Reviewerly/exHarmony huggingface dataset:
Sarcasm Detection Approaches for English Language

Human emotions have always been a mystery. It is tough to infer what a person wants to convey by just reading a sentence written by her. Sentiment analysis (opinion mining) has tried to use the accuracy of a computer and natural language processing (NLP) to make computers detect human emotions in various types of text available online. With the changing trends and times, humans have learnt new ways of expressing their feelings. Sarcasm is the most popular of them. People these days, say words and sentences that are not literally meant by the speaker or there is some hidden meaning to it that is supposed to be understood by the listener. Several efforts have been done by the researchers to make machines capable of understanding such sentences too. This chapter aims to be an introduction to the world of sarcasm and the methods of detecting it. It gives the reader a complete sense of the role of sarcasm in the field of sentiment analysis and how machines can be made capable of understanding sarcasm.
Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras

This paper proposes a novel unified framework to solve the 3-D localization and tracking problem that occurs multiple camera settings with overlapping views. The main challenge is to overcome the uncertainty of the back projection arising from the challenges of ground point detection in an environment that includes severe occlusions and the unknown heights of people. To tackle this challenge, we establish a Bayesian learning framework that maximizes a posterior over the trajectory assignments and 3-D positions for given detections from multiple cameras. To solve the Bayesian learning problem in a tractable form, we develop an expectation-maximization scheme based on the variation inference approximation, where the probability distributions are designed to follow Boltzmann distributions of seven terms that are induced from multicamera tracking settings. The experimental results show that the proposed method outperforms the state-of-the-art methods on the challenging multicamera data sets.
Social Media and Workers Rights: What Is at Stake?

This article addresses the way in which the growing use of social media is changing the employment relationship. Technology has given rise to a huge increase in the amount of information about employees available to employers, while allowing them to engage in invasive monitoring of employee access to the internet and social media. This highlights the importance of regulatory techniques, as employees have become more exposed to monitoring by the employer and potentially to discrimination. In certain cases, the employee’s freedom of expression is at risk. To counteract these tendencies, privacy laws, recently reinforced in the EU by the General Data Protection Regulation or GDPR, and more generally the principle of proportionality, can represent effective instruments to prevent technology from exacerbating the condition of subordination of employees.
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.

Traditional clustering algorithms such as K-means rely heavily on the natureof the chosen metric or data representation. To get meaningful clusters, theserepresentations need to be tailored to the downstream task (e.g. cluster photosby object category, cluster faces by identity). Therefore, we frame clusteringas a meta-learning task, few-shot clustering, which allows us to specify how tocluster the data at the meta-training level, despite the clustering algorithmitself being unsupervised. We propose Centroid Networks, a simple and efficient few-shot clusteringmethod based on learning representations which are tailored both to the task tosolve and to its internal clustering module. We also introduce unsupervisedfew-shot classification, which is conceptually similar to few-shot clustering,but is strictly harder than supervised* few-shot classification and thereforeallows direct comparison with existing supervised few-shot classificationmethods. On Omniglot and miniImageNet, our method achieves accuracy competitivewith popular supervised few-shot classification algorithms, despite using *nolabels* from the support set. We also show performance competitive withstate-of-the-art learning-to-cluster methods.
Evaluating Security, Privacy and Usability Features of QR Code Readers

The widespread of smartphones with advanced capabilities has motivated developers to design new mobile applications that are used as barcode scanners. Although several barcode readers are available, they still have security and privacy limitations. In this paper, we first present a comprehensive and systematic review of barcode reader applications by analyzing their security, privacy and usability features. We categorize these apps into four groups depending on their properties: URLs security, Crypto-based security, Popular applications, and Save-privacy. We also highlight their weaknesses and present design recommendations for usable, secure and privacy-guaranteed scanner applications. Based on our recommendations, we have developed BarSec Driod a proof-of-concept secure barcode reader Android app that exploits some features of other applications and at the same time overcomes their limitations. We have performed a user usability and security survey, on BarSec Driod and two other popular QR code readers, KasperSky and QR Droid Private. The results show that BarSec Driod is easy to use, satisfies the expectations of the users and is secure. Moreover, we have observed that following the design tips, user’s security awareness and usability increase.
Top down, bottom up and classroom reading anxiety and their effect on reading performance of undergraduate engineering students in Pakistan

