Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Predicting Customer Reviews using a Hybrid Approach

Sentiment Analysis is a widely used text classification technique. It breaks down any given text or comments and classify the text either as positive or negative based on the views conveyed in it. Previous works done on sentiment classification used either lexicon based approach or machine learning techniques. Likewise, the major drawback of the existing systems was the focus on only binary classification of review such as positive or negative. Ignorance of the neutral review will result in misinterpretation of a customer's opinion about a product or movie, which will degrade the business or trend. In case of using only lexicon based approach, the system highly depends on the selection of lexicon resource and dictionary. In case of system built only using machine learning approach, the performance of the system depends on the algorithms chosen. This work presents a hybrid model to resolve the neutral class too. The proposed work combines a lexical approach (SentiWordNet) with the machine learning algorithms such as Support Vector Machine, Decision Tree, Logistic Regression and Naive Bayes for sentiment analysis to resolve the neutral opinions beyond the binary categorization of the customer's review. We have also compared the performance of these four machine learning algorithms along with the lexicon approach. The results proved that Support Vector Machine and Logistic Regression algorithms outperform the other two algorithms with an accuracy of about 80% which is on average differs by 6% to 10% when compared to other algorithms.
A <i>Group</i>-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannot-links) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by using the neural network, which can explicitly provide nonlinear mappings. Experimental results based on both synthetic and real-world datasets show that the proposed methods yield much better performance compared to other distance learning methods using pairwise constraints.
What questions reveal about novices’ attempts to make sense of data visualizations: Patterns and misconceptions

Data visualization literacy has attracted widespread interest due to the urgent need to analyze unprecedented volumes of data we have nowadays. Much work on visualization literacy focuses on asking people to answer specific questions about the data depicted in a visual representation, in an attempt to try to understand how people make sense of the underlying data. In this work, we investigate, through a user survey, the initial questions people pose when first encountering a visualization. We analyzed a set of 1058 questions that 22 participants created about 20 different visualizations, deriving templates for the recurring types of questions that emerged as information-seeking patterns, and classifying the various kinds of errors they introduced in the questions. By understanding the common mistakes they made when asking data-related questions, we now feel better equipped to inform further research on producing and consuming data visualization concepts. The results of the study reported in this paper can be used in teaching data visualization, as they uncover and classify frequent errors people make when trying to make sense of data represented visually. The study may also contribute to the design of visualization recommender systems, as the question patterns revealed what people expect to answer with each visualization.
Autonomous drone with ability to track and capture an aerial target

This article presents an autonomous aerial system developed for the first challenge of Mohamed Bin Zayed International Robotics Competition (MBZIRC) 2020, consisting of the autonomous interception of an aerial target flying on a variable trajectory and speed, and also bursting several balloons randomly placed over a certain area, also fully autonomous. This work presents a prototype of a capture aerial robot designed specifically for the scenario under consideration, with the aim of integrating the necessary equipment to autonomously detect, track and capture the target drone. Both the hardware architecture and the software that allows to perform this fully autonomous complex mission are described in the paper, as well as the tools used for the integration and execution of the different functional modules. Apart from that, the Guidance, Navigation and Control (GNC) algorithms implemented for the tracking of the target are also explained. Finally the system is tested in a simulation environment and also validated with real experiments, which most interesting results are presented.
An Approximate Low-Power Lifting Scheme Using Reversible Logic

Haar Wavelet transform is an efficacious class of wavelet transform that satisfies both symmetry and orthogonality properties which are crucial in handling boundary distortion and energy preservation in image processing applications. Such applications demand power efficient design solutions that deliver high performance. Reversible logic has emerged as a solution that incorporates logical and physical reversibility to realise low power designs. This paper presents a reversible logic based design of Haar wavelet transform and lifting scheme for Haar wavelet transform, a first in literature of reversible logic. The designs are analysed to measure the efficiency of reversible logic implementations in terms of Quantum Cost (QC), Constant Inputs (CI), Garbage Outputs (GO) and Gate Count (GC). Furthermore, this paper proposes two architectures for Reversible Approximate Full Adder (RAFA) - RAFA-1 and RAFA-2; optimised explicitly for reversible logic based implementation. The proposed architectures have 25% Error Rate (ER) and optimised QC, CI, GC and GO when compared to existing exact and approximate full adder architectures implemented using reversible logic. Functional verification of the proposed architectures are performed on FPGA using 512 × 512 image. The efficiency of the image processing application is projected in terms of Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR). Average SSIM and average PSNR are found to be 0.9679 and 31.81dB for RAFA-1 and 0.9696 and 32.15dB for RAFA-2 which are comparable with exact full adder based design.
Efficient and Phase-Aware Video Super-Resolution for Cardiac MRI

Cardiac Magnetic Resonance Imaging (CMR) is widely used since it can illustrate the structure and function of the heart in a non-invasive and painless way. However, it is time-consuming and high-cost to acquire high-quality scans due to the hardware limitation. To this end, we propose a novel end-to-end trainable network to solve CMR video super-resolution problem without the hardware upgrade and the scanning protocol modifications. We incorporate the cardiac knowledge into our model to assist in utilizing the temporal information. Specifically, we formulate the cardiac knowledge as the periodic function, which is tailored to meet the cyclic characteristic of CMR. Besides, the proposed residual of residual learning scheme facilitates the network to learn the LR-HR mapping in a progressive refinement fashion. This mechanism enables the network to have the adaptive capability by adjusting refinement iterations depending on the difficulty of the task. Extensive experimental results on large-scale datasets demonstrate the superiority of the proposed method compared with numerous state-of-the-art methods.
Efficient Secure Computation of Order-Preserving Encryption

Order-preserving encryption (OPE) allows encrypting data, while still enabling efficient range queries on the encrypted data. Moreover, it does not require any change to the database management system, which makes OPE schemes very suitable for data outsourcing with threats from weak adversaries. However, all OPE schemes are necessarily symmetric limiting the use case to one client and one server. Imagine a scenario where a Data Owner (DO) outsources encrypted data to the Cloud Service Provider (CSP) and a Data Analyst (DA) wants to execute private range queries on this data. Then either the DO must reveal its encryption key or the DA must reveal the private queries. In this paper, we overcome this limitation by allowing the equivalent of a public-key OPE. We present a secure multiparty protocol that enables secure range queries for multiple users. In this scheme, the DA cooperates with the DO and the CSP in order to order-preserving encrypt the private range queries without revealing any other information to the parties. The basic idea of our scheme is to replace encryption with a secure, interactive protocol. In this protocol, we combine OPE based on binary search trees with homomorphic encryption and garbled circuits (GC) achieving security against passive adversaries with sublinear communication and computation complexity. We apply our construction to different OPE schemes including frequency-hiding OPE and OPE based on an efficiently searchable encrypted data structure which can withstand many of the popularized attacks on OPE. We implemented our scheme and observed that if the database size of the DO has 1 million entries it takes only about 0.3 s on average via a loopback interface (1.3 s via a LAN and 15.6 s via a WAN with about 200 ms round-trip time) to encrypt an input of the DA. Moreover, while the related work has an overhead of 10 to 100 seconds compared to a plaintext MySQL range query on a database with 10 million entries, our scheme has an overhead of only 360 milliseconds.
Robust Filtering Options for Higher-Order Strain Fields Generated by Digital Image Correlation

