Reviewerly/exHarmony huggingface dataset:
Analogue to Digital Telecare: Findings and Themes from a User-Centred Study to Help People Live in the Community Safely

Telecare services include personal alarms, home sensors and activity monitoring to enable people to remain safe and independent in their own home.Telecare has traditionally used analogue connectivity, however internationally, there is a shift to digital connectivity.This presents a rare opportunity to fundamentally redesign telecare, address current barriers to uptake, and help more people live in the community safely.This paper describes a user-centred study to design innovative digital telecare concepts, involving key stakeholders (a supplier, a manufacturer, 13 end users, 32 informal carers and 29 health and social care professionals).There are currently limited examples of digital telecare internationally.The main contributions of this paper are: an overview of key challenges and opportunities for telecare, not emphasised in existing literature within the context of the analogue to digital switchover; findings from user engagement activities, which identified issues that may be more important to users when designing telecare (e.g.self-concept) and less important (e.g.privacy); and the synthesis of ideas generated through the design process, which identified four themes that should prove useful to practitioners and researchers working in the field: community-based support, telecare you don't wear or notice, expand the use of telecare, and introduce telecare earlier.Telecare.Technology enabled care.Assisted living technology.Telehealthcare.Older people.
Sparse Trust Data Mining

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information.However, large recommendation systems with trust data suffer from the sparse trust problem , which leads to grade inflation and severely affects the reliability of trust propagation.This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework.It lays a foundation for the trust-related research in large recommended systems.The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system.We conducts a comprehensive performance evaluation on both real-world and synthetic datasets.The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.
TODAY’S HIGHER EDUCATION AT A CROSSROADS - The critical point and paradigm shift in the educator’s role

Observing the different progress rates of the available data, information and knowledge (on one side) and human capacity to process these available data, information and knowledge (on the other side), the author becomes aware of the higher pace of the first –in the midst of impetus of new communication and information technologies –and argues that, at this point in time, we are eye-witnessing a real education paradigm shift. The education system is at a critical point in time (call it critical point in education –CPE) when the educator’s role must change from knowledge repository to skilled, expert knowledge explorer and identifier, switching from teaching the subject to teach students how to pick the right and relevant information related to the subject –from the ocean of available data. The current corona-crisis –which started by the time when the ideas for this chapter were put together –made this question more acute, asking for re-thinking the education system and educator’s role. This chapter launches the thesis of the education paradigm shift –in that respect of the educator’s role in the predictable future, to provoke a discussion, and to open a research path, for higher education strategists, policy makers, scholars and educators.
Discovering Interdisciplinarily Spread Knowledge in the Academic Literature

With the increase in scientific publications and the diversification of research areas, science has become complex and interdisciplinary. Discovering important knowledge has become difficult even for researchers in specific domains. Previously proposed keyphrase extraction methods focus mainly on detecting intensively discussed topics in specific domains but do not distinguish concepts with interdisciplinary spread from those discussed in narrow areas. Here, we propose a diffusion meme score that evaluates the knowledge diffusion distance in a paper citation network. The distances between papers that contain specific terms are measured by the network embedding space of the citation network. Using 57 million publication records from 48 years of Scopus, we evaluated newly appearing terms in and after 1975 in biomedical science papers using the proposed indicator. Approximately half of the top 20 terms were related to Nobel Prize or Clarivate Citation Laureates, and the top terms of the indicators were more likely to appear in Wikipedia than terms extracted using existing methods. Moreover, the top terms were unlikely to include specific minor diseases, which are often extracted using existing methods. Therefore, the diffusion meme score evaluates important terms from scientific literature and citation networks that are more interdisciplinary. Our method improves the understanding of young researchers regarding domains, the development of the history of science, and the evaluation of researcher contributions.
The FAIRness of data management plans: an assessment of some European DMPs

