It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

A review on transfer learning in EEG signal analysis

Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications – motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks – are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

This study presents a case application of a socio-technical framework to assess and predict the development of the Internet of Things (IoT) in Korea. Applying a socio-technical system approach to the IoT, this paper seeks a clear understanding of how the IoT will evolve and stabilize in a smart environment. It investigates the complex interaction between social and technical aspects of the IoT, by highlighting the co-evolution, interaction, and interface, which constitute the next generation network environment. It describes the challenges in designing, deploying, and sustaining the diverse components of the IoT, and provides a snapshot of Korea's current approach to meeting this challenge. Finally, the findings of this study provide insights into these challenges and opportunities, by offering a socio-technical analysis of IoT development. The insights help to conceptualize how the IoT can be designed and situated within human-centered contexts.

https://www.econstor.eu/bitstream/10419/106844/1/816915229.pdf

A socio-technical framework for Internet-of-Things design

Industrial Internet of Things and its Applications in Industry 4.0: State of The Art

In order to solve real-life problems, several metaheuristic optimization algorithms have been developed. The Moth-Flame Optimization (MFO) algorithm is a search algorithm based on a mechanism called transverse orientation. In this mechanism, the moths tend to maintain a fixed angle with respect to the moon. MFO suffers from the degeneration of the global search capability and convergence speed. To overcome these imperfections, an Improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main novelty of the proposed approach is the definition of a hybrid phase between exploration and exploitation. This phase is characterized by a fitness depended weight factor for updating the moths positions. IMFO is tested on selected benchmark functions, CEC2014 test functions and 6 design problems, and compared with recent well-known optimization algorithms. The results show that IMFO achieves the best results with respect to the comparison algorithms in terms of search capability and convergence performances.

An Improved Moth-Flame Optimization algorithm with hybrid search phase

Intelligent food processing: Journey from artificial neural network to deep learning

The present study focuses on the problem of automatic generation control (AGC) by employing the design of fractional-order proportional–integral–derivative controller (FOPID). A hydrothermal power system with governor dead band (GDB) and generation rate constraint (GRC) is considered for investigation. FOPID controller optimal values are obtained by using teacher learning-based optimization (TLBO) technique, and the employed objective function is integral time-multiplied absolute error (ITAE). The supremacy of the proposed controller is also shown by comparing with PID controller. Further, the performance of the system is analyzed by varying parameter values of the system. From the simulation results, it reveals that TLBO optimized FOPID controller minimizes the errors in frequency of the control areas and tie-line power effectively.

TLBO Algorithm Optimized Fractional-Order PID Controller for AGC of Interconnected Power System

Internet of Things (IoT) is a new age technology, developed with the vision to connect and interconnect all the objects everywhere. This technology enables an overwhelming smartness, which helps the humankind in many ways. Connecting the objects around us, make them communicate with each other towards a mission of intelligent healthcare, safety, Industrial processing applications. As the Internet of Things involved in many various entities and diverse applications, that the vulnerability to unauthorized access is much higher. Today, cyber-attacks faced by the communication networks are very strong and critically alarming. This research represents an intelligent technique or methodology to defend the security breach , developed with the enhancement of Deep Learning algorithms (Deep Belief Network), i.e., Deep Belief Network . This intelligent intrusion detection methodology scrutinizes the malicious activity that is active inside the network, and one tries to get its entry. In this paper, the investigation of embedding the Deep learning methodology is discussed. The DBN enhancement to the security network is compared with standard DGAs and IDS algorithms, and the results are analyzed.

Danilo Pelusi

Papers

Industrial Internet of Things and its Applications in Industry 4.0: State of The Art

An Improved Moth-Flame Optimization algorithm with hybrid search phase

Intelligent food processing: Journey from artificial neural network to deep learning

TLBO Algorithm Optimized Fractional-Order PID Controller for AGC of Interconnected Power System

Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things