Reviewerly/exHarmony huggingface dataset:
Introduction

Personal data is increasingly important in our lives. We use personal data to quantify our behaviour, through health apps or for ‘personal branding’ and we are also increasingly forced to part with our data to access services. With a proliferation of embedded sensors, the built environment is playing a key role in this developing use of data, even though this remains relatively hidden. Buildings are sites for the capture of personal data. This data is used to adapt buildings to people's behaviour, and increasingly, organisations use this data to understand how buildings are occupied and how communities develop within them. A whole host of technical, practical, social and ethical challenges emerge from this still developing area across interior, architectural and urban design, and many open questions remain. This book makes a contribution to this on-going discourse by bringing together a community of researchers interested in personal informatics and the design of interactive buildings and environments. The book’s aim is to foster critical discussion about the future role of personal data in interactions with the built environment.
Extraction Method for Constructive Proposals based on Online Comments

In the flipped classroom teaching, it transfers the traditional teaching process from in-class to out-of-class, which is transforming the traditional education mode. In order to make good use of students' feedback data, and improve the quality of the online course and the effect of class teaching, this paper proposes an Extraction Method for constructive Proposals based on online Comments (EMPC). First, EMPC obtains the target words of course comments through BERT (Bidirectional Encoder Representations from Transformers) and the target filter algorithm, then it inputs target words into Bi-LSTM (Bi-directional Long Short-Term Memory) module with attention mechanism to obtain the opinion words. After all the target-opinion pairs are obtained, the algorithm based on k-means is used to extract constructive proposals. We conducted experiments by using MOOC (Massive Open Online Course) courses comment data, the experimental results show that EMPC achieves a high accuracy in both target word extraction and opinion word extraction, and it can get the proposals mentioned by students most frequently in order to improve the flipped classroom teaching.
Overview of Search and Rescue From Robotics to Wireless Sensors and Robots Networks

In the recent years, many researchers have shown interest in developing search and rescue system composed of one or multiple robots, which have the mission of finding victims and identifying the potential hazards. To enhance the robotic systems there is a growing trend of integrating wireless sensor networks (WSNs) to robots and multi-robot systems, which gives more awareness of the environments. In the first part of this article, the authors present a review of robotic system and their environments in search and rescue systems. Additionally, they explain challenges related to these systems and tasks that a robot or a multi-robot system should execute to fulfil the search and rescue activities. As a second part, the authors expose the system that integrates WSNs with robots and the advantages that brings this latter. In addition, they cite tasks and missions that are achieved in a better way with a cooperation of WSN and robots. Furthermore, the authors expose and discuss the remarkable research, challenges and the open research challenges that includes this cooperation.
An Efficient Server Minimization Algorithm for Internet Distributed Systems

The increasing use of online services leads to an unequal distribution of the loads among the servers. As a result, the problem is to balance the loads among the servers such that the total number of active servers is minimized. One of the possible solutions is to transfer the loads from the underutilized server to a suitable server and make the underutilized server to sleep mode. In this paper, a server minimization algorithm (SMA) is proposed for the solution of server minimization and the load balancing problem. The proposed algorithm reduces the number of servers by merging the loads of the two least loaded servers. Then it determines the standard deviation of the server loads for load balancing. The proposed SMA is compared with an existing load balancing algorithm using the number of minimized servers, load standard deviation and load factor. The simulation results show the efficacy of the SMA.
Recognition on Images From Internet Street View Based on Hierarchical Features Learning With CNNs

This article describes hierarchical features with unsupervised learning on images from internet street view images. This is due to the time spent by trained researchers on feature construction steps with traditional methods. This article focuses on the activation of each layer of with convolutional neural networks (CNNs) on Internet street view images detection and compared similarities and differences among them on each layer. The experiment results achieved error rates of 21% on recognition which work went relatively well than the traditional machine learning techniques, such as Parallel SVM.
Conditions for Effective Macroprudential Policy Interventions

