Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch

This thesis is concerned with type-logical grammars and their practical applicability as tools of reasoning about sentence syntax and semantics. The focal point is narrowed to Dutch, a language exhibiting a large degree of word order variability. In order to overcome difficulties arising as a result of that variability, the thesis explores and expands upon a type grammar based on Multiplicative Intuitionistic Linear Logic, agnostic to word order but enriched with decorations that aim to reduce its proof-theoretic complexity. An algorithm for the conversion of dependency-annotated sentences into type sequences is then implemented, populating the type logic with concrete, data-driven lexical types. Two experiments are ran on the resulting grammar instantiation. The first pertains to the learnability of the type-assignment process by a neural architecture. A novel application of a self-attentive sequence transduction model is proposed; contrary to established practices, it constructs types inductively by internalizing the type-formation syntax, thus exhibiting generalizability beyond a pre-specified type vocabulary. The second revolves around a deductive parsing system that can resolve structural ambiguities by consulting both word and type information; preliminary results suggest both excellent computational efficiency and performance.
Adversarial Large-Scale Root Gap Inpainting

Root imaging of a growing plant in a non-invasive, affordable, and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-filled transparent container, imaging them with digital cameras, and segmenting root from soil background. However, due to soil occlusion and the fact that digital imaging is a 2D projection of a 3D object, gaps are present on the segmentation masks, which may hinder the extraction of finely grained root system architecture (RSA) traits. Herein, we develop an image inpainting technique to recover gaps from disconnected root segments. We train a patch-based deep fully convolutional network using a supervised loss but also use adversarial mechanisms at patch and whole root level. We use Policy Gradient method, to endow the model with large-scale whole root view during training. We train our model using synthetic root data. In our experiments, we show that using adversarial mechanisms at local and whole-root level we obtain a 72% improvement in performance on recovering gaps of real chickpea data when using only patch-level supervision.
A System Uncertainty Propagation Approach With Model Uncertainty Quantification in Multidisciplinary Design

The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g. input variability) or epistemic (e.g. model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplinary design system, uncertainty propagation refers to the analysis that quantifies the overall uncertainty of system QOIs resulting from all sources of aleatory and epistemic uncertainty originating in the individual disciplines. However, due to the complexity of multidisciplinary simulation, especially the coupling relationships between individual disciplines, many uncertainty propagation approaches in the existing literature only consider aleatory uncertainty and ignore the impact of epistemic uncertainty. In this paper, we address the issue of efficient uncertainty quantification of system QOIs considering both aleatory and epistemic uncertainties. We propose a spatial-random-process (SRP) based multidisciplinary uncertainty analysis (MUA) method that, subsequent to SRP-based disciplinary model uncertainty quantification, fully utilizes the structure of SRP emulators and leads to compact analytical formulas for assessing statistical moments of uncertain QOIs. The proposed method is applied to a benchmark electronics packaging problem. To demonstrate the effectiveness of the method, the estimated low-order statistical moments of the QOIs are compared to the results from Monte Carlo simulations.Copyright © 2014 by ASME
Robust online adaptive sensor-driven survey planning for single and multiple autonomous underwater vehicles