Digital image correlation (DIC) systems have been used in many engineering fields to obtain surface full-field strain distribution. However, noise affects the accuracy and precision of the measurements due to many factors. The aim of this study was to find out how different filtering options; namely, simple mean filtering, Gaussian mean filtering and Gaussian low-pass filtering (LPF), reduce noise while maintaining the full-field information based on constant, linear and quadratic strain fields. Investigations are done in two steps. First, linear and quadratic strain fields with and without noise are simulated and projected to discrete measurement points which build up strain window sizes consisting of 6×5, 12×11, and 26×17 points. Optimal filter sizes are computed for each filter strategy, strain field type, and strain windows size, with minimal impairment of the signal information. Second, these filter sizes are used to filter full-field strain distributions of steel samples under tensile tests by using an ARAMIS DIC system to show their practical applicability. Results for the first part show that for a typical 12×11 strain window, simple mean filtering achieves an error reduction of 66–69%, Gaussian mean filtering of 72–75%, and Gaussian LPF of 66–69%. If optimized filters are used for DIC measurements on steel samples, the total strain error can be reduced from initial 240−300 μstrain to 100–150 μstrain. In conclusion, the noise-floor of DIC signals is considerable and the preferable filters were a simple mean with s*¯ = 2, a Gaussian mean with σ*¯ = 1.7, and a Gaussian LPF with D0*¯ = 2.5 in the examined cases.
The Democratic Socialisation of Knowledge: Integral to an Alternative to the Neoliberal Model of Development

This chapter begins with a democratic socialist critique of the mainstream neoclassical reading of Hardin’s famous tale of the ‘Tragedy of the Commons’. Then, it considers the neoliberal-led commodification of academic knowledge and counter projects of the ‘Public Digital University’ and ‘knowledge socialism’ as a springboard for examining the broader context of the neoliberal-led commodification of the immaterial world and resulting unstable and unsustainable societal outcomes. Discussion concentrates first on political and economic consequences of privatised regulation of the global information commons that I examine in relation to the democratic ideal of a well-formed global virtual civil society. Following on, this chapter turns to examine globally unstable economic effects of capitalism’s uneven development and the limitations of existing socialist critiques. The final section links ‘cosmopolitan democracy’ with a ‘world knowledge bank’ as key features of a democratic socialist alternative to the neoliberal model of development.
GMDE: Extracting quantitative information from geologic maps

Abstract GMDE is a program, available on the desktop for Apple Macintosh, Microsoft Windows, and Linux platforms and in a mobile version for iOS, that enables geologists to extract quantitative structural information from geologic maps and satellite images. The program facilitates the digitizing of strikes and dips or calculating them from three-point problems, calculation of stratigraphic map thickness, determination of piercing points on faults, and the construction of down-plunge projections and vertical cross sections with projected apparent dips, contacts, and cylindrical folds. The program also enables the automatic plotting of planar contacts across topography based on orientation calculated from three clicked points, which can be carried out in the field for immediate hypothesis testing. Error propagation is built into many of the calculations. Maps and satellite images require no projection or datum information, just four points with known latitude and longitude information. Alternatively, the user can enter the map or image in MBTiles format. Users can easily extract X-Y-Z data for any clicked or calculated point or polygon, enabling them to make their own calculations.
Fast-UAP: An algorithm for expediting universal adversarial perturbation generation using the orientations of perturbation vectors

Convolutional neural networks (CNNs), which are popular machine-learning tools, are being applied in various tasks. However, CNN models are vulnerable to universal perturbations, which despite being usually quasi-imperceptible to the human eye can cause natural images to be misclassified with high probability. The original algorithm of generating universal perturbations (the algorithm is called UAP for brevity) only aggregates minimal perturbations in each iteration without considering the orientations of perturbation vectors; consequently, the magnitude of the universal perturbation cannot efficiently increase at each iteration, thereby resulting in slow universal perturbation generation. Hence, we propose an optimized algorithm to enhance the performance of generating universal perturbations based on the orientations of perturbation vectors. At each iteration, rather than choosing the minimal perturbation vector, we choose the perturbation whose orientation is similar to that of the current universal perturbation; therefore, the magnitude of the aggregation of both the perturbations will be maximized. The experimental results show that compared with UAP, we could generate universal perturbations in a shorter time using a smaller number of training images. Furthermore, we empirically observed that compared with the universal perturbations generated using UAP, the ones generated using our proposed algorithm achieved an average fooling-rate increment of 9% in white-box and black-box attacks.
Background Learnable Cascade for Zero-Shot Object Detection

