Reviewerly/exHarmony huggingface dataset:
Multi-Level Network for High-Speed Multi-Person Pose Estimation

In multi-person pose estimation, the left/right joint type discrimination is always a hard problem because of the similar appearance. Traditionally, we solve this problem by stacking multiple refinement modules to increase network's receptive fields and capture more global context, which can also increase a great amount of computation. In this paper, we propose a Multi-level Network (MLN) that learns to aggregate features from lower-level (left/right information), upper-level (localization information), joint-limb level (complementary information) and global-level (context) information for discrimination of joint type. Through feature reuse and its intra-relation, MLN can attain comparable performance to other conventional methods while runtime speed retains at 42 FPS.
Context-sensitive and attribute-based sentiment classification of online consumer-generated content

Purpose Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC. Design/methodology/approach A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words. Findings Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains. Practical implications Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach. Originality/value This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.
SplitSlider: A Tangible Interface to Input Uncertainty

Experiencing uncertainty is common when answering questionnaires. E.g., users are not always sure to answer how often they use trains. Enabling users to input their uncertainty is thus important to increase the data’s reliability and to make better decision based on the data. However, few interfaces have been explored to support uncertain input, especially with TUIs. TUIs are more discoverable than GUIs and better support simultaneous input of multiple parameters. It motivates us to explore different TUI designs to input users’ best estimate answer (value) and uncertainty. In this paper, we first generate 5 TUI designs that can input both value and uncertainty and build low-fidelity prototypes. We then conduct focus group interviews to evaluate the prototypes and implement the best design, SplitSlider, as a working prototype. A lab study with SplitSlider shows that one third of the participants (4/12) were able to discover the uncertainty input function without any explanation, and once explained, all of them could easily understand the concept and input uncertainty.
Collective Classification for Social Opinion Spam Detection

With increasingly more firms using online social media to market their products and services, so are the widely spread attacks to the consumer opinions posted to social media, namely the social opinion spam. Fake social opinions may inflate firms' own product reputation or defame competitors' product reputation. Accordingly, there is a pressing need to develop effective detection method to identify and remove social opinion spam in order to facilitate fair online trading and improve the effectiveness of consumer decision-making. The main contribution of our research work is to design and evaluate several collective classification methods to detect social opinion spam on online social media. In particular, experiments based on the Yelp social opinion dataset reveal that state-of-the-art collective classification algorithms can achieve the detection performance of 72.5% in terms of F-score. However, selecting the effective relational features is critical for achieving good performance of collective classification. Moreover, our experiments also show that more recent deep learning techniques such as DeepWalk can be incorporated into the iterative collective classification to further bootstrap the performance of social opinion spam detection. More studies are needed to further examine deep learning techniques and investigate into why they cannot bootstrap social opinion spam detection. As a whole, our work contributes to develop a new generation social opinion spam detection methodology to improve the hygiene of social opinions and facilitate fair online trading and effective consumer decision-making.
Performance Evaluation of Applying N-Gram Based Naïve Bayes Classifier for Hierarchical Classification

Text classification is a process of allocating one or more class label to a text document. If the text classification problem has too many categories, and there are certain categories with less number of training documents, text classification task becomes difficult. Recall will be less for categories with less number of training documents. To handle text classification problem with too many categories and to take into consideration parent-child/sibling relationships between categories in user profile and document profile for content-based filtering, hierarchical classification is better approach. The main issue with hierarchical classification is error propagation. The error that occurs at early level in hierarchy will carry forward to all the levels below it. So, misclassification at early level in hierarchy needs to be reduced. Term ambiguity may be one of the reasons for classification error. Naive Bayes classification method is mostly used in text classification problem as it takes less time for training and testing. Naive Bayes model considers that terms are not dependent on each other for a given class. For data where terms are dependent on each other, performance of naive Bayes is degraded. In this paper, word-level n-gram based Multinomial Naive Bayes classification method is combined with hierarchical classification to reduce misclassification that occur at early level in hierarchy & improve content-based filtering. Proposed algorithm also suggests a way to reduce execution time requirements for calculating probabilities of terms for n-gram naive bayes model.
Maximum Search Limitations: Boosting Evolutionary Particle Swarm Optimization Exploration

