hate speech posts on social media platforms. The enormous data volume makes it hard to capture such cases and either moderate or delete them. This paper presents an approach to detect and visualize online aggression, a special case of hate speech, over social media. Aggression is categorized into overtly aggressive (OAG), covertly aggressive (CAG), and non-aggressive labels (NAG). We have designed a user interface based on a web browser plugin over Facebook and Twitter to visualize the aggressive comments posted on the Social media user’s timelines. This plugin interface might help to the security agency to keep a tab on the social media stream. It also provides citizens with a tool that is typically only available for large enterprises. The availability of such a tool alleviates the technological imbalance between industry and citizens. Besides, the system might be helpful to the research community to create further tools and prepare weakly labeled training data in a few minutes using comments posted by users on celebrity’s Facebook, Twitter timeline. We have reported the results on a newly created dataset of user comments posted on Facebook and Twitter using our proposed plugins and the standard Trolling Aggression Cyberbullying 2018 (TRAC) dataset in English and code-mixed Hindi. Various classifiers like Support Vector Machine (SVM), Logistic regression, deep learning model based on Convolution Neural Network (CNN), Attention-based model, and the recently proposed BERT pre-trained language model by Google AI, have been used for aggression classification. The weighted F1-score of around 0.64 and 0.62 is achieved on TRAC Facebook English and Hindi datasets while on Twitter English and Hindi datasets, the weighted F1-score is 0.58 and 0.50, respectively.
Experimental kernel-based quantum machine learning in finite feature space

We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels' ability to separate points, i.e., their resolution, under the constraint of finite, fixed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classification tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature.
Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

Image segmentation is considered as a key research topic in the area of computer vision. It is pivotal in a broad range of real-life applications. Recently, the emergence of deep learning drives significant advancement in image segmentation; the developed systems are now capable of recognizing, segmenting, and classifying objects of specific interest in images. Generally, most of these techniques primarily focused on the asymmetric field of view or frontal view objects. This work explores widely used deep learning-based models for person segmentation using top view data set. The first model employed in this work is Fully Convolutional Neural Network (FCN) with Resnet-101 architecture. The network consists of a set of max-pooling and convolution layers to identify pixel-wise class labels and prediction of the mask. The second model is based on FCN called U-Net with Encoder-Decoder architecture. The encoder is mainly comprised of a contracting path, also called an encoder, which captures the context in the image and symmetric expanding path called decoder to enable accurate location. The third model used for top view person segmentation is a DeepLabV3 model also with encoder-decoder architecture. The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. The decoder is used for up-sampling and reconstruction of output using important information extracted by the encoder. All segmentation models are firstly tested using pre-trained models (trained on frontal view data set). To improve the performance, these models are further trained using person data set captured from a top view. The output of all models consists of a segmented person in the top view images. The experimental results reveal the effectiveness and performance of segmentation models by achieving IoU of 83%, 84%, and 86% and mIoU of 80% 82% and 84% for FCN, U-Net, and DeepLabv3 respectively. Furthermore, the discussion is provided for output results with possible future guidelines.
Efficient privacy-preserving authentication protocol using PUFs with blockchain smart contracts

The Internet of Things devices generates a huge amount of sensitive data. Machine learning is the standard processing paradigm for intelligently handling the huge amount of data. Unfortunately, the IoT devices have limited resources to handle the performance of big data feature learning with machine learning techniques. IoT devices often compromise the privacy of users and make them vulnerable to numerous cyber-attacks. In this paper, we propose an efficient privacy-preserving authentication protocol based on blockchain technology and the secret computational model of physically unclonable function (denoted by PUF model). The proposed protocol guarantees the users privacy with a decentralized smart contract blockchain with the PUF model. In practice, the proposed protocol guarantees that IoT devices and the miner are authenticated in a faster authentication process compared to current blockchain techniques. In addition, Blockchain and PUF combine to ensure data provenance and data transparency in IoT networks. Blockchain-based smart contracts provide decentralized digital ledgers that are able to withstand data tampering attacks. This ensures the security and privacy of outsourced big data in IoT environments. We also investigated the privacy implications of using IoT devices with various security analysis, and avenues for research to extenuate the privacy concerns in IoT environments.
Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain

Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit transactions has become a challenging problem to unveil illicit services over the network. Graph Convolutional Network, a graph neural network based spectral approach, has recently emerged and gained much attention regarding graph-structured data. Previous research has highlighted the degraded performance of the latter approach to predict illicit transactions using, a Bitcoin transaction graph, so-called Elliptic data derived from Bitcoin blockchain. Motivated by the previous work, we seek to explore graph convolutions in a novel way. For this purpose, we present a novel approach that is modelled using the existing Graph Convolutional Network intertwined with linear layers. Concisely, we concatenate node embeddings obtained from graph convolutional layers with a single hidden layer derived from the linear transformation of the node feature matrix and followed by Multi-layer Perceptron. Our approach is evaluated using Elliptic data, wherein efficient accuracy is yielded. The proposed approach outperforms the original work of same data set.
Neural-network quantum state tomography in a two-qubit experiment

We study the performance of efficient quantum state tomography methods based on neural-network quantum states using measured data from a two-photon experiment. Machine-learning-inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e., to positive semidefinite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.
Movie Recommender System Using Collaborative Filtering

