Face liveness detection by rPPG features and contextual patch-based CNN

Abstract. Face anti-spoofing plays a vital role in security systems including face payment systems and face recognition systems. Previous studies showed that live faces and presentation attacks have significant differences in both remote photoplethysmography (rPPG) and texture information. We propose a generalized method exploiting both rPPG and texture features for face anti-spoofing task. First, we design multi-scale long-term statistical spectral (MS-LTSS) features with variant granularities for the representation of rPPG information. Second, a contextual patch-based convolutional neural network (CP-CNN) is used for extracting global-local and multi-level deep texture features simultaneously. Finally, weight summation strategy is employed for decision level fusion of the two types of features, which allow the proposed system to be generalized for detecting not only print attack and replay attack, but also mask attack. Comprehensive experiments were conducted on five databases, namely 3DMAD, HKBU-Mars V1, MSU-MFSD, CASIA-FASD, and OULU-NPU, to show the superior results of the proposed method compared with state-of-the-art methods.Tiivistelmä. Kasvojen anti-spoofingilla on keskeinen rooli turvajärjestelmissä, mukaan lukien kasvojen maksujärjestelmät ja kasvojentunnistusjärjestelmät. Aiemmat tutkimukset osoittivat, että elävillä kasvoilla ja esityshyökkäyksillä on merkittäviä eroja sekä etävalopölymografiassa (rPPG) että tekstuuri-informaatiossa, ehdotamme yleistettyä menetelmää, jossa hyödynnetään sekä rPPG: tä että tekstuuriominaisuuksia kasvojen anti-spoofing -tehtävässä. Ensinnäkin rPPG-informaation esittämiseksi on suunniteltu monivaiheisia pitkän aikavälin tilastollisia spektrisiä (MS-LTSS) ominaisuuksia, joissa on muunneltavissa olevat granulariteetit. Toiseksi, kontekstuaalista patch-pohjaista konvoluutioverkkoa (CP-CNN) käytetään globaalin paikallisen ja monitasoisen syvään tekstuuriominaisuuksiin samanaikaisesti. Lopuksi, painoarvostusstrategiaa käytetään päätöksentekotason fuusioon, joka auttaa yleistämään menetelmää paitsi hyökkäys- ja toistoiskuille, mutta myös peittää hyökkäyksen. Kattavat kokeet suoritettiin viidellä tietokannalla, nimittäin 3DMAD, HKBU-Mars V1, MSU-MFSD, CASIA-FASD ja OULU-NPU, ehdotetun menetelmän parempien tulosten osoittamiseksi verrattuna uusimpiin menetelmiin

Identifying Mislabeling in Machine Learning Based Intrusion Detection System

Machine learning has shown strong potential in improving the performance of an Intrusion Detection Systems (IDS). In a machine learning based IDS, the problem is commonly formulated as a supervised classification, in which various training datasets are used to train a selected model to learn how various network features are related to different types (i.e., benign traffic or a type of network attack) of network traffic. Each training dataset usually includes a large amount of data samples, and each data sample contains many network features and their associated type of traffic called label. Most recent studies focus on developing a better machine learning model to achieve higher performance in an IDS. Very little research has been done in understanding the quality of training datasets, especially mislabeling affects the performance of a machine learning based IDS.In this thesis, we focus on the mislabeling issue in a machine learning based IDS. We first show the impact of mislabeling on the performance of such an IDS. Then, we propose a new algorithm called Heuristic Mislabel Identification (HMI) based on Data Shapley [6] to identify mislabels in training datasets. Based on different mislabeling scenarios, HMI heuristically and iteratively divides a training dataset into multiple groups to narrow down the location or range of mislabels. We have evaluated our method using a widely adopted IDS training dataset (i.e., CICIDS2017). The evaluation results show that HMI can identify 84% random mislabels and 78% mislabels from a single data source. The precision on both experiment above is 100% which means the suspect group must contain mislabeling samples

On dialogues between sound and performance physicality: Compositional Experimentation, Embodiment, and Placement of the Self

This body of work consists of eleven original musical compositions of a varied format that encompasses live solo or chamber instrumental concert music performance, performance art, site-specific/responsive performance installation, digital production of audio-visual content, alongside an accompanying critical and reflective commentary. Created as part of my practice-led research concerning an entangled relationship between sound and performance physicality, this work connects to and extrapolates from an array of existing, heterogeneous theoretical and practical discourses on instrumental theatre, the involvement of the human body in sound-making, a normalised composer-performer hierarchy, technology, and an elusive interstitial territory between sound’s multi-faceted articulations. This research, therefore, addresses issues surrounding compositional experimentation and embodiment, as well as sonic and human agency in music-making, drawing on features of autoethnography and a hybrid model of musical practice, in which the acts of composing, performing, devising, curating collectively afford an understanding of an emergent transnational creative identity. The eleven compositions chronicle the manifestation of an expanding and expansive compositional vocabulary of my own. Through interrogating the cultural and historical significances associated with the musical score, and through foregrounding and recontextualising a range of peripheral and understated actions and objects found within a conventional instrumental performance practice, these compositions eventually outline a new compositional and artistic paradigm that is intrinsically shaped by my lived and living experience of being Chinese inside a Western society. This research gives rise to a highly personal contribution to a growing area of scholarship that considers subjectivity, identity, and holistic ontological transformations as inherent facets of, and catalysts for an embodied practice of musical and compositional experimentation. It is an invitation for new ways of contextualising transnational encounters into the process of making music, thus normalising a multitude of resistances–especially those towards stereotyping and misrepresentation–as a mediating facilitator of compositional and artistic intentions