This paper aims at identifying effective macroprudential policy (MPP) interventions and analysing the macroeconomic conditions that promote them. We define effective MPP interventions as those that stabilize its underlying target variable, such as credit growth, house price growth, etc. For our analysis, we construct a new database that documents the use of a large number of MPP instruments for 61 advanced and emerging market economies from 2000 to 2016. The new feature of the database is that it maps every recorded MPP intervention in these economies and over this period to stabilize a specific target variable category for banking, health, domestic loans, the exchange rate, foreign capital movements, and house prices. Using this dataset, we introduce a practical way for defining the macroprudential policy effectiveness. We find that MPP interventions are more likely to be effective when several prudential measures are taken together, but at the same time avoid the diminishing returns of repeated MPP tightening. Monetary tightening seems to override the effectiveness of MPP instruments. The output gap, credit cycle, external debt, current account, and global risk appetite also count for the likelihood of MPP successes. The paper provides a guideline for the effective conduct of MPPs.
Document Recommendation Based on Interests of Co-authors for Brain Science

Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators' interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.
An improved Wang-Mendel algorithm in Engine Test Bench control system

The Engine Test Bench system needs to tune the PID parameters every time the engine or dynamometer is replaced. In order to realize the self-tuning of PID parameters, a kind of Wang-Mendel algorithm (WM) which is improved by the Fuzzy C-Means Clustering (FCM) and Genetic Algorithm (GA) is applied to the system. According to the characteristics of the Engine Test Bench system, Data of different characteristics is selected to train the prediction model, so as to predict the PID parameters. This paper first introduces the control structure of the Engine Test Bench system, next introduces the fuzzy prediction model, and then uses the FCM algorithm for data mining and GA to optimize the weight in the WM algorithm. Finally, the prediction model is obtained and the prediction results are used in the actual engineering for testing. The results show that the improved algorithm has higher accuracy and practicability.
The Understanding of Spatial-Temporal Behaviors

This chapter introduces a cutting-edge research field of computer vision and image understanding – the spatial-temporal behavior understanding. The main concepts, the focus of research, the typical technology, the fast development, etc. of this new field in recent years are overviewed. An important task in computer vision and image understanding is to analyze the scene through image operation on the image of scene in order to guide the action. To do this, one needs to locate the objects in the scene, and to determine how they change its position, attitude, speed, and relationships in the space over time. In short, it is to grasp the action in time and space, to determine the purpose of the operation, and thus to understand the semantics of the information they passed. This is referred ti as the understanding of spatial-temporal behaviors.
Clothing Material Design Concept Based on Big Data and Information Technology

Fashion design belongs to the category of art and craft, which is an art form combining practicality and artistry. With the development of science and technology, network technology has been fully integrated into social life. The emergence of big data has changed the traditional economic and social form, and put forward new requirements for fashion design profession in the field of education. Based on big data, this paper studies the concept of Chinese clothing material design through hash algorithm of big data. Therefore, in order to adapt to the requirements of the era of big data, this paper believes that the fashion design major of higher professional universities should start with the education reform, strengthen the education mode in line with the times, improve the market demand, and provide professional fashion design talents for the society. Recently, China's garment industry needs a lot of experts. Many enterprises can't find talents suitable for many positions every year. Graduates are still facing employment problems. The imbalance of demand and supply is reflected in the talent training methods can not meet the requirements of the clothing industry. In the context of big data today, the innovation of fashion design should be the main task to develop, massive information can be easily presented in front of people. Therefore, we need to cater to the pace of fashion elements to meet the existing needs. In the source of fashion elements, the innovation of fashion design and the pursuit of fashion is no longer a lot of designers. Massive data resources use traditional fashion prediction methods to predict the results of its development trend, but the actual embodiment of life and culture. The trend of fashion design has been unable to achieve. In the context of the new era, the development of the Internet has led to information innovation, which is not limited to the structure of style and color, but in the development of clothing data.
Accurate Step Count With Generalizable Deep Learning on Accelerometer Data

Physical Activity (PA) is a globally recognized pillar of general health. However, there is no widely accepted measure to quantify PA. Step count, as one measure of PA, is a well known predictor of long term morbidity and mortality. Although step count is widely available in consumer grade mobile and wearable devices, a lack of methodological stan-dards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on developing device-specific step count algorithms in controlled experimental settings, and often employ sensor modalities such as gyroscopes that may not be widely available. This limits step count suitability in clinical scenarios as algorithms might not generalize across devices and patient populations. In this paper, we trained neural network models on publicly available data and directly tested on an independent cohort without further tuning. Herein we assess step count accuracy of neural network architectures trained on accelerometer-only activity signals from one device and applied to accelerometer-only data obtained from a separate subject cohort wearing multiple distinct devices. We found highly accurate step count estimates using accelerometer-only activity signals (96 % - 99 % with the best model). The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step count. Thus it positions step count as a reliable mobility endpoint and a strong candidate for clinical validation.
Copyright in Cyberspace

