Reviewerly/exHarmony huggingface dataset:
Intuitive Ontology-Based SPARQL Queries for RDF Data Exploration

This paper introduces a strategy for both the retrieval and analysis of linked open data (LOD) based on the use of visual tools. Retrieving and understanding data from triplestores (such as SPARQL) requires technical knowledge and proves to be challenging with large datasets, which result in an increase in the mental overload when unknown ontologies are involved in the creation of complex queries. These two problems benefit greatly from visual techniques that allow for executing them in an easier and more intuitive manner. These techniques have already been applied to each problem separately; however, we propose combining them to lower the complexity of triple-store data retrieval and empower its exploration and analysis through specific data visualizations. To demonstrate the suitability of this strategy, a web-based tool was implemented. It allows for the creation of interactive queries using node-link ontology representations, and a subsequent filtering and analysis through a configurable dashboard with different data visualizations. This paper also presents a user-centred design process and evaluation of the proposed tool.
Expert Finding Considering Dynamic Profiles and Trust in Social Networks

Recently, social network services that express individual opinions and thoughts have been significantly developed. As unreliable information is generated and shared by arbitrary users in social network services, many studies have been conducted to find users who provide reliable and professional information. In this paper, we propose an expert finding scheme to discover users who can answer users’ questions professionally in social network services. We use a dynamic profile to extract the user’s latest interest through an analysis of the user’s recent activity. To improve the accuracy of the expert finding results, we consider the user trust and response quality. We conduct a performance evaluation with the existing schemes through various experiments to verify the superiority of the proposed scheme.
Similarity mapping for robust face recognition via a single training sample per person

Face recognition using a single training sample per person (STSPP) is very challenging due to significant variations in the query sample. Although most of sparse representation based methods eliminate the variations to obtain discriminative power, their recognition performance usually degrades as the number of training samples per person decreases. To improve recognition performance on the STSPP problem, in this paper, we propose a novel face recognition method: generating new training samples by preserving the variations of each query sample onto training samples instead of eliminating the variations, and then identifying the query one via the most similar generated training sample. Specifically, we formulate face recognition as an patch-based sparse representation problem, and then efficiently solve it by dividing the optimization procedure into three individual sub-problems: selecting training patches, weighting selected patches and calculating residuals. Meanwhile, we interpret the proposed algorithm from a probability modeling viewpoint. Experiments on multiple benchmark databases demonstrate its superior recognition performance to state-of-the-art methods on faces under illumination changes, occlusions and facial expressions, with lower test time costs.
Relationships within the Fraud Diamond: The Decision Processes That Influence Fraudulent Intentions in Online Consumer Fraud

ABSTRACT Online consumer fraud is a problem with significant consequences. While a substantial body of research examines the strategies used to defraud consumers in online environments, little is known about the decision processes that perpetrators follow before engaging in fraud. To address this issue, we develop an ethical decision-making model of online consumer fraud based on the fraud diamond. The model also includes anonymity, a key feature of online environments, which can influence sellers' ethical decision-making processes. We empirically evaluate the model first by asking participants to consider the misrepresentation of an asset's value in an online transaction, and then by having participants engage in a real-life version of that scenario. Results indicate that perceived anonymity affects the influences of capability, opportunity, and motivation on rationalization. Further, greater perceived anonymity increases the influence of rationalization on one's intent to act.
Information Extraction in Handwritten Marriage Licenses Books

Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
Facial Expression Recognition via Relation-based Conditional Generative Adversarial Network

Recognizing emotions by adapting to various human identities is very difficult. In order to solve this problem, this paper proposes a relation-based conditional generative adversarial network (RcGAN), which recognizes facial expressions by using the difference (or relation) between neutral face and expressive face. The proposed method can recognize facial expression or emotion independently of human identity. Experimental results show that the proposed method provides higher accuracies of 97.93% and 82.86% for CK+ and MMI databases, respectively than conventional method.
A News Recommender System for Media Monitoring

