Miracle-dz/UniFAR-FTUs huggingface dataset:
Event Structure and Verbal Decomposition

This chapter explores the relationship between constrained semantic representations of events, and structured syntactic representations that express them. I show that these representations track each other systematically, and that argument structure generalizations emerge in lock-step with these structures. I therefore propose a system in which those generalizations follow from the following general principles of structural interpretation: (i) embedding corresponds to the cause/leads to relation; (ii) each subevental structure is related potentially to a participant NP; (iii) event-recursion is limited to structures with at most one dynamic predication per event phase. The maximal subevental structure consists of a stative predication embedding a dynamic one, and the dynamic one in turn embedding a stative one. This structure and its proper subsets exhaust the event types built by the grammar. These principles ensure the relative prominence of the different argument positions as well as specific entailments for the different positions.
Exploiting Cross-Lingual Representations For Natural Language Processing

Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages. In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Dan Roth
Sensor Selection for TDOA-Based Localization in Wireless Sensor Networks With Non-Line-of-Sight Condition

This paper investigates the selection of a subset of sensors for time difference of arrival (TDOA) localization under the non-line-of-sight (NLOS) condition in wireless sensor networks (WSN). Specifically, we aim to optimize the sensor activation for the sake of minimizing the localization error when considering NLOS condition subject to energy constraints. In contrast to existing sensor selection strategies, two independent Boolean selection vectors are utilized to determine the reference sensor and other sensors simultaneously in TDOA localization. Upon presenting expressions of the Cramer-Rao lower bound (CRLB) under three different scenarios, including: 1) line-of-sight (LOS), 2) Prior statistics unknown NLOS (PSU-NLOS), 3) Prior statistics known NLOS (PSK-NLOS), the optimization problems for sensor selection are formulated to minimize the CRLB based on two independent Boolean selection vectors. Analytical scheme is developed by solving the tractable semidefinite program (SDP) problems which are converted from the original nonconvex problem. Furthermore, two low-complexity heuristic algorithms, namely best option filling (BOF) algorithm and iterative swapping greedy (ISG) algorithm, are proposed for the sake of practical implementation. Simulation results validate that the localization accuracy for sensors selected by the SDP with randomization algorithm and the ISG algorithm achieves the exhaustive search method. Additionally, these two algorithms are stable under several random sensor network geometries.
FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING

Semantic image segmentation, the task of assigning a label to each pixel in an image, is a major challenge in the field of computer vision. Semantic image segmentation using fully convolutional neural networks (FCNNs) offers an online solution to the scene understanding while having a simple training procedure and fast inference speed if designed efficiently. The semantic information provided by the semantic segmentation is a detailed understanding of the current context and this scene understanding is vital for scene modification in augmented reality (AR), especially if one aims to perform destructive scene augmentation. Augmented reality systems, by nature, aim to have a real-time modification of the context through head-mounted see-through or video-see-through displays, thus require efficiency in each step. Although there are many solutions to the semantic image segmentation in the literature such as DeeplabV3+ [1], Deeplab DPC [2], they fail to offer a low latency inference due to their complex architectures in aim to acquire the best accuracy. As a part of this thesis work, we provide an efficient architecture for semantic image segmentation using an FCNN model and achieve real-time performance on smartphones at 19.65 frames per second (fps) while maintaining a high mean intersection over union (mIOU) of 67.7% on Cityscapes validation set with our “Basic” variant and 15.41 fps and 70.3% mIOU on Cityscapes test set using our “DPC” variant. The implementation is open-sourced and compatible with Tensorflow Lite, thus able to run on embedded and mobile devices. Furthermore, the thesis work demonstrates an augmented reality implementation where semantic segmentation masks are tracked online in a 3D environment using Google ARCore [3]. We show that the frequent calculation of semantic information is not necessary and by tracking the calculated semantic information in 3D space using inertial-visual odometry that is provided by the ARCore framework, we can achieve savings on battery and CPU usage while maintaining a high mIOU. We further demonstrate a possible use case of the system by inpainting the objects in 3D space that are found by the semantic image segmentation network. The implemented Android application performs real-time augmented reality at 30 fps while running the computationally efficient network that was proposed as a part of this thesis work in parallel.
Service discovery and composition : PreDiCtS approach