Movies are one of the sources of entertainment, but the problem is in finding the desired content from the ever-increasing millions of content every year. However, recommendation systems come much handier in these situations. The aim of this paper is to improve the accuracy and performance of a regular filtering technique. Although varieties of methods are used to implement a recommendation system, Content-based filtering is the simplest method. Which takes input from the users, rechecks his/her history/past behavior, and recommends a list of similar movies. In this paper, to prove the effectiveness, K-NN algorithms and collaborative filtering are used to mainly focus on enhancing the accuracy of results as compared to content-based filtering. This approach is based on cosine similarity using k-nearest neighbor with the help of a collaborative filtering technique, at the same time removing the drawbacks of the content-based filtering. Although using Euclidean distance is preferred, cosine similarity is used as the accuracy of cosine angle and the equidistance of movies remain almost the same.
Grey wolf optimizer with an enhanced hierarchy and its application to the wireless sensor network coverage optimization problem

Grey wolf optimizer (GWO), which is inspired by the social behaviours of grey wolf packs, is a nature-inspired and population-based algorithm. The GWO technique has the advantage of conceptual simplicity and shows good results for solving various real-world problems. However, this technique has the drawback of premature convergence and is prone to stagnation in local optima. The leadership hierarchy is the paramount characteristic of the GWO and influences its searching precision. Therefore, a grey wolf optimizer with enhanced hierarchy (GWO-EH) is proposed to overcome these deficiencies. Firstly, fitness-based self-adaptive weight coefficients are introduced to better imitate the hierarchy of the grey wolves, which also have a positive effect on the convergence speed. Then, we propose an improved position-updating equation to enhance the leadership of the high-ranking wolves, whereby the global exploration ability of the GWO is strengthened. Finally, the strategy of repositioning wolves around the leading wolves is designed to keep a perfect balance between exploration and exploitation. The search ability of the GWO-EH is thoroughly compared with the GWO itself, some promising GWO variants, and several well-established algorithms on twenty-three widely used benchmark functions. Empirical studies reveal that GWO-EH has a competitive overall performance according to the average value (standard deviation), Wilcoxon rank-sum test results, and convergence curve. Moreover, our method is applied to address the wireless sensor network coverage optimization problem, and the applicability and validity of the GWO-EH are indicated by the experimental results.
Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs

Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in large-scale deep learning applications, while it is known that the communication bottleneck limits the scalability of the distributed system. Gradient sparsification is a promising technique to significantly reduce the communication traffic, while pipelining can further overlap the communications with computations. However, gradient sparsification introduces extra computation time, and pipelining requires many layer-wise communications which introduce significant communication startup overheads. Merging gradients from neighbor layers could reduce the startup overheads, but on the other hand it would increase the computation time of sparsification and the waiting time for the gradient computation. In this paper, we formulate the trade-off between communications and computations (including backward computation and gradient sparsification) as an optimization problem, and derive an optimal solution to the problem. We further develop the optimal merged gradient sparsification algorithm with SGD (OMGS-SGD) for distributed training of deep learning. We conduct extensive experiments to verify the convergence properties and scaling performance of OMGS-SGD. Experimental results show that OMGS-SGD achieves up to 31% end-to-end time efficiency improvement over the state-of-the-art sparsified SGD while preserving nearly consistent convergence performance with original SGD without sparsification on a 16-GPU cluster connected with 1Gbps Ethernet.
Don’t click: towards an effective anti-phishing training. A comparative literature review

Abstract Email is of critical importance as a communication channel for both business and personal matters. Unfortunately, it is also often exploited for phishing attacks. To defend against such threats, many organizations have begun to provide anti-phishing training programs to their employees. A central question in the development of such programs is how they can be designed sustainably and effectively to minimize the vulnerability of employees to phishing attacks. In this paper, we survey and categorize works that consider different elements of such programs via a clearly laid-out methodology, and identify key findings in the technical literature. Overall, we find that researchers agree on the answers to many relevant questions regarding the utility and effectiveness of anti-phishing training. However, we identified influencing factors, such as the impact of age on the success of anti-phishing training programs, for which mixed findings are available. Finally, based on our comprehensive analysis, we describe how a well-founded anti-phishing training program should be designed and parameterized with a set of proposed research directions.
Quantum Power Method by a Superposition of Time-Evolved States

We propose a quantum-classical hybrid algorithm of the power method, here dubbed as the quantum power method, to evaluate H^n|ψ⟩ with quantum computers, where n is a non-negative integer, H^ is a time-independent Hamiltonian of interest, and |ψ⟩ is a quantum state. We show that the number of gates required for approximating H^n scales linearly in the power and the number of qubits, making it a promising application for near-term quantum computers. Using numerical simulation, we show that the power method can control systematic errors in approximating the Hamiltonian power H^n for n as large as 100. As an application, we combine our method with a multireference Krylov-subspace-diagonalization scheme to show how one can improve the estimation of ground-state energies and the ground-state fidelities found using a variational-quantum-eigensolver scheme. Finally, we outline other applications of the quantum power method, including several moment-based methods. We numerically demonstrate the connected-moment expansion for the imaginary-time evolution and compare the results with the multireference Krylov-subspace diagonalization.16 MoreReceived 11 August 2020Revised 19 November 2020Accepted 7 January 2021DOI:https://doi.org/10.1103/PRXQuantum.2.010333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum gatesQuantum simulationQuantum Information
Law versus technology: Blockchain, GDPR, and tough tradeoffs

Inconsistency between the way in which the law is structured, and the way in which technologies actually operate is always an interesting and useful topic to explore. When a law conflicts with a business model, the solution will often be changing the business model. However, when the law comes into conflict with the architecture of hardware and software, it is less clear how the problem will be managed. In this paper, we analyze the contradiction of blockchain technology and the requirements of GDPR. The three contradictions we examine are (i) right to be forgotten versus irreversibility/immutability of records, (ii) data protection by design versus tamper-proofness and transparency of blockchain, and (iii) data controller versus decentralized nodes. We highlight that the conflicts can be handled through focusing on commonalities of GDPR and the blockchain, developing new approaches and interpretations, and tailoring the blockchain technology according to the needs of data protection law.