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Artificial Intelligence Usage Opportunities in Smart City Data Management

Authors:
  • NextTechnologies Ltd
  • NextTechnologies Kft

Abstract and Figures

In our current study, we are aiming to explore data management methods in Smart City systems. In data management, AI (Artificial Intelligence) can be used as well. Solutions for the usage of AI and integration into Smart City concept will be researched as well. Main motivation of the study is to draw attention to one of the most important element of Smart Cities, to the flow of data. Our study provide a possible solution for managing data and keep data up-to-date in such systems with the usage of newest technology possibilities. While explaining the solution, we will give all the necessary details about the data flow model between the AI based system and humans who are using the Smart City. For managing the data-flow, we would like to use Big Data methods extended with other required methods. We are using the term of Big Data as a technology maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets [1] connecting with AI solutions.
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Interdisciplinary Description of Complex Systems 18(3), 382-388, 2020
*Corresponding author,
: adylaszlo@nexttechnologies.hu; -;
*H- 2234 Maglód, Sugár út 44.
ARTIFICIAL INTELLIGENCE USAGE
OPPORTUNITIES IN SMART CITY
DATA MANAGEMENT
Luca F. Hudasi and László Ady*
NextTechnologies Ltd. Complex Systems Research Institute
Budapest, Hungary
DOI: 10.7906/indecs.18.3.8
Regular article
Received: 17 February 2020.
Accepted: 28 June 2020.
ABSTRACT
In our current study, we are aiming to explore data management methods in Smart City systems. In
data management, Artificial Intelligence can be used as well. Solutions for the usage of Artificial
Intelligence and integration into Smart City concept will be researched as well. Main motivation of
the study is to draw attention to one of the most important element of Smart Cities, to the flow of data.
Our study provides a possible solution for managing data and keep data up-to-date in such systems
with the usage of newest technology possibilities. While explaining the solution, we will give all the
necessary details about the data flow model between the Artificial Intelligence based system and
humans who are using the Smart City. For managing the dataflow, we would like to use Big Data
methods extended with other required methods. We are using the term of Big Data as a technology
maximizing computation power and algorithmic accuracy to gather, analyse, link, and compare large data
sets [1] connecting with Artificial Intelligence solutions.
KEY WORDS
smart city, artificial intelligence, data, management, innovation
CLASSIFICATION
ACM: 10010147.10010178
JEL: C8
Artificial intelligence usage opportunities in smart city data management
383
INTRODUCTION
Intelligent systems, such as Smart Cities are based on the flow of information [2]. Another
important aspect of a good smart city is to make good decisions. It is logical, that if a system
has bad or missing information, it cannot make good decisions [3].
‘The first step in a city becoming a “smart city” is collecting more and better data. [4] says John
Walker in his study. Therefore the following main areas are important to cover upon collecting data:
Develop an automata data collector system.
Develop a people triggered data collector system.
Develop a data sharing and correction system.
The following figure shows the flow of data based on collection and sharing:
Figure 1. Data flow of a smart city.
On figure 1, arrows show the direction of data flow. Although people triggered data and Automatic
data collection is mainly one directional, data sharing is not. Data Sharing part has many
subcomponent and the data flow in this case bidirectional since the participants not just providing but
getting data as well. In the following sections we discuss the details of each components.
DEVELOP AN AUTOMATA DATA COLLECTOR SYSTEM
Developing an automata data collector system covers the already well-known methods, such
as having traffic monitoring systems, automata government administration bodies (for voting,
for taxes, etc.), and an automata traffic-, weather forecast-, and energy distribution system.
To build fully automated systems using big data, it is a requirement to have a built-out sensor
network such as camera, temperature measurers, motion detectors and GPS based devices [5].
It does not mean these systems do not require a supervisor, but they can operate in an independent
way and processing data coming from sensors. Many of these systems are already existing in
testing or in live format, for example in Singapore, London, Barcelona, etc. [4, 6]. It is
important to use the experiences from these cities to build a more reliable one.