Zero-shot detection (ZSD) is crucial to large-scale object detection with the aim of simultaneously localizing and recognizing unseen objects. There remain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over state-of-the-art methods.
Privacy-Preserving Distributed Graph Filtering

With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools for processing sensitive data. As a step towards a solution, we demonstrate the privacy-preserving capabilities of variants of the so-called distributed graph filters. Such implementations allow each node to compute a desired linear transformation of the networked data while protecting its own private data. In particular, the proposed approach eliminates the risk of possible privacy abuse by ensuring that the private data is only available to its owner. Moreover, it preserves the distributed implementation and keeps the same communication and computational cost as its non-secure counterparts. Furthermore, we show that this computational model is secure under both passive and eavesdropping adversary models. Finally, its performance is demonstrated by numerical tests and it is shown to be a valid and competitive privacy-preserving alternative to traditional distributed optimization techniques.
Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality

Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the interplay between political actors over time. However, as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use. In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization. We first propose a design space for how textual narratives can be used to describe causal data. We then present results from a crowdsourced user study where participants were asked to recover causality information from two causality visualizations-causal graphs and Hasse diagrams-with and without an associated textual narrative. Finally, we describe Causeworks, a causality visualization system for understanding how specific interventions influence a causal model. The system incorporates an automatic textual narrative mechanism based on our design space. We validate Causeworks through interviews with experts who used the system for understanding complex events.
An Effective Neural Machine Translation for English to Hindi Language

Machine translation involves the conversion of text from one language to the other language. In the world of web, a huge number of resources are made available in English. Many of the people are not familiar with this global language. Manually transmuting them into native languages such as Hindi (Indian National language) is a tedious task. In such scenarios, automatic machine translation is an efficient approach. In our work, 8 advanced architectures have been experimented and contrasted their efficiencies. Six different Indian languages such as Hindi, Bengali, Gujarati, Malayalam, Tamil and Telugu is worked on. How BLEU varies with the usage of Word embedding technique have been clearly shown.. Encoder to decoder networks are found fine for short sentences. But if the length of the sentence exceeds 20, then attention architecture is suitable. The 4 Layer Bi-directional LSTM is a great choice in these networks to achieve higher BLEU is also observed. In our work, CFILT, UFAL, ILCC datasets have been considered and achieved a BLEU score of 21.97.
Postdigital Knowledge Socialism

This chapter historicises Michael Peters' concept of knowledge socialism and situates it in a wider postdigital context. Looking at theory, the chapter shows that our analyses of knowledge socialism should reach beyond dichotomy with knowledge capitalism. Knowledge socialism does not need to be digital, but the digital has significantly and irreversibly shaped knowledge socialism. The chapter identifies the need to develop a distinct philosophy of knowledge socialism based on a complete erasure of borders between low and high theory. Through an examination of historical struggle over freedom and openness of information, the chapter shows that the dynamic between knowledge capitalism and knowledge socialism is a never-ending story of appropriations and re-appropriations of concepts and ideas. Analysing examples of knowledge socialism in educational practice, the chapter shows that present technological affordances for knowledge socialism are much more advanced than its theory and political economy. Based on these analyses, the chapter warns against repeating errors made by earlier generations of leftist scholars and practitioners such as a strong orientation to the immaterial aspects of digital cultures, a dualistic approach to our postdigital reality, a disconnect between high and low theory, and a disconnect between theory and practice.
Descriptions from the Customers

Product descriptions play an important role in the e-commerce ecosystem. Yet, on leading e-commerce websites product descriptions are often lacking or missing. In this work, we suggest to overcome these issues by generating product descriptions from user reviews. We identify the set of candidates using a supervised approach that extracts review sentences in their original form, diversifies them, and selects the top candidates. We present extensive analyses of the generated descriptions, including a comparison to the original descriptions and examination of review coverage. We also perform an A/B test that demonstrates the impact of presenting our descriptions on user traffic.