Reviewerly/exHarmony huggingface dataset:
Blockchain Adaptation of Remote Patient Monitoring with Internet of Medical Things

IoT is transforming the world of information significantly. It has allowed administration services without human interventions for a range of applications including healthcare with Remote Patient Monitoring (RPM). Blockchain is broadly utilized in IoT applications like smart health monitoring that provides privacy and security. Blockchain innovation presents freedom for the medical services industry, for example, reduced transaction costs, transparency for reporting regulatory entities, productive medical care information, privacy, confidentiality and universality of healthcare records. This chapter deals with the collaboration of blockchain innovation in the IoT security in terms of the RPM framework. Patient Centric Agent is used for end-to-end design for nonstop tolerant checking. The patient agent deals with a segment of blockchain to provide protection when streaming information from body area sensors that need to be securely stored and analysing critical parts of blockchain to a wellbeing application network where patient’s information can be utilized to make alarms that are essential to validate medical services suppliers in a protected way. This methodology is valuable as they assemble information for longer time frames. The access control manager stores and safely accesses information needed by the classifier during retraining progressively in real-time from an external data storage.
Augmented Co-Design Methods for Climate Smart Environments

The research is based on the hypothesis that integrating site-specific and global data into the design process requires a methodological design approach, which connects local to global systems and extends the application of available predefined algorithmic scripts and singular solutions. These tools allow the designer to apprehend and simulate possible future scenarios with unparalleled precision and speed. Computational design thinking will help us master increasingly complex design challenges as well as build a profound theoretical knowledge base to meaningfully integrate current and future technologies. After re-evaluating the principles of the computational pioneers, computationally driven methods for pressing urban challenges through data-informed design speculations are discussed. Cutting-edge design speculations aim to open up new immersive design simulation and participatory processes in environmental design and urban development and give sustainable answers to societal and environmental challenges, ultimately shaping our future world.
Robotics Technology in the Tourism and Hospitality Sector in the Wake of the COVID-19 Health Crises

During the global pandemic, the worldwide tourism and hospitality sector has affected severely, and robotics can be a good instrument for providing significant physical distance during the pandemic as humans avoid physical interaction. In this chapter, examination has been performed on whether it would be helpful or whether it will have adverse effects on using robotic systems to offer physical distance for the tourism and hospitality sector. The chapter further revealed that robotics technology builds a technological wall among tourists and staff, enhancing physical and emotional distance between them. Therefore, tourism and hospitality businesses need to augment robots with other technologies to promote social interaction and counteract the adverse consequences of complete distance.
The economic and environmental analysis of solar energy development under climate impacts

Solar energy is a promising renewable technology to secure energy security and reduce emissions. While there are several solar energy studies, the intensified climate change has altered the climate pattern such as regional sunny hours and the strength of solar radiation. To investigate the impact of climate change on the regional solar energy potential, this study analyses the average sunny hour and solar radiation from monthly data from Jan. 2009 to Apr. 2021 and applies the ARIMA and ARDL models to predict the potential changes of these factors for the period of May 2021 to Dec. 2025. The results show the impact of climate change on solar energy generation potential is geographically different. Based on the historical data, the estimated electricity generation potential from conventional PV, PV/PCM, and PVT/PCM technologies are 2,636, 2,747, and 2868 kWh per m 2 , respectively. However, under climate impacts, their power potential will be greatly reduced to 2,422, 2,525, and 2636 kWh per m 2 . Specifically, the future power potential will decline up to 8%, and thus the policymakers should incorporate the climate influence on solar energy development and application to minimize power fluctuation.
Voice Assistants Have a Plurilingualism Problem

Intelligent personal assistants (IPAs) using speech interfaces have historically been limited to monolingual use in pre-selected languages. Although recent developments in some IPAs have allowed for increased multilingual flexibility, the plurilingual competence – ability to utilise more than one language in the frame of a single interaction – of state-of-the-art IPAs still falls short. This is demonstrated in a pilot study, where two widely used IPAs are shown to consistently fail in plurilingual interactions across 3 core tasks.
Unifying Recommender Systems and Conversational User Interfaces

