The enforcement of sensitive policies in untrusted environments is still an open challenge for policy-based systems. On the one hand, taking any appropriate security decision requires access to these policies. On the other hand, if such access is allowed in an untrusted environment then confidential information might be leaked by the policies. The key challenge is how to enforce sensitive policies and protect content in untrusted environments. In the context of untrusted environments, we mainly distinguish between outsourced and distributed environments. The most attractive paradigms concerning outsourced and distributed environments are cloud computing and opportunistic networks, respectively. In this dissertation, we present the design, technical and implementation details of our proposed policy-based access control mechanisms for untrusted environments. First of all, we provide full confidentiality of access policies in outsourced environments, where service providers do not learn private information about policies during the policy deployment and evaluation phases. Our proposed architecture is such that we are able to support expressive policies and take into account contextual information before making any access decision. The system entities do not share any encryption keys and even if a user is deleted, the system is still able to perform its operations without requiring any action. For complex user management, we have implemented a policy-based Role-Based Access Control (RBAC) mechanism, where users are assigned roles, roles are assigned permissions and users execute permissions if their roles are active in the session maintained by service providers. Finally, we offer the full-fledged RBAC policies by incorporating role hierarchies and dynamic security constraints. In opportunistic networks, we protect content by specifying expressive access control policies. In our proposed approach, brokers match subscriptions against policies associated with content without compromising privacy of subscribers. As a result, an unauthorised broker neither gains access to content nor learns policies and authorised nodes gain access only if they satisfy fine-grained policies specified by publishers. Our proposed system provides scalable key management in which loosely-coupled publishers and subscribers communicate without any prior contact. Finally, we have developed a prototype of the system that runs on real smartphones and analysed its performance.

Multilingual semantic linguistic resource is critical for many applications in Natural Language Processing (NLP). While, building large-scale lexico-semantic resources manually from scratch is extremely expensive, which promoted the applications of automatic extraction or merger algorithms. These algorithms did benefit us in creation of large-scale resources, but introduced many kinds of errors as the side effect. For example, Chinese WordNet follows the WordNet structure and is generated via several algorithms. This automatic generation of resources introduces many kinds of errors such as wrong translation, typos and false mapping between multilingual terms. The quality of a linguistic resource influences the performance of the further applications direct- ly, which means the quality of a linguistic resource should be the higher the better. Thus, finding errors is inevitable. However, till now, there is not any efficient method to find errors from a large-scale and multi- lingual resource. Validating manually by experts could be a solution, but it is very expensive, where the obstacles come from not only the large-scale dataset, but also multilingual. Even though crowdsourcing is a method for solving large-scale and tedious task, it is still costly. By thinking in this scenario, we plan to find an effective method that can help us finding errors in low cost. We use games as our solution and adopt Universal Knowledge Core (UKC) with respect to Chinese language as our case study. UKC is a multi-layered multilingual lexico-semantic resource where a common lexical element from a different language is mapped to a formal concept. In this dissertation, we present a non-immersive game named Concept Challenge Game to find the errors that exist in English-Chinese lexico-semantic resource. In this game, people will face challenges in English synsets and have to choose the most appropriate option from the listed Chinese synsets. The players are unaware when finding errors in the lexico-semantic resource. Our evaluation shows that people are spending a significant amount of time playing and able to find differ- ent erroneous mappings. Moreover, we further extended our game to Italian version, the result is promising as well, indicating that our game has the ability to figure out errors in multilingual linguistic resources.

Quantum computing theory posits that a computer exploiting quantum mechanics can be strictly more powerful than classical models. Several quantum computing devices are under development, but current technology is limited by noise sensitivity. Quantum Annealing is an alternative approach that uses a noisy quantum system to solve a particular optimization problem. Problems such as SAT and MaxSAT need to be encoded to make use of quantum annealers. Encoding SAT and MaxSAT problems while respecting the constraints and limitations of current hardware is a difficult task. This thesis presents an approach to encoding SAT and MaxSAT problems that is able to encode bigger and more interesting problems for quantum annealing. A software implementation and preliminary evaluation of the method are described.

