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"Personal Computing"
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The handbook of personal area networking technologies and protocols
\"This handbook offers an unparalleled view of wireless personal area networking technologies and their associated protocols. It lifts the lid on their growing adoption within the consumer electronics, home automation, sports, and health and well-being markets. Bluetooth low energy, ZigBee, EnOcean, and ANT+ are comprehensively covered, along with other WPAN technologies including NFC, Wi-Fi, Bluetooth classic and high speed, and WHDI. It also features 802.11ac, the Internet of Things, Wireless USB, WiGig, and WirelessHD. The handbook shows how white space radio, cellular, and femtocells have inadvertently blurred the boundaries between personal and wide area communications, creating disruptive topologies through technology convergence. It explores how pervasive WAN technologies have spawned a new generation of consumers through the Lawnmower Man Effect and explains how our personal space has become integral to social media streams, including Twitter, Facebook, and Pinterest. An essential read for students, software engineers and developers, product planners, technical marketers, and analysts\"-- Provided by publisher.
Book
Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining
Traditional regression-based approaches do not provide good results in diagnosis and prediction of occurrences of cardiovascular diseases (CVD). Therefore, the goal of this paper is to propose a deep learning–based prediction model of occurrence of major adverse cardiac events (MACE) during the 1, 6, 12 month follow-up after hospital admission in acute myocardial infarction (AMI) patients using knowledge mining. We used the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, a cardiovascular disease database registered in 52 hospitals in Korea between 1 January, 2005, and 31 December, 2008. Among 14,885 AMI patients, 10,813 subjects in age from 20 to 100 years with the 1-year follow-up traceability without coding errors were finally selected. For our experiment, the training/validation/test dataset split is 60/20/20 by random sampling without replacement. The preliminary deep learning model was first built by applying training and validation datasets and then a new preliminary deep learning model was generated using the best hyperparameters obtained from random hyperparameter grid search. Lastly, the preliminary prediction model of MACE occurrences in AMI patients is evaluated by test dataset. Compared with conventional regression-based models, the performances of machine/deep learning–based prediction models of the MACE occurrence in patients with AMI, including deep neural network (DNN), gradient boosting machine (GBM), and generalized linear model (GLM), are also evaluated through a matrix with sensitivity, specificity, overall accuracy, and the area under the ROC curve (AUC). The prediction results of the MACE occurrence during the 1, 6, and 12-month follow-up in AMI patients were the AUC of DNN (1 M 0.97, 6 M 0.94, 12 M 0.96), GBM (0.96, 0.95, 0.96), and GLM (0.76, 0.67, 0.72) in machine learning–based models as well as GRACE (0.75, 0.72, 0.76) in regression model. Compared with previous models, our deep learning–based prediction models significantly had the accuracy of 95% or higher and outperformed all machine learning and regression-based prediction models. This paper was the first trial of deep learning–based prediction model of the MACE occurrence in AMI clinical data. We found that the proposed prediction model applied different risk factors except the attribute “age” by using knowledge mining and directly used the raw data as input.
Journal Article
Get going with Amazon Echo and Alexa : in easy steps
\"The days of only being able to search for items on computers using text searches are long gone: voice search is rapidly becoming one of the most popular ways to find content on computing devices and the Web. One of the leaders in this area is the Amazon Echo, a high-quality speaker which uses Alexa ... to perform a range of tasks from playing music and making calls to smartphones, to answering questions and even controlling compatible devices in the home, such as turning on the heating ... [This book] leads you through the process of setting up the Amazon Echo, connecting it to your home wifi network and then controlling much of its functionality, so that you can start making the most of your digital personal assistant\"--ONIX annotation.
Book
Enhanced fault identification and optimal task prediction (EFIOTP) algorithm during multi-resource utilization in cloud-based knowledge and personal computing
Virtualization technology is playing an important role in cloud computing for efficient task scheduling and application deployment. Cloud computing offers a platform to store and retrieve a large volume of information without any restriction on time or location. The system optimizes the available resource based on the user application requirement. Server and data storage devices can access distributed data residing in remote places via virtualization mechanism, where cloud applications are easily migrated from one server to another. Issues related to fault identification and resource optimization problems often occur in a cloud environment. To resolve these issues, an enhanced fault identification and optimal task prediction (EFIOTP) algorithm are proposed for finding and preventing faults during task execution with multiple resources. The research work objective is to design a deadline-determined resource allocation model with the VM resource isolation method in a cloud. The proposed work evaluates the maximum amount of task execution by considering different types of resources to identify and predict the faults at various levels and to minimize the occurrence of faults and task execution time. Based on the experiment evaluation, the proposed EFIOTP algorithm reduces 775 task completions (TCT), 0.237 datacenter server utilization (DCSU), 2% virtual machine cost (VMC), and improves the 0.39 hypervolumes (HV) on several parameters and scientific workflow application.