This paper considers unifying research on conversational user interfaces and recommender systems. Studies on conversational user interfaces (CUIs) typically examine how conversations can be facilitated (i.e., optimizing the means). Recommender systems research (RecSys) aims to retrieve and present recommendations in a user's session (i.e., optimizing the ends). Though these aims are overlapping across both areas, they can be better examined together to target the means and ends of what people can achieve with technology as conversational recommender systems (CRSs). We discuss the intersection of conversational user interfaces, recommender systems, and conversational recommender systems. We argue how conversations and recommendations can be designed holistically, in which recommendations can also be a means to foster engaging conversational interaction, while conversations as ends can better sustain curated, long-term recommendations.
Beyond Subservience: Using Joint Commitment to Enable Proactive CUIs for Mood Logging

Conversational user interfaces (CUIs) are a promising interaction modality to engage people with self-report activities that are widely used to study people's experiences and support them with their mental health. However, this potential is limited by the prevailing CUI interaction paradigm being subservience to the user, which constrains self-reporting to being user initiated. A more effective approach would be for CUIs to proactively engage users with self-reporting, particularly at opportune moments. This paper proposes that joint action theory, specifically joint commitment, can be an effective framework to support designers in designing effective proactive CUI interactions. Using mood logging as a use case, we highlight three key areas where joint commitment can impact proactive CUI design. We also discuss wider challenges and future areas of research needed to identify the opportunities and challenges of using joint commitment within proactive CUI research and development.
PeaceTech

In recent years, the world’s enthusiasm for new technology has been accompanied by growing concern over the harmful social, environmental, and economic impacts of these same technologies. In response to what is often referred to as a “techlash” <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , a new focus on “tech for good” can be found in the private sector as well as in higher education and the realm of governments. For example, Communitech, an organization dedicated to supporting tech startups in the region of Waterloo, ON, Canada, rebranded its annual tech summit around the theme of tech for good in 2018 <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . This included the development and launch of the Canadian Tech for Good Declaration, an effort to build the commitment of companies to a set of six principles, including things like “leave no one behind” and “think inclusively at every stage” <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> .
Design and implementation of blockchain based peer to peer energy trading platform

Abstract Blockchain is an emerging technology which is known for its popularity and reliability. First introduced in 2008, it has now expanded to many areas such as IoT, cryptocurrency and Smart contracts. A blockchain consists of blocks assembled in the form of a chain. Data from each participant in the network is stored in these blocks. It is done in a block format called a series of transactions. The Internet of Things (IoT) has transformed many traditional lifestyles. The IoT has enabled cities, housing, pollution control, energy saving and intelligent transportation systems. Blockchain technology and the Internet of Things can increase the efficiency of peer-to-peer energy trading platforms. The open-source peer to peer (P2P) energy trading system is designed on the blockchain. Peer to peer (P2P) is a decentralized communication model in which each party has the same functionality and each party can initiate a communication session. The system collects real time data, monitors and controls it using MQTT Protocol. The trading activities takes place on a web interface which uses a public test Ethereum blockchain, they are tamper-proof. IoT is used to monitor and control the energy. The energy data is acquired and processed using Wi-Fi Based microcontrollers.
RecGuard: An efficient privacy preservation blockchain-based system for online social network users

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and e-commerce. However, it raises several questions about privacy, especially for users who prefer to remain anonymous, especially on online social networks (OSNs). Moreover, due to the commercialization of online users' data, some service providers sell users' data to third parties at the blind side of the users, which leads to trust issues between users and service providers. Such matters call for a system that gives online users much-needed control and autonomy of their data. With the advancement of blockchain technology, many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and hacks. To resolve these limitations, we propose RecGuard, a privacy preservation blockchain-based network system. We developed two smart contracts, RG-SH and RG-ST, to ensure the security and privacy of user data. The RG-SH manages user data, whereas the RG-ST stores data. A graph convolutional network (GCN) was integrated with the blockchain-based system to detect malicious nodes. Finally, we implemented our framework prototype on a locally simulated network. The analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.
Word elongation as an audience-design strategy in social media marketing in Palestine

