Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.

Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition

This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

Nowadays, Internet of Things (IoT) devices can collect a large amount of data and infer the context they are operating in. One of the most pervasive IoT device is the smartphone. Among the plethora of sensors that such device has, microphone is probably the most versatile. It can be used to infer information about the context by acquiring the environmental sound. In this paper, we propose an active ultrasonic-based method able to sense context information. The approach is based on the emission of periodic ultrasonic impulses, called pings, whose echoes are continuously acquired to extract a proper set of features. Using a classifier the context information is then retrieved. Two applications are presented: 1) an indoor/outdoor detector and 2) an earphone wearing state detector. In both cases a smartphone is the employed device. The former senses if the smartphone is in an indoor or outdoor environment while the latter detects if a user is wearing or not his earphones. The obtained results are encouraging for both the solutions that have been stressed in different working conditions and employed within proper application frameworks.

Ultrasounds-Based Context Sensing Method and Applications Over the Internet of Things

Recently, people have been interested in sharing what they are watching on TV, allowing the development of Social TV Applications often based on mobile devices. In this context, this paper proposes IRTR (Improved Real-Time TV-channel Recognition): a new method aimed at recognizing in real time (live) what people are watching on TV without any active user interaction. IRTR uses the audio signal of the TV program recorded by smartphones and is performed through two steps: i) fingerprint extraction and ii) TV channel real-time identification. Step i) is based on the computation of the Audio Fingerprint (AF). The AF computation has been taken from the literature and has been improved in terms of power consumption and computation speed to make the smartphone implementation feasible by using an ad hoc cost function aimed at selecting the best set of AF parameters. Step ii) is aimed at deciding the TV channel the user is watching. This step is performed using a likelihood estimation algorithm proposed in this paper. The consumed power, computation and response time, and correct decision rate of IRTR, evaluated through experimental measures, show very satisfying results such as a correct decision rate of about 95%, about 2s of computation time, and above 90% power saving with respect to the literature.

A Television Channel Real-Time Detector using Smartphones

Television audience estimation is an important task for advertisement placement. In this paper we present a system based on a client-server architecture able to recognize a live television show. The clients are implemented throughout smartphones, thus a well known audio fingerprint algorithm has been modified to fit smartphone needs. To reach this aim, the optimization of an ad-hoc cost function has been introduced. Server and client are generally far from being synchronized, due to the variety of broadcasting media (aerial, satellite or streaming over the Internet) leading to different and unpredictable delays. For this reason, we present a new likelihood estimate designed to overcome the lack of synchronization issue.

Alessandro Delfino

Papers

Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition

Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

Ultrasounds-Based Context Sensing Method and Applications Over the Internet of Things

A Television Channel Real-Time Detector using Smartphones

Opportunistic estimation of television audience through smartphones