333 research outputs found
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Software doping – Theory and detection
Software is doped if it contains a hidden functionality that is intentionally included by the manufacturer and is not in the interest of the user or society. This thesis complements this informal definition by a set of formal cleanness definitions that characterise the absence of software doping. These definitions reflect common expectations on clean software behaviour and are applicable to many types of software, from printers to cars to discriminatory AI systems. We use these definitions to propose white-box and black-box analysis techniques to detect software doping. In particular, we present a provably correct, model-based testing algorithm that is intertwined with a probabilistic-falsification-based test input selection technique. We identify and explain how to overcome the challenges that are specific to real-world software doping tests and analyses. The most prominent example of software doping in recent years is the Diesel Emissions Scandal. We demonstrate the strength of our cleanness definitions and analysis techniques by applying them to emission cleaning systems of diesel cars. All our car related research is unified in a Car Data Platform. The mobile app LolaDrives is one building block of this platform; it supports conducting real-driving emissions tests and provides feedback to the user in how far a trip satisfies driving conditions that are defined by official regulations.Software ist gedopt wenn sie eine versteckte Funktionalität enthält, die vom Hersteller beabsichtigt ist und deren Existenz nicht im Interesse des Benutzers oder der Gesellschaft ist. Die vorliegende Arbeit ergänzt diese nicht formale Definition um eine Menge von Cleanness-Definitionen, die die Abwesenheit von Software Doping charakterisieren. Diese Definitionen spiegeln allgemeine Erwartungen an "sauberes" Softwareverhalten wider und sie sind auf viele Arten von Software anwendbar, vom Drucker über Autos bis hin zu diskriminierenden KI-Systemen. Wir verwenden diese Definitionen um sowohl white-box, als auch black-box Analyseverfahren zur Verfügung zu stellen, die in der Lage sind Software Doping zu erkennen. Insbesondere stellen wir einen korrekt bewiesenen Algorithmus für modellbasierte Tests vor, der eng verflochten ist mit einer Test-Input-Generierung basierend auf einer Probabilistic-Falsification-Technik. Wir identifizieren Hürden hinsichtlich Software-Doping-Tests in der echten Welt und erklären, wie diese bewältigt werden können. Das bekannteste Beispiel für Software Doping in den letzten Jahren ist der Diesel-Abgasskandal. Wir demonstrieren die Fähigkeiten unserer Cleanness-Definitionen und Analyseverfahren, indem wir diese auf Abgasreinigungssystem von Dieselfahrzeugen anwenden. Unsere gesamte auto-basierte Forschung kommt in der Car Data Platform zusammen. Die mobile App LolaDrives ist eine Kernkomponente dieser Plattform; sie unterstützt bei der Durchführung von Abgasmessungen auf der Straße und gibt dem Fahrer Feedback inwiefern eine Fahrt den offiziellen Anforderungen der EU-Norm der Real-Driving Emissions entspricht
Cyber-Human Systems, Space Technologies, and Threats
CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp
ON EXPRESSIVENESS, INFERENCE, AND PARAMETER ESTIMATION OF DISCRETE SEQUENCE MODELS
Huge neural autoregressive sequence models have achieved impressive performance across different applications, such as NLP, reinforcement learning, and bioinformatics. However, some lingering problems (e.g., consistency and coherency of generated texts) continue to exist, regardless of the parameter count. In the first part of this thesis, we chart a taxonomy of the expressiveness of various sequence model families (Ch 3). In particular, we put forth complexity-theoretic proofs that string latent-variable sequence models are strictly more expressive than energy-based sequence models, which in turn are more expressive than autoregressive sequence models. Based on these findings, we introduce residual energy-based sequence models, a family of energy-based sequence models (Ch 4) whose sequence weights can be evaluated efficiently, and also perform competitively against autoregressive models. However, we show how unrestricted energy-based sequence models can suffer from uncomputability; and how such a problem is generally unfixable without knowledge of the true sequence distribution (Ch 5).