The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods.
Dependent object types with implicit functions

DOT (Dependent Object Types) is an object calculus with path-dependent types and abstract type members, developed to serve as a theoretical foundation for the Scala programming language. As yet, DOT does not model all of Scala's features, but a small subset. We present the calculus DIF (DOT with Implicit Functions), which extends the set of features modelled by DOT to include implicit functions, a feature of Scala to aid modularity of programs. We show type safety of DIF, and demonstrate that the generic programming focused use cases for implicit functions in Scala are also expressible in DIF.
The local and global imagery of women in Russian advertising

Abstract Russian advertisements geared to women display both local and global tendencies. The alignment exists among the elements such as imagery of women and the promoted products, reason or ‘tickle’ advertising techniques, language usage, and value messages. The local imagery of women such as housewife and average woman are visually presented by all age groups and promote goods produced locally, use ‘reason’ explanations, deliver the spoken or written messages mostly in the Russian language, and reflect traditional Russian collectivistic values. The global imagery of women such as sexy beauties or ‘glocal’ imagery of women such as career woman and celebrity employ English only or code‐mixing of English and Russian in the name of the product, label and trade brand, text, slogan attention getter, and ending lines. The English language serves as a sign of modernity, high social status, and prestige and a ‘tickle’ strategy which is used when model‐looking young women promote foreign‐made goods.
Autonomous Two-Stage Object Retrieval Using Supervised and Reinforcement Learning

Humans have been sending tele-operated robots into hazardous areas in an attempt to preserve life for many years. The task they are presented with is often challenging and requires cognitive abilities, that is, the ability to process information in order to apply it to a different situation. In this work, we proposed an autonomous approach employing both supervised and reinforcement learning for hidden-object retrieval in two stages. Stage 1 used both learning methods to find a hidden object whereas stage 2 only used reinforcement learning to isolate it. The method is targeted towards field robots with reduced computational power hence we explored the viability of tabular reinforcement learning algorithms. We used a convolutional neural network (CNN) to interpret the state of the scene from images and a reinforcement learning agent for each stage of the task. The robot was presented with a workspace containing piles of rubble under one of which an object was buried. The robot must learn to find and isolate it. We compared the performance of four reinforcement learning algorithms over 500 episodes and found that Sarsa (λ) and RMax were most appropriate for stage 1 and 2 respectively. The approach allowed a robot to learn to complete a search and retrieval task by interpreting images. This could lead to the deployment of such robots in disaster areas eliminating the need for tele-operated platforms.
Computational and Human Evaluations of Orthogonal Graph Drawings

Graphs are often drawn into straight-line node-link diagrams for better understanding of the underlying data. However, curves have also been used in graph visualization for various purposes. Orthogonal drawing is one type of graph drawings in which each edge is made up with an alternating sequence of vertical and horizontal line segments. In the past, many algorithms have been developed for orthogonal drawing so that the resulting drawings meet some pre-defined aesthetic criteria and constraints. Experiments have also been conducted to evaluate performance of algorithms and effectiveness of orthogonal drawings. In this paper, we briefly summarize the research that has been done in relation to orthogonal drawing and provide directions for future research.
Determining Perceptual Similarity Among Readers Based on Eyegaze Dynamics