This dissertation demonstrates the feasibility of three novel low-power and low-noise schemes for the readout interfaces of MEMS Capacitive Microphones (MCM) by presenting their detailed design descriptions and measurement results as application-specific ICs (ASIC) in CMOS technology developed to exploit their application scope in consumer electronics and hearing aids. MCMs are a new generation of acoustic sensors, which offer a significant scope to improve miniaturization, integration and cost of the acoustic systems by leveraging the MEMS technology. Electret-Condenser-Microphones (ECM) are the current market solution for acoustic applications; however, MCMs are being considered as the future microphone-of-choice for mobile phones in consumer electronics and for hearing aids in medical applications. The readout interface of MCM in an acoustic system converts the output of the MEMS sensor into an appropriate electrical representation (analog or digital). The output of a MCM is in the form of capacitive-variations in femto-Farad range, which necessitates a low-noise signal-translation employed by the readout interface together with a low-power profile for its portable applications. The main focus of this dissertation is to develop novel readout schemes that are low-noise, low-power, low-cost and batch-producible, targeting the domains of consumer electronics and hearing-aids. The presented readout interfaces in this dissertation consist of a front-end, which is a preamplifier, and a backend which converts the output of the preamplifier into a digital representation. The first interface presents a bootstrapped preamplifier and a third-order sigma-delta modulator (SDM) for analog-to-digital conversion. The preamplifier is bootstrapped to the MCM by tying its output to the sensorâ€TMs substrate. This bootstrapping technique boosts the MCM signal by ~17dB and also makes the readout insensitive to the parasitic capacitors in MCM electro-mechanical structure, achieving 55dBA/Pa of SNDR. The third-order low-power SDM converts output of the PAMP into an over-sampled digital bitstream demonstrating a dynamic-range (DR) of 80dBA. This ASIC operates at 1.8V single-supply and 460uA of total current consumption; thus, highlighting the feasibility of low-power integrated MCM readout interface. This ASIC is also acoustically characterized with a MCM, bonded together in a single package, demonstrating a reasonable agreement with the expected performance. The second interface presents a readout scheme with force-feedback (FFB) for the MCM. The force-feedback is used to enhance the linearity of the MCM and minimize the impact of drift in sensor mechanical parameters. Due to the unavailability of the sensor, the effect of FFB could not be measured with an MCM; however, the presented results point out a significant performance improvement through FFB. The preamplifier in this ASIC utilizes a high-gain OTA in a capacitive-feedback configuration to achieve parasitic insensitive readout in an area and power-efficient way, achieving 40dBA/Pa of SNDR. The digital output of the third-order SDM achieved 76dBA of DR and was also used to apply the electrostatic FFB by modulating the bias voltage of the MCM. A dummy-branch with dynamic matching converted the single-ended MCM into a pseudo-differential sensor to make it compatible with force-feedback. This interface operates at 3.3V supply and consumes total current of 300uA. The third interface presents a chopper-stabilized multi-function preamplifier for MCM. Unlike typical MCM preamplifiers, this preamplifier employs chopper-stabilization to mitigate low-frequency noise and offset and it also embeds extra functionalities in the preamplifier core such as controllable gain, controllable offset and controllable high-pass filtering. This preamplifier consists of two stages; the first stage is a source-follower buffering the MCM output into a voltage signal and the second-stage is a chopper-stabilized controllable capacitive gain-stage. This preamplifier employs MΩ bias resistors to achieve consistent readout sensitivity over the audio band by utilizing the miller effect, avoiding the conditionally-linear GΩ bias resistors. The offset control functionality of this preamplifier can be used to modulate idle tones in the subsequent sigma-delta modulator out of the audio-band. The high-pass filtering functionality can be used to filter-out low-frequency noises such as wind-hum. This preamplifier operates at 1.8V and consumes total current of 50u with SNDR of 44dB/PA, demonstrating the feasibility of a low-power low-noise multifunction preamplifier for the MCM sensor.

