Multimedia Context Awareness for Smart Mobile Environments

openNowadays the development of the IoT framework and the resulting huge number of smart connected devices opens the door to exploit the presence of multiple smart nodes to accomplish a variety of tasks. Multimedia context awareness, together with the concept of ambient intelligence, is tightly related to the IoT framework, and it can be applied to a large number of smart scenarios. In this thesis, the aim is to study and analyze the role of context awareness in different applications related to smart mobile environments, such as future smart spaces and connected cities. Indeed, this research work focuses on different aspects of ambient intelligence, such as audio-awareness and wireless-awareness. In particular, this thesis tackles two main research topics: the first one, related to the framework of audio-awareness, concerns a multiple observations approach for smart speaker recognition in mobile environments; the second one, tied to the concept of wireless-awareness, regards Unmanned Aerial Vehicle (UAV) detection based on WiFi statistical fingerprint analysis.openXXXI CICLO - SC. E TECN. ING. ELETTR. E DELLE TEL. - Ambienti cognitivi interattiviGaribotto, Chiar

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Measuring Information Security Awareness Efforts in Social Networking Sites – A Proactive Approach

For Social Network Sites to determine the effectiveness of their Information Security Awareness (ISA) techniques, many measurement and evaluation techniques are now in place to ensure controls are working as intended. While these techniques are inexpensive, they are all incident- driven as they are based on the occurrence of incident(s). Additionally, they do not present a true reflection of ISA since cyber-incidents are hardly reported. They are therefore adjudged to be post-mortem and risk permissive, the limitations that are inacceptable in industries where incident tolerance level is low. This paper aims at employing a non-incident statistic approach to measure ISA efforts. Using an object- oriented programming approach, PhP is employed as the coding language with MySQL database engine at the back-end to develop sOcialistOnline – a Social Network Sites (SNS) fully secured with multiple ISA techniques. Rather than evaluating the effectiveness of ISA efforts by success of attacks or occurrence of an event, password scanning is implemented to proactively measure the effects of ISA techniques in sOcialistOnline. Thus, measurement of ISA efforts is shifted from detective and corrective to preventive and anticipatory paradigms which are the best forms of information security approach

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Deep Spoken Keyword Spotting:An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS

Opportunistic and Context-aware Affect Sensing on Smartphones: The Concept, Challenges and Opportunities

Opportunistic affect sensing offers unprecedented potential for capturing spontaneous affect ubiquitously, obviating biases inherent in the laboratory setting. Facial expression and voice are two major affective displays, however most affect sensing systems on smartphone avoid them due to extensive power requirement. Encouragingly, due to the recent advent of low-power DSP (Digital Signal Processing) co-processor and GPU (Graphics Processing Unit) technology, audio and video sensing are becoming more feasible. To properly evaluate opportunistically captured facial expression and voice, contextual information about the dynamic audio-visual stimuli needs to be inferred. This paper discusses recent advances of affect sensing on the smartphone and identifies the key barriers and potential solutions of implementing opportunistic and context-aware affect sensing on smartphone platforms

DolphinAtack: Inaudible Voice Commands

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems(VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though hidden, are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.Comment: 15 pages, 17 figure