Reviewerly/exHarmony huggingface dataset:
CARESSES: The World's First Culturally Sensitive Robots for Elderly Care

Cyberpsychology, Behavior, and Social NetworkingVol. 22, No. 6 CyberEuropeCARESSES: The World's First Culturally Sensitive Robots for Elderly CareCompiled by: Giuseppe Riva and Eleonora RivaCompiled by: Giuseppe RivaAddress correspondence to: Dr. Giuseppe Riva, Department of Psychology, Catholic University of Milan, Largo Gemelli 1-20123 Milan, Italy E-mail Address: giuseppe.riva@unicatt.itDepartment of Psychology, Catholic University of Milan, Milan, Italy.Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.Search for more papers by this author and Eleonora RivaDr. Eleonora Riva, Department of Cultural Heritage and Environment, University of Milan, Milan, Italy E-mail Address: eleonora.riva@unimi.itDepartment of Cultural Heritage and Environment, University of Milan, Milan, Italy.Search for more papers by this authorPublished Online:7 Jun 2019https://doi.org/10.1089/cyber.2019.29155.ceuAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"CARESSES: The World's First Culturally Sensitive Robots for Elderly Care." Cyberpsychology, Behavior, and Social Networking, 22(6), p. 430FiguresReferencesRelatedDetailsCited byHumanoid service robots: The future of healthcare?23 June 2021 | Journal of Information Technology Teaching Cases, Vol. 12, No. 2Human-Robot Interaction and User Manipulation21 March 2022Categorizing Social Robots with Respect to Dimensions Relevant to Ethical, Social and Legal Implications7 April 2020 | i-com, Vol. 19, No. 1 Volume 22Issue 6Jun 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Giuseppe Riva and Eleonora Riva.CARESSES: The World's First Culturally Sensitive Robots for Elderly Care.Cyberpsychology, Behavior, and Social Networking.Jun 2019.430-430.http://doi.org/10.1089/cyber.2019.29155.ceuPublished in Volume: 22 Issue 6: June 7, 2019PDF download
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems

We study learning of a matching model for response selection in retrieval-based dialogue systems. The problem is equally important with designing the architecture of a model, but is less explored in existing literature. To learn a robust matching model from noisy training data, we propose a general co-teaching framework with three specific teaching strategies that cover both teaching with loss functions and teaching with data curriculum. Under the framework, we simultaneously learn two matching models with independent training sets. In each iteration, one model transfers the knowledge learned from its training set to the other model, and at the same time receives the guide from the other model on how to overcome noise in training. Through being both a teacher and a student, the two models learn from each other and get improved together. Evaluation results on two public data sets indicate that the proposed learning approach can generally and significantly improve the performance of existing matching models.
Research on Text Mining of Railway Safety Supervisors Performance Based on BiLSTM and CRF

Abstract In the era of big data with unstructured data, the potential value of unstructured data mining is an effective way to solve the industry problem. Based on a large number of data recorded from the actual text of the performance plan of railway security supervisors, this paper proposes a text similarity calculation method based on text mining technology to calculate whether the personnel performance plan matches the reality. First, the text Named Entity Recognition(NER) is realized by using the combination of Bi-directional Long Short Term Memory (BiLSTM) and the Conditional Random Fields (CRF). Then based on the concept similarity calculation of HowNet, we designed the method of calculating the similarity between the same named entity. After that, different weights are given according to the importance of named entity, and a method of computing similarity between named entities is designed.. Finally, according to the given threshold, to obtain whether planning and realistic matching, and the actual personnel working situation, provide the basis for personnel assessment for the management personnel.
Introduction to Voice Presentation Attack Detection and Recent Advances

Over the past few years, significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evaluation protocols has enabled, for the first time, the meaningful benchmarking of different PAD solutions. This chapter summarises the progress, with a focus on studies completed in the last 3 years. The article presents a summary of findings and lessons learned from three ASVspoof challenges, the first community-led benchmarking efforts. These show that ASV PAD remains an unsolved problem and further attention is required to develop generalised PAD solutions which have the potential to detect diverse and previously unseen spoofing attacks.
Computational intelligence-based steganalysis comparison for RCM-DWT and PVA-MOD methods

This research article proposes data hiding technique for improving the data hiding procedure and securing the data transmission with the help of contrast mapping technique along with advanced data encryption standard. High data hiding capacity, image quality and security are the measures of steganography. Of these three measures, number of bits that can be hidden in a single cover pixel, bits per pixel (bpp), is very important and many researchers are working to improve the bpp. We propose an improved high capacity data hiding method that maintains the acceptable image quality that is more than 30 dB and improves the embedding capacity higher than that of the methods proposed in recent years. The method proposed in this paper uses notational system and achieves higher embedding rate of 4 bpp and also maintain the good visual quality. To measure the efficiency of the proposed information hiding methodology, a simulation system was developed with some of impairments caused by a communication system. PSNR (Peak Signal to Noise ratio) is used to verify the robustness of the images. The proposed research work is verified in accordance to noise analysis. To evaluate the defencing performance during attack RS steganalysis is used.
Combining Parametric and Nonparametric Models for Off-Policy Evaluation