The FAIR principles have become a data management instrument for the academic and scientific community, since they provide a set of guiding principles to bring findability, accessibility, interoperability and reusability to data and metadata stewardship. Since their official publication in 2016 by Scientific Data – Nature, these principles have received worldwide recognition and have been quickly endorsed and adopted as a cornerstone of data stewardship and research policy. However, when put into practice, they occasionally result in organisational, legal and technological challenges that can lead to doubts and uncertainty as to whether the effort of implementing them is worthwhile. Soon after their publication, the European Commission and other funding agencies started to require that project proposals include a Data Management Plan (DMP) based on the FAIR principles. This paper reports on the adherence of DMPs to the FAIR principles, critically evaluating ten European DMP templates. We observed that the current FAIRness of most of these DMPs is only partly satisfactory, in that they address data best practices, findability, accessibility and sometimes preservation, but pay much less attention to metadata and interoperability.
Improved Meta-Learning Training for Speaker Verification

Meta-learning (ML) has recently become a research hotspot in speaker verification (SV). We introduce two methods to improve the meta-learning training for SV in this paper. For the first method, a backbone embedding network is first jointly trained with the conventional cross entropy loss and prototypical networks (PN) loss. Then, inspired by speaker adaptive training in speech recognition, additional transformation coefficients are trained with only the PN loss. The transformation coefficients are used to modify the original backbone embedding network in the x-vector extraction process. Furthermore, the random erasing (RE) data augmentation technique is applied to all support samples in each episode to construct positive pairs, and a contrastive loss between the augmented and the original support samples is added to the objective in model training. Experiments are carried out on the Speaker in the Wild (SITW) and VOiCES databases. Both of the methods can obtain consistent improvements over existing meta-learning training frameworks. By combining these two methods, we can observe further improvements on these two databases.
Under the Concealing Surface

Given the central role of webcams in monitoring physical surroundings, it behooves the research community to understand the characteristics of webcams' distribution and their privacy/security implications. In this paper, we conduct the first systematic study on live webcams from both aggregation sites and individual webcams (webpages/IP hosts). We propose a series of efficient, automated techniques for detecting and fingerprinting live webcams. In particular, we leverage distributed algorithms to detect aggregation sites and generate webcam fingerprints by utilizing the Graphical User Interface (GUI) of the built-in web server of a device. Overall, we observe 0.85 million webpages from aggregation sites hosting live webcams and 2.2 million live webcams in the public IPv4 space. Our study reveals that aggregation sites have a typical long-tail distribution in hosting live streams (5.8% of sites contain 90.44% of live streaming contents), and 85.4% of aggregation websites scrape webcams from others. Further, we observe that (1) 277,239 webcams from aggregation sites and IP hosts (11.7%) directly expose live streams to the public, (2) aggregation sites expose 187,897 geolocation names and more detailed 23,083 longitude/latitude pairs of webcams, (3) the default usernames and passwords of 38,942 webcams are visible on aggregation sites in plaintext, and (4) 1,237 webcams are detected as having been compromised to conduct malicious behaviors.
Development of CPA Coaching Model for Professional Coaching -ACTIVE Coaching Conversation Model-based C-PLUS Coaching Program

The purpose of this study is to develop a professional coaching program that can be provided to those who want to become professional coaches. To this end, I collected data on professional coaching education at domestics and abroad and developed an integrated CPA (C-PLUS Active Coaching Model) coaching model for the development of professional coaches based on several workshops. CPA Coaching Integrated Model stands for C-PLUS combined with Active Coaching Model, and the ultimate goal of C-PLUS Coaching Program is to create a positive (+) effect in life through coaching so that anyone can live A's life. To ensure that The C-PLUS coaching program for professional coaches consists of 20 hours and contains the contents necessary for professional coaches based on the basic theory and skills of coaching. The coaching model used in this process is the CPA model, and the coaching conversation model is the ACTIVE Coaching conversation model based on the C-PLUS coaching principle. The specific program consisted of the basics of coaching, the principle of coaching, coaching conversation model, coaching skills, coaching style, and coaching practice. ACTIVE Coaching Conversational Model-based C-PLUS Coaching Program developed in this study is designed to provide a coaching approach that enables anyone to easily understand coaching and become a professional coach or use coaching in their lives through coaching education. It is meaningful in that it is provided systematically.
Smart Interconnected Infrastructures for Security and Protection: The DESMOS Project

