Sistem Klasifikasi Jenis dan Warna Kendaraan Secara Real-time Menggunakan Metode k-Nearest Neighbor dan Framework YOLACT

Peningkatan jumlah dan variasi jenis kendaraan terus berkembang seiring dengan meningkatnya permintaan pasar. Permasalahan baru timbul akibat meningkatnya jumlah dan variasi jenis kendaraan seperti meningkatnya pelanggaran lalu lintas dan kriminalitas. Dengan kondisi ini, pengawasan pelanggar lalu lintas dan kriminalitas secara manual oleh pihak berwajib akan lebih sulit dilakukan terutama di daerah perkotaan. Sistem pengenalan jenis dan warna kendaraan atau Vehicle Color, Make and Model Recognition (VCMMR) adalah komponen penting dalam pengembangan sistem pengawasan keamanan di era otomatisasi. Dengan memanfaatkan CCTV, sistem ini dapat diaplikasikan pada sistem gerbang otomatis, pengawasan kendaraan otomatis, pemantauan lalu lintas, dll. Sistem VCMMR yang dapat bekerja secara real-time dapat meningkatkan keamanan dengan menghasilkan data kendaraan lengkap berupa warna, merek dan model kendaraan selain menggunakan pengenalan plat nomor kendaraan. Penelitian ini menggunakan metode k-Nearest Neighbor untuk mengklasifikasikan warna kendaraan dan framework YOLACT dengan arsitektur ResNet-50 yang telah dilatih untuk mengenali merek dan model kendaraan. Dataset dalam penelitian ini terdiri dari 10 jenis kendaraan dengan 40 citra data latih tiap kelas dan 10 warna dengan 25 citra data latih tiap kelas. Pengujian dilakukan menggunakan enam model YOLACT dengan epoch berbeda dan tiga variasi frame sampling untuk mengurangi waktu komputasi. Hasil pengujian pada video data uji empat kendaraan menunjukkan bahwa frame sampling 250 milidetik menghasilkan performa terbaik dengan waktu komputasi rata-rata 16,08 detik. Model YOLACT dengan epoch yang lebih besar mampu mengenali kendaraan yang berada jauh dari kamera (objek kecil) dengan lebih baik, akurasi yang diperoleh yaitu 91,67% pada epoch 517

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Affective Speech Recognition

Speech, as a medium of interaction, carries two different streams of information. Whereas one stream carries explicit messages, the other one contains implicit information about speakers themselves. Affective speech recognition is a set of theories and tools that intend to automate unfolding the part of the implicit stream that has to do with humans emotion. Application of affective speech recognition is to human computer interaction; a machine that is able to recognize humans emotion could engage the user in a more effective interaction. This thesis proposes a set of analyses and methodologies that advance automatic recognition of affect from speech. The proposed solution spans two dimensions of the problem: speech signal processing, and statistical learning. At the speech signal processing dimension, extraction of speech low-level descriptors is dis- cussed, and a set of descriptors that exploit the spectrum of the signal are proposed, which have shown to be particularly practical for capturing affective qualities of speech. Moreover, consider- ing the non-stationary property of the speech signal, further proposed is a measure of dynamicity that captures that property of speech by quantifying changes of the signal over time. Furthermore, based on the proposed set of low-level descriptors, it is shown that individual human beings are different in conveying emotions, and that parts of the spectrum that hold the affective information are different from one person to another. Therefore, the concept of emotion profile is proposed that formalizes those differences by taking into account different factors such as cultural and gender-specific differences, as well as those distinctions that have to do with individual human beings. At the statistical learning dimension, variable selection is performed to identify speech features that are most imperative to extracting affective information. In doing so, low-level descriptors are distinguished from statistical functionals, therefore, effectiveness of each of the two are studied dependently and independently. The major importance of variable selection as a standalone component of a solution is to real-time application of affective speech recognition. Although thousands of speech features are commonly used to tackle this problem in theory, extracting that many features in a real-time manner is unrealistic, especially for mobile applications. Results of the conducted investigations show that the required number of speech features is far less than the number that is commonly used in the literature of the problem. At the core of an affective speech recognition solution is a statistical model that uses speech features to recognize emotions. Such a model comes with a set of parameters that are estimated through a learning process. Proposed in this thesis is a learning algorithm, developed based on the notion of Hilbert-Schmidt independence criterion and named max-dependence regression, that maximizes the dependence between predicted and actual values of affective qualities. Pearson’s correlation coefficient is commonly used as the measure of goodness of a fit in the literature of affective computing, therefore max-dependence regression is proposed to make the learning and hypothesis testing criteria consistent with one another. Results of this research show that doing so results in higher prediction accuracy. Lastly, sparse representation for affective speech datasets is considered in this thesis. For this purpose, the application of a dictionary learning algorithm based on Hilbert-Schmidt independence criterion is proposed. Dictionary learning is used to identify the most important bases of the data in order to improve the generalization capability of the proposed solution to affective speech recognition. Based on the dictionary learning approach of choice, fusion of feature vectors is proposed. It is shown that sparse representation leads to higher generalization capability for affective speech recognition

A Novel User Oriented Network Forensic Analysis Tool

In the event of a cybercrime, it is necessary to examine the suspect’s digital device(s) in a forensic fashion so that the culprit can be presented in court along with the extracted evidence(s). But, factors such as existence and availability of anti-forensic tools/techniques and increasing replacement of hard disk drives with solid state disks have the ability to eradicate critical evidences and/or ruin their integrity. Therefore, having an alternative source of evidence with a lesser chance of being tampered with can be beneficial for the investigation. The organisational network traffic can fit into this role as it is an independent source of evidence and will contain a copy of all online user activities. Limitations of prevailing network traffic analysis techniques – packet based and flow based – are reflected as certain challenges in the investigation. The enormous volume and increasing encrypted nature of traffic, the dynamic nature of IP addresses of users’ devices, and the difficulty in extracting meaningful information from raw traffic are among those challenges. Furthermore, current network forensic tools, unlike the sophisticated computer forensic tools, are limited in their capability to exhibit functionalities such as collaborative working, visualisation, reporting and extracting meaningful user-level information. These factors increase the complexity of the analysis, and the time and effort required from the investigator. The research goal was set to design a system that can assist in the investigation by minimising the effects of the aforementioned challenges, thereby reducing the cognitive load on the investigator, which, the researcher thinks, can take the investigator one step closer to the culprit. The novelty of this system comes from a newly proposed interaction based analysis approach, which will extract online user activities from raw network metadata. Practicality of the novel interaction-based approach was tested by designing an experimental methodology, which involved an initial phase of the researcher looking to identify unique signatures for activities performed on popular Internet applications (BBC, Dropbox, Facebook, Hotmail, Google Docs, Google Search, Skype, Twitter, Wikipedia, and YouTube) from the researcher’s own network metadata. With signatures obtained, the project moved towards the second phase of the experiment in which a much larger dataset (network traffic collected from 27 users for over 2 months) was analysed. Results showed that it is possible to extract unique signature of online user activities from raw network metadata. However, due to the complexities of the applications, signatures were not found for some activities. The interaction-based approach was able to reduce the data volume by eliminating the noise (machine to machine communication packets) and to find a way around the encryption issue by using only the network metadata. A set of system requirements were generated, based on which a web based, client-server architecture for the proposed system (i.e. the User-Oriented Network Forensic Analysis Tool) was designed. The system functions in a case management premise while minimising the challenges that were identified earlier. The system architecture led to the development of a functional prototype. An evaluation of the system by academic experts from the field acted as a feedback mechanism. While the evaluators were satisfied with the system’s capability to assist in the investigation and meet the requirements, drawbacks such as inability to analyse real-time traffic and meeting the HCI standards were pointed out. The future work of the project will involve automated signature extraction, real-time processing and facilitation of integrated visualisation