Media monitoring services allow their customers, mostly companies, to receive, on a daily basis, a list of documents from mass media that discuss topics relevant to the company. However, media monitoring services often generate these lists by using keyword-filtering techniques, which introduce many false positives. Hence, before the end users, i.e., the employees of the company, may consult these lists and find relevant documents, a human editor must inspect the keyword-filtered documents and remove the false positives. This is a time consuming job. In this paper we present a recommender system that aims at reducing the number of documents that the editor needs to inspect every day. The proposed solution classifies documents (represented with TF-IDF and embeddings features) using techniques trained on data containing the editors’ past actions (i.e. the removals of false positives). The proposed technique is shown to be able to correctly predict the true positives, thus reducing the number of documents that the editor needs to inspect every day.
Application of Improved Wavelet De-noising Method in MEMS-IMU Signals

Aiming at the problem of reconstructed signal oscillation caused by discontinuity of hard threshold de-noising method, and the low precision of reconstructed signal caused by constant deviation in soft threshold de-noising method in traditional wavelet de-noising, this paper proposed an improved wavelet threshold function. This function adds a time-varying coefficient ahead of the threshold of the soft threshold function, which overcomes the discontinuity problem in the hard threshold and the fixed deviation problem in the soft threshold. Applying this method to the MEMS-IMU signals de-noising, the experimental results show that the improved wavelet threshold de-noising method can effectively reduce the random noises in MEMS-IMU. And compared with the traditional wavelet de-noising method, the signal is smoother and the de-noising effect is more pronounced.
Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey

Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm. Users lacking in expertise or computational resources can outsource their data and database management needs to a third-party service provider. Outsourcing, however, raises an important issue of result integrity: how can the client verify with lightweight overhead that the query results returned by the service provider are correct (i.e., the same as the results of query execution locally)? This survey focuses on categorizing and reviewing the progress on the current approaches for result integrity of SQL query evaluation in the DaaS model. The survey also includes some potential future research directions for result integrity verification of the outsourced computations.
CountMarks: Multi-finger Marking Menus for Mobile Interaction with Head-Mounted Displays

We designed, implemented, and evaluated CountMarks, a novel menu that extends marking menus with multi-touch input on a secondary touchscreen, for use with smart glasses and head-mounted displays. We experimentally compared CountMarks to marking menus. Results indicate that CountMarks offers faster selection and better search accuracy, but slightly worse selection accuracy. A second experiment compared standing vs. walking while using CountMarks, and handheld vs. on-leg interaction. Results indicate that CountMarks can be used effectively with a handheld device while both standing and walking. We present an example application employing CountMarks in a mock Netflix UI, to demonstrate how the technique can be applied in existing applications.
Strengthening Security in Industrial Settings: A Study on Gaze-Based Biometrics through Free Observation of Static Images

As security becomes crucial in an increasing number of industrial contexts, the need arises for new ways to check or authenticate the identity of people. In this paper, we present a method that exploits gaze data to implement a soft biometric technique. Specifically, the user's gaze behavior is inspected during the unconstrained observation of different kinds of static images. The obtained results, achieved using a machine learning approach, are generally satisfying, although more experiments will be necessary to fully confirm the viability of the proposed method.
Generating Cuneiform Signs with Cycle-Consistent Adversarial Networks

The efficient use of machine learning methods in the context of word spotting on cuneiform datasets is usually limited by the number of annotated training samples available. In view of the large amount of required data, their manual generation is a tedious task for 2D and 3D datasets. In this work we present an automatic approach for generating training samples using Generative Adversarial Networks for Domain Adaptation. Our approach adapts between the visual domains of hand-drawn cuneiform autographs and 2D-projections of 3D-scanned cuneiform tablets without any class information. This way, promising results are achieved, as shown in our experiments.
Imbalanced Learning Techniques for Improving the Performance of Statistical Models in Automated Essay Scoring

The problem of class imbalance occurs in many application domains, as in the case of essays. The imbalance of the number or samples among the classes presents a problem for predictive algorithms, both for classification or for regression. In this paper, we present imbalanced learning techniques for improving the performance of statistical models in automated essay grading. We analyze the performance of different types of statistical algorithms using machine learning and classification techniques, combined with different balancing methods. The results indicated a significant improvement of accuracy and average error when the algorithms were applied in a balanced corpus, comparing with the unbalanced ones.
Am I Coughing More Than Usual?