Purpose The purpose of this paper is to explore EFL reading anxiety of first-year undergraduate engineering students and its effect on their reading performance in a public sector engineering university in Pakistan. It specifically aims to explore their top-down, bottom-up and classroom EFL reading anxiety. Design/methodology/approach Data for the present study were collected from 200 first-year engineering students to explore their reading anxiety. A 20-item questionnaire developed by Zoghi and Alivandivafa (2014) was used to measure students’ EFL reading anxiety, while an IELTS academic reading test was used to measure their reading performance. The data were analyzed through exploratory factorial analysis and multiple regression analysis to determine which type of reading anxiety has a significant effect on students’ reading performance. Findings It was found that the bottom-up reading anxiety and the classroom reading anxiety have a significant negative impact on the reading performance of the first-year undergraduate engineering students of a Pakistani university. However, top-down reading anxiety has an insignificant negative impact on the reading performance of university students. Research limitations/implications The data for the current study were drawn from one Pakistani public sector engineering university, and all the students were first-year undergraduates. The data were collected through a self-reported questionnaire and IELTS (academic) reading test. Some of the students may be unfamiliar with the IELTS test pattern, so their reading performance might have been affected. Practical implications Teachers should adopt such a methodology in their EFL classrooms which helps students reduce their reading anxiety. Reading texts must be selected considering the proficiency level of students, and reading strategies must be explicitly taught to reduce bottom-up and top-down reading anxieties. Teachers should create a positive learning environment in their classroom by encouraging students to make an effort to improve their reading skills in order to deal with classroom reading anxiety. Students must be explained that they should help one another rather than ridiculing each other’s reading mistakes. Differentiated instruction can also be adopted to facilitate weak readers. The teachers can provide additional/out of the class support to weak readers in order to help them deal with reading anxiety. Originality/value The EFL reading anxiety among university students in the Pakistani context has received little attention from the researchers. Furthermore, although the impact of EFL reading anxiety on EFL students’ reading performance has been explored previously, the impact of three types of EFL reading anxiety on EFL learners’ reading performance has not been adequately investigated.
NeuroCore: Guiding High-Performance SAT Solvers with Unsat-Core Predictions.

The NeuroSAT neural network architecture was recently introduced for predicting properties of propositional formulae. When trained to predict the satisfiability of toy problems, it was shown to find solutions and unsatisfiable cores on its own. However, the authors saw no obvious path to using the architecture to improve the state-of-the-art. In this work, we train a simplified NeuroSAT architecture to directly predict the unsatisfiable cores of real problems. We modify several high-performance SAT solvers to periodically replace their variable activity scores with NeuroSAT's prediction of how likely the variables are to appear in an unsatisfiable core. The modified MiniSat solves 10% more problems on SAT-COMP 2018 within the standard 5,000 second timeout than the original does. The modified Glucose solves 11% more problems than the original, while the modified Z3 solves 6% more. The gains are even greater when the training is specialized for a specific distribution of problems; on a benchmark of hard problems from a scheduling domain, the modified Glucose solves 20% more problems than the original does within a one-hour timeout. Our results demonstrate that NeuroSAT can provide effective guidance to high-performance SAT solvers on real problems.
Few-Shot Text Classification with Induction Network.

