Reviewerly/exHarmony huggingface dataset:
FaceOff

Designing desirable and aesthetical manifestation of web graphic user interfaces (GUI) is a challenging task for web developers. After determining a web page's content, developers usually refer to existing pages, and adapt the styles from desired pages into the target one. However, it is not only difficult to find appropriate pages to exhibit the target page's content, but also tedious to incorporate styles from different pages harmoniously in the target page. To tackle these two issues, we propose FaceOff, a data-driven automation system that assists the manifestation design of web GUI. FaceOff constructs a repository of web GUI templates based on 15,491 web pages from popular websites and professional design examples. Given a web page for designing manifestation, FaceOff first segments it into multiple blocks, and retrieves GUI templates in the repository for each block. Subsequently, FaceOff recommends multiple combinations of templates according to a Convolutional Neural Network (CNN) based style-embedding model, which makes the recommended style combinations diverse and accordant. We demonstrate that FaceOff can retrieve suitable GUI templates with well-designed and harmonious style, and thus alleviate the developer efforts.
Offensive communications: exploring the challenges involved in policing social media

The digital revolution has transformed the potential reach and impact of criminal behaviour. Not only has it changed how people commit crimes but it has also created opportunities for new types of crimes to occur. Policymakers and criminal justice institutions have struggled to keep pace with technological innovation and its impact on criminality. Criminal law and justice, as well as investigative and prosecution procedures, are often outdated and ill-suited to this type of criminality as a result. While technological solutions are being developed to detect and prevent digitally-enabled crimes, generic solutions are often unable to address the needs of criminal justice professionals and policymakers. Focussing specifically on social media, this article offers an exploratory investigation of the strengths and weaknesses of the current approach used to police offensive communications online. Drawing on twenty in-depth interviews with key criminal justice professionals in the United Kingdom, the authors discuss the substantial international challenges facing those seeking to police offensive social media content. They argue for greater cooperation between policymakers, social science and technology researchers to develop workable, innovative solutions to these challenges, and greater use of evidence to inform policy and practice.
A Prototype Mobile Augmented Reality Systems for Cultural Heritage Sites

Augmented Reality (AR) is an emerging technology that is currently revolutionising the worldly activities. The evolution of mobile devices has pioneered AR as a state-of-the-art technology in the last decade giving rise to more and more Location-Based Mobile AR (LBMAR) systems. This technology is widely been used by heritage industry to create the missing sparks from their static environment thus making it livelier and entertaining. Yet, current systems have not exploited the emerging sensory technologies in smartphones which led to low-quality AR experiences. In this work, we will model a real-time Mobile AR system for a cultural site in Mauritius that will both promote the cultural tourism and enhance the visiting experience. Moreover, the proposed prototype will also attract historians and students so that they can carefully study those heritage aspects that were not accessible before.
Across-Time Comparative Summarization of News Articles

Comparative summarization is an effective strategy to discover important similarities and differences in collections of documents biased to users' interests. A natural method of this task is to find important and corresponding content. In this paper, we propose a novel research task of automatic query-based across-time summarization in news archives as well as we introduce an effective method to solve this task. The proposed model first learns an orthogonal transformation between temporally distant news collections. Then, it generates a set of corresponding sentence pairs based on a concise integer linear programming framework. We experimentally demonstrate the effectiveness of our method on the New York Times Annotated Corpus.
A Two-Stage Attribute-Constraint Network for Video-Based Person Re-Identification

Person re-identification has gradually become a popular research topic in many fields such as security, criminal investigation, and video analysis. This paper aims to learn a discriminative and robust spatial–temporal representation for video-based person re-identification by a two-stage attribute-constraint network (TSAC-Net). The knowledge of pedestrian attributes can help re-identification tasks because it contains high-level information and is robust to visual variations. In this paper, we manually annotate three video-based person re-identification datasets with four static appearance attributes and one dynamic appearance attribute. Each attribute is regarded as a constraint that is added to the deep network. In the first stage of the TSAC-Net, we solve the re-identification problem as a classification issue and adopt a multi-attribute classification loss to train the CNN model. In the second stage, two LSTM networks are trained under the constraint of identities and dynamic appearance attributes. Therefore, the two-stage network provides a spatial–temporal feature extractor for pedestrians in video sequences. In the testing phase, a spatial–temporal representation can be obtained by inputting a sequence of images to the proposed TSAC-Net. We demonstrate the performance improvement gained with the use of attributes on several challenging person re-identification datasets (PRID2011, iLIDS-VID, MARS, and VIPeR). Moreover, the extensive experiments show that our approach achieves state-of-the-art results on three video-based benchmark datasets.
Combined shape measurement based on locating and tracking of an optical scanner