DEVELOP A PEOPLE TRIGGERED DATA COLLECTOR SYSTEM
People triggered data collectors are Intelligent Systems where the information is coming from
an active human input. Such as if an inhabitant visit an authorized system (through internet)
and report or handle an administrative hace they need. For example request a credit, rent a
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car, report a misdemeanour activity, etc. Here, the smart system has to be prepared to be able
to serve the customers need and make fair and clear decisions. This is the most dangerous
form of Data Collection because the final decision is made by an Artificial Intelligence (AI)
using a learnt scheme. The discrimination factor is too high, and there are already case
studies for detecting and removing discriminative part from the software [7]. Some
companies curtailed their customers credit if charges appeared for counselling, because
depression and marital strife were signs of potential job loss or expensive litigation[8] says
Racher ODwyer and this was just one example from the many. The question which should
be considered is: is it ethical and legal to allow such kind of discrimination? If the answer is
no, then the information system should be prepared to prevent this, or supervised for such
actions. Connecting psychology and big data in the field of allowance is a new immature area
which needs further studies and testing before essential decisions are made based on the
result. Therefore, we strongly recommend to use a bipolar system in this case: first, the AI
based system make the decisions using the source from collected Big Data, then a human
supervisor should overview the output with the factors used in the decision, and validate or
decline it. Using this two steps verification looks longer, but it is not so much. Collecting data
would be still the responsibility of the automata system and this is the most time-wasting part
of the process. Educated human supervising would correct and develop the AI to make better
decisions in the future. Once the system works measurably stable and ethical, the supervising
work can be decreased.
Another important point of data collection is to collect quality data [4], otherwise the
information the systems decisions are based on is corrupt or missing, therefore the decisions
will be similarly wrong. To achieve this, our proposed solution is to include the inhabitants of
the city to clarify data.
One area where inhabitants can participate is the social-, public administration improvements.
The system would be capable of filling out data and do pre-tasks for the inhabitants (for example:
doing the tax, requesting for new social cards when the existing ones are going out-of-date,
providing public utility usages, making renting, other billing tasks, etc.) but the citizen would
have the opportunity to monitor the decisions, and correct them if needed. Next time the system
would learn from the mistakes and from the habits of the people, and would make better decisions.
Another are would be for extra comfort services, where people would voluntarily provide
information for the system which then can help them to take away tasks from their shoulders,
such as organizing trips, ordering and delivering food, other supplies, or appointments with
doctors or similar. With more up-to-date corrections of the information and decisions the
system is operating with are made, the better intelligent services could be provided. It is
always very important to leave an opportunity to supervise the decisions the AI is doing in
the place of people, to avoid discrimination and bad decisions (detailed above).
DEVELOP A DATA SHARING AND CORRECTION SYSTEM
To be able to cooperate with citizens in the development of data and decision-making, it is
important to make the information the smart city collects as transparent as possible. People
should see the base information of some bad decisions to be able to help to correct them.
While the smart city would provide transparent decision making for the citizens, it is critical
to guard the sensitive data. Big data collection is always a hazard factor. More information
the system provide, more value it represents and it will be more interesting for non-ethical
parties. Therefore, any data which can be provided to third party, should be depersonalized
carefully. To manage the proper depersonalization, is a key factor of the data flow. Creation
of standards document for depersonalization of Smart City data is a requirement.
Artificial intelligence usage opportunities in smart city data management
385
The main industry, which is collecting and using data is the advertisement industry. It can
also produce a great income for the city by using the data collected, but the depersonalization
should be act in the process here as well. People should been informed that the collected data
are provided and generating income, but the system should also keep peoples trust while do
so. The first aim of a smart city is always to provide a better and easier life for people, get rid
of discrimination and unethical decisions. Ethical and correct advertising is the part of this,
but it should not lead to people exposed to direct marketing harassments which can lead to
people leaving social media platforms and losing trust [9, 10].
There is another part of the industry, which is dealing with data: A company can provide
other useful data for the smart city and in exchange get data as well which is important for the
company. This can be a government level company as well which is collecting data and
provide the result as a service for the people.
STRATEGY TO PROCESS DATA
After discussing the aspects of collecting and managing quality data, the next important step
is to have the intelligent system make good decisions. We emphasized the need for
supervising decisions till they become trustable in the aspects of avoiding discrimination and
improvements. We offered the idea to include citizens in the supervising work as well. Also
mentioned to use up data and experience collected from other smart cities. These ideas can
help maintaining data decisions. Although we have to know that a good system beside having
the correct data and algorithms also needs to process data quick and effectively. A good
strategy to process data is taking the following steps: data filtering, pre-processing, processing
and decision support. Figure 2 shows the processing stages with their dependencies.
