Reviewerly/exHarmony huggingface dataset:
WACline: A Software Product Line to harness heterogeneity in Web Annotation

a b s t r a c t A significant amount of research project funding is spent creating customized annotation systems, reinventing the wheel once and again, developing the same common features.In this paper, we present WACline, a Software Product Line to facilitate customization of browser extension Web annotation clients.WACline reduces the development effort by reusing common features (e.g., highlighting and commenting) while putting the main focus on customization.To this end, WACline provides already implemented 111 features that can be extended with new ones.In this way, researchers can reduce the development and maintenance costs of annotation clients.
Effects of Feature-Based Explanation and Its Output Modality on User Satisfaction With Service Recommender Systems

Recent advances in natural language based virtual assistants have attracted more researches on application of recommender systems (RS) into the service product domain (e.g., looking for a restaurant or a hotel), given that RS can assist users in more effectively obtaining information. However, though there is emerging study on how the presentation of recommendation (vocal vs. visual) would affect user experiences with RS, little attention has been paid to how the output modality of its explanation (i.e., explaining why a particular item is recommended) interacts with the explanation content to influence user satisfaction. In this work, we particularly consider feature-based explanation, a popular type of explanation that aims to reveal how relevant a recommendation is to the user in terms of its features (e.g., a restaurant's food quality, service, distance, or price), for which we have concretely examined three content design factors as summarized from the literature survey: feature type, contextual relevance, and number of features. Results of our user studies show that, for explanation presented in different modalities (text and voice), the effects of those design factors on user satisfaction with RS are different. Specifically, for text explanations, the number of features and contextual relevance influenced users' satisfaction with the recommender system, but the feature type did not; while for voice explanations, we found no factors influenced user satisfaction. We finally discuss the practical implications of those findings and possible directions for future research.
A transfer learning approach to predict shipment description quality

International shipments always have a harmonized system code (HSCode) associated with them, to determine the tariff for the custom declaration. The HSCode is derived from the shipment description that the customer provides, which makes the quality of the description important to assign the correct code. When the description is too generic or incomplete, the logistic company will have to contact the customer in order to find out the content of the shipment. Due to the fact that there is no effective way to identify the quality of description, we developed a description quality evaluation model, based on deep learning combined with domain knowledge. By using a 2000 shipments data set with scores ranging from 0 to 4 provided by experts, where 4 represents the best quality possible, the developed model can classify 45.17% of the data correctly and 43.95% of the data with 1 score difference(i.e predict label 1 as 2 or 0) from the human annotated ground truth. This model can be used for historical data analysis, and potentially giving customers on-site feedback when they are providing a bad description for the shipment content.
Linear and Deep Order-Preserving Wasserstein Discriminant Analysis

Supervised dimensionality reduction for sequence data learns a transformation that maps the observations in sequences onto a low-dimensional subspace by maximizing the separability of sequences in different classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. In this paper, we propose a linear method, called order-preserving Wasserstein discriminant analysis (OWDA), and its deep extension, namely DeepOWDA, to learn linear and non-linear discriminative subspace for sequence data, respectively. We construct novel separability measures between sequence classes based on the order-preserving Wasserstein (OPW) distance to capture the essential differences among their temporal structures. Specifically, for each class, we extract the OPW barycenter and construct the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the OPW distance between the corresponding barycenters. We learn the linear and non-linear transformations by maximizing the inter-class distance and minimizing the intra-class scatter. In this way, the proposed OWDA and DeepOWDA are able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments on four 3D action recognition datasets show the effectiveness of OWDA and DeepOWDA.
A generalized framework for recognition of expiration dates on product packages using fully convolutional networks

It is important to understand the expiration date. However, it is challenging for machines to understand it. Most previous methods recognize expiration dates in limited conditions. To address this problem, a generalized framework for detecting and understanding expiration dates has been proposed. This framework handles challenging cases and distinguishes 13 different date formats. Unlike previous methods, a neural network-based date parser is adopted in the framework to understand the meaning of an expiration date by identifying the day, month, and year. The experimental results demonstrate the proposed framework achieves 97.74% recognition accuracy for expiration dates in various formats and challenging cases. Since there is no publicly available dataset of expiration dates, a novel dataset collection named ExpDate was created and opened.
Use of the <scp>Weibull</scp> model on sizing thickeners—Part <scp>II</scp>: Methods of thickener sizing

Abstract Among several methods employed for sizing thickeners available in the literature, the Kynch, Biscaia Jr., Talmadge and Fitch, Roberts, Coe and Clevenger, and Oltmann methods use experimental data from sedimentation curves and graphical approaches. By using the Weibull distribution, it is possible to represent sedimentation curves with algebraic equations, which does not require the use of graphical approaches and provides more accuracy and speed for sizing calculations. In the present work, the main objective is the development of a set of equations for sizing continuous thickeners, for six conventional methods found in the literature, using the Weibull model. A comparative analysis of calculated and literature diameters for each graphical method presented variations between 0.73% and 8.93%. The use of the Weibull model presented the best accuracy for the Biscaia Jr. method, with a 0.73% average absolute error.
Sustainable Interactions as Design Objects That Promote Digital Humanism

The main purpose of sustainable interaction design is to meet global challenges in society, the economy, and the environment to an extent that goes beyond the well-being of human beings and attempts to promote the well-being of all beings.This paper examines the relationship between sustainable interaction design and digital humanism.Interactions as objects of design could/should be characterized as able to afford sustainable behavior for the following reasons: their very existence is sustainable, they are designed to evoke sustainability, or they are part of a sustainable system.Humanity needs to realize that every single human being is part of the humanitarian and ecological crisis.Through sustainable interaction design, we could really help users feel that they are part of the solution and that their actions can truly change lives.We seek answers related to the design, materiality, and contribution of sustainable interactions in shaping more humane interfaces.
Adjoint dynamical kernel density for anomaly detection

Anomalies affect data quality and lead to unexpected analysis results in data mining. Although techniques to handle anomalies do exist, they can fail in considering density changes of object along with incremental neighbors. This paper proposes an outlier factor based on the change of adjoint dynamical kernel density (ADD) to represent the degree of the object being an anomaly. The factor is equal to the ratios of the adjoint dynamical kernel density fluctuation (ADDF) of the object and the average ADDF of its neighborhood. ADDF is estimated by the difference of ADD, which describes the difference of every two consecutive adjoint kernel densities (AKD) of the object. AKD indicates kernel densities of the object while its neighbors are added one by one. Importantly, the kernel function is adopted to measure the distance between objects where the kernel trick improves discriminability between objects and reduces the computational burden of the algorithm. The experiments are performed on eight datasets to evaluate the effectiveness of the proposed method with different kernel functions. The experimental results have shown that the proposed method with the Gaussian kernel function has better performance of anomalies recognition and higher adaption of the parameter k of k-nearest neighbors over some other anomaly detection methods.
A Bayesian network-based model to understand the role of soft requirements in technology acceptance

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of software products, where soft requirements need to be taken into account. Therefore, we address this need by using a novel Bayesian network approach that allows the prediction of end users' decisions and ranks soft requirements' importance when making these decisions. The approach offers insights that help requirements engineers better understand which soft requirements are essential for particular software to be accepted by its target users. We have implemented a Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied to the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). Our findings show that soft requirements such as Responsibility and Trust (e.g. Trust in the supplier/brand) are relevant for the COVID-19 app acceptance. However, the importance of soft requirements is also contextual and time-dependent. For example, Fear of infection was an essential soft requirement, but its relevance decreased over time. The results are reported as part of a two stage-validation of the model.
Enhanced pre-processing for deep learning in MRI whole brain segmentation using orthogonal moments

This paper introduces an orthogonal moment pre-processing method to enhance convolutional neural network outcomes for whole brain image segmentation in magnetic resonance images. The method implements kernel windows based on orthogonal moments to transform the original image into a modified version with orthogonal moment properties. The transformed image contains the optimal representation of the coefficients of the Legendre, Tchebichef and Pseudo-Zernike moments. The approach was evaluated on three distinct datasets; NFBS, OASIS, and TCIA, and obtained an improvement of 4.12%, 1.91%, and 1.05%, respectively. A further investigation employing transfer learning using orthogonal moments of various orders and repetitions, achieved an improvement of 9.86% and 7.76% on the NFBS and OASIS datasets, respectively, when trained using the TCIA dataset. In addition, the best image representations were used to compare different convolutional neural network architectures, including U-Net, U-Net++, and U-Net3+. U-Net3+ demonstrated a slight improvement over U-Net in an overall accuracy of 0.64 % for the original image and 0.33 % for the modified orthogonal moment image.
Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

Abstract Text sentiment analysis is mainly used to the customers benefits. In the existing works, the text sentiment analysis faces more troubles such as, disambiguation (removing unwanted terms), discussions, contrast, intensity, and excessive flections and complex sound structure with less accuracy. In this article, the text sentiment analysis on E‐shopping product using chaotic coyote optimized deep belief network approach is proposed to minimize the troubles in the sentiment analysis and increase the accuracy. The other name of sentiment analysis is subjective analysis. The main objective of this article is “classify the text sentiments according to the polarity (positive and negative) and to increase the accuracy.” Here, the E‐shopping Kaggle datasets are preprocessed and the features are extracted. Then, the extracted features of the trained data's are given using deep belief network (DBN) classifier to get pure sentiments (positive or negative) with accuracy. Here, the performance metrics of the accuracy, recall, and precision, F‐measure, specificity, and sensitivity are calculated. The simulation process is executed in Python platform. The proposed chaotic coyote optimized deep belief network (CCO‐DBN) attains accuracy 9.8%, precision 17.2%, recall 5.61%, F‐measure 17.07%, specificity 2.247%, sensitivity 13.25% is higher than the existing methods such as FCM‐DFA, GA, SVM‐RFA.
AAAN: Anomaly Alignment in Attributed Networks

Anomaly subgraph detection is an important problem that has been well researched in various applications, ranging from cyberattacks in computer networks to malicious activities in social networks. Most existing approaches detect anomaly subgraphs in attributed networks with sufficient features. However, multitudinous industry data with insufficient anomalous attributes are the main challenge for traditional anomaly subgraph detection algorithms. In particular, none of the literature focuses on connected anomaly subgraph detection with insufficient anomalous features. To address this problem, we propose Anomaly Alignment in Attributed Networks (AAAN), which first detects connected anomaly subgraphs by aligning anomalies in two attributed networks (one with insufficient anomalous features and the other with sufficient anomalous features). Extensive experiments on three real-world datasets (the Weibo, Baidu migration network, and COVID-19 pandemic datasets) show the effectiveness and efficiency of our algorithm. We can identify the crime hotspots in terms of city blocks from urban traffic networks, which are aligned with the criminal events reported on social networks. The results also demonstrate how AAAN outperforms competitive approaches in the COVID-19 outbreak anomaly subgraph detection and urban crime hotspot detection tasks.
Ensuring privacy in the application of the Brazilian general data protection law (LGPD)

Currently, many organizations make use of the personal data of their users. Personal data is the set of information that can lead to the identification of a specific person and, therefore, this information is generally vital for the operations and business continuity of organizations. Consequently, the relevance of adopting methodologies that guarantee the protection and privacy of user information is indispensable to prevent the leaking of sensible information. Therefore, laws were created to establish essential requirements for organizations to provide support and protection to the personal data of users, such as the General European Data Protection Regulation (GDPR) and the Brazilian General Data Protection Law (LGPD). This work aims to develop a framework to support ICT professionals in adapting companies to the requirements demanded by the LGPD. To achieve the purpose, a framework based on the BEST methodology (Business Engaged Security Transformation) was proposed. This framework has a sustainable approach and can be implemented by any organization. A survey was carried out to collect the perception of Information and Communication Technology (ICT) practitioners in relation to adherence to LGPD adaptation actions by organizations. As a result, we identified a weakness in the privacy and information security management methodology implemented in organizations, which, in the future, may result in risks and damage to user information.
Inferring User Height and Improving Impersonation Attacks in Mobile Payments using a Smartwatch

In this paper, we show that as a user makes mobile payments with a smartwatch, the height of the user can be inferred purely from inertial sensor data captured on the watch (with R<sup>2</sup> scores of up to 0.77). Besides unwanted information exposure, we also show that users of a similar height are more difficult to distinguish between in terms of their tap gesture data and that an attacker who chooses a victim of a similar height can improve the success chance of impersonation (by increasing the false acceptance rate by up to 20.6%).
Federated Learning Within Pervasive Heterogeneous Environments