The existing track-based methodology for shape measurement is a combination of binocular vision system and optical scanner. We study a dedicated and effective track-based measurement system with a laser scanner, which has characteristics of non-contact, high-precision, and convenience. Although, several related approaches have been proposed, dedicated locating and tracking processing algorithm still has some improvements. This paper focuses on locating and tracking of laser scanner in overall scanning process. In this work, we propose a simple but useful five-marker target which is attached on a laser scanner, as well as the target detection and local coordinate system establishment method. We also provide details of global calibration method and scanner pose calculation method. The effectiveness of our methodology and measurement system in this paper are validated by experimental tests. We perform scanning tests on complex shape and work piece, separately, and the results show good performance in our special combined shape measurement.
Evaluating the use of digital image correlation for strain measurement in historic tapestries using representative deformation fields

Abstract An analysis technique to assess the viability of digital image correlation (DIC) in tracking the full‐field strains across the surface of hanging historic tapestries is presented. Measurement uncertainty related to the use of the inherent tapestry image in tracking displacements is investigated through use of “synthetic” deformation fields. The latter are generated by mapping the details of a given tapestry image into finite element analyses. The combination of self‐weight loading, material non‐linearity, and image specific heterogeneity (related to slit stitching, damage, and patch‐restorations) serve to generate a bespoke deformation field complex enough to assess the reliability of DIC measurements. Accuracy is evaluated by comparing measured results with the original known deformations. The technique demonstrates that the optimum imaging settings and the choice of subset size for DIC analysis are strongly influenced by the tapestry image and the goal of the measurement, they are found using a compromise between conflicting objectives: minimising measurement error while maximising resolution.
Enhanced feature fusion through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis

Feature fusion aims to provide enhancements of data authenticity in both traditional and deep learning pattern analysis. Canonical correlation analysis (CCA) based feature fusion is a main technique for exploring the mutual relationships of multiple feature sets. In traditional CCA-based feature fusion, the dimensionality of each feature set is usually first reduced using principal component analysis (PCA), linear discriminant analysis (LDA) etc. to ensure non-singularity and invertibility of covariance matrices. One issue with the above standard CCA-based feature fusion is that the reduced feature sets generated by PCA or LDA may neglect certain correlation information among different feature sets which is useful for CCA, and this in turn may degrade the following classification performance. Another issue is that most CCA fused features may still contain redundancies due to the correlation criterion. These redundancies may be relevant or irrelevant to class labels. The irrelevant redundancies may degrade the pattern recognition performance, while the relevant redundancies can make the pattern recognition system more robust. In this paper, we propose an enhanced feature fusion scheme through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis (IEDCA-IRE) addressing the above two issues. IEDCA-IRE explores the intra-class correlation including both the pairs-wise correlation like CCA-based feature fusion approaches and the correlation across different features within the same class. By incorporating kernelized IEDCA into minimum redundancy maximum relevance (mRMR) criterion, only the relevant redundancy is retained in the fused feature. Our proposed IEDCA-IRE can be used in unimodal feature fusion, multimodal feature fusion, fusion of deep features extracted from different deep neural network models as well as fusion of deep features and handcrafted features. Extensive experiments have proved its effectiveness.
Atomic Head Movement Analysis for Wearable Four-Dimensional Task Load Recognition

Physical activity recognition using wearable sensors has achieved good performance in discriminating heterogeneous activities for health monitoring, but there has been less investigation of sedentary activities, e.g., desk work, which is often physically homogenous, to improve health in office environments. In this study, we explored head movement as a new sensing modality for physical and mental activity analysis. A new algorithm which segments gyroscope signals into atomic head movement events is proposed. Instead of recognizing activities in terms of predefined categories, we recognized four dimensions of task load: cognitive, perceptual, communicative, and physical, analogous to current manual workload assessment methods like NASA-TLX. We collected head movement data from 24 participants who wore a tri-axial inertial sensor at head while performing multiple tasks with varying load levels at office. An average of 70% accuracy was achieved for recognizing cognitive load levels, and more than 80% for the other three load types. The proposed event features outperformed a set of 181 features from previous physical activity recognition studies. We also demonstrated that these atomic event features are diagnostic of different load types in cross-load type classification, showing the promise of physical and mental load monitoring for health.
Accurate Smoothing Methods for State Estimation of Continuous-Discrete Nonlinear Dynamic Systems