Text classification tends to struggle when data is deficient or when it needsto adapt to unseen classes. In such challenging scenarios, recent studies oftenuse meta learning to simulate the few-shot task, in which new queries arecompared to a small support set on a sample-wise level. However, thissample-wise comparison may be severely disturbed by the various expressions inthe same class. Therefore, we should be able to learn a general representationof each class in the support set and then compare it to new queries. In thispaper, we propose a novel Induction Network to learn such generalizedclass-wise representations, innovatively combining the dynamic routingalgorithm with the typical meta learning framework. In this way, our model isable to induce from particularity to university, which is a more human-likelearning approach. We evaluate our model on a well-studied sentimentclassification dataset (English) and a real-world dialogue intentclassification dataset (Chinese). Experiment results show that, on bothdatasets, our model significantly outperforms existing state-of-the-art modelsand improves the average accuracy by more than 3%, which proves theeffectiveness of class-wise generalization in few-shot text classification.
Boolean functions as points on the hypersphere in the Euclidean space

Abstract A new approach to the study of algebraic, combinatorial, and cryptographic properties of Boolean functions is proposed. New relations between functions have been revealed by consideration of an injective mapping of the set of Boolean functions onto the sphere in a Euclidean space. Moreover, under this mapping some classes of functions have extremely regular localizations on the sphere. We introduce the concept of curvature of a Boolean function, which characterizes its proximity (in some sense) to maximally nonlinear functions.
Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Region-of-interest (ROI) segmentation is an important critical step and challenging task in the evolution of computer-aided detection (CAD) system for breast cancer. The discovery of breast cancer in early stages can save many women lives. However, most of the early detection systems are costly in terms of complexity, price and processing time; that make it unsuited for developing countries. The digital mammography is proven to be one of the most important diagnostic techniques for breast cancer tumors. Therefore, this work proposes a CAD system for breast cancer detection from digital mammography based on Gaussian Mixture Model (GMM) followed by Support Vector Machine (SVM). The best contribution of our proposed system is the usage of GMM for the first time in the literature for mammogram images segmentation into ROI areas. Besides, the discrimination between the three classes of tissues as normal, benign or malignant, is used without previous knowledge of mammogram images' type. Moreover, the proposed system is fully automated in all of its stages with reduced computation compared with recent used methods. Hence, it offers a suitable early detection system to our country regarding moneywise, timewise, and reduced complexity. A non-linear multi-class SVM is used for classifying the ROI into three classes: normal, benign or malignant tissue. The experiments show overall average classification accuracy of 90% for detecting normal, malignant or benign on randomly chosen 90 cases from the benchmark mini-MIAS dataset. On the other hand, the proposed method achieves 92.5% accuracy when classifying the benign from malignant cases.
TOWARDS AN EDUCATIONAL MATHEMATICAL ONTOLOGY

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 6823-6829ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1657Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
The Future of Tangible User Interfaces

Tangible user interfaces have a rich history in HCI research ever since their introduction two decades ago. But what are the practical implications, the commercial potential, and the future of this influential paradigm? This panel starts by looking into the importance of tangible interaction and its current role. It will then draw on the expertise of both the panelists and the audience to speculate about its future and new opportunities for the field. The panelists represent a variety of perspectives from both industry and academia, and includes some of the most well-known innovators in the field. The format builds on the CHI 2006 panel The state of tangible interfaces: projects, studies, and open issues, which shared some of the same organizers.
SGD: Decentralized Byzantine Resilience.

The size of the datasets available today leads to distribute Machine Learning(ML) tasks. An SGD--based optimization is for instance typically carried out bytwo categories of participants: parameter servers and workers. Some of thesenodes can sometimes behave arbitrarily (called \emph{Byzantine} and caused bycorrupt/bogus data/machines), impacting the accuracy of the entire learningactivity. Several approaches recently studied how to tolerate Byzantineworkers, while assuming honest and trusted parameter servers. In order toachieve total ML robustness, we introduce GuanYu, the first algorithm (to thebest of our knowledge) to handle Byzantine parameter servers as well asByzantine workers. We prove that GuanYu ensures convergence against$\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers,which is optimal in asynchronous networks (GuanYu does also tolerate unboundedcommunication delays, i.e. asynchrony). To prove the Byzantine resilience ofGuanYu, we use a contraction argument, leveraging geometric properties of themedian in high dimensional spaces to prevent (with probability 1) any drift onthe models within each of the non-Byzantine servers. % To convey itspracticality, we implemented GuanYu using the low-level TensorFlow APIs anddeployed it in a distributed setup using the CIFAR-10 dataset. The overhead oftolerating Byzantine participants, compared to a vanilla TensorFlow deploymentthat is vulnerable to a single Byzantine participant, is around 30\% in termsof throughput (model updates per second) - while maintaining the sameconvergence rate (model updates required to reach some accuracy).
Improved Cryptanalysis of Provable Certificateless Generalized Signcryption

Certificateless generalized signcryption adaptively work as certificateless signcryption, signature or encryption scheme having single algorithm for suitable storage-constrained environments.Recently, Zhou et al. proposed a novel Certificates generalized scheme, and proved its ciphertext indistinguishability under adaptive chosen ciphertext attacks (IND-CCA2) using Gap Bi-linear Diffie-Hellman and Computational Diffie-Hellman assumption as well as proved existential unforgeability against chosen message attacks (EUF-CMA) using the Gap Bi-linear Diffie-Hellman and Computational Diffie-Hellman assumption in random oracle model.In this paper, we analyzed Zhou et al. scheme and unfortunately proved IND-CCA2 insecure in encryption and signcryption modes in defined security model.We also present a practical and improved scheme, provable secure in random oracle model.
Recommendations from cold starts in big data

Abstract This paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data. We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressing these challenges, the Transitive Semantic Relationships (TSR) model, which infers potential relationships from user and item text content and few labelled examples. We perform both implicit and explicit evaluation of TSR as a recommender system and from new user cold starts we achieve a hit-rate@10 of 77% on a collection of 630 items with only 376 supply-chain consumer labels, and 67% with only 142 supply-chain supplier labels, demonstrating a high level of performance even with extremely few labels in challenging cold-start scenarios. TSR is suitable for any dataset featuring few labels and user and item content, where similarity of content indicates similar relationship forming capability. TSR can be used as a standalone recommender system or to complement existing high-performance recommender models that require more labels or do not support cold starts.
Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0

Industry is changing rapidly under industry 4.0. The manufacturing process and its cyber-physical systems (CPSs) produce large amounts of data with many relationships and dependencies in the data. Outlier detection and problem solving is difficult in such an environment. We present an unsupervised outlier detection method to find outliers in temporal spatial log data without domain-specific knowledge. Our method is evaluated with real-world unlabeled CPS log data extracted from a quality glass inspection machine used in production. As a measurement metric for success, we set reasonable outlier areas in cooperation with a domain expert. Using our proposed method, we were able to find all known outlier areas. In addition, we found outliers that were not previously known and have been verified as outliers by a domain expert ex post.
Image Invariants to Anisotropic Gaussian Blur

The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated. Such blurring is a semi-group action in the image space, where the orbits are classes of blur-equivalent images. We propose a non-linear projection operator which extracts blur-insensitive component of the image. The invariants are then formally defined as moments of this component but can be computed directly from the blurred image without an explicit construction of the projections. Image description by the new invariants does not require any prior knowledge of the particular blur kernel shape and does not include any deconvolution. Potential applications are in blur-invariant image recognition and in robust template matching.
Measuring the Performance on Load Balancing Algorithms

Load balancing is an integrated aspect of the environment in cloud computing. Cloud computing has lately outgoing technology. It has getting exoteric day by day residence widespread chance in close to posterior. Cloud computing is defined as a massively distributed computing example that is moved by an economic scale in which a repertory of abstracted virtualized energetically. The number of clients in cloud computing is increasing exponentially. The huge amount of user requests attempt to entitle the collection for numerous applications. Which alongside with heavy load not far afield off from cloud server. Whenever particular (Virtual Machine) VMs are overloaded then there are no more duties should be addressed to overloaded VM if under loaded VMs are receivable. For optimizing accomplishment and better response or reaction time the load has to be balanced between overloaded VMs (virtual machines). This Paper describes briefly about the load balancing accession and identifies which is better than others (load balancing algorithm).
Vehicle Detection in Aerial Images Based on Hyper Feature Map in Deep Convolutional Network

Vehicle detection based on aerial images is an interesting and challenging research topic.Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs.Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN.However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view.In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed.Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features.Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection.We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods.Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.