This paper presents “DESMOS”, a novel ecosystem for the interconnection of smart infrastructures, mobile and wearable devices, and applications, to provide a secure environment for visitors and tourists. The presented solution brings together state-of-the-art IoT technologies, crowdsourcing, localization through BLE, and semantic reasoning, following a privacy and security-by-design approach to ensure data anonymization and protection. Despite the COVID-19 pandemic, the solution was tested, validated, and evaluated via two pilots in almost real settings—involving a fewer density of people than planned—in Trikala, Thessaly, Greece. The results and findings support that the presented solutions can provide successful emergency reporting, crowdsourcing, and localization via BLE. However, these results also prompt for improvements in the user interface expressiveness, the application’s effectiveness and accuracy, as well as evaluation in real, overcrowded conditions. The main contribution of this paper is to report on the progress made and to showcase how all these technological solutions can be integrated and applied in realistic and practical scenarios, for the safety and privacy of visitors and tourists.
Aircraft Parking Trajectory Planning in Semistructured Environment Based on Kinodynamic Safety RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math>

To improve the safety and effectiveness of autonomous towing aircraft aboard the carrier deck, this study proposes a velocity-restricted path planner algorithm named as kinodynamic safety optimal rapidly exploring random tree (KS-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> ) to plan a near time-optimal path. First, a speed map is introduced to assign different maximum allowable velocity for the sampling points in the workspace, and the traverse time is calculated along the kinodynamic connection of two sampling points. Then the near time-optimal path in the tree-structured search map can be obtained by the rewiring procedures, instead of a distance-optimal path in the original RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> algorithm. In order to enhance the planner’s performance, goal biasing scheme and fast collision checking technique are adopted in the algorithm. Since the sampling-based methods are sensitive to their parameters, simulation experiments are first conducted to determine the optimal input settings for the specific problem. The effectiveness of the proposed algorithm is validated in several common aircraft parking scenarios. Comparing with standard RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> and human heuristic driving, KS-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> demonstrates a higher success rate, as well as shorter computation and trajectory time. In conclusion, KS-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> algorithm is suitable to generate a near time-optimal safe path for autonomous high density parking in semistructured environment.
Türkiye’de İşaret Dili Çevirmenliği için Ulusal Standart ve Ulusal Yeterlilik Çalışmaları

In 2012-2013 Turkey began working on drafting a national standard for translators and interpreters, including sign language interpreters.By 2017-2018, the same group moved on to drafting their national qualifications.In 2020 the National Standard was enlarged to include a wider scope for sign language interpreting and the National Qualifications for Sign Language Interpreters were approved and published.These developments marked an important step for sign language interpreting in Turkey, shedding light on realities of professionals in the country.The results of this long and carefully planned process led to the drafting of two documents that will hopefully contribute to sign language interpreting.The standard and qualification referred to may be able to lead to steps to improve the professionalism of the sign language interpreters, the recognition of the skill sets of interpreters, areas of expertise in sign language interpreting, life long learing in sign language interpreting, the importance of the recognition of training in the field and studies in sign language interpreting.The following paper summarizes this long process, and outlines the model and rationale behind the model that Turkey embraced.The paper aims to a) provide an overview of sign language interpreting in Turkey; b) model the process for similar endeavours; and c) explain the importance of such initiatives for improving the practice of sign language interpreting.The intention of this primarily descriptive paper is to promote an awareness of the developments in Turkey; the hope is that this awareness will promote cooperation, which will further contribute to the profession.
Fast Video Summary Generation Based On Low Rank Tensor Decomposition

This paper deals with the problem of large number of parameters and complex calculation in video abstract generation of Fully Connected Network and Convolutional Neural Network. At the same time, the training and testing of such model need a lot of time and computer resources. We came up with a deep learning network parameter compression method based on Singular Value Decomposition(SVD) and Trucker Decomposition (TD) is proposed to generate the video summaries. The experiment was compared with other methods on TVSum and SumMe dataset, and the F1 value was 55.3% in TVSum dataset and 46.8% in SumMe dataset. At the same time, the degree of test time shortening under the same data volume is taken as the evaluation basis. The experimental results show that the proposed method achieves 1.04 times of acceleration in the SVD forward calculation, and 1.29 times of acceleration in the TD forward calculation. In a conclusion, the neural network model based on low-rank decomposition can effectively save computer resources and the time consumed by running programs.
Sentiment Analysis on COVID-19 Related Social Distancing Across the Globe Using Twitter Data

The Covid-19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of COVID-19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression.
Overview of the 2021 SDP 3C Citation Context Classification Shared Task

