Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Understanding for Computers

A long-standing challenge for computers communicating with humans is to pass the Turing test, i.e., to communicate in such a way that it is impossible for humans to determine whether they are talking to a computer or another human being. The field of natural language understanding — which studies automatic means of capturing the semantics of textual content — plays a central part in this long-term goal of artificial intelligence research. Natural language understanding can itself be understood at different levels. In this thesis, we make contributions to automatic understanding of text at the level of words, short texts, and full documents. Understanding texts at the word level, means understanding how words relate to each other semantically. For example, do two words or phrases mean approximately the same thing? Does a particular word still mean the same thing it used to, say, 50 years ago? Or, as is the question central to the first part of this thesis, can we automatically detect which words people used in different periods in time to refer to a particular concept? The question we ask ourselves in the second part of this thesis is: can we automatically determine, from the word-level up, if two sentences have a similar meaning? Finally, in the third part of this thesis, document-level text understanding is the focus of our interest. In particular, we study multiple approaches to the reading comprehension task, where a computer reads a document and answers questions about it.
The Effect of Knowledge Breadth and Depth on New Product Performance

This study distinguishes two features of a firm's knowledge base - breadth and depth - and elucidates their interplay in determining new product performance. Papers drawing from the knowledge-based view of the firm often argue a positive role for knowledge base in new product development, however empirical evidence shows an equivocal relationship between knowledge base and new product performance. The empirical results from a sample of 192 high-tech firms indicate that a deep knowledge in a specific industry is imperative to a firm's new product success. However, the effect of knowledge breadth is contingent on knowledge depth: a firm's deep knowledge in a specific field causes a systematic shift in the effect of knowledge breadth, from a negative to a positive effect. In other words, knowledge breadth has a negative effect on new product performance for lower levels of knowledge depth, but a positive effect for higher levels of knowledge depth. These findings offer valuable managerial implications for knowledge management, i.e. if firms expand their knowledge base across various fields or submarkets, they need to correspondingly accumulate deep knowledge in each specific field to take advantage of a broad knowledge base.
Effectiveness of Project-Based Learning as a Strategy for Property Education

Abstract Project-based learning (PBL) is a student-centred instructional approach used to promote active and deep learning by involving students in investigating real-world issues in a collaborative environment. In this paper, we discuss how PBL was implemented in an introductory property course running in semester one, year one, which is offered in multiple modes of study. Two qualitative student surveys were conducted during the study period to solicit feedback from students on their learning experience. Comments from students suggested that PBL provides beneficial insight into the valuation process, and the field work and group exercises help motivate students and make the subject matter more interesting. University conducted, end of study period evaluations were used to compare the outcomes of the new course to other courses in the property program and the University. Finally, we identify a number of challenges in implementing PBL; these include workload issues, teachers ‘ content knowledge, lack of experience from both teachers and students, and the need to develop specialised material for off-campus study.
Image context for object detection, object context for part detection

Objects and parts are crucial elements for achieving automatic image understanding. The goal of the object detection task is to recognize and localize all the objects in an image. Similarly, semantic part detection attempts to recognize and localize the object parts. This thesis proposes four contributions. The first two make object detection more efficient by using active search strategies guided by image context. The last two involve parts. One of them explores the emergence of parts in neural networks trained for object detection, whereas the other improves on part detection by adding object context. First, we present an active search strategy for efficient object class detection. Modern object detectors evaluate a large set of windows using a window classifier. Instead, our search sequentially chooses what window to evaluate next based on all the information gathered before. This results in a significant reduction on the number of necessary window evaluations to detect the objects in the image. We guide our search strategy using image context and the score of the classifier. In our second contribution, we extend this active search to jointly detect pairs of object classes that appear close in the image, exploiting the valuable information that one class can provide about the location of the other. This leads to an even further reduction on the number of necessary evaluations for the smaller, more challenging classes. In the third contribution of this thesis, we study whether semantic parts emerge in Convolutional Neural Networks trained for different visual recognition tasks, especially object detection. We perform two quantitative analyses that provide a deeper understanding of their internal representation by investigating the responses of the network filters. Moreover, we explore several connections between discriminative power and semantics, which provides further insights on the role of semantic parts in the network. Finally, the last contribution is a part detection approach that exploits object context. We complement part appearance with the object appearance, its class, and the expected relative location of the parts inside it. We significantly outperform approaches that use part appearance alone in this challenging task.
Digital Image and video processing Based on Edge Detection: A Review

The images Edge detection is one of the most important concerns in digital image and video processing. In the real time video and image processing, edge detection has been greatly benefited and thus, new avenues for research opened up. The digital image and video processing consists of the implementation of various image processing algorithms like edge detection using Sobel, Prewitt, Canny and Robert etc. In this paper various research on image edge detection is studied and concluded the best one for further improvement in the same field.
Securing Cloud Computations with Oblivious Primitives from Intel SGX

We are witnessing a confluence between applied cryptography and secure hardware systems in enabling secure cloud computing. On one hand, work in applied cryptography has enabled efficient, oblivious data-structures and memory primitives. On the other, secure hardware and the emergence of Intel SGX has enabled a low-overhead and mass market mechanism for isolated execution. By themselves these technologies have their disadvantages. Oblivious memory primitives carry high performance overheads, especially when run non-interactively. Intel SGX, while more efficient, suffers from numerous software-based side-channel attacks, high context switching costs, and bounded memory size. In this work we build a new library of oblivious memory primitives, which we call ZeroTrace. ZeroTrace is designed to carefully combine state-of-art oblivious RