Additionally, we introduced heuristics to improve the efficiency of the routing method for larger circuits. As shown in the results, the complexity of finding optimal solution increases by increasing the number of DFG nodes as well as the number of I/O of the circuit.
7 Conclusions
Sensors can be classified based on the specific physical or chemical property they measure (e.g. air pressure, temperature, acceleration) or – in the case of fusion sensors that combine the results of multiple underlying sensors – the calculated result they provide (e.g. eg absolute orientation of a unit). Sensor data collected through consumer IoT devices is particularly sensitive when it can be linked to the real identity of the user. It can be assumed that this threat will continue to grow with further improvements in sensor technologies in terms of size, cost and accuracy, further advances in machine learning methods and – most importantly – the predicted rapid proliferation of consumer IoT devices [17 ] .
Evans, D.: The Internet of Things - How the Next Evolution of the Internet is Changing Everything. The rise of the Internet of Things (IoT) has brought new improvement and development opportunities to the automotive industry, such as electric vehicles (EVs). Another limitation was that a full evaluation of the Data Integration Platform has yet to be completed.
There are a number of design flaws, but I wouldn't say the stress aspect is a flaw in the system, instead it has… it's the human factor. SystemCo further noted that the challenge to enable successful implementation was that team leaders needed to be able to "sell" the system to management as a necessity, as well as a willingness to work with the system on the part of all participants to engage them and provide them with adequate resources to would avoid problems, something that previous research [4,7] shows to be important. Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress and Prosperity in an Age of Brilliant Technologies.
Case studies of the use of IoT in the WASH sector in developing countries and, where possible, in Africa provided additional background. One of the new developments in ICT, the "Internet of Things" (IoT), enables the integration of the digital and physical worlds, resulting in the creation of new services that can be used for positive impact. In: Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT)–Enabling Internet Evolution, p.
Santucci, G.: The Internet of Things: Between the Internet Revolution and the Metamorphosis of Objects (2011).
IoTutor: How Cognitive Computing Can Be Applied to Internet of Things
Education
Therefore, adequate education of the future workforce is a prerequisite for success in the increasingly relevant field of IoT. A cognitive computing system [8,19,23] such as IBM Watson [20] associates a passage of text (that is, a question) with another passage of text (that is, an expected corresponding answer) using machine learning. Predicting the likely answer involves determining the salient features of a question by generating hypotheses and evaluating possible answers given context, and iteratively learning from each instance of interaction with the cognitive system.
In this paper, we propose to use cognitive computing for educating students in the IoT domain. We describe the design and implementation of IoTutor that we use for empirical evaluation of our approach. We implemented the IoTutor as a cross-platform web-based application using a collection of the IBM Watson cloud services, including the discovery service, text-to-speech, and speech-to-text services.
We have trained the IoTutor with selected scientific publications and course books relevant to the IoT education. To complete the assignment, they were instructed and encouraged to use IoTutor alongside Google Scholar and other digital libraries. Via a user experience questionnaire, participants were asked to express their opinions regarding the attractiveness, transparency, efficiency, stimulation and novelty of the IoTutor.
2 Related Work
3 Design and Implementation
Design
If the user wants to use voice commands, the microphone button must be pressed, which will establish a flow through the backend between IoTutor and the speech-to-text Watson, and the recognized words will be displayed in the question box. Unless the user toggles the microphone from on to off, IoTutor will continue to recognize the voice commands and display the text on the question box. Otherwise, if the user decides to use text to ask questions, the question can be typed on the question box.
When the microphone is stopped or the send button is pressed, the question will be displayed in the conversation area and then sent to the backend. Passages of answers are displayed first, which the user can click to expand, and then the answer is displayed in a modal (pop-up) window with more details. These details include a link to the full article and an option to have IoTutor read the entire text to the user.
If the link to the full article is clicked, the user will be redirected to the full pdf file. When the close button is clicked, the modal window will be closed, and the user can either choose to expand another answer, or ask a new question. For each question, we added a natural language query to the Watson Discovery service for training (activity 3.2).
Implementation Details
For design and security reasons, the front-end communicates with Watson services through the back-end. The backend has an environment file (env.json) that contains the credentials (such as username, password, url, workspace, version, collection ID, configuration ID, and environment ID) for each of the Watson Cloud services, including discovery, text conversion to speech and speech to text. The discovery component is a simple application programming interface (API) that takes requests (in this case, questions) from the front end and sends them to the Watson Cloud Discovery service.
When a response is received from the Watson Discovery service, the back-end forwards the response to the front-end. In addition, the backend has a database of files that have been imported into Watson Discovery and can easily map the response to an actual scientific publication, so if the user wants to read more, the backend can provide a link to the paper. Basically, it can set up a stream that can listen to the user's microphone and display the recognized text in the IoTutor GUI input field, and can create an audio file corresponding to the given text input.
Similar to the discovery component, it first authenticates to the text-to-speech or speech-to-text service using the information provided by the env.json file and can then send specific requests to the Watson Cloud text-to-speech or speech services in the text. The discovery service allowed us to extract useful information from various scientific publications related to the Internet of Things, such that when the user asks a question, we can query the service and get a list of sorted answers (publications, sections of publications or a specific sentence or paragraph in such articles) that are relevant to the question being asked. This service will listen to the microphone and provide a recognized text stream in response.
4 Evaluation
Demonstration
To implement our application, we have used HTML5, CSS and JS in the front-end, whereas NodeJS [2] is used in the back-end. In the back-end, we used, among other things, the Watson Node SDK [3] to access the IBM Watson Developer Cloud services. In order for the voice commands to work on the front-end, which requires data to be encrypted, we enabled Hyper Text Transfer Protocol Secure (HTTPS) on our server (Table 1).
A view of the conversation area, including a welcome message, questions, answer list, question box, and microphone toggle button. Extended response view, including article information (title, authors, year), text-to-speech functionality, and extended response.
Evaluation Method and Results
Most of the participants were students taking an Internet of Things master's course at Linnaeus University. IoTutor 229 The results of the UEQ show a tendency for the participants to express an overall positive attitude towards the tool. Most aspects/dimensions (12 out of 17) were rated higher than the neutral value, while the rest were slightly lower than the neutral value (see Fig. 6).
Looking cumulatively at each of the five aspects/dimensions, all ratings appear above the neutral value (see Fig. 7). Considering that aspects of transparency and efficiency measure the utility of the tool, and aspects of simulation and novelty measure the user experience, the indication is that both show similar ratings. Slightly higher ratings were shown for perspicacity and novelty, which is an indication that the participants did not have difficulty getting used to the tool and the participants had recognized the innovativeness and creativity of the tool.
6. Mean and standard deviation for each of the responses in the user experience questionnaire. In addition to the UEQ results, we had the opportunity to briefly discuss the tool with two participants after they had used the tool and provided their UEQ responses. A major concern expressed was that the tool provided answers based on keywords, which was not expected given that the interface tool was accepting natural queries.
5 Conclusions and Future Work
Participants have expressed their opinions regarding the attractiveness, insight, efficiency, stimulation and innovation of IoTutor. In the future, we plan to use a larger digital library of scientific publications during the training process of IoTutor to measure its impact on increasing the quality of the answers provided by the tool. In: Proceedings of the Machine Learning Driven Technologies and Architectures for Intelligent Internet of Things (ML-IoT), pp.
Chen, Y., Argentinis, J.E., Weber, G.: IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Chozas, A.C., Memeti, S., Pllana, S.: Using cognitive computing to teach parallel programming: an IBM Watson solution. In: Proceedings of 9th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE 2016, pp.
High, R.: The age of cognitive systems: an inside look at IBM Watson and how it works. Mercer, C.: 16 Innovative Businesses Using IBM Watson: Which Companies Are Using Big Data and Watson Analytics to Power Their Businesses. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution, and reproduction in any medium or form. , as long as you give proper credit to the original author(s) and source, provide a link to the Creative Commons license, and indicate if changes have been made.