Federated learning (FL), a recent collaborative learning paradigm, has raised plenty of opportunities to bring learning to the edge, but it also faces many obstacles. We focus on the challenges explicitly imposed by heterogeneity within FL in a pervasive computing environment. We summarize three main methods that the scientific community has proposed to tackle this challenge (improved aggregation techniques, regularization of clients learning, and clustering of similar clients). The research objectives of this thesis work are to develop upon these proposed methods to build robust FL schemes to benefit from user diversity while also mitigating its detrimental effects. To fulfill this objective, we follow a three-step approach: (a) analyze and evaluate different FL approaches for pervasive environments (b) experiments and the proposal of the three different fields of FL for heterogeneity (c) integration of the three proposed methods on real-devices.
An English Teaching Ability Assessment Method Based on Fuzzy Mean-Shift Clustering

This paper presents an in-depth study and analysis of the assessment of English teaching ability using the algorithm of fuzzy mean-shift clustering. The paper proposes an automatic scoring model for Chinese-English sentence-level interpretation based on semantic scoring. The automatic scoring model calculates candidates’ Chinese-English sentence interpretation scores by fusing the feature parameters at both the phonological and content levels. Adaptive weights are introduced to fuse the current pixel and the neighborhood mean with adaptive weighting, and the weighted entropy constraint term is embedded in the clustering objective function to solve the selection problem of the weighting parameters. Finally, the graphical fuzzy division information of the neighboring pixels is used to construct the local spatial information constraint term of the current pixel, and the graphical fuzzy division term of the current pixel is adjusted to correct the clustering center obtained from the iteration. Fluency is selected as the feature scoring parameter at the speech level, and the automatic scoring model directly scores the fluency features of the candidates’ recordings; two feature scoring parameters, keywords, and sentence semantics are selected at the content level, and the content features are scored after converting the candidates’ recordings to text by manual conversion. The zero-energy product method is used to extract speech features to calculate fluency feature scores; the semantic scoring model introduced in this paper is used to calculate keyword and sentence semantic feature scores; finally, the random forest algorithm is used to fuse the above three feature scoring parameters to obtain the total quality score of Chinese-English interpretation. Considering the correlation of neighborhood pixel affiliation, the KL scatter and affiliation space information is used to supervise the current pixel affiliation, to further improve the segmentation accuracy of the algorithm; finally, segmentation tests are conducted on synthetic images, medical images, and remote sensing images. The results show that the proposed algorithm has a stronger noise suppression ability and can obtain more satisfactory segmentation results than other robust fuzzy clustering algorithms.
Lessons Learned from an Eye Tracking Study for Targeted Advertising in the Wild

With the ambitious objective in mind to assess the validity of gaze-based interest prediction and test its application for targeted advertising on a self-order terminal, we set out to conduct a user study at a fast food restaurant. We recorded the customers&#x2019; eye movements while they were placing their order, and used the collected data to infer which information about the meals they were interested in. We then wanted to test whether the predictions can be used to display targeted advertisements, and influence the customers&#x2019; purchase intention for a complementary product. The results did not corroborate the hypothesized relationship between visual attention and interest. However, due to the multiplicity of underlying assumptions, we were unable to identify whether a link does indeed not exist, or whether the result was caused by the experimental setup.In this paper, we describe the design and results of our user study, and discuss our lessons learned. Our observations shall guide other researchers to avoid these common pitfalls.
Generalized graph pattern discovery in linked data with data properties and a domain ontology

Nowadays, in many practical situations, analytical tasks need to be performed on complex heterogeneous data, often described by a domain ontology (DO). Such cases abound in life science fields such as agro-informatics, where observations and measures on animals/plants are logged for subsequent mining. The data is naturally structured as graph(s), unlabelled and missing some values, hence it fits well pattern mining. In our own precision farming project aimed at decision support for dairy cow management, we mine for knowledge in milk production data. In one task, we aim at contrast patterns explaining the relative impact of independent production factors. To that end, ontologically-generalized graph patterns (OGPs), a variety of generalized graph patterns, where vertices and edges are labelled by DO classes and properties, respectively, were defined. A mining methodology was also designed that reconciles OWL DOs, abstraction from RDF graphs and literals in data. To address the well-known cost-related limitations of graph mining -exacerbated here by class/property specializations and data properties- we split the mining task into (1) mining of generic object property topology patterns and (2) label refinement. Those focus on two sorts of OGPs, called topologies and class stars, respectively, which, after being mined separately, get (3) assembled into fully-fledged OGPs.