The proliferation of Web Services is fostering the need for service-discovery and composition tools to provide more personalisation during the service retrieval process. In this paper, we describe the motivating details behind PreDiCtS, a framework for personalised service-retrieval. In our approach we consider that similar service composition problems can be tackled in a similar manner by reusing and adapting past composition best practices or templates. The proposed retrieval process uses a mixedinitiative technique based on Conversational Case-Based Reasoning (CCBR), that provides i) for a clearer identification of the user’s service requirements and ii) based on these requirements, finds suitable service templates that satisfy the user’s goal. We discuss how retrieval can vary through the use of different CCBR algorithms and how adaptation can be performed over the retrieved templates thus providing the personalisation feature in PreDiCtS.
Analysis of complex predicates in Mandarin Chinese

This project examines complex predicates in Mandarin, which is a language abundant in complex predicates. The approach of syntactic and semantic analyses in this study is based on the Simpler Syntax model developed by Culicover and Jackendoff (2005), which incorporates the LCS (Lexical Conceptual Structure) approach of Jackendoff (1990), with some minor adjustments to account for my analysis of eventuality structure. This study is also supplemented by approaches taken from other previous studies as necessary. This study proposes a two-dimensional classification for Mandarin complex predicates. Complex predicates in this study are classified as monoclausal or polyclausal in the syntactic dimension and as resultatives, depictives, directionals, etc. in the semantic dimension. Monoclausal resultatives and polyclausal serial verb constructions with bleached verbs are given particular focus in the thesis and discussed in detail. Naturally occurring sentences show that some monoclausal resultatives can take imperfective aspect categories. I suggest two sources for the compatibility between these resultatives and the imperfective aspect categories: (a) iterative readings and (b) focus on the process portions of resultatives. A questionnaire undertaken with native speakers shows that compatibility between resultatives and the Progressive Aspect may also be influenced by polarity. The verb na, which has a full verb meaning of ‘take’ with the hand as the default body part, can be used as a bleached verb and employed as an instrument marker in polyclausal serial verb constructions. A corpus of mouth and foot painting artists-related articles are collected for the analysis of na, where the executive body part is not the hand. The analysis of sentences in the corpus shows that the distinction between full and bleached verbs is not categorical and that verb bleaching can be influenced by pragmatic factors such as context.
Qualitative Risk Assessment of Gas Pipelines by Using of Indexing System Method in GIS environment

Nowadays the urbanization is developing rapidly, and it leads to growing demand for gas; which resulted in denser pipeline network, by the following increase in the pipeline network congestion, accidents will become inevitable. So Pipelines are a remarkable source of hazard for their adjacent society. Usually Indexing system method is used for pipe line risk assessment. This method assesses risk by using an index system, which includes a sum index and a leak impact index. Each index is made up of several sub factors which represent the major cause of failure in the network of gas pipelines. Using spatial analysis, changes in position, status and location of every factor in the vicinity of the pipeline can be checked and it helps to improve pipeline risk assessment. To increase accuracy of result, in this research, an indexing system method, is integrated with abilities of GIS. GIS can also be used as a calculating engine for produce risk scores. The developed model is implemented on a part with 52 km length of greatest Iranian hydrogen sulfide pipeline. The results show that the highest risk rating is 3 and the lowest number is 42. 12 sections of pipeline have low or moderate relative risk due to low leak impact index and lower hazard after gas leakage in these sections. 42 sections of pipeline have lower scores than 10 that show potential failure. In this regard, it is necessary to take measures to reduce risk. The implementation of this research in GIS, increase the accuracy of the risk calculation.
ANALYSIS OF SYSTEM QUALITY, INFORMATION QUALITY AND PERCEIVED USEFULNESS TO USER SATISFACTION

This study aims to test and analyze the quality of the system against perceived usefulness, the quality of information against perceived usefulness, the quality of the system to user satisfaction, Information Quality to user satisfaction and perceived usefulness to user satisfaction. The population in this research is employees teller and customer service department of Bank BJB in subang city. Sampling technique is nonprobability sampling by using purposive sampling method. The type of data used is primary data with questionnaire data collection method. Hypothesis testing used is Structural Equation Modeling (SEM) approach with variance Structural equation test or better known as Partial Least Square (PLS). The result of the research shows that Quality System has a significant positive effect on Perceived Usefulness, Quality of Information has a significant positive effect on Perceived Usefulness, Quality of System has a significant positive effect on user satisfaction, Information Quality has positive significant effect on user satisfaction, and Perceived Usefulness has no effect to User Satisfaction
User Developed Applications and Information Systems Success: A Test of DeLone and McLean's Model

DeLone and McLean’s (1992) model of information systems success has received much attention amongst researchers. This study provides the first empirical test of an adaptation of DeLone and McLean’s model in the user-developed application domain. The model tested was only partially supported by the data. Of the nine hypothesized relationships tested, four were found to be significant and the remainder not significant. The model provided strong support for the relationships between perceived system quality and user satisfaction, perceived information quality and user satisfaction, user satisfaction and intended use, and user satisfaction and perceived individual impact. This study indicates that user perceptions of information systems success play a significant role in the user-developed application domain. There was, however, no relationship between user developers’ perceptions of system quality and independent experts’ evaluations, and user ratings of individual impact were not associated with organizational impact measured as company performance in a business simulation. Further research is required to understand the relationship between user perceptions of IS success and objective measures of success, and to provide a model of IS success appropriate to end user development.
Confirmation Bias Estimation from Electroencephalography with Machine Learning

Cognitive biases are known to affect human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, post-hoc behavioral analysis is used to measure the level of bias in a decision. However, these techniques can be hindered by subjective factors and cannot be collected in real-time. This pilot study collects behavior patterns and physiological signals present during biased and unbiased decision-making. Supervised machine learning models are trained to find the relationship between Electroencephalography (EEG) signals and behavioral evidence of cognitive bias. Once trained, the models should infer the presence of confirmation bias during decision-making using only EEG - without the interruptions or the subjective nature of traditional confirmation bias estimation techniques.
Synthetic Data Augmentation for Facial Re-identification

Facial Re-identification datasets which facilitate the training of Deep Neural Networks (DNNs), tend to be high quality images of celebrities harvested from the internet. There is however a domain gap between these datasets, and the low quality samples used in real-world systems and scenarios such as surveillance footage. In this work we describe a novel process of data augmentation using synthetically generated images, which aids cross-domain generalisability, without the need to acquire large amounts of real data in the target domain. We also contribute a new dataset derived from this process: syn-Face . Our approach is validated by training with standard high quality datasets with synthetic augmentation and testing in 2 different realistic sets.
Antenna Design for Underwater Applications

Research in the field of underwater communications continues to generate a lot of interest worldwide, due to a wide range of applications that this technology covers, which include: offshore oil and gas field monitoring, coastline protection and surveillance, oceanographic data collection which will require data exchange between two or more Autonomous Underwater Vehicles (AUVs) and underwater environmental observation for exploration. In this regard, underwater radio waves technology is proposed as a means to deliver high-speed communications at a short range between two underwater bodies. This will be of great importance to scuba divers, underwater photographers or videographers, scientific divers and other underwater explorers. Though this technology suffers greatly from attenuation in the underwater environment. Consequently this work has been considered mainly in lower frequencies in the high-frequency band where the attenuation effect is minimal. Therefore, based on mathematical calculations and considerations for the conductivity and permittivity of the medium, underwater antennas were developed in this work for usage in aqueous environments. The developed antennas can be grouped into two with respect to bandwidth and their radiation characteristics. These includes narrow, dual and wideband antennas as well as those with omnidirectional, bi-directional and directional radiation characteristics. Finally, realized gains of underwater antennas for underwater communication systems are as well presented. This research work can be summarily divided into four categories, which are: investigation of relevant theoretical analyses of radio waves propagation in lossy medium, extensive computer simulation using FEKO electromagnetic simulation software, fabrication of antennas and experimental validation of the work done, which include measurement of radiation characteristics of the antennas in the medium. Hence, fabrication of the designs with good results was performed in the INESC TEC facilities. Also the manufactured antennas were measured with the aid of Vector Network Analyzer (VNA) and these were carried out in a freshwater tank located at INESC TEC, with the antennas at a depth of 2.5 m from the surface and placed on the centre of the tank (which has dimensions of 10 m × 6 m × 5.5 m and the conductivity of water was 0.0487 S/m at 25◦C).
A multifaceted framework for adoption of cloud computing in Malaysian SMEs