Figure 2. Logical architecture of data processing steps.
DATA FILTERING
The aim of data filtering is to collect data which the system really needs during the
processing, to minimize the amount to store and process. An existing solution to filter out too
many data (mainly when the system has limitations of processing quickly) is CERNs
solution. CERN had a problem of bypassing all the measured data to their servers. With
Intels help, they developed a specific FPGA which help to filter out the relevant data before
bypassing it to the servers. This way, they minimized the load on the centre [11].
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PRE-PROCESSING
It is a good speeding strategy to execute pre-processing tasks as close at the point of data
collection as possible. This means that the stage when the data comes in the system, stored
procedures executed immediately and decode the input such as voice to text, picture to sort
and identify, etc. Sorting (mainly of pictures) is an effective way because dedicated devices
are already a stage of the sorting. As an example, a parking lot could have a dedicated camera
to recognize empty spaces and another dedicated one for recognizing unauthorized parking.
For pre-processing the pictures, the area reservation database needs to be presented at the
moment the picture is catched and pre-processed by the camera system. This is the stage
when the data depersonalization should be performed as well in case it is needed.
PROCESSING
Processing has many types how it can be performed. These types can be individually used or
together as well. These types are the following:
1st type: Stored procedures. In this context, stored procedure means a logically separated unit
of functions for performing a specific task. The sorting component will decide which stored
procedure can be executed on the data by sorting it to categories. These procedures need to be
written, but on the contrary it takes less processor time while they are running on production.
2nd type: AI. During the processing, an AI module will decide which AI based stored
procedure can process the data,. These AI based stored procedures will only get pre-
processed data. Raw data will be pre-processed by the sorting unit. These AI units (stored
procedures) are looking for relations in data.
3rd type: Data mining. Using the opportunities of data mining, the system can learn models
from big data to predict problems. After detecting upcoming problems, there is a possibility
to make decisions to prevent them and create a safer environment. Difficulties that need to
be addressed during data mining include data gathering, data labelling, data and model integration,
and model evaluation [12]. Data gathering and data labelling can happen in the pre-processing
stage, data and model integration, and model evaluation should happen in the processing stage.
4th type: Manual. Manual actions needed In cases the system can not recognize and process a
certain data from the pre-processing stage (because it is not prepared for it). The system will
display the details on a graphical interface and will ask a human to decide the next steps, such
as sorting the data into one of the existing categories. This type of processing is more like an
extend method for “error handling” together with other AI based solutions.
DECISION SUPPORT
This stage of data processing is responsible to make automated decisions or help the human
decision making.
Result after the processing stage is stored in a database. The decision supporting unit has the
knowledge of the connections between result types and actions. Lets take an example: There
is a processing result that contains a picture that a car took a parking place. The connected
action is to decrease the number of free spaces. The digital table in front of the parking lot
will change and display the new data with the amount of free parking places. Or lets take a
more complex case example when the camera system detects that a big container occupied
the public space near a building. The first action will be to check the permission of taking the
place at the related authorities. In case there is no permission, the second action to execute is
to create a draft report for the police (or related authority) and put the case up to a human
supervisor to accept or decline. It is important to make this case half-automated with human
Artificial intelligence usage opportunities in smart city data management
387
supervising, because making a punishment should not be full automated. The decision
support unit has to contain a set of rules about which action can be done in which case: for
example, at a heating system there could be a rule that after switching off the gas unit, it
cannot be turned back in the next 5 minutes (safety period of the gas unit to chill down). If an
automated decision would be made to turn on the heating because it is too cold within this 5
minutes, this rule would write it over. All the decisions which were made, should be logged
for possible investigations and later improvements.
PROTECTION OF DATA FLOW
Finally, since smart cities are operating with sensitive data, it is also a part of data
management to save the data from being stolen, unauthorised modifications and destruction.
Beside using the well-known defensive solutions since the system is based on artificial
intelligence and there are couple of paradigms available of normal behaviours we can use
these resources to add another level of defence for the systems protection. We propose to
build an alarm system, which by monitoring the information flow can alert about
disharmonious data detections. Which means, if the data flowing through the system does not
follow a continuously measured norm, it could be considered that the data was manipulated.