Deploying a system in a safe and secure manner requires ensuring the tech- nical and procedural levels of assurance also with respect to social and regu- latory frameworks. This is because threats and attacks may not only derive from pitfalls in complex security critical system, but also from ill-designed procedures. However, existing methodologies are not mature enough to em- brace procedural implications and the need for multidisciplinary approach on the safe and secure operation of system. This is particularly common in electronic voting (e-voting) systems. This dissertation focuses along two lines. First, we propose an approach to guarantee a reasonable security to the overall systems by performing for- mal procedural security analysis. We apply existing techniques and define novel methodologies and approaches for the analysis and verification of procedural rich systems. This includes not only the definition of adequate modeling convention, but also the definition of general techniques for the injection of attacks, and for the transformation of process models into rep- resentations that can be given as input to model checkers. With this it is possible to understand and highlight how the switch to the new tech- nological solution changes security, with the ultimate goal of defining the procedures regulating system and system processes that ensure a sufficient level of security for the system as well as for its procedures. We then investigate the usage of formal methods to study and analyze the strength and weaknesses of currently deployed (e-voting) system in order to build the next generation (e-voting) systems. More specifically, we show how formal verification techniques can be used to model and reason about the security of an existing e-voting system. To do that, we reuse the methodology propose for procedural security analysis. The practical applicability of the approaches is demonstrated in several case studies from the domain of public administrations in general and in e-voting system in particular. With this it can be possible to build more secure, reliable, and trustworthy e-voting system.

This thesis investigates the possibility to exploit human language resources and knowledge extraction techniques to build STaRS.sys, a software system designed to support therapists in the rehabilitation of Italian anomic patients. After an introductory section reviewing classification, assessment, and remediation methods for naming disorders, we analyze the current trends in the exploitation of computers for the rehabilitation of language disorders. Starting from an analysis of the needs of speech therapists in their daily work with aphasic patients, the requirements for the STaRS.sys application are defined, and a number of possible uses identified. To be able to implement these functionalities, STaRS.sys needs to be based on a lexical knowledge base encoding, in a explicit and computationally tractable way, at least the kind of semantic knowledge contained in the so called feature norms. As a backbone for the development of this semantic resource we chose to exploit the Italian MultiWordNet lexicon derived from the original Princeton WordNet. We show that the WordNet model is relatively well suited for our needs, but that an extension of its semantic model is nevertheless needed. Starting from the assumption that the kinds composing the feature types classifications exploited for encoding feature norms can be mapped onto semantic relations in a WordNet-like semantic network, we identified a set of 25 semantic relations that can cover all the information contained in these datasets. To demonstrate the feasibility of our proposal, we first asked to a group of therapists to use our feature types classification for classifying a set of 300 features. The analysis of the inter-coder agreement shows that the proposed classification can be used in a reliable way by speech therapists. Subsequently, we collected a new set of Italian feature norms for 50 concrete concepts and analyze the issues raised by the attempt to encode them into a version of MultiWordNet extended to include the new set of relations. This analysis shows that, in addition to extending the relation set, a number of further modifications are needed, for instance to be able to encode negation, quantifications or the strength of a relation. Information that, we will show, isn't well represented in the existing feature norms either. After defining an extended version of MultiWordNet (sMWN), suitable to encode the information contained in feature norms, we deal with the issue of automatic extraction of such semantic information from corpora. We applied to an Italian a corpus state of the art machine-learning-based method for the extraction of common-sense conceptual knowledge from corpora, previously applied to English. We tried a number of modifications and extensions of the original algorithm, with the aim of improving its accuracy. Results and limitations are presented and analyzed, and possible future improvement discussed.

