Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

RESTful service composition at a glance

The Web of Things is an active research field which aims at promoting the easy access and handling of smart things' digital representations through the adoption of Web standards and technologies. While huge research and development efforts have been spent on lower level networks and software technologies, it has been recognized that little experience exists instead in modeling and building applications for the Web of Things. Although several works have proposed Representational State Transfer (REST) inspired approaches for the Web of Things, a main limitation is that poor support is provided to web developers for speeding up the development of Web of Things applications while taking full advantage of REST benefits. In this paper, we propose a framework which supports developers in modeling smart things as web resources, exposing them through RESTful Application Programming Interfaces (APIs) and developing applications on top of them. The framework consists of a Web Resource information model, a middleware, and tools for developing and publishing smart things' digital representations on the Web. We discuss the framework compliance with REST guidelines and its major implementation choices. Finally, we report on our test activities carried out within the SmartSantander European Project to evaluate the use and proficiency of our framework in a smart city scenario.

/pdf/a-web-of-things-framework-for-restful-applications-and-its-4ltjvfyp0j.pdf

A Web of Things Framework for RESTful Applications and Its Experimentation in a Smart City

Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable in IoT environments, a protocol conversion scheme between ISO/IEEE 11073 protocol and oneM2M protocol, and a Multiclass Q-learning scheduling algorithm based on the urgency of biomedical data delivery to medical staff are proposed. In addition, for the sake of patients’ privacy, two security schemes are proposed—the separate storage scheme of data in parts and the Buddy-ACK authorization scheme. The experiment on the constructed system showed that the system worked well and the Multiclass Q-learning scheduling algorithm performs better than the Multiclass Based Dynamic Priority scheduling algorithm. We also found that the throughputs of the Multiclass Q-learning scheduling algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the Multiclass Based Dynamic Priority algorithm increase with decreases in the increasing ratio.

An IoT System for Remote Monitoring of Patients at Home

After the traditional document-centric Web 1.0 and user-generated content focused Web 2.0, Web 3.0 has become a repository of an ever growing variety of Web resources that include data and services associated with enterprises, social networks, sensors, cloud, as well as mobile and other devices that constitute the Internet of Things. These pose unprecedented challenges in terms of heterogeneity (variety), scale (volume), and continuous changes (velocity), as well as present corresponding opportunities if they can be exploited. Just as semantics has played a critical role in dealing with data heterogeneity in the past to provide interoperability and integration, it is playing an even more critical role in dealing with the challenges and helping users and applications exploit all forms of Web 3.0 data. This book presents a unified approach to harness and exploit all forms of contemporary Web resources using the core principles of ability to associate meaning with data through conceptual or domain models and semantic descriptions including annotations, and through advanced semantic techniques for search, integration, and analysis. It discusses the use of Semantic Web standards and techniques when appropriate, but also advocates the use of lighter weight, easier to use, and more scalable options when they are more suitable. The authors' extensive experience spanning research and prototypes to development of operational applications and commercial technologies and products guide the treatment of the material. Table of Contents: Role of Semantics and Metadata / Types and Models of Semantics / Annotation -- Adding Semantics to Data / Semantics for Enterprise Data / Semantics for Services / Semantics for Sensor Data / Semantics for Social Data / Semantics for Cloud Computing / Semantics for Advanced Applications

Semantics Empowered Web 3.0: Managing Enterprise, Social, Sensor, and Cloud-based Data and Services for Advanced Applications

Representational State Transfer (REST), as an architectural style for distributed hypermedia systems, enables scalable operation of the World Wide Web (WWW) and is the foundation for its future evolution. However, although described over 10 years ago, no comprehensive formal model for representing RESTful systems exists. The lack of a formal model has hindered understanding of the REST architectural style and the WWW architecture, consequently limiting Web engineering advancement. In this paper we present a model of RESTful systems based on a finite-state machine formalism. We show that the model enables intuitive formalization of many REST's constraints, including uniform interface, stateless client-server operation, and code-on-demand execution. We describe the model's mapping to a system-level view of operation and apply the model to an example Web application. Finally, we outline benefits of the model, ranging from better understanding of REST to designing frameworks for RESTful system development.

/pdf/formal-modeling-of-restful-systems-using-finite-state-4gx9r398q9.pdf

Formal modeling of RESTful systems using finite-state machines

Representational State Transfer (REST), as an architectural style for distributed hyper-media systems, enables scalable operation of the World Wide Web and is the foundation for its future evolution. However, although described over 10 years ago, no formal model for representing RESTful systems exists that is comprehensive in following REST principles, intuitive to Web engineers and researchers alike, and offers practical development guidelines. The lack of such formal models has hindered understanding of both the REST architectural style and the Web architecture, consequently limiting Web engineering advancement. In this paper we present a generic model of RESTful systems based on a finite-state machine formalism. We show that the model enables intuitive formalization of REST design principles, including uniform interface, stateless client-server operation, and code-on-demand execution. Furthermore, we describe the model's mapping to a system-level view of operation and apply the model to an example Web application and several real-word Web applications. Finally, we explore the practical challenges and benefits of using the model in the field of Web engineering, ranging from better understanding of REST to designing frameworks for RESTful system development.

/pdf/a-finite-state-machine-approach-for-modeling-and-analyzing-vqj93ltfsu.pdf

A finite-state machine approach for modeling and analyzing restful systems

In recent years, tools for computer aided learning have become widespread on all levels of education. These tools are used as replacements or additions to traditional methods of instruction, in order to reduce the time students require to absorb the taught course. On the other hand, these tools ease the burden of course lecturers when preparing complex examples since these can be created and executed in real time and then analyzed in class. To increase the interactivity of computer science education, we have developed RegExpert, a tool for regular language manipulation and visualization. The main goal of the RegExpert tool is to simplify and visually present complex concepts and mathematical models of automata theory fundamentals. RegExpert converts user defined or automatically generated regular expression to an equivalent NFA-epsilon (nondeterministic finite automaton with epsilon transitions) and presents it to the user in a form of state diagram. In this paper, we present the structure and implementation of the tool, as well as the ideas for future development.

/pdf/regexpert-a-tool-for-visualization-of-regular-expressions-46v7wqzhat.pdf

RegExpert: A Tool for Visualization of Regular Expressions

Demand for more advanced Web applications is the driving force behind Web browser evolution Recent requirements for Rich Internet Applications, such as mashing-up data and background processing, are emphasizing the need for building and executing Web applications as a coordination of browser execution contexts Since development of such Web applications depends on cross-context communication, many browser primitives and client-side frameworks have been developed to support this communication In this paper we present a systematization of cross-context communication systems for Web browsers Based on an analysis of previous research, requirements for modern Web applications and existing systems, we extract a framework for classifying cross-context communica-tion systems Using the framework, we evaluate the current ecosystem of cross-context communication and outline directions for future Web research and engineering

pdf/a-classification-framework-for-web-browser-cross-context-25lwlfkefk.pdf

A Classification Framework for Web Browser Cross-Context Communication

We present peer-tutor recommender system for service composition.Peer-tutoring process is based on socially-intelligent computing.Expert peer tutors are identified by analysis of existing service compositions.The peer tutoring system was implemented and evaluated through a consumer study.Results show significant improvements in terms of consumer performance and QoE. With continued development towards the Internet of Things, services are making their way from enterprise solutions to our offices and homes. This process is a major driving force in consumerization of IT, because sustainable application development at this scale will not be possible without direct involvement and innovation from consumers themselves. In this paper, we present our work on consumerization of service composition tools. First, we describe how consumer-facing services can be presented in a usable and intuitive way. Then, combining social computing with machine intelligence, we define a recommender system that supports consumers in sharing their knowledge and creativity in peer-tutored service composition, thus empowering consumers to create their own applications. This system recommends consumers with the required service composition knowledge based on mining procedural knowledge stored in previously defined compositions. Once such a group of consumers is identified, social computing tools are used to allow them to share this knowledge with their peers. To demonstrate the effectiveness of this peer-tutored service composition model, we performed consumer satisfaction studies on our consumerized service composition tool Geppeto, which we extended with the described recommender system. Results show significant improvements in service composition in terms of performance and quality of experience.

Consumerized and peer-tutored service composition

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ivan Budiselic

Author Tools

Chat about Author