Abstract This chapter is an introduction to the domain of intellectual property (IP) rights, notably copyright. For computer scientists, the most relevant part of copyright law concerns copyright on computer programs, or software. Copyright on software is the enabling precondition for the General Public Licence (GPL) and the open source initiative. Before discussing copyright on software, however, this chapter first investigates the position of IP law in the context of constitutional democracy and clarifies that IP law is private law. From there, the chapter provides an overview of the various types of IP that are most relevant, after which it turns to the history, objectives, and scope of copyright protection. Finally, this chapter discusses EU copyright law and the issues of open source and free access.
Book Reviews

Neil Gandal of Tel Aviv University and CEPR reviews “Blockchain and the Law: The Rule of Code,” by Primavera De Filippi and Aaron Wright. The Econlit abstract of this book begins: “Explores the dual nature of blockchain technology, focusing on the emergence of lex cryptographica—systems that create order without law and implement what can be thought of as private regulatory frameworks—and outlines potential avenues for regulation.”
Chapter 22: Professional Communications

PreviousNext No AccessThe Complete PCOA® ReviewChapter 22: Professional Communicationshttps://doi.org/10.21019/9781582123417.ch22Authors:Jeremy ThomasJeremy ThomasSearch for more papers by this author SectionsSupplemental MaterialAboutFull TextReferences ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Abstract: Key components of interpersonal communication are the sender, the receiver, and the message. Pharmacists should identify barriers to communication and seek to resolve those barriers. Pharmacists should identify patients with low health literacy and develop appropriate visual or written materials. Open-ended questions will provide the pharmacist with more information than traditional closed-ended questions. A medication history includes allergies, current and past medications, adverse reactions, over-the-counter medications, and adherence. 22-16Additional ResourcesBeardsley RS, Kimberlin CL, Tindall WN. Communication Skills in Pharmacy Practice. 6th ed. Baltimore, MD: Lippincott, Williams & Wilkins; 2012. Google Scholar, Berger BA. Communication Skills for Pharmacists. 3rd ed. Washington, DC: American Pharmacists Association; 2009. Abstract, Google Scholar, Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561–566. Crossref, Medline, Google Scholar, Haig KM, Sutton S, Whittington J. SBAR: a shared mental model for improving communication between clinicians. Jt Comm J Qual Patient Saf. 2006;32:165–175. Google Scholar, Health Literacy. Centers for Disease Control and Prevention. Available at: https://www.cdc.gov/healthliteracy/learn/index.html. Accessed November 30, 2017. Google Scholar, Jones RM. Patient Assessment in Pharmacy Practice. 3rd ed. Baltimore, MD: Lippincott, Williams & Wilkins; 2015. Google Scholar, Rantucci MJ. Pharmacists Talking with Patients: A Guide to Patient Counseling. 2nd ed. Baltimore, MD: Lippincott, Williams & Wilkins; 2007. Google Scholar, USP Pictogram Library. United States Pharmacopeia. Available at: http://www.usp.org/health-quality-safety/usp-pictograms. Accessed November 30, 2017. Google Scholar, PreviousNext Session Activity Recently Viewed Chapter 22: Professional Communications Jeremy Thomas Recently Searched No search history FiguresReferencesRelatedDetails Published: 29 July 2020eISBN: 1-58212-341-1 Copyright & Permissions© 2020 by the American Pharmacists Association. All rights reserved.© 2020 by the American Pharmacists Association. All rights reserved Publisher American Pharmacists Association HistoryPublished: 29 July 2020 Citation Information Jeremy Thomas, (2020), "Chapter 22: Professional Communications," The Complete PCOA® Review https://doi.org/10.21019/9781582123417.ch22 Loading ...
Enhancing an Academic Library's Social Media Presence Using Peer-to-Peer Marketing

The San José State University Library first became involved in social media in 2011 with the formation of a social media team. The team quickly realized that maintaining an academic library's social media presence—creating original content, monitoring posts and feeds, and maximizing exposure to the library's target audience—can take a sizeable chunk of time. Additionally, it is important to understand student preferences regarding the kind of content they are interested in seeing from their university library on social media so that the time spent creating content has not been wasted. This chapter will discuss how the social media team dealt with these questions and others, such as how to adapt to the regularly changing world of social media and how to reap the benefits of hiring student assistants to help create an engaging, relevant social media presence.
Green Mountain kimberlite, Colorado: mineralogy and petrology