An increasing number telehealth systems continuously collect selfreported data from patients. Objective and subjective collection of health data facilitates early detection and treatment of chronic conditions, but patient needs in these telehealth contexts are poorly understood. It is for example not clear how to support patients' reflection on their daily self-reported data. Inadequate support can result in fragmented daily health monitoring and poor adherence. This paper contributes 1) a synthesis of the related but hitherto disjunct personal informatics literature on self-tracking and 2) an indepth field study on how six people suffering chronic obstructive pulmonary disease (COPD) used a telehealth system as part of their health self-tracking. Our analysis showed that a telehealth solution which relegated patients to mere data suppliers missed out on opportunities to address user needs. We extended Li's 5-stage model to show where reflection manifested when interacting with the telehealth system.
Genetic-based Multi-criteria Workflow Scheduling with Dynamic Resource Provisioning in Hybrid Large Scale Distributed Systems

Nowadays, scientific progress in multiple disciplines gave rise to conducting large scale scientific experiments and applications known as High Performance Computing (HPC). These HPC applications are commonly structured as workflows of heavy tasks with large data size and intricate dependencies which are typically performed in large-scale distributed systems (LSDS) such as clusters, Grids and recently Cloud infrastructures. In fact, workflow scheduling in distributed systems, especially in Clouds, is proved to be an NP hard problem. Our main target in this paper is to design a workflow scheduling approach based on the Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II) in hybrid distributed systems by optimizing the Makespan and cost. In this work, we also studied the improvement of the Makespan-Cost trade-off with the scalability concept in the Cloud with our designed approach. For that, we proposed different scenarios dealing with the provisioning strategy of processing nodes alongside an existing resource pool. Conducted experiments show the advantage of Cloud infrastructures against other distributed systems and allow investigating the different factors in correlation with resources provisioning process.
An Industrial Multi-Agent System (MAS) Platform

When it comes to address challenges in the area of distributed and parallel computing, Multi-Agent Systems (MAS) are emerging as a key architecture. Characterized as a collection of autonomous software (agents) which are able to cooperate in a distributed environment, MAS-based applications have proven capabilities when using cognitive processes, reasoning and knowledge representation in order to develop functionality related to complex and dynamic scenarios where the contribution of a single agent is computationally limited. In this paper, we propose an industrial platform which fully supports the development, deployment and maintenance cycle of MAS-based applications.
A Convenient and Extensible Offline Chinese Speech Recognition System Based on Convolutional CTC Networks

Deep learning methods have been widely used in automatic speech recognition (ASR), and they have achieved significant improvement in accuracy. The deep CNN structure can significantly improve the performance of the HMM speech recognition system. In addition, the CNN model has a good translation invariance in the time-frequency domain, making the model more robust (noise resistance). In this paper, we use the acoustic model based on CNN+CTC+Self-Attention and the corresponding language model to construct an end-to-end Chinese speech recognition system as a pre-training model. On this basis, we do not repeat the training of acoustic models. We propose a method combining Levenshtein Distance and hashing method to construct an off-line Chinese speech recognition system for a specific scene. The experimental results show that using the deep convolution CTC (Connectionist Temporal Classification) time series automatic speech recognition model, we have achieved a total error rate (WER) of 18% on the standard data set THRHS-30 and Free ST Chinese Mandarin Corpus. In addition, the combination of Levenshtein Distance and hash language model can achieve an accuracy of more than 90% on specific phrases. The whole model has strong expansibility and practicability.
What's behind a choice? Understanding Modality Choices under Changing Environmental Conditions