Drawing on a combination of sociolinguistic and psychological models, this study examines sponsored Facebook ads over a six-month period in Palestine. While language has been a worthwhile area of study in the field of marketing, this article examines the linguistic feature of word elongation, the duplication of the same letter, as a strategy of marketing and enhancing customers’ engagement with the advertised products. The importance of this study emanates from the fact that, to the best of our knowledge, there has not been any study on word elongation as a strategy for marketing. In addition to scrutinizing the marketing function of this linguistic phenomenon in Palestine, we discuss the methods of translating word elongation in the light of Newmark’s theory of translation. The researchers have come to the conclusion that although communicative translation of ads is of great importance, the translator needs to take the additional semantic features of elongated words into consideration as they may have grammatical as well as social functions.
An Ensemble Model for Stance Detection in Social Media Texts

The aim of this paper is to develop a model to classify the stance expressed in social media texts. More specifically, the work presented focuses on tweets. In stance detection (SD) tasks, the objective is to identify the stance of a person towards a target of interest. In this paper, a model for SD is established and its variations are evaluated using different classifiers. The single models differ based on the pre-processing and the combination of features. To reduce the dimensionality of the feature space, analysis of variance (ANOVA) test is used. Then, two classifiers are employed as base learners including Random Forests (RF) and Support Vector Machines (SVM). Experimental analyses are conducted on SemEval dataset that is used as a benchmark for SD. Finally, the base learners that resulted from different design alternatives, are combined into three ensemble models. Experimental results show the significance of the used features and the effectiveness of a manually built dictionary that is used in the pre-processing stage. Moreover, the proposed ensembles outperform the state-of-the-art models in the overall test score, which suggests that ensemble learning is the best tool for effective SD in tweets.
Financial Development, Political Instability, Trade Openness and Growth in Brazil: Evidence from a New Dataset, 1890-2003

Abstract What is the relationship between financial development, political instability, trade openness and economic growth and how does it change over time? This paper examines these links using a new econometric approach and unique data set. In this paper, we apply the logistic smooth transition model (LST) to annual data for Brazil from 1890 to 2003. The main finding is that financial development has a time-varying effect on economic growth, which depends significantly on (jointly estimated) trade openness thresholds. In addition, political instability displays a negative effect on growth whereas trade openness a positive one. Finally, our estimates show that in 56% of the years in which financial development has a ‘below the mean’ effect, we find that trade openness experiences a substantial ‘above the mean’ change.
BookSampo Fiction Literature Knowledge Graph Revisited: Building a Faceted Search Interface with Seamlessly Integrated Data-Analytic Tools

BookSampo – Finnish Fiction Literature on the Semantic Web is a Linked Data (LD) service and portal deployed in 2011 by the Public Libraries of Finland with nearly 2 million annual users. However, BookSampo's user interface (UI) on top of the underlying knowledge graph (KG) in a SPARQL endpoint has been created using traditional search and data exploration methods, and the full potential of the KG—8.74 million triples today—has not been fully utilized: the data, covers all Finnish fiction literature and beyond and is interesting from a Digital Humanities (DH) research perspective, too. This paper presents first results in creating a new BookSampo UI using faceted semantic search with data-analytic tools for Digital Humanities (DH) research, based on the Sampo Model and Sampo-UI framework.
Ship Detection in SAR Images via Cross-Attention Mechanism

Deep learning has been widely applied to ship detection in Synthetic Aperture Radar (SAR) images. Unlike optical images, the current object detection methods have the problem of weak feature representation due to the low object resolution in SAR images. In addition, disturbed by chaotic noise, the features of classification and location are prone to significant differences, resulting in classification and location task misalignment. Therefore, this paper proposes a novel SAR ship target detection algorithm based on Cross-Attention Mechanism (CAM), which can establish the information interaction between the classification and localization task and strengthen the correlation between features through attention. In addition, to suppress the noise in multi-scale feature fusion, we designed an Attention-based Feature Fusion Module (AFFM), which uses the attention information between channels to perform the re-weighting operation. This operation can enhance useful feature information and suppress noise information. Experimental results show that on a benchmark SAR Ship Detection Dataset (SSDD), the Fully Convolutional One-Stage Object Detector (FCOS) with ResNet-50 backbone network was optimized to improve AP by 6.5% and computational cost by 0.51%. RetinaNet with ResNet-50 backbone network was optimized to improve AP by 1.8% and computational cost by 0.51%.
Automatic Generation of Coherent Image Galleries in Virtual Reality