Advancing Electric Flight through Innovative Materials in Aerospace Propulsion Systems

The advent of electric aircraft heralds a transformative era in aviation, offering a sustainable alternative to conventional aircraft that significantly contribute to carbon emissions. This paper discusses the application of advanced materials in overcoming the technical hurdles associated with electric propulsion systems, focusing on their application in airframe construction, electrical conductors, thermal management, and battery technology to enhance the performance and sustainability of electric aircraft. Advanced composites like carbon fiber reinforced polymer (CFRP) are explored for their potential to reduce aircraft weight and improve mechanical properties. The paper also addresses the challenges of thermal management in electric propulsion systems, highlighting the use of phase change materials (PCMs) and advanced ceramics for efficient heat dissipation. Furthermore, the exploration of high-energy-density cathode materials, innovative anode materials, and solid-state electrolytes is discussed in the context of developing lightweight, high-capacity batteries for electric aircraft. Despite the promising advancements in material science and the potential benefits of electric aviation, the paper acknowledges the existing challenges, including the high cost of advanced materials, the need for improved energy storage solutions, and the environmental impact of material production

DataSpread: Unifying Databases and Spreadsheets.

Spreadsheet software is often the tool of choice for ad-hoc tabular data management, processing, and visualization, especially on tiny data sets. On the other hand, relational database systems offer significant power, expressivity, and efficiency over spreadsheet software for data management, while lacking in the ease of use and ad-hoc analysis capabilities. We demonstrate DataSpread, a data exploration tool that holistically unifies databases and spreadsheets. It continues to offer a Microsoft Excel-based spreadsheet front-end, while in parallel managing all the data in a back-end database, specifically, PostgreSQL. DataSpread retains all the advantages of spreadsheets, including ease of use, ad-hoc analysis and visualization capabilities, and a schema-free nature, while also adding the advantages of traditional relational databases, such as scalability and the ability to use arbitrary SQL to import, filter, or join external or internal tables and have the results appear in the spreadsheet. DataSpread needs to reason about and reconcile differences in the notions of schema, addressing of cells and tuples, and the current pane (which exists in spreadsheets but not in traditional databases), and support data modifications at both the front-end and the back-end. Our demonstration will center on our first and early prototype of the DataSpread, and will give the attendees a sense for the enormous data exploration capabilities offered by unifying spreadsheets and databases

高効率有機汚染物質酸化に向けた鉄系金属-有機骨格由来の不均一フェントン類似体の機構的起源の解明

東京都立大

Adaptivescreen: an adaptive news browser for laptops and mobile devices

Optimizing interactive information retrieval interfaces is a new trend in IR which focuses more on the interface design and formalizes interactive IR. However, none of existing work has proposed a system to support both adaptive web design and navigational interface, a primary goal of this thesis. We propose the very first browsing system that can adapt to screen size and inferred user need potentially during the process of information retrieval at every interaction. AdaptiveScreen not only presents a user-friendly interface with adaptive web design but also connects with a novel interface card model which formally models the interactive retrieval task. We show that AdaptiveScreen improves upon the prototype system from the perspective of system architecture and system implementations. AdaptiveScreen is redesigned in the manner of classic software architectural pattern Model-View-Controller. By comparing the screen shots and performance between AdaptiveScreen and the prototype system on both laptops and movable devices, we can conclude that AdaptiveScreen successfully leverages the effectiveness of Interface Card Model (ICM) proposed before and overcomes the weakness of prototype system. In the end, we hope the new system can demonstrate the potential applications of algorithms based on ICM and stimulate other researchers in the field of interactive IR

Learning to learn: a reflexive case study of PRiSM SampleRNN

The emergence of neural audio synthesis technology has opened up many new creative and collaborative avenues for musical practitioners in recent years. With a growing number of software tools becoming openly accessible, many composers and sound artists start to map their music-making processes into a nebulous, data-informed collaborative framework. This often puts the practice of data curation, generative machine-learning models, as well as the artistic usage of machine-generated outputs into a state of play, whereby much of the idiosyncrasy of the resultant work is shaped by fine-tuning deep-learning algorithms. However, issues surrounding agency, distributed creativity, and access to computational resources / specialists tend to surface. This paper looks at these issues within the existing infrastructure of a Music Conservatoire, where to engage creatively and strategically with data and artificial intelligence tools becomes an increasingly important skill for artists to adopt outside their conventional musical training. Through the lens of the work of PRiSM (The RNCM Centre for Practice & Research in Science & Music) and the rollout of PRiSM SampleRNN between 2020-2022, we identify an emergent model of musical training and research that institutionally facilitates knowledge exchange and collaborative dialogues between practitioners, pedagogues, as well as research software engineers who are often not considered part of the existing conservatoire establishment