Journal Article
Lightweight knowledge-based authentication model for intelligent closed circuit television in mobile personal computing
In the intelligent CCTV surveillance environment, personal identity is confirmed based on face recognition. However, the recognition rate of the current face recognition technology is still faulty. In particular, face recognition may not work correctly due to various causes such as CCTV shot quality, weather, personal pose and facial expression, hairstyle, lighting condition, and so on. In this case, there is a great risk of exposing an object’s privacy information in the video surveillance environment due to erroneous object judgment. This paper proposes a video surveillance-based access control technique that combines a facial recognition system using CCTV machine learning with radio-frequency identification (RFID). The proposed method is implemented when accurate facial recognition is difficult to achieve due to poor video quality or low levels of similarity against feature vectors, in which cases multi-channel authentication is performed with the use of RFID features available on a mobile device in possession of the individual. The dual-channel authentication approach can still help identify the entity and protect his or her privacy with greater security even if the RFID tags for authentication are breached or accurate facial detection becomes challenging due to various factors such as CCTV video quality deterioration.
Journal Article
Addressing Brazilian diversity in personal computing systems with a tailoring-based approach
Access to knowledge, information, and technology is a key element for the development of individuals and society as a whole. While computing systems play a fundamental role in this process, efforts aimed at diminishing the worldwide digital divide are still scarce. In this study, we propose a tailoring-based approach for personal systems design as a way to promote digital and social inclusion in contexts of highly unbalanced access to computing technology. Our approach uses theories and methods of participatory design and semiotics to tailor user interfaces according to principles of universal access. We propose the integration of the PLuRaL framework for the system conception and design with the FAN (flexibility through AJAX and norms) framework for implementation and deployment of tailorable user interfaces, reaching a complete approach for the creation of systems. The approach, frameworks, and methods were effectively analyzed during the design of an inclusive social network system in Brazil. In addition, we present an evaluation of the developed system and discuss possible impacts on digital inclusion.
Journal Article
Musical Metaverse: vision, opportunities, and challenges
The so-called metaverse relates to a vision of a virtual, digital world which is parallel to the real, physical world, where each user owns and interact through his/her own avatar. Music is one of the possible activities that can be conducted in such a space. The “Musical Metaverse” (MM), the metaverse part which is dedicated to musical activities, is currently in its infancy, although is a concept that is constantly evolving and is progressing at a steady pace. However, to the best of the author’s knowledge, as of today an investigation about the opportunities and challenges posed by the MM has not been conducted yet. In this paper, we provide a vision for the MM and discuss what are the opportunities for musical stakeholders offered by current implementations of the MM, as well as we envision those that are likely to occur as the metaverse emerges. We also identify the technical, artistic, ethical, sustainability, and regulatory issues that need to be addressed so for the MM to be created and utilized in efficient, creative, and responsible ways. Given the importance and timeliness of the MM, we believe that a discussion on the related opportunities and concerns is useful to provide developers with guidelines for creating better virtual environments and musical interactions between stakeholders.
Journal Article
A systematic literature review of data governance and cloud data governance
Data management solutions on their own are becoming very expensive and not able to cope with the reality of everlasting data complexity. Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Forward-thinking organizations believe that the only way to solve the data problem will be the implementation of an effective data governance. Attempts in governing data failed before, as they were driven by IT, and affected by rigid processes and fragmented activities carried out on system by system basis. Up to very recently governance is mostly informal with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organization. Despite its highly recognized importance, the area of data governance is still under developed and under researched. Since data governance is still under researched, there is need to advance research in data governance in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of data governance. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the state-of-the-art of research in data governance. The objective of the study is to provide a credible intellectual guide for upcoming researchers in data governance to help them identify areas in data governance research where they can make the most impact. The systematic literature review covered published contributions from both academia and industry. The presented SLR searches and examines most relevant published work since year 2000 to-date for data governance for non-cloud, and for cloud computing since 2007. Only 52 studies met the inclusion criteria, which are critically reviewed.
Journal Article
The Chatbot Usability Scale: the Design and Pilot of a Usability Scale for Interaction with AI-Based Conversational Agents
Standardised tools to assess a user’s satisfaction with the experience of using chatbots and conversational agents are currently unavailable. This work describes four studies, including a systematic literature review, with an overall sample of 141 participants in the survey (experts and novices), focus group sessions and testing of chatbots to (i) define attributes to assess the quality of interaction with chatbots and (ii) the designing and piloting a new scale to measure satisfaction after the experience with chatbots. Two instruments were developed: (i) A diagnostic tool in the form of a checklist (BOT-Check). This tool is a development of previous works which can be used reliably to check the quality of a chatbots experience in line with commonplace principles. (ii) A 15-item questionnaire (BOT Usability Scale, BUS-15) with estimated reliability between .76 and .87 distributed in five factors. BUS-15 strongly correlates with UMUX-LITE by enabling designers to consider a broader range of aspects usually not considered in satisfaction tools for non-conversational agents, e.g. conversational efficiency and accessibility, quality of the chatbot’s functionality and so on. Despite the convincing psychometric properties, BUS-15 requires further testing and validation. Designers can use it as a tool to assess products, thus building independent databases for future evaluation of its reliability, validity and sensitivity.
Journal Article