The objective of the thesis is to propose new survey planning methods for autonomous underwater vehicles (AUVs). Historical approaches to the problem usually involve preplanning paths before the start of the survey in a structured, either zig-zag or lawn mower, pattern. This has three notable shortcomings: 1) The AUV may not be able to follow the prescribed path exactly, for example if there are unexpected disturbances, 2) Object detection in imagery obtained from the survey is dependent on many factors, some of which may not be known a priori, resulting in overly conservative plans, and 3) In the absence of pre-placed beacons, once the vehicle submerges, its location estimate will drift, again resulting in deviation from the pre-specified path. These issues are overcome here using new online and adaptive approaches to survey planning. First, an algorithm is developed that maintains a model of the area coverage over the workspace in real-time and plans paths within that workspace. This sensor-driven algorithm is also able to account for uncertainty in the factors that affect object detection while underway. Second, the algorithm is augmented to explicitly account for the uncertainty in the AUV location estimate. The link between area coverage and state estimation is made. It is motivated that for coverage, estimating the full trajectory (or smoothing) as opposed to just the present pose (or filtering) is beneficial. In addition, the planning strategies previously developed are augmented to operate within the new probabilistic framework. The main benefit of the proposed approach is that it is robust to localization sensor noise and can guarantee coverage in a real field coverage sense not just a planning sense. Finally, the algorithms are extended to coverage with multiple AUVs. If a team of AUVs possess precisely synchronized clocks, then when they communicate underwater they can also calculate their relative ranges from the time-of-flight of the acoustic signals. A cooperative trajectory estimation algorithm is developed that is particularly well-suited to operate within the challenging underwater communications channel. Data to be transmitted in each broadcast communication scales linearly with the number of vehicles in the AUV team and does not backlog in the case of communication failures, which are common underwater. The algorithms are tested in simulation and in the field on the Iver2 AUV. These tests show the effectiveness of the proposed methods.
Location Sensitive Recommendations in Ad-hoc Social Network Environments Generated using Learning-to-Rank Algorithm

: Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. The selection of the best service from the ones available is a conundrum to predict as different users will follow different selection techniques. Selection of the web service is directly related to the quality of service (QoS) provided. This paper proposes a learning-to-rank algorithm to comprehend the decision strategy of users in choosing the specific web service. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user‟s preference and location.
Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees.
An Augmented Reality Mirror Exergame using 2D Pose Estimation

Exergames have become very popular for fitness and rehabilitation purposes. They usually rely on RGB-D sensors to estimate human 3D body pose and, therefore, allow users to interact with virtual environments. Currently, a new generation of deep learning techniques enable the estimation of 2D body pose from video sequences. These video sequences could be augmented with the estimated pose and other virtual objects, resulting in augmented reality mirrors where players can see their reflection along with other visual cues that guide them through exercises. The main benefit of using this approach would be replacing RGB-D cameras with simpler and more widely available webcams. This approach is explored in this work with the development of the ExerCam exergame. This application relies on a single webcam and the OpenPose library to allow users to perform exercises where they have to reach virtual targets appearing on the screen. A preliminary study has been performed in order to explore the technical viability and usability of this application, with promising results.
PXIe-based multi-channel data acquisition system of soft X-ray camera

Soft X-ray cameras will be widely used in the ITER (Interna- tional Thermonuclear Experimental Reactor) diagnostic system. To acquire their output data to display the signal changes on a computer, a PXIe (PCI Express eXtensions for Instrumentation)-based multi-channel data acquisition system for a soft X-ray camera has been designed and is presented in this paper. The Xilinx FPGA (Field-Programmable Gate Array) chip Kintex7-325T-900-2 is adopted as a controller in this system. The hardware ’ s composition and the PXIe bus transmission of this system are presented and discussed. The acquisition and transmission of the soft X-ray camera ’ s multi-channel data has been accomplished with subse-quent processing. Currently, this system can collect signals within 1MHz, and the system ’ s signal resolution has achieved 0.122mV.
A fast building demand response method based on supply–demand coordination for urgent responses to smart grids

Many demand response (DR) measures are available for building air-conditioning systems, which can be categorized as demand-side-based controls and supply-side-based controls. However, due to the limitations of response speed and/or thermal comfort control, existing methods cannot economically and effectively meet the urgent DR requests from grids in emergency situations. This paper proposes a fast building demand response method based on supply-demand-side coordination for urgent responses to smart grids. It combines both the demand-side-based and supply-side-based control measures simultaneously. On the supply side, direct load control actions are employed to provide immediate power reductions. On the demand side, indoor air temperature set-points are adjusted stepwise according to an “incremental schedule” to achieve a uniform indoor temperature rise in different zones. In addition, two performance indexes are newly proposed to evaluate the sacrifice degree of thermal comfort. Measures for avoiding power rebound in post-DR periods are also considered. The proposed DR method is tested as a case study in a virtual building. The results show that through the coordinated control of the supply and demand side, a fast and effective DR (e.g., 18.3% of power reduction) is achieved with a uniform sacrifice of thermal comfort.
Algorithms for recursive delegation