We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local accuracy of each model and then using a planner to select which model to use at every time step as to minimize the return error estimate along entire trajectories. Across a variety of domains, our mixture-based approach outperforms the individual models alone as well as state-of-the-art importance sampling-based estimators.
FASTER Recurrent Networks for Video Classification.

Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities. The FASTER framework can integrate high quality representations from expensive models to capture subtle motion information and lightweight representations from cheap models to cover scene changes in the video. A new recurrent network (i.e., FAST-GRU) is designed to aggregate the mixture of different representations. Compared with existing approaches, FASTER can reduce the FLOPs by over 10x? while maintaining the state-of-the-art accuracy across popular datasets, such as Kinetics, UCF-101 and HMDB-51.
Definition of approaches to complex research of information educational environment in general, professional and additional education systems

Introduction and goal. The article deals with the actual problem of the lack of assessment of the current state and effectiveness of the functioning of the information educational environment of individual educational organizations, at the regional and national level. The purpose of the described research was to determine the approaches to assessing the state of such an environment in the systems of general, vocational and additional education. Materials and methods. The definition of approaches to the assessment of the modern stage of the formation and development of information educational media has been carried out on the basis of the method of modeling, analytical activity, the use of elements of graph theory, and the analysis of scientific literature. Provides for the use of telecommunication technologies to collect the necessary information. Results. The stages and technologies for carrying out a comprehensive study of the state of the information educational environment in systems of general, vocational and additional education have been developed and described. Such stages include the preliminary construction of a scientifically based model, the development of computer tools, including questionnaire templates for surveys of workers and students of educational institutions, the collection and statistical processing of data, a comparative analysis of the existing stage of development of the information educational environment and the results of previous studies, the publication of analysis results and developed recommendations in telecommunication networks. The proposed technology provides for the possibility of a unified collection and publication of information on examples of the most effective practical experience in the formation and use of information educational environments. Conclusions. The developed approaches application will make it possible to identify measures aimed at improving the efficiency of projects for the formation and development of information educational environments and will contribute to a more accurate forecasting of the prospects for informatization of education.
The Impact of Agile Methodologies and Cost Management Success Factors: An Empirical Study

Software cost management is a significant feature of project management. As such, it needs to be employed in a project or line of work. Software cost management is integral to software development failures, which, in turn, cause software failure. Thus, it is imperative that software development professionals develop their cost management skills to deliver successful software projects. The aim of this study is to examine the impact of cost management success factors with project management factors and three agile methodologies – Extreme Programming (XP), Scrum and Kanban methodologies which are used in the Pakistani software industry. To determine the results, the researchers applied quantitative approach through an extensive survey on 52 agile software development companies in Pakistan. Statistical techniques, such as Pearson’s correlation and mean and standard deviation were performed to examine the results. Following this analysis, we found that cost management has a positive effect on other project management factors, which are schedule, scope, risk, resources, and quality. Furthermore, it is determined that, in general, Kanban performed better than both, Scrum and XP in the context of project management factors.
Deep Short Text Classification with Knowledge Powered Attention

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention.
Prognostic potential of mathematical experiment planning

The ground-experimental testing of the elements of space technology is a crucial stage in the creation of space products, in fact, this is the ground «flight» of the devices being created. And how well the operating conditions will be reproduced at this stage and the successful functioning of the devices under the expected conditions is ensured, its successful operation in real flight during the service life depends. Electronic equipment (apparatus) of the spacecraft is one of the essential elements that ensure the fulfillment of target tasks, which should confirm their readiness for the forthcoming work at the stage of ground-based experimental testing. The technology of experimental development at a time when the scientific and production association of applied mechanics (now the JSC Academician M. F. Reshetnev Information Satellite Systems) was part of the circle of enterprises that created space technology. And at the same time, the peak of research and implementation in the engineering practice of mathematical experiment planning turned out to be very timely. There was a backlog of applied work in various branches of science and technology and a minimum of work on radio-electronic topics, since the element base of the latter did not allow managing the internal parameters of electronic components, i. e., to reveal the influence of internal factors. This became an obstacle to the implementation of the capabilities of the method in research and improvement of electronic equipment. The article shows how the developers of the enterprise equipment overcame the existing limitations and successfully applied mathematical planning of the experiment for the first time in Russian space technology. Using the capabilities of the methodology, the tasks of optimization of circuit design and design implementation of devices, the choice of the element base for space equipment, the establishment of tolerances and the formation of test modes, etc. were solved.
Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation

In this paper, we propose two novel methods for domain adaptation for the attention-only neural machine translation (NMT) model, i.e., the Transformer. Our methods focus on training a single translation model for multiple domains by either learning domain specialized hidden state representations or predictor biases for each domain. We combine our methods with a previously proposed black-box method called mixed fine tuning, which is known to be highly effective for domain adaptation. In addition, we incorporate multilingualism into the domain adaptation framework. Experiments show that multilingual multi-domain adaptation can significantly improve both resource-poor in-domain and resource-rich out-of-domain translations, and the combination of our methods with mixed fine tuning achieves the best performance.
The Role of Emotional Expression in Accessing Social Networks: The Case of Newcomers' Blogs

Research has established the critical role of social networks in facilitating adjustment to foreign environments.Increasingly, social interactions are happening through computer mediated technology.This paper explores the role of emotional expression in newcomers' blogs in developing and interacting with social networks in a new country.This research uses a dictionary-based text analytics approach to detect emotional expression in newcomers' blog posts and their associated discussions.Blog posts with more emotional expression had more associated responses; discussions tended to be more positive than posts; and the relative amount of negative emotion in the discussions increases as posts become more negative.Results suggest that expression of emotion in blogs can facilitate access to social networks and increase engagement in online communities by increasing the amount of responses and triggering congruent emotional response from blog readers, which is a precursor to affiliation and understanding.The findings in this paper highlight the role of emotional expression in blog posts and discussions, and its connection to developing social networks and engaging in online communities which has the potential to facilitate access to social support.
How Large Are Lions? Inducing Distributions over Quantitative Attributes

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as "Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.
Crowdsourcing and Aggregating Nested Markable Annotations

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markablesin our case, the (potentially nested) noun phrases in coreference resolution (or mentions).In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model.The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F 1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector.One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task-and language-independent and are thus applicable to a variety of other annotation scenarios.
Definition of the Criteria for Layout of the UML Use Case Diagrams

Abstract Nowadays, the topicality and applicability of model-driven development in the object-oriented development approach has increased, so it is important that created models and diagrams display not only the content, but also visually reflect information. Transparent diagram placement that influences work productivity is important for displaying information. Manual layout of diagrams is a time-consuming activity, which can also be ineffective, so in this paper the application of UML use case automatic layout is reviewed. The paper also examines the requirements of use case diagrams and placement criteria, which will serve as a basis for the creation of an automatic use case diagram layout algorithm.
A User Constraint Awareness Approach for QoS-Based Service Composition

Web service composition adopts functional features including the inputs and outputs, and non-functional features including quality of service (QoS), conditional structure constraints, user preferences, and trusts to compose homogeneous or heterogeneous services together in order to create value-added services. However, in some complex practical application scenarios, the web services with the same function can provide the generous differentiated contents, and there is no approach to focus on the user’s constraints on the content provided by the web services. In this paper, we focus on handling three composition dimensions simultaneously including functional features, QoS and the user’s constraints on the contents provided by the web services. Therefore, an improved genetic algorithm to obtain an optimal solution for this task is applied. In addition, we also take it into consideration that the over-constrained problem caused by implicit conflicting constraints and improve a constraint correction approach to solve this problem with less cost of consistency checks. Experimental results using the real datasets about travel demonstrate the effectiveness of our approach in creating the fully functional and quality-optimized solutions, on the premise that the users constraints on the content are satisfied.
Temporal density extrapolation using a dynamic basis approach

Density estimation is a versatile technique underlying many data mining tasks and techniques, ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data. With the pervasiveness of embedded systems and digitisation, this latter type of streaming and evolving data becomes more important. Nevertheless, research in density estimation has so far focused on stationary data, leaving the task of of extrapolating and predicting density at time points outside a training window an open problem. For this task, temporal density extrapolation (TDX) is proposed. This novel method models and predicts gradual monotonous changes in a distribution. It is based on the expansion of basis functions, whose weights are modelled as functions of compositional data over time by using an isometric log-ratio transformation. Extrapolated density estimates are then obtained by extrapolating the weights to the requested time point, and querying the density from the basis functions with back-transformed weights. Our approach aims for broad applicability by neither being restricted to a specific parametric distribution, nor relying on cluster structure in the data. It requires only two additional extrapolation-specific parameters, for which reasonable defaults exist. Experimental evaluation on various data streams, synthetic as well as from the real-world domains of credit scoring and environmental health, shows that the model manages to capture monotonous drift patterns accurately and better than existing methods. Thereby, it requires not more than 1.5 times the run time of a corresponding static density estimation approach.