With the rapidly increasing size of digitized and born-digital multimedia collections in archives, museums and private collections, manually curating collections becomes a nearly impossible task without disregarding large parts of the collection. In this paper, we propose the use of Self-Organizing Maps (SOMs) to automatically generate coherent image galleries that allow intuitive, user-driven exploration of large multimedia collections in virtual reality (VR). We extend the open-source VR museum VIRTUE to support such exhibitions and apply it on different collections using various image features. A successful pilot test took place at the Basel Historical Museum with more than 300 participants.
FAIROs: Towards FAIR Assessment in Research Objects

The FAIR principles have become a popular means to guide researchers when publishing their research outputs (i.e., data, software, etc.) in a Findable, Accessible, Interoperable and Reusable manner. In order to ease compliance with FAIR, different frameworks have been developed by the scientific community, offering guidance and suggestions to researchers. However, scientific outputs are rarely published in isolation. Research Objects have been proposed as a framework to capture the relationships and context of all constituents of an investigation. In this paper we present FAIROs, a framework for assessing the compliance of a Research Object (and its constituents) against the FAIR principles. FAIROs reuses existing FAIR validators for individual resources and proposes i) two scoring methods for assessing the fairness of Research Objects, ii) an initial implementation of the scoring methods in the FAIROs framework, and iii) an explanation-based approach designed to visualize the obtained scores. We validate FAIROs against 165 Research Objects, and discuss the advantages and limitations of different scoring systems.
Visual Noise‐Canceling HMD: Toward Reduced Reality

Abstract Unique methods aim to suppress unnecessary visual information and focus on desired objects.
Talent Management Recommendation Technology Based on Deep Learning

Nowadays, with the vigorous development of information management technology, talent management has become a hot field that scholars pay attention to. The flow of talent between companies has become increasingly frequent. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of data on what to do. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recommendation, when an enterprise introduces high-quality talents, provides valuable reference suggestions and selects candidates. When introducing talents, enterprises should not only consider the ability level of talents but also consider the cooperative relationship between them and enterprise personnel. Therefore, it is necessary to analyze the network of partners to find out the rules. There are only author nodes in the isomorphic collaborator network, and the connection between nodes is the cooperative relationship. On this basis, this paper constructs a heterogeneous collaborator network; that is, there are multiple types of nodes and connections in the network. The main research problem of this paper is to find an indicator to measure the strength of association between scholars in the collaborator network and to recommend potential academic talents for enterprises. Based on the data of academic research cooperation network, we carried out sufficient experiments to demonstrate the effectiveness of the heterogeneous cooperation network model proposed in this paper.
Identification of Review Helpfulness Using Novel Textual and Language-Context Features

With the increase in users of social media websites such as IMDb, a movie website, and the rise of publicly available data, opinion mining is more accessible than ever. In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. Connotation words have a different meaning than their literal meanings. While representing a word, the context in which the word is used changes the semantics of words. In this research work, categorizing movie reviews with good F-Measure scores has been investigated with Word2Vec and three different aspects of proposed features have been inspected. First, psychological features are extracted from reviews positive emotion, negative emotion, anger, sadness, clout (confidence level) and dictionary words. Second, readablility features are extracted; the Automated Readability Index (ARI), the Coleman Liau Index (CLI) and Word Count (WC) are calculated to measure the review’s understandability score and their impact on review classification performance is measured. Lastly, linguistic features are also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting 50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualized embedding of words into vectors with 50, 100, 150 and 300 dimensions.The pretrained Word2Vec model converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning (ML) algorithms are applied and evaluated according to performance measures: accuracy, precision, recall and F-Measure. The results indicate Support Vector Machine (SVM) using self-trained Word2Vec achieved 86% F-Measure and using psychological, linguistic and readability features with concatenation of Word2Vec features SVM achieved 87.93% F-Measure.