Task delegation is essential to many applications, ranging from outsourcing of work to the design of routing protocols. Much research in computational trust has been devoted to the generation of policies to determine which agent should the task be delegated to, given the agent’s past behaviour. Such work, however, does not consider the possibility of the agent delegating the task onwards, inducing a chain of delegation events before the task is ﬁnally executed. In this paper, we consider the process of delegation chain formation, introducing a new algorithm based on quitting games to cater for recursive scenarios. We evaluate our proposal under various network topologies, consistently demonstrating its superiority with respect to recursive adaptations of existing multi-armed bandit algorithms.
Thermal-Aware Design and Simulation Approach for Optical NoCs

For chip multiprocessors, one major challenge is to bridge the increasing speed gap between processor and the global on-chip interconnect delay. By integrating optical interconnects in network-on-chip (NoC) architectures, optical NoCs can overcome the power and bandwidth bottleneck of traditional electrical on-chip networks. However, while considering the thermal sensitivity of silicon photonic devices used in optical NoCs, optical interconnects may not have advantages in power efficiency as compared with their electrical counterparts. To tackle this problem, in this article, we propose a thermal-aware design and simulation approach for optical NoCs. Key techniques include thermal-sensitive optical power loss models from device level to network level, a thermal-aware adaptive routing mechanism, and a thermal-aware simulation platform. The thermal-aware simulation platform enables optical NoC simulation together with on-chip temperature simulation as well as optical thermal effect modeling. With the proposed thermal-aware simulation platform, we conducted a case study of an $8\times 8$ mesh-based optical NoC under a set of synthetic traffic patterns as well as real applications at typical temperature scenarios. By comparing and analyzing different temperature distributions, we can conclude that it can achieves a better optimization effect for the temperature distributions where the hot spots are scattered across the chip.
Adaptive Flow-Level Resource Allocation for Wireless Mesh Networks

In this paper, we study how to achieve network utility maximization (NUM) in flow-level wireless networks, which can be characterized by the random arrivals and departures of flow-level traffic in such networks. Existing work in this area has mainly concentrated on finding the stability region in such networks while achieving the NUM objective. In this paper, we propose a fully distributed adaptive CSMA based flow-level cross-layer resource allocation algorithm (FLRA), which achieves the NUM goal while satisfying desired delay requirement via optimized link scheduling at the MAC layer and optimal flow-level admission control at the transport layer. Detailed algorithm design is presented. We analyze the convergence property of FLRA and its optimality in terms of network utility while meeting desired delay requirement. We further reveal the relationship among maximum network utility, desired delay, and number of active flows when a network reaches stability. We conduct extensive simulations and the results validate the effectiveness of our analytical results.
Deep Neural Networks for Sound Event Detection