In the second part of the thesis, we study practical sequence model families and algorithms based on theoretical findings in the first part of the thesis. We introduce neural particle smoothing (Ch 6), a family of approximate sampling methods that work with conditional latent variable models. We also introduce neural finite-state transducers (Ch 7), which extend weighted finite state transducers with the introduction of mark strings, allowing scoring transduction paths in a finite state transducer with a neural network. Finally, we propose neural regular expressions (Ch 8), a family of neural sequence models that are easy to engineer, allowing a user to design flexible weighted relations using Marked FSTs, and combine these weighted relations together with various operations
Analysis, Design and Fabrication of Micromixers
This book includes an editorial and 12 research papers on micromixers collected from the Special Issue published in Micromachines. The topics of the papers are focused on the design of micromixers, their fabrication, and their analysis. Some of them proposed novel micromixer designs. Most of them deal with passive micromixers, but two papers report studies on electrokinetic micromixers. Fully three-dimensional (3D) micromixers were investigated in some cases. One of the papers applied optimization techniques to the design of a 3D micromixer. A review paper is also included and reports a review of recently developed passive micromixers and a comparative analysis of 10 typical micromixers
Automatic Speech Recognition without Transcribed Speech or Pronunciation Lexicons
Rapid deployment of automatic speech recognition (ASR) in new languages, with very limited data, is of great interest and importance for intelligence gathering, as well as for humanitarian assistance and disaster relief (HADR). Deploying ASR systems in these languages often relies on cross-lingual acoustic modeling followed by supervised adaptation and almost always assumes that either a pronunciation lexicon using the International Phonetic Alphabet (IPA), and/or some amount of transcribed speech exist in the new language of interest. For many languages, neither requirement is generally true -- only a limited amount of text and untranscribed audio is available. This work focuses specifically on scalable techniques for building ASR systems in most languages without any existing transcribed speech or pronunciation lexicons.
We first demonstrate how cross-lingual acoustic model transfer, when phonemic pronunciation lexicons do exist in a new language, can significantly reduce the need for target-language transcribed speech. We then explore three methods for handling languages without a pronunciation lexicon. First we examine the effectiveness of graphemic acoustic model transfer, which allows for pronunciation lexicons to be trivially constructed. We then present two methods for rapid construction of phonemic pronunciation lexicons based on submodular selection of a small set of words for manual annotation, or words from other languages for which we have IPA pronunciations. We also explore techniques for training sequence-to-sequence models with very small amounts of data by transferring models trained on other languages, and leveraging large unpaired text corpora in training. Finally, as an alternative to acoustic model transfer, we present a novel hybrid generative/discriminative semi-supervised training framework that merges recent progress in Energy Based Models (EBMs) as well as lattice-free maximum mutual information (LF-MMI) training, capable of making use of purely untranscribed audio.
Together, these techniques enabled ASR capabilities that supported triage of spoken communications in real-world HADR work-flows in many languages using fewer than 30 minutes of transcribed speech. These techniques were successfully applied in multiple NIST evaluations and were among the top-performing systems in each evaluation
Collected Papers (Neutrosophics and other topics), Volume XIV
This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar
Securing the Next Generation Web
With the ever-increasing digitalization of society, the need for secure systems is growing. While some security features, like HTTPS, are popular, securing web applications, and the clients we use to interact with them remains difficult.To secure web applications we focus on both the client-side and server-side. For the client-side, mainly web browsers, we analyze how new security features might solve a problem but introduce new ones. We show this by performing a systematic analysis of the new Content Security Policy (CSP)\ua0 directive navigate-to. In our research, we find that it does introduce new vulnerabilities, to which we recommend countermeasures. We also create AutoNav, a tool capable of automatically suggesting navigation policies for this directive. Finding server-side vulnerabilities in a black-box setting where\ua0 there is no access to the source code is challenging. To improve this, we develop novel black-box methods for automatically finding vulnerabilities. We\ua0 accomplish this by identifying key challenges in web scanning and combining the best of previous methods. Additionally, we leverage SMT solvers to\ua0 further improve the coverage and vulnerability detection rate of scanners.In addition to browsers, browser extensions also play an important role in the web ecosystem. These small programs, e.g. AdBlockers and password\ua0 managers, have powerful APIs and access to sensitive user data like browsing history. By systematically analyzing the extension ecosystem we find new\ua0 static and dynamic methods for detecting both malicious and vulnerable extensions. In addition, we develop a method for detecting malicious extensions\ua0 solely based on the meta-data of downloads over time. We analyze new attack vectors introduced by Google’s new vehicle OS, Android Automotive. This\ua0 is based on Android with the addition of vehicle APIs. Our analysis results in new attacks pertaining to safety, privacy, and availability. Furthermore, we\ua0 create AutoTame, which is designed to analyze third-party apps for vehicles for the vulnerabilities we found
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