Interacting with the physical and digital environment multimodally enhances user flexibility and adaptability to different scenarios. A body of research has focused on comparing the efficiency and effectiveness of different interaction modalities in digital environments. However, little is known about user behavior in an environment that provides freedom to choose from a range of modalities. That is why, we take a closer look at the factors that influence input modality choices. Building on the work by Jameson & Kristensson, our goal is to understand how different factors influence user choices. In this paper, we present a study that aims to explore modality choices in a hands-free interaction environment, wherein participants can choose and combine freely three hands-free modalities (Gaze, Head movements, Speech) to execute point and select actions in a 2D interface. On the one hand, our results show that users avoid switching modalities more often than we expected, particularly, under conditions that should prompt modality switching. On the other hand, when users make a modality switch, user characteristics and consequences of the experienced interaction have a higher impact in the choice, than the changes in environmental conditions. Further, when users switch between modalities, we identified different types of switching behaviors. Users who deliberately try to find and choose an optimal modality (single switcher), users who try to find optimal combinations of modalities (multiple switcher), and a switching behavior triggered by error occurrence (error biased switcher). We believe that these results help to further understand when and how to design for multimodal interaction in real-world systems.
Estimating Uncertainty in Task-Oriented Dialogue

Situated multimodal systems that instruct humans need to handle user uncertainties, as expressed in behaviour, and plan their actions accordingly. Speakers’ decision to reformulate or repair previous utterances depends greatly on the listeners’ signals of uncertainty. In this paper, we estimate uncertainty in a situated guided task, as leveraged in non-verbal cues expressed by the listener, and predict that the speaker will reformulate their utterance. We use a corpus where people instruct how to assemble furniture, and extract their multimodal features. While uncertainty is in cases verbally expressed, most instances are expressed non-verbally, which indicates the importance of multimodal approaches. In this work, we present a model for uncertainty estimation. Our findings indicate that uncertainty estimation from non-verbal cues works well, and can exceed human annotator performance when verbal features cannot be perceived.
Biometric Tools for Learner Identity in e-Assessment

Biometric tools try model a person by means of its intrinsic properties or behaviours. Every person has a set of unique physical traits derived from genetics and vital experience. Although there are many traits that can be used to verify the identity of a learner, such as the voice, appearance, fingerprints, iris, or gait among others, most of them require the use of special sensors. This chapter presents an analysis of four biometric tools based on standard sensors and used during the TeSLA project pilots. Those tools are designed to verify the identity of the learner during an assessment activity. The data for on-site and on-line institutions is used in order to compare the performance of such tools in both scenarios.
Energy-Efficient Device Activation, Rule Installation and Data Transmission in Software Defined DCNs

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.
Combine or connect: Practical experiences querying library linked data

ABSTRACT Linked Data provides a conceptual foundation for creating unified views across Digital Libraries, but implementation challenges must be overcome to realize the vision of computationally assisted cross‐corpus research. We report practical experiences comparing two alternative workset building approaches across combined datasets: the HathiTrust Digital Library and the Early English Books Online Text Creation Partnership. In one experiment we combine both datasets within one triplestore using a single ontology and apply consolidated querying; in the other we build two distributed triplestores, each dataset conforming to its own ontology, and connected through federated querying. Each solution presents tradeoffs in complexity, system efficiency and responsiveness, and in the workload of configuring new methods providing access to Digital Libraries. We demonstrate that choosing a consolidated or federated approach fundamentally alters the dataset configuration process for cross‐corpora workset building, so should be considered early in deployment specification and design. As both approaches provide equivalent functionality to the end‐user, the practice and experience documented here inform design and development of distributed Linked Data Digital Libraries offering combined collection querying.