In this note, we extend the accurate continuous-discrete extended-cubature Kalman filter and continuous-discrete cubature Kalman filter to deal with the problem of Bayesian optimal smoothing in nonlinear dynamic systems. The dynamics can be modeled with nonlinear stochastic differential equations (SDEs) and the noise corrupted measurements are obtained at discrete time instants. To be consistent with the literature, the resulting nonlinear smoothers are referred to as the accurate continuous-discrete extended-cubature Kalman smoother and the continuous-discrete cubature Kalman smoother, respectively. We first present two approximation methodologies to solve the SDE encountered in the prediction step of continuous-discrete filter. Then, two types of novel Gaussian approximation smoothing methods are derived based on the fixed-interval Rauch-Tung-Striebel smoother, which computes the smoothing solution according to the stored filtering results. The performances of the proposed smoothing methods are demonstrated in a simulated application and the numerical results show that the newly presented approaches are more flexible and robust than other smoothing algorithms with lower computational cost.
Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems

Building complex automation solutions, common to process industries and building automation, requires the selection of components early on in the engineering process. Typically, recommender systems guide the user in the selection of appropriate components and, in doing so, take into account various levels of context information. Many popular shopping basket recommender systems are based on collaborative filtering. While generating personalized recommendations, these methods rely solely on observed user behavior and are usually context free. Moreover, their limited expressiveness makes them less valuable when used for setting up complex engineering solutions. Product configurators based on deterministic, handcrafted rules may better tackle these use cases. However, besides being rather static and inflexible, such systems are laborious to develop and require domain expertise. In this work, we study various approaches to generate recommendations when building complex engineering solutions. Our aim is to exploit statistical patterns in the data that contain a lot of predictive power and are considerably more flexible than strict, deterministic rules. To achieve this, we propose a generic recommendation method for complex, industrial solutions that incorporates both past user behavior and semantic information in a joint knowledge base. This results in a graph-structured, multi-relational data description – commonly referred to as a knowledge graph. In this setting, predicting user preference towards an item corresponds to predicting an edge in this graph. Despite its simplicity concerning data preparation and maintenance, our recommender system proves to be powerful, as shown in extensive experiments with real-world data where our model outperforms several state-of-the-art methods. Furthermore, once our model is trained, recommending new items can be performed efficiently. This ensures that our method can operate in real time when assisting users in configuring new solutions.
SMS-Based ICT Tool for Knowledge Sharing in Agriculture

Agriculture is the world’s major industry with 60% of the global population depending on it.  Being the major source of livelihood, agriculture is challenged by issues such as food shortage, food security problems, climate change affecting crop yield, land degradation, decrease of crop varieties and others. There is a huge amount of knowledge products generated by the government agencies, local universities, private civic groups, local government units and non-government organizations (NGOs) to address these issues however the information has difficulty reaching the intended farm practitioners as end-user. With this aforesaid challenge, a technology based information sharing and access among stakeholders such as agricultural knowledge experts (academe) and knowledge end-users (farmers) is a dire need.  Information and Communications Technology (ICT) has always been an indispensable tool that can provide an environment where knowledge generators and knowledge users can use to exchange information any time and place.  Retooling and scaling up the process how information and knowledge products is being accessed and shared is indeed a critical consideration. Using descriptive research and qualitative approach, this study determined an ICT tool, a managed SMS-based system to be highly acceptable to the identified stakeholders as an information sharing medium and model and is therefore recommended for use.  The result of this research shall serve as an implementation guide for government, non-government organizations(NGOs), extension service providers, system integrators, researchers, and other related groups offering or planning to engage in similar service.
A kernel semi-supervised distance metric learning with relative distance: Integration with a MOO approach