In order to display human-like intelligence, advanced computational systems should have access to the vast network of generic facts about the world that humans possess and that is known as commonsense knowledge (books have pages, grocery has a price, ...). Developers of AI applications have long been aware of this, and, for decades, they have invested in the laborious and expensive manual creation of commonsense knowledge repositories. An automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI. Two relatively recent developments in computer science and computational linguistics that may provide an answer to the commonsense collection problem are text mining from large amounts of data, something that has become possible with the massive availability of text on the Web, and human computation, which is a workaround technique implemented by outsourcing the 'hard' sub-steps of a problem to people. Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends to be extremely noisy. Human computation is also a challenging problem because people can provide unreliable data and may lack motivation to solve problems on behalf of researchers and engineers. A clever, and recently popularized, technique to motivate people to contribute to such projects it to pose the problems as entertaining games and let people solve those problems while they play a game. This technique, commonly known as games-with-a-purpose approach, has proved a very powerful way of recruiting laypeople on the Web. The focus of this thesis is to study methods to collect common sense from people via human computation and from text via text mining, and explore the opportunities in bringing these two types of methods together. The first contribution of my study is the introduction of a novel text miner trained on a set of known commonsense facts. The text miner is called BagPack and it is based on a vector-space representation of concept pairs, that also captures the relation between the pairs. BagPack harvests a large number of facts from Web-based corpora and these facts constitute a -- possibly noisy -- set of candidate facts. The second contribution of the thesis is Concept Game, a game with a purpose which is a simple slot-machine game that presents the candidate facts -- that are mined by BagPack -- to the players. Players are asked to recognize the meaningful facts and discard the meaningless facts in order to score points. Thus, as a result, laypeople verify the candidate set and we obtain a refined, high-quality dataset of commonsense facts. The evaluation of both systems suggests that text mining and human computation can work very efficiently in tandem. BagPack acts as an almost-endless source of candidate facts which are likely to be true, and Concept Game taps laypeople to verify these candidates. Using Web-based text as a source of commonsense knowledge has several advantages with respect to a purely human-computation system which relies on people as the source of information. Most importantly, we can tap domains that people do not talk about when they are directly asked. Also, relying on people just as a source of verification makes it possible to design fast-paced games with a low cognitive burden. The third issue that I addressed in this thesis is the subjective and stereotypical knowledge which constitutes an important part of our commonsense repository. Regardless of whether one would like to keep such knowledge in an AI system, being able to identify the subjectivity and detect the stereotypical knowledge is an important problem. As a case study, I focused on stereotypical gender expectations about actions. For this purpose, I created a gold standard of actions (e.g., pay bill, become nurse) rated by human judges on whether they are masculine or feminine actions. After that, I extracted, combined, and evaluated two different types of data to predict the gold standard. The first type of data depends on the metadata provided by social media (in particular, the genders of users in a microblogging site like Twitter) and the second one depends on Web-corpus-based pronoun/name gender heuristics. The metadata about the Twitter users helps us to identify which actions are mentioned more frequently by which gender. The Web-corpus-based score helps us to identify which gender is more frequently reported to be carrying out a given action. The evaluation of both methods suggests that 1) it is possible to predict the human gold standard with considerable success, 2) the two methods capture different aspects of stereotypical knowledge, and 3) they work best when combined together.

The goal of information retrieval (IR) is to map a natural language query, which specifies the user information needs, to a set of objects in a given collection, which meet these needs. Historically, there have been two major approaches to IR that we call syntactic IR and semantic IR. In syntactic IR, search engines use words or multi-word phrases that occur in document and query representations. The search procedure, used by these search engines, is principally based on the syntactic matching of document and query representations. The precision and recall achieved by these search engines might be negatively affected by the problems of (i) polysemy, (ii) synonymy, (iii) complex concepts, and (iv) related concepts. Semantic IR is based on fetching document and query representations through a semantic analysis of their contents using natural language processing techniques and then retrieving documents by matching these semantic representations. Semantic IR approaches are developed to improve the quality of syntactic approaches but, in practice, results of semantic IR are often inferior to that of syntactic one. In this thesis, we propose a novel approach to IR which extends syntactic IR with semantics, thus addressing the problem of low precision and low recall of syntactic IR. The main idea is to keep the same machinery which has made syntactic IR so successful, but to modify it so that, whenever possible (and useful), syntactic IR is substituted by semantic IR, thus improving the system performance. As instances of the general approach, we describe the semantics enabled approaches to: (i) document retrieval, (ii) document classification, and (iii) peer-to-peer search.