The objective of this thesis is to develop novel classification and feature learning techniques for the task of sound event detection (SED) in real-world environments. Throughout their lives, humans experience a consistent learning process on how to assign meanings to sounds. Thanks to this, most of the humans can easily recognize the sound of a thunder, dog bark, door bell, bird singing etc. In this work, we aim to develop systems that can automatically detect the sound events commonly present in our daily lives. Such systems can be utilized in e.g. contextaware devices, acoustic surveillance, bio-acoustical and healthcare monitoring, and smart-home cities. In this thesis, we propose to apply the modern machine learning methods called deep learning for SED. The relationship between the commonly used timefrequency representations for SED (such as mel spectrogram and magnitude spectrogram) and the target sound event labels are highly complex. Deep learning methods such as deep neural networks (DNN) utilize a layered structure of units to extract features from the given sound representation input with increased abstraction at each layer. This increases the network’s capacity to efficiently learn the highly complex relationship between the sound representation and the target sound event labels. We found that the proposed DNN approach performs significantly better than the established classifier techniques for SED such as Gaussian mixture models. In a time-frequency representation of an audio recording, a sound event can often be recognized as a distinct pattern that may exhibit shifts in both dimensions. The intra-class variability of the sound events may cause to small shifts in the frequency domain content, and the time domain shift results from the fact that a sound event can occur at any time for a given audio recording. We found that convolutional neural networks (CNN) are useful to learn shift-invariant filters that are essential for robust modeling of sound events. In addition, we show that recurrent neural networks (RNN) are effective in modeling the long-term temporal characteristics of the sound events. Finally, we combine the convolutional and recurrent layers in a single classifier called convolutional recurrent neural networks (CRNN), which emphasizes the benefits of both and provides state-of-the-art results in multiple SED benchmark datasets.
• DESIGN AND DEVELOPMENT OF SANITIZATION ALGORITHM FOR MINING PRIVACY - PRESERVING FREQUENT ITEMSETS

ABSTRACT Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. In order to preserve the privacy of the client in data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper we concentrate on Data sanitization problem by providing a Non-uniform Randomized sanitization algorithm for sanitizing the original database to transform it into a sanitized database devoid of any sensitive patterns specified by the data owner. The Uniform randomized sanitization algorithm considers any item in a restricted itemset as a victim item to be removed from sensitive transactions with an equal probability. But the Non-uniform Randomized sanitization algorithm prefers items with high support as victim items, thereby minimizing the effect on non-sensitive patterns. As a result accuracy will be increased. Keywords-frequent patterns, sensitive patterns, non-sensitive patterns, legitimate patterns, sanitization, privacy preserving
Forwarding Strategies in NDN-Based Wireless Networks: A Survey

Named Data Networking (NDN) is a promising Internet architecture paradigm of the future that adopts the Content Centric Networking (CCN) approach. Applications and users only care about the contents, while the Internet protocol (i.e., TCP/IP) focuses on communication by specifying and discovering the device location, which makes it more difficult and limiting. NDN supports the efficient distribution of the content, and secures the content rather than securing data channels. The forwarding mechanism in NDN-based wireless networks including MANET, WSN, VANET, and WMN are very important for efficient communication. NDN provides us a robust and simple communication mechanism. Two types of packets are exchanged over the networks in NDN forwarding: INTEREST and DATA. In this paper, a detailed survey is provided regarding forwarding strategies and forwarding issues in NDN/CCN based MANETs, VANETs, WSNs, and WMNs. Future challenges regarding forwarding in wireless environment are also highlighted.
Spectrum Cost Optimization for Cognitive Radio Transmission over TV White Spaces using Artificial Neural Networks

In this paper, the use of TV White Spaces (TVWS) by small cognitive radio wireless network operators (SCWNOs) is considered in order to support the growing demands for IoT applications in smart grid and smart cities. In order to support the wide range of services and applications that are being offered by SCWNOS, spectrum leasing could be considered as an alternative solution to achieve improved Quality of Service (QoS). We consider a situation whereby in order to satisfy the QoS requirements, SCWNOs can decide to lease a certain part of the TVWS spectrum that is referred to as high priority TVWS channel (HPC) for a certain period and pay a fee depending on the duration of HPC spectrum usage. We develop an Artificial Neural Networks (ANN) based online algorithm to determine the optimal transmission decision per time slot that would minimise the overall HPC leasing cost of the SCWNOs while satisfying the QoS constraints. The simulations results shows that our proposed ANN based online algorithms outperforms the Lyapunov based online algorithm while its performance is very close to the optimal offline solution with 99% accuracy.