The Green Mountain diatreme,
Studies on Rich Media and Related Knowledge Organization

Rich media is introduced and analyzed from the concept itself, typical applications in different fields, the characteristics and influence in two sizes.Knowledge organization and traditional knowledge organization systems are also analyzed and most of them are suitable for rich media.In this paper, 9 typical applications are reorganized from four dimensions of contents, time, space and human.This paper also points out the feature representation and mapping problems in rich media knowledge organization work.
Tufan: Low-Power Throughput Architecture for Acceleration of EfficientNet on Cloud FPGAs

In recent years, we observe active growth in designing application-specific architectures to accelerate the Convolutional Neural Network (CNN). Among CNN architectures, the recently introduced EfficientNet has emerged as the state-of-the-art CNN, which presents an extensible compound scaling architecture to enhance network capacity to achieve higher accuracy with relatively lower computation demand. However, we see a lack of application-specific architecture support to capitalize on the nuances and benefits of EfficientNet fully. This paper presents Tufan, a throughput-oriented architecture for the acceleration of EfficientNet on Cloud FPGAs. Tufan is a unified framework that supports various EfficientNet family architectures, demonstrating structural sparsity. The accelerator design introduces parameterizable, configurable, and scalable compute units that can be configured based on the user-specific requirement, EfficientNet model, and batch size. We assess the energy efficiency of Tufan for executing a set of EfficientNet family configurations implemented on Xilinx’s Alveo U50 FPGA board and Nvidia Tesla P100 GPU. Our experimental results confirmed that Tufan enhances energy efficiency by 7.81% over P100 GPGPU for a batch size of 28 at 300MHz.
Counterfactual Autoencoder for Unsupervised Semantic Learning

Deep Neural Networks (DNNs) are best known for being the state-of-the-art in artificial intelligence (AI) applications including natural language processing (NLP), speech processing, computer vision, etc. In spite of all recent achievements of deep learning, it has yet to achieve semantic learning required to reason about the data. This lack of reasoning is partially imputed to the boorish memorization of patterns and curves from millions of training samples and ignoring the spatiotemporal relationships. The proposed framework puts forward a novel approach based on variational autoencoders (VAEs) by using the potential outcomes model and developing the counterfactual autoencoders. The proposed framework transforms any sort of multimedia input distributions to a meaningful latent space while giving more control over how the latent space is created. This allows us to model data that is better suited to answer inference-based queries, which is very valuable in reasoning-based AI applications.
Blurring Boundaries

Software development has become increasingly software 'product' development, without an authoritative 'customer' stakeholder that many requirements engineering processes assume exists in some form. Many progressive software product companies today are empowering cross-functional product teams to 'own' their product – to collectively understand the product context, the true product needs, and manage its on-going evolution – rather than develop to a provided specification. Some teams do this better than others and neither established requirements elicitation and validation processes nor conventional team leadership practices explain the reasons for these observable differences. This research examines cross-functional product teams and identifies factors that support or inhibit the team's ability to collectively create and nurture a shared mental model that accurately represents the external product domain and its realities. The research also examines how teams use that collective understanding to shape development plans, internal and external communications, new team member onboarding, etc. We are engaged with several software product companies using a constructivist Grounded Theory method towards the research question. Early results are emerging as organisational factors, within and surrounding the teams. One emerging observation relates to the degree to which functional distinctions are treated as demarcations or blurred boundaries. The other observation is the impact an expectation of mobility has on an individual's sense of feeling part of the collective team versus solely being a functional expert. This also becomes a factor in the first observation. The research is in-progress but early observations are consistent with a basic element of empathy that a certain blurring of the boundaries is necessary for a period of time in order to better understand the other context. Future research will examine whether the observed organisational factors are pre-conditions for the team being able to collectively understand the context of the product requirements, collectively and deeply.
The 142nd Regional Meeting (Kanto Area)

This presentation focuses on the English needed to present and discuss a poster.I will introduce the English that presenters need in order to give a clear and concise summary of their research and discuss the contents of their poster.Examples of poster previews and poster presentation overviews will be given with emphasis on key sentences.Additionally, I will focus on how to engage with visitors to your poster, and also on how to stay in control of a discussion.