Due to the increasing demand for 3D vision systems, many efforts have been recently concentrated to achieve complete 3D information analogous to human eyes. Scannerless optical range imaging systems are emerging as an interesting alternative to conventional intensity imaging in a variety of applications, including pedestrian security, biomedical appliances, robotics and industrial control etc. For this, several studies have reported to produce 3D images including stereovision, object distance from vision system and structured light source with high frame rate, accuracy, wide dynamic range, low power consumption and lower cost. Several types of optical techniques for 3D imaging range measurement are available in the literature, among them one of the most important is time-of-flight (TOF) principle that is intensively investigated. The third dimension, i.e. depth information, can be determined by correlating the reflected modulated light signal from the scene with a reference signal synchronous with the light source modulation signal. CMOS image sensors are capable of integrating the image processing circuitry on the same chip as the light sensitive elements. As compared to other imaging technologies, they have the advantages of lower power consumption and potentially lower price. The merits make this technology competent for the next-generation solid-state imaging applications. However, CMOS process technologies are developed for high-performance digital circuits. Different types of 3D photodetectors have been proposed for three-dimensional imaging. A major performance improvement has been found in the adoption of inherently mixing detectors that incorporate the role of detection and demodulation in a single device. Basically, these devices use a modulated electric field to guide the photo generated charge carriers to different collection sites in phase with a modulation signal. One very promising CMOS photonic demodulator based on substrate current modulation has recently been proposed. In this device the electric field penetrates deeper into the substrate, thus enhancing the charge separation and collection mechanism. A very good sensitivity and high demodulation efficiency can be achieved. The objective of this thesis has been the design and characterization of a Current Assisted Photo mixing Demodulator (CAPD) to be applied in a TOF based 3D CMOS sensing system. At first, the experimental investigation of the CAPD device is carried out. As a test vehicle, 10×10 pixel arrays have been fabricated in 0.18Âμm CMOS technology with 10×10 Âμm2 pixel size. The main properties of CAPD devices, such as the charge transfer characteristic, modulation contrast, noise performance and non-linearity problem, etc. have been simulated and experimentally evaluated. Experimental results demonstrate a good DC charge separation efficiency and good dynamic demodulation capabilities up to 45MHz. The influence of performance parameters such as wavelength, modulation frequency and voltage on this device is also discussed. This test device corresponds to the first step towards incorporating a high resolution TOF based 3D CMOS image sensor. The demodulator structure featuring a remarkably small pixel size 10 × 10 Âμm2 is used to realize a 120 × 160 pixel array of ranging sensor fabricated in standard 0.18Âμm CMOS technology. Initial results demonstrate that the demodulator structure is suitable for a real-time 3D image sensor. The prototype camera system is capable of providing real-time distance measurements of a scene through modulated-wave TOF measurements with a modulation frequency 20 MHz. In the distance measurement, the sensor array provides a linear distance range from 1.2m to 3.7m with maximum accuracy error 3.3% and maximum pixel noise 8.5% at 3.7m distance. Extensive testing of the device and prototype camera system has been carried out to gain insight into the characteristics of this device, which is a good candidate for integration in large arrays for time-of-flight based 3D CMOS image sensor in the near future.

(Self-)Adaptive software systems monitor the status of their requirements and adapt when some of these requirements are failing. The baseline for much of the research on adaptive software systems is the concept of a feedback loop mechanism that monitors the performance of a system relative to its requirements, determines root causes when there is failure, selects an adaptation, and carries it out. The degree of adaptivity of a software system critically depends on the space of possible adaptations supported (and implemented) by the system. The larger the space, the more adaptations a system is capable of. This thesis tackles the following questions: (a) How can we define multi-dimensional adaptation spaces that subsume proposals for requirements- and architecture-based adaptation spaces? (b) Given one of more failures, how can we select an optimal adaptation with respect to one or more objective functions? To answer the first question, we propose a design process for three-dimensional adaptation spaces, named the Three-Peaks Process, that iteratively elicits control and environmental parameters from requirements, architectures and behaviours for the system-to-be. For the second question, we propose three adaptation mechanisms. The first mechanism is founded on the assumption that only qualitative information is available about the impact of changes of the system's control parameters on its goals. The absence of quantitative information is mitigated by a new class of requirements, namely Adaptation Requirements, that impose constraints on the adaptation process itself and dictate policies about how conflicts among failing requirements must be handled. The second mechanism assumes that there is quantitative information about the impact of changes of control parameters on the system’s goals and that the problem of finding an adaptation is formulated as a constrained multi-objective optimization problem. The mechanism measures the degree of failure of each requirement and selects an adaptation that minimizes it along with other objective functions, such as cost. Optimal solutions are derived exploiting OMT/SMT (Optimization Modulo Theories/Satisfiability Modulo Theories) solvers. The third mechanism operates under the assumption that the environment changes dynamically over time and the chosen adaptation has to take into account such changes. Towards this direction, we apply Model Predictive Control, a well-developed theory with myriads of successful applications in Control Theory. In our work, we rely on state-of-the-art system identification techniques to derive the dynamic relationship between requirements and possible adaptations and then propose the use of a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost-function. This adaptation mechanism can guarantee a certain level of requirements satisfaction over time, by dynamically composing adaptation strategies when necessary. Finally, each piece of our work is evaluated through experimentation using variations of the Meeting-Scheduler exemplar.