IST Austria Thesis

Autism spectrum disorders (ASD) are a group of genetic disorders often overlapping with other neurological conditions. Despite the remarkable number of scientific breakthroughs of the last 100 years, the treatment of neurodevelopmental disorders (e.g. autism spectrum disorder, intellectual disability, epilepsy) remains a great challenge. Recent advancements in geno mics, like whole-exome or whole-genome sequencing, have enabled scientists to identify numerous mutations underlying neurodevelopmental disorders. Given the few hundred risk genes that were discovered, the etiological variability and the heterogeneous phenotypic outcomes, the need for genotype -along with phenotype- based diagnosis of individual patients becomes a requisite. Driven by this rationale, in a previous study our group described mutations, identified via whole - exome sequencing, in the gene BCKDK – encoding for a key regulator of branched chain amin o acid (BCAA) catabolism - as a cause of ASD. Following up on the role of BCAAs, in the study described here we show that the solute carrier transporter 7a5 (SLC7A5), a large neutral amino acid transporter localized mainly at the blood brain barrier (BBB), has an essential role in maintaining normal levels of brain BCAAs. In mice, deletion of Slc7a5 from the endothelial cells of the BBB leads to atypical brain amino acid profile, abnormal mRNA translation and severe neurolo gical abnormalities. Additionally, deletion of Slc7a5 from the neural progenitor cell population leads to microcephaly. Interestingly, we demonstrate that BCAA intracerebroventricular administration ameliorates abnormal behaviors in adult mutant mice. Furthermore, whole - exome sequencing of patients diagnosed with neurological dis o r ders helped us identify several patients with autistic traits, microcephaly and motor delay carrying deleterious homozygous mutations in the SLC7A5 gene. In conclusion, our data elucidate a neurological syndrome defined by SLC7A5 mutations and support an essential role for t he BCAA s in human bra in function. Together with r ecent studies (described in chapter two) that have successfully made the transition into clinical practice, our findings on the role of B CAAs might have a crucial impact on the development of novel individualized therapeutic strategies for ASD

Efficient Support for Application-Specific Video Adaptation

As video applications become more diverse, video must be adapted in different ways to meet the requirements of different applications when there are insufficient resources. In this dissertation, we address two sorts of requirements that cannot be addressed by existing video adaptation technologies: (i) accommodating large variations in resolution and (ii) collecting video effectively in a multi-hop sensor network. In addition, we also address requirements for implementing video adaptation in a sensor network. Accommodating large variation in resolution is required by the existence of display devices with widely disparate screen sizes. Existing resolution adaptation technologies usually aim at adapting video between two resolutions. We examine the limitations of these technologies that prevent them from supporting a large number of resolutions efficiently. We propose several hybrid schemes and study their performance. Among these hybrid schemes, Bonneville, a framework that combines multiple encodings with limited scalability, can make good trade-offs when organizing compressed video to support a wide range of resolutions. Video collection in a sensor network requires adapting video in a multi-hop storeand- forward network and with multiple video sources. This task cannot be supported effectively by existing adaptation technologies, which are designed for real-time streaming applications from a single source over IP-style end-to-end connections. We propose to adapt video in the network instead of at the network edge. We also propose a framework, Steens, to compose adaptation mechanisms on multiple nodes. We design two signaling protocols in Steens to coordinate multiple nodes. Our simulations show that in-network adaptation can use buffer space on intermediate nodes for adaptation and achieve better video quality than conventional network-edge adaptation. Our simulations also show that explicit collaboration among multiple nodes through signaling can improve video quality, waste less bandwidth, and maintain bandwidth-sharing fairness. The implementation of video adaptation in a sensor network requires system support for programmability, retaskability, and high performance. We propose Cascades, a component-based framework, to provide the required support. A prototype implementation of Steens in this framework shows that the performance overhead is less than 5% compared to a hard-coded C implementation