This paper provides an overview of the 2021 3C Citation Context Classification shared task. The second edition of the shared task was organised as part of the 2nd Workshop on Scholarly Document Processing (SDP 2021). The task is composed of two subtasks: classifying citations based on their (Subtask A) purpose and (Subtask B) influence. As in the previous year, both tasks were hosted on Kaggle and used a portion of the new ACT dataset. A total of 22 teams participated in Subtask A, and 19 teams competed in Subtask B. All the participated systems were ranked based on their achieved macro f-score. The highest scores of 0.26973 and 0.60025 were reported for sub-task A and B, respectively.
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion

We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously.Multilingual models are often challenged by model capacity and severe data distribution skew across languages.While prior works largely focus on monolingual models, we propose Conditional Generative Matching models (CGM), optimized within a Variational Autoencoder framework to address challenges arising from multilingual RS.CGM does so with expressive message conditional priors, mixture densities to enhance multi-lingual data representation, latent alignment for language discrimination, and effective variational optimization techniques for training multi-lingual RS.The enhancements result in performance that exceed competitive baselines in relevance (ROUGE score) by more than 10% on average, and 16% for low resource languages.CGM also shows remarkable improvements in diversity (80%) illustrating its expressiveness in representation of multi-lingual data.
Research result recording of forensic histological examination course in Russia

Microscopy is the main research method during forensic histological examinations. Section images obtained using microscopy in the form of a mental image are stored in the memory of a forensic histologist. In addition, visual information is sketched, described, or recorded using photography and videography. Aims: This study aimed to identify recording microscopy result features in the case of forensic histological examination in Russia at present and its development prospects in the future. Research material included conclusions of an expert (specialist) of state forensic medical institutions in Russia. Methods used to study the material were as follows: comparative-historical analysis, microscopic, thanatogenetic analysis, microphotography, and histological section description. The authors analyzed two methods of recording of microscopy results in detail, which are frequently used in forensic histological examinations at present in Russia. The article reflects historical, legal, and forensic aspects, as well as some unresolved problems of recording information obtained during forensic histological research. The authors also express their opinion about the use of computer vision of histological section microscopy in the near future, as a supplement but not the opposite of biological vision.
GIS-AIDED GEOGRAPHICAL AND METEOROLOGICAL DATA OVERVIEW OF SOLAR RADIATION MAPPING FOR MALAYSIA – AN EXPLANATORY STUDY BASED ON SOLAR RADIATION PREDICTION MODELING USING NEURAL NETWORK APPROACH

Solar radiation mapping has used geographical and meteorological data. To obtain geographical and meteorological data, a Geographic Information System (GIS) is required. GIS is defined as an integrated geographic resource that presents data in terms of spatial information. This data is important for Neural Networks as it will be used as input parameters for the development of solar radiation prediction models. Solar radiation prediction is one way to map the sun's rays in certain places where there are insufficient resources or space to build a complete solar radiation measurement station. Since predictions about solar radiation require meteorological and geographical data, this paper will give an overview of GIS-assisted geographical and meteorological data to be used as input parameters for solar radiation mapping which will eventually be used as input for prediction models developed for the whole country of Malaysia using Neural Networks. Based on the results, the prediction model developed managed to obtain a coefficient of determination, R2 value of 0.9329.
Mineral Potential Modeling of Porphyry Copper Deposits using Continuously-Weighted Spatial Evidence Layers and Union Score Integration Method

There are different exploration methods, each of which may introduce a number of promising exploration targets. However, due to the financial and time constraints, only a few of them are selected as the exploration priorities. Instead of the individual use of any exploration method, it is common to integrate the results of different methods in an interdependent framework in order to recognize the best targets for further exploration programs. In this work, the continuously-weighted evidence maps of proximity to intrusive contacts, faults density, and stream sediment geochemical anomalies of a set of porphyry copper deposits in the Jiroft region of the Kerman Province in Iran are first generated using the logistic functions. The weighted evidence maps are then integrated using the union score integration function in order to model the deposit type in the studied area. The weighting and integration approaches applied avoid the disadvantages of the traditional methods in terms of carrying the bias and error resulting from the weighting procedure. Evaluation of the ensuing prospectivity model generated demonstrate that the prediction rate of the model is acceptable, and the targets generated are reliable to follow up the exploration program in the studied area.