Metric learning, which aims to determine an appropriate distance function to measure the similarity and dissimilarity between data points accurately, is one of the most popular methods to enhance the performance of many machine learning methods such as K-means clustering and K nearest neighbor classifier algorithms. These algorithms may not perform well because of the use of normal Euclidean distance function that ignores any statistical regularities that might be estimated from a large training set of labeled examples. In many real-world applications, the Euclidean distance may not be fit to capture the intrinsic similarity and dissimilarity between the data points. Compared to existing metric learning algorithms, which use large amount of labeled data in the form of must-link (ML) and cannot-link constraints as side information where the granularity of true clustering is unknown, our proposed approach uses few labeled data in the form of relative-distance constraints such as equality constraints, Ceq, and inequality constraints, Cneq. For satisfying such constraints, we need to project the initial Euclidean distance matrix by using Bregman projection on the convex subset of constraints in such a way that all the constraints are satisfied. Since Bregman projection is not orthogonal, means while satisfying the current constraint previously satisfied constraints may get unsatisfied, we need to select a proper subset of constraints for learning better distance function. The multi-objective framework is utilized for selecting a good subset of constraints which can help in getting the proper labeling of the data set. The selected subset of constraints is used for adjusting the initial kernel-matrix. K-means clustering technique is applied to the adjusted kernel matrix to label the data set. In order to evaluate the quality of obtained labeling, different external and internal cluster validity indices are deployed. The values of these indices are simultaneously optimized using the search capability of MOO with the aim of selecting the appropriate subset of constraints. The proposed approach is evaluated on UCI Human Activity Recognition using Smartphone Dataset v1.0 along with nine other popular data sets. Results show that our approach outperforms the state of the art semi-supervised metric learning algorithms with respect to different internal and external cluster validity indices.
Invisible Geniuses: Could the Knowledge Frontier Advance Faster?

A better understanding of the determinants of idea/knowledge production remains critical for long-run growth. Toward this end, this paper establishes two results using data from the International Mathematical Olympiad (IMO). First, individuals who excelled in teenage years are especially capable of advancing the knowledge frontier. Second, such talented individuals born in poorer countries are systematically less likely to engage in knowledge production. IMO participants from low-income countries produce 34 percent fewer publications and 56 percent fewer citations than equally talented rich-country counterparts. Policies to encourage talented youth to pursue scientific careers—especially those from poorer countries—could advance the knowledge frontier faster. (JEL D83, O30, O47)
Analyzing compliance and application of usability guidelines and recommendations by web developers

There are many guidelines and recommendations for improving the user experience in websites. Web developers can follow these recommendations to improve the usability of the website and the user experience. Although guidelines and recommendations could not be the final solution to get the optimum usability level, they are useful and fast to apply, in comparison to other kind of usability improvements may involve many time and resources. Currently we can find many websites which contain big usability problems, some of these problems could be solved applying usability guidelines and recommendations. In this research work we analyse the degree of compliance of usability recommendations by web developers. We did an extensive compilation of usability recommendations designed to focus the user attention in the important parts of the website and reduce noise generated by unimportant parts. We analyse the behaviour of 20 web developers to obtain the degree of application of usability recommendations, before they received a specific course on usability, and after it. The purpose of the research is to analyse the degree of compliance with the usability recommendations focused on reducing noise. The two main objectives are (1) Determine which recommendations are the most forgotten by web developers without training in usability. (2) Determine which recommendations are most important according to web developers' thinking. In the conclusions we determine which are the most important recommendations. What are the most applied recommendations and their degree of compliance. Reflections are also extracted about the relationship of the results obtained.
Bearings-Only Tracking Using Augmented Ensemble Kalman Filter

Tracking targets with bearings-only measurement is a great challenge caused by poor observability and highly nonlinear estimation. In this brief, a novel augmented ensemble Kalman filter (AEnKF) is presented to address this bearings-only tracking problem. Different from the conventional ensemble Kalman filter (EnKF), the AEnKF overcomes the limitation of the linear measurement update rule in the linear minimum mean-square error (LMMSE) framework. The AEnKF utilizes a nonlinear transform of the measurement, called uncorrelated conversion (UC), to augment the measurement space. This conversion serves as a pseudomeasurement and is uncorrelated with the original measurement statistically. Unlike other UC filters based on the Gaussian assumption in the existing literature, the AEnKF does not impose any assumption on the probability density of the measurement by using generalized orthogonal polynomials to construct the UCs in a systematic way. The simulation results show that the AEnKF outperforms the conventional EnKF and other UC filters in the bearings-only tracking problem.