Purpose The purpose of this study is to develop a model to understand the relationships among technology, organizational and environmental (TOE) contexts, intention to adopt cloud computing (IACC) and actual usage of cloud computing (AUCC) in small and medium enterprises (SMEs) in Malaysia as a developing country. More specifically, this paper seeks to explore the mediation effect of IACC on the relationship between TOE context and AUCC. Design/methodology/approach A positivist research approach was selected for this study. Drawing largely upon the TOE framework, this study uses survey data from 209 Malaysian SMEs. Structural equation modelling (SEM) based on partial least squares (PLS) was used to assess the structural relations of the research model. Findings The results of the structural model show that data security, technology readiness, top management support, competitive pressure and innovativeness are the most significant factors in predicting the adoption of cloud computing in Malaysian SMEs. Further, the results indicate that intention to adopt cloud computing can play a mediating role between TOE factors and the actual usage of cloud computing. Research limitations/implications The focus upon Malaysian SMEs may diminish the generalizability of the findings. This study provides profound insight into the management and foundation of cloud computing, different types of cloud services and deployment models that could facilitate the management of enterprise strategic resources and contribute to the performance improvement. This study also provides another important implication for practitioners regarding the absolute necessity of value drivers’ identification within enterprise and understand the causal relationships, which are vital in driving those values. Practical implications This study provides several practical guidance for practitioners in deploying cloud services which are most suitable option for their specific technology requirement in their enterprise to enjoy the full benefits of their intangible assets. Another significant implication of this study lies in the fact that it may require a different emphasis on nature and adoption design when there is a higher level of stress on technology-related and cloud computing resources. Originality/value This study contributes to the extant literature by developing an integrative model to identify how a wide set of contextual factors can determine the intention to adopt cloud computing and, in turn, influence the actual usage of cloud computing in SMEs in Malaysia as a developing country.
Picture Fuzzy Weighted Distance Measures and their Application to Investment Selection

The picture fuzzy set (PFS) is a powerful tool to collect and handle large amounts of uncertain assess information in a new light. In this study, we explore some distance measures for the PFSs and propose Picture fuzzy ordered weighted distance measure and Picture fuzzy hybrid weighted distance measure. Some of their properties are also mathematically explored. Moreover, we introduce a model for the aforesaid distance measures to solve multiple attribute group decision making (MAGDM) method in an updated way. And at the end of our paper a practical application of investment alternatives selection is provided to illustrate the validity and applicability of the presented work
Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs

Image co-saliency detection via fusion-based or learning-based methods faces cross-cutting issues. Fusion-based methods often combine saliency proposals using a majority voting rule. Their performance hence highly depends on the quality and coherence of individual proposals. Learning-based methods typically require ground-truth annotations for training, which are not available for co-saliency detection. In this work, we present a two-stage approach to address these issues jointly. At the first stage, an unsupervised deep learning model with stacked autoencoder (SAE) is proposed to evaluate the quality of saliency proposals. It employs latent representations for image foregrounds, and auto-encodes foreground consistency and foreground-background distinctiveness in a discriminative way. The resultant model, SAE-enabled fusion (SAEF), can combine multiple saliency proposals to yield a more reliable saliency map. At the second stage, motivated by the fact that fusion often leads to over-smoothed saliency maps, we develop self-trained convolutional neural networks (STCNN) to alleviate this negative effect. STCNN takes the saliency maps produced by SAEF as inputs. It propagates information from regions of high confidence to those of low confidence. During propagation, feature representations are distilled, resulting in sharper and better co-saliency maps. Our approach is comprehensively evaluated on three benchmarks, including MSRC, iCoseg, and Cosal2015, and performs favorably against the state-of-the-arts. In addition, we demonstrate that our method can be applied to object co-segmentation and object co-localization, achieving the state-of-the-art performance in both applications.