4,685 research outputs found
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
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Guide Me in Analysis: A Framework for Guidance Designers
Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
A Review and Characterization of Progressive Visual Analytics
Progressive Visual Analytics (PVA) has gained increasing attention over the past years.
It brings the user into the loop during otherwise long-running and non-transparent computations
by producing intermediate partial results. These partial results can be shown to the user
for early and continuous interaction with the emerging end result even while it is still being
computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth
various interpretations and instantiations that have created a research domain of competing terms,
various definitions, as well as long lists of practical requirements and design guidelines spread across
different scientific communities. This makes it more and more difficult to get a succinct understanding
of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and
discussion of PVA presented in this paper address these issues and provide (1) a literature collection
on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical
recommendations for implementing and using PVA-based visual analytics solutions
Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation
Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods
On The Privacy Of Cloud Computing
Cloud computing is a model for providing on-demand access to computing service via the Internet. In this instance, the Internet is the transport mechanism between a client and a server located somewhere in cyberspace, as compared to having computer applications residing on an “on premises” computer. Adoption of cloud computing practically eliminates two ongoing problems in IT service provisioning: the upfront costs of acquiring computational resources and the time delay of building and deploying software applications. The technology is not without a downside, which in this case is the privacy of business and personal information. This paper provides a conspectus of the major issues in cloud computing privacy and should be regarded as an introductory paper on this important topic.  
Artificial Text Detection with Multiple Training Strategies
As the deep learning rapidly promote, the artificial texts created by
generative models are commonly used in news and social media. However, such
models can be abused to generate product reviews, fake news, and even fake
political content. The paper proposes a solution for the Russian Artificial
Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish
which model within the list is used to generate this text. We introduce the
DeBERTa pre-trained language model with multiple training strategies for this
shared task. Extensive experiments conducted on the RuATD dataset validate the
effectiveness of our proposed method. Moreover, our submission ranked second
place in the evaluation phase for RuATD 2022 (Multi-Class).Comment: Accepted by Dialogue-2022 Conference. 7 pages, 2 figures, 2 table
Temporal, cognitive and behavioral dimensions of transaction costs: To an understanding of hybrid vertical inter-firm relations
transaction costs;costs and cost price
FAKE REVIEWS AND MANIPULATION: DO CUSTOMER REVIEWS MATTER?
With the prevalence of fake reviews across web and e-commerce platforms it has become difficult for the customers to make an informed purchase decision. Considering this we examine the influence of review manipulation on customer’s purchase decision. A qualitative approach employing interviews with frequent online shoppers was employed to explore the phenomenon. The results of the study suggest that customers accord recommendations from their social network more weightage than the reviews available on an e-commerce platform. Further, we found that customers apply either or both interactive and extractive strategies to deal with review manipulation. Keywords: information processing, review manipulation, fake reviews, grounded theory
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