This prevention method can be considered as a “software” type prevention. Physical prevention
means that important data is allowed to travel on a way that is theoretically impossible to
interfered or read by malicious bodies without immediate detection. In practise, this means
the usage of optical cables where the network can detect any interference if the specifications
of the light change. Using IEC 62443 standard is highly advised [13]. As a conclusion, we
suggest to consider using the mentioned techniques and processes from this article when new
smart cities are designed and build, and also for existing smart cities to develop.
REFERENCES
[1] Boyd, D. and Crawford, K.: Critical questions for big data.
Information, Communication and Society 15(5), 662-679, 2012,
http://dx.doi.org/10.1080/1369118X.2012.678878,
[2] Tokody, D.; Papp, J.; Iantovics, L.B. and Flammini, F.: Complex, Resilient and Smart
Systems.
In: Flammini F., eds: Resilience of Cyber-Physical Systems. Advanced Sciences and
Technologies for Security Applications. Springer, Cham, 2019,
[3] Tokody, D.; Schuster, G. and Papp, J.: Smart City, Smart Infrastructure, Smart Railway.
International Conference on Applied Internet and Information Technologies, October 23, 2015.
Technical faculty “Mihajlo Pupin” Zrenjanin, Zrenjanin, 2015,
[4] Walker, J.: Smart City Artificial Intelligence Applications and Trends.
https://emerj.com/ai-sector-overviews/smart-city-artificial-intelligence-applications-trends, accessed
2nd February 2019,
[5] Neirotti, P.; De Marco, A.; Cagliano, A.C.; Mangano. G. and Scorrano, F.: Current
trends in Smart City initiatives: some stylised facts.
Cities 38(2014), 25-36, 2014,
http://dx.doi.org/10.1016/j.cities.2013.12.010,
[6] Ismail, N.: What are the most advanced smart cities in the world?
https://www.information-age.com/advanced-smart-cities-world-123470745, accessed 2nd
February 2019,
[7] Galhotra, S.; Brun, Y. and Meliou, A.: Fairness Testing: Testing Software for
Discrimination.
ESEC/FSE17, 4-8 September, 2017, ACM, Paderborn, 2017,
L.F. Hudasi and L. Ady
388
[8] ODwyer, R.: Algorithms are making the same mistakes assessing credit scores that humans
did a century ago.
https://qz.com/1276781/algorithms-are-making-the-same-mistakes-assessing-credit-scores-that-huma
ns-did-a-century-ago, accessed 2nd February 2019,
[9] Morrison, K.: Consumers Dont Like and Dont Trust Digital Advertising (Infographic).
https://www.adweek.com/digital/consumers-dont-like-and-dont-trust-digital-advertising-infograp
hic, accessed 3rd February 2019,
[10] Tenzer, A. and Chalmers H.: When trust falls down.
https://www.ipsos.com/sites/default/files/2017-06/Ipsos_Connect_When_Trust_Falls_Down.pdf,
accessed 3rd February 2019,
[11] Barney, L.: The FPGA based Trigger and Data Acquisition system for the CERN NA62
experiment.
https://www.hpcwire.com/2017/04/14/xeon-fpga-processor-tested-at-cern, accessed 7th February
2019,
[12] Létourneau, S.; Famili, F. and Matwin, S.: Data Mining to Predict Aircraft Component
Replacement.
IEEE Intelligent Systems 14, 59-66, 1999,
http://dx.doi.org/10.1109/5254.809569,
[13] Elder, J.: Understanding the Importance of Physical Security for Industrial Control
Systems (ICS).
https://applied-risk.com/blog/understanding-importance-physical-security-industrial-control-syst
ems-ics, accessed 6th February 2019.
... In the medical field, technologies are used to improve hospital inpatient care. Smart city data management systems provide the collected data and generate revenue, but the system should also maintain people's trust while doing so [318]. Managing and building people trust is a key challenge in sustainable smart city development. ...
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Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation man-agement, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key chal-lenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established.
... In the medical field, technologies are used to improve hospital inpatient care. Smart city data management systems provide the collected data and generate revenue, but the system should also maintain people's trust while doing so [318]. Managing and building people trust is a key challenge in sustainable smart city development. ...
Article
Full-text available
Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.