Understanding of a readingSinha, Aniruddha material depends on lexical processing and formation ofKumar Saha, Sanjoy perception. Differences between individuals exist due toBasu, Anupam various cognitive capabilities and psychological factors. In this paper, we focus on grouping of individuals according to their reading traits based on the eye movement behavior using affordable eye-tracking device. Detailed characterization of eye gaze, namely the distribution of the spatial spread and change in drift direction in fixations, is considered for analyzing the reading behavior. They relate to the perceptual span and attention, respectively. Analysis of variance (ANOVA) is performed with the features derived from the perceptual distance between pairwise individuals. Multidimensional scaling is used to obtain the relative perceptual orientation and attention among the individuals. Results demonstrate the feasibility of forming homogeneous groups based on the eye movement behavior.
Blood Glucose Classification to Identify a Dietary Plan for High-Risk Patients of Coronary Heart Disease Using Imbalanced Data Techniques

Coronary heart disease (CHD) is a deadly disease that affects people around the world, yet early prevention can help to decrease mortality numbers. An increase in total blood glucose levels is one of the high-risk factors for CHD occurrence. Therefore, it is important to control glucose regulation and slow down the development of diabetes. Advances in the machine learning approach helps to create a multiclass classifier capable of categorising patients as normal, prediabetic or diabetic based on blood glucose levels. In addition, a modifiable diet based on the classified patient's glucose levels aids in reducing the risk of developing heart disease. This study aims to classify blood glucose levels for CHD risk factors using the highly imbalanced Framingham Heart Study dataset and to identify a suitable dietary plan that reduces the risk of developing CHD. The efficiency of classification depends on the balanced dataset; hence, the pro-posed study aims to apply effective imbalanced data techniques such as Random Oversampling, SMOTE, SMOTEENN and SMOTETomek to enhance the classification performance. Various imbalanced data techniques with different classifiers are used for investigation. The results suggest that the voting ensemble classifier with the random oversampling technique recorded the highest balanced accuracy, outperforming other classifiers with AUC (73%), specificity (77.45%), sensitivity (72%), F1 measures (78%) and balanced accuracy (67.94%). The pro-posed study suggests a suitable dietary plan, including preventive tips for normal patients, a low glycaemic diet for prediabetes and a diabetic diet for diabetes patients, which help to reduce the development of CHD risk factors.
Evaluation of Effectiveness of Glyphs to Enhance ChronoView

ChronoView is a visualization method of representing periodic features of the occurrence of events. It expresses a set of time stamps in the position on a plane. Although ChronoView offers high space efficiency, it can generate ambiguous representations. To solve this problem, glyphs have been exploited as ChronoView markers. This paper explains a user study conducted to investigate the effectiveness of the Star Glyph and Ring Glyph. The study shows that glyphs contribute to an accurate reading of temporal features. It also shows that Star Glyph and Ring Glyph have different features. It is clear that, whereas Ring Glyph dominates the time range reading, Star Glyph dominates the comparison of frequencies in the unit of time.
Detection of fake opinions on online products using Decision Tree and Information Gain

Online reviews are one of the major factors for the customers to purchase any product or to get service from many sources of information that can be used to determine the public opinion on the products. Fake reviews will be published intentionally to drive the web traffic towards the particular products. These fake reviewers mislead the customers to distract the purchasers mind. Reviewers behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. In this work the reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using decision tree classifier and Information Gain. Significance of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficacy of the proposed approach.
Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imagery are increased for more detailed and precise information in land planning, urban planning, and Earth observation. Commonly, feature-based methods are applied for image registration. In these methods, first control or key points are detected using feature detector such as scale-invariant feature transform (SIFT). The numbers and the distribution of these control points are important for the remaining steps of registration. These methods provide reasonable performance; however, they suffer from high computational cost and irregular distribution of control points. To overcome these limitations, we propose an area-based registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input datasets. The results demonstrate its efficacy and accuracy in image registration.
Towards a visualization of deep neural networks for rough line images

Low dose scanning electron microscope (SEM) images are an attractive option to estimate the roughness of nanos- tructures. We recently proposed two deep convolutional neural network (CNN) architectures named “LineNet” to simultaneously perform denoising and edge estimation on rough line SEM images. In this paper we consider multiple visualization tools to improve our understanding of LineNet1; one of these techniques is new to the visualization of denoising CNNs. We use the resulting insights from these visualizations to motivate a study of two variations of LineNet1 with fewer neural network layers. Furthermore, although in classification CNNs edge detection is commonly believed to happen early in the network, the visualization techniques suggest that important aspects of edge detection in LineNet1 occur late in the network.
Design Study of 6-DOF Grinding Robot

Following the need for improvement of efficiency and quality of grinding operation, the design of dedicated six degree of freedom (6-DOF) robot is proposed. The mechanical structure of the 6-DOF grinding robot is derived considering the properties of grinded objects and the grinding head. The MDA350 grinding head produced by Mannesmann-Demag is considered as the tool. The direct and inverse kinematic problem is solved using Denavit-Hartenberg method. Next, the robot dynamics using the simulation method is investigated. Considering the actual working load, the strength of the relevant links is analyzed using ANSYS software.
Non-English Words in <i>The Spanish Tragedy</i>

The juiciest mysteries of authorship are those which present unusual challenges. These mysteries stretch the capacities of scholars of attribution and, eventually, drive methodological innovation. ...
Ontology-Based Conceptualisation of Text Mining Practice Areas for Education

Text mining is highly multi-disciplinary field including various techniques of text analysis. These techniques are used for uncovering hidden information and knowledge in semi-structured and non-structured texts. Text mining concepts are spread among different, but related practice areas. It is often difficult to receive fast insight into this amount of concepts for a non-professional, e.g. for a student. The paper presents the OWL ontology-based prototype which should ease education and learning of facts which are used in the text mining domain. It is mainly aimed to the university students studying text mining at the introductory level. It can also be used as a formal vocabulary of text mining concepts for understanding of methods, techniques, concepts and relations between them by machines.
Light Field Stitching Targeting Focal Length Inconsistency

Focal length inconsistency is a common problem in capturing multiple light fields (LFs), which reduces the accuracy in feature matching and LF registration, and limits the quality of LF stitching. In this paper, a novel method is proposed to stitch the LFs captured at different focal length accurately. First, refocus matching is proposed to find the best-matched-focused regions in the focal stack to improve the quality of feature matching. Then, depth distances and refocusing features are exploited to register central sub-aperture images (CSI) to handle focal length variation. Finally, CSI-based 4D warping is applied to preserve the LF angular consistency. Experimental results show that the proposed method outperforms the existing approaches obviously in terms of the stitched LFs and the coherence in epipolar plane image (EPI), especially for the LFs with the focal length inconsistency.
About the Use of a New Set of Quadriphase Sequences for Increasing Security of PMR over LTE Primary Synchronization

Current 4G mobile communication systems are relying on LTE standard and on its evolution, namely LTE-A. Thanks to its widespread diffusion and to the capability of efficiently supporting broadband multimedia data exchange, LTE considers some particular extensions of the standard for private mobile radio (PMR) networks in the framework of public security communications. Unfortunately, some open issues of LTE in terms of vulnerability to security attacks may hinder such kind of safety-critical applications. In this paper, we propose a novel solution to one of these issues, namely the primary synchronization signal (PSS) spoofing, based on a new set of quadriphase sequences: the Even-Odd-Equivalent (EOE) pseudo De Bruijn (DB) sequences, obtained by a simple transformation of binary De Bruijn sets. EOE pseudo DB sets are characterized by equal even and odd crosscorrelation, near-impulsive autocorrelation and very large cardinality. This last feature makes the use of EOE pseudo DB sets an efficient countermeasure against PSS spoofing. As compared with the standard solution based on the use of Constant-Amplitude Zero Autocorrelation (CAZAC) Frank-Zadoff-Chu (FZC) sequences, the proposed solution increases the PSS robustness against spoofing attacks, performing very close to FZC in terms of cell ID detection probability.