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Systematic method has become an important method for people to understand and analyze things in modern science. The core idea of systematic view is the overall concept of system [13]. In general, in order to improve the basic shape of sports, improve the system initiative of athletes’ organs, and give full play to the best mode of sports mechanism and effect, the physical fitness index system is taken as an important reference standard in the process of training. It belongs to the basic index of technical training and tactical training and has a positive impact on the technology, tactics, load training, physical condition, and sports life of special sports. The establishment of a reasonable physical fitness index system can be used as a powerful carrier for the selection mechanism of athletes in reality [14]. Physical fitness is the foundation of young athletes and provides strong support for their technical level. Ordinary teenagers are mostly in the system of compulsory education or secondary and higher education, and their training purposes and means are different from those of young athletes. As far as the means of physical training are concerned, athletes will be better than ordinary teenagers in terms of selection, training, competition, and other aspects, but from the perspective of physiological development characteristics, they are in the second peak of development. The stimulation of training means will have a more obvious effect on athletes’ training, which can provide training support for ordinary teenagers [15]. This will make the competition time longer and test the physical fitness of athletes. If one side’s physical condition is not strong, there will be calf muscle cramps, or even acute sports injury. On the court, long-term muscle contraction and ball extension, such as fast movement, kicking, swinging, and wrist strength, are different from the periodic endurance of other sports. Athletes must have special endurance quality, special strength quality, special speed quality, etc. that change with the change of competition intensity [16]. 2.3. Pedometer APP The primary task of the pedometer algorithm is to obtain the original three-axis acceleration data based on the sensor module and then perform data analysis and algorithm design based on the entire waveform. The actual test shows that there are many interference clutters in the acceleration signal generated by the human body when counting steps in various scenes. Therefore, it is very important to preprocess the original data before formally analyzing the motion waveform [17]. The data collection function of the pedometer is realized by the main controller reading data from the sensor, and its core is the acceleration sensor. The use of analog signal sensors requires additional analog-to-digital converters, which will increase the complexity of the circuit and the space utilization rate; the use of digital signal sensors avoids this problem while using high-precision sensors to ensure the reliability of data. In addition, it is necessary to ensure a higher speed data interface, a certain processing capacity, and lower power consumption in the selection of the main controller and the sensor [18]. The overall architecture of pedometer is shown in Figure 1. According to the function requirement analysis, the acceleration and angular velocity data selected collection of six-axis accelerometer and gyroscope inertial sensor MPU6050, master controller selects 16 ultra-low power consumption microprocessor MSP430G2553. Data transmission can use serial port transmission or wireless module transmission, and the programming of the main controller can be realized through online programmable function [19].
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Smart city is an inevitable development trend in the future, which has a strong role in promoting urban development. Promoting the construction of smart city can not only improve people’s living standards and quality of life, but also effectively promote urban development. This paper first gives an overview of smart city, then briefly introduces the characteristics of smart city, and finally analyzes the key supporting technologies and applications of smart city construction, including cloud computing technology, big data technology, Internet of things technology, artificial intelligence technology and 3D Printing technology. Smart city is a new urban form. In the process of building a smart city, the core is the key supporting technology. Therefore, it is necessary to strengthen the research on the key supporting technology and combine it with the actual situation of the city to achieve effective use.
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Our hypothesis was that the ecosystem of self-driving cars could be treated as a complex system. The proof of this was based on the definition of self-driving car ecosystem and definition of complex system. We not found definition for ecosystem of self-driving cars. That's why we made our own definition of self-driving car ecosystem. Self-driving car ecosystem is all technology and person and service that connect to the self-driving car and have effect to the self-driving car technology, self-driving car design, self-driving car traffic, self-driving car environment (infrastructure), self-driving car maintenance, self-driving car education and self-driving car law.
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“Cyber-Physical Systems or “smart” systems are co-engineered interacting networks of physical and computational components. These systems will provide the foundation of our critical infrastructure, form the basis of emerging and future smart services, and improve our quality of life in many areas.” (National Institute of Standards and Technology: Cyber-physical systems. [Online]. Available: https://www.nist.gov/el/cyber-physical-systems. Accessed 31 Dec 2017, 2017). The concept of Smartness has been increasingly used as a marketing catchphrase. This study seeks to explain that smartness can be a serious indicator which can help to describe the machine intelligence level of different devices, systems or networks weighted by, among others, the usability index. The present study aims to summarize the implementation of complex, resilient and smart system on the level of devices, systems and complex system networks. The research should consider a smart device as a single agent, the system as a multi-agent system, and the network of complex systems has been envisaged as an ad hoc multi-agent system (Farid AM: Designing multi-agent systems for resilient engineering systems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9266, pp 3–8, 2015) organised in a network. The physical incarnations of this latter could be, for example, the subsystems of a smart city. In order to determine the smartness of a certain system, the Machine Intelligence Quotient (MIQ) (Iantovics LB, Gligor A, Georgieva V: Detecting outlier intelligence in the behavior of intelligent coalitions of agents. In: 2017 IEEE congress on evolutionary computation (CEC), pp 241–248, 2017; Park H-J, Kim BK, Lim KY: Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems. IEEE Trans Syst Man Cybern – Part A Syst Humans 31(2):89–96, 2001; Park HJ, Kim BK, Lim GY: Measuring machine intelligence for human-machine coop-erative systems using intelligence task graph. In: Proceedings 1999 IEEE/RSJ international conference on intelligent robots and systems. Human and environment friendly robots with high intelligence and emotional quotients (Cat. No.99CH36289), vol 2, pp 689–694, 1999; Ozkul T: Cost-benefit analyses of man-machine cooperative systems by assesment of machine intelligence quotient (MIQ) gain. In: 2009 6th international symposium on mechatronics and its applications, pp 1–6, 2009), Usability Index (UI) (Li C, Ji Z, Pang Z, Chu S, Jin Y, Tong J, Xu H, Chen Y: On usability evaluation of human – machine interactive Interface based on eye movement. In: Long S, Dhillon BS (eds) Man-machine-environment system engineering: proceedings of the 16th international conference on MMESE. Springer, Singapore, pp 347–354, 2016; Szabó G: Usability of machinery. In: Arezes P (ed) Advances in safety management and human factors: proceedings of the AHFE 2017 international conference on safety management and human factors, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA. Springer International Publishing, Cham, pp 161–168, 2018; Aykin N (ed): Usability and internationalization of information technology. Lawrence Erlbaum Associates, Inc., Publishers, Mahwah, 2005) and Usability Index of Machine (UIoM), Environmental Performance Index (Hsu A et al: Global metrics for the environment. In: The environmental performance index ranks countries’ performance on high-priority environmental issues. Yale University, New Haven, 2016) of Machine (EPIoM) indexes will be considered. The quality of human life is directly influenced by the intelligence and smart design of machines (Farid AM: Designing multi-agent systems for resilient engineering systems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9266, pp 3–8, 2015; Liouane Z, Lemlouma T, Roose P, Weis F, Liouane Z, Lemlouma T, Roose P, Weis F, Neu HMAG: A genetic neural network approach for unusual behavior prediction in smart home. In: Madureira AM, Abraham A, Gamboa D, Novais P (eds) Advances in intelligent systems and computing, vol 2016. Springer International Publishing AG, Porto, pp 738–748, 2017). Smartness of systems have an indispensable role to play in enabling the overall resilience of the combined cyber-physical system.
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The main goal of the NA62 experiment at CERN is to measure the branching ratio of the ultra-rare K+→π+νbar nu decay, collecting about 100 events to test the Standard Model of Particle Physics. Readout uniformity of sub-detectors, scalability, efficient online selection and lossless high rate readout are key issues. The TDCB and TEL62 boards are the common blocks of the NA62 TDAQ system. TDCBs measure hit times from sub-detectors, TEL62s process and store them in a buffer, extracting only those requested by the trigger system following the matching of trigger primitives produced inside TEL62s themselves. During the NA62 Technical Run at the end of 2012 the TALK board has been used as prototype version of the L0 Trigger Processor.
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The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
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  • G Schuster
  • J Papp
Tokody, D.; Schuster, G. and Papp, J.: Smart City, Smart Infrastructure, Smart Railway. International Conference on Applied Internet and Information Technologies, October 23, 2015. Technical faculty "Mihajlo Pupin" Zrenjanin, Zrenjanin, 2015,
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  • N Ismail
Ismail, N.: What are the most advanced smart cities in the world? https://www.information-age.com/advanced-smart-cities-world-123470745, accessed 2 nd February 2019,
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  • R O'dwyer
O'Dwyer, R.: Algorithms are making the same mistakes assessing credit scores that humans did a century ago.
Understanding the Importance of Physical Security for Industrial Control Systems (ICS)
  • J Elder
Elder, J.: Understanding the Importance of Physical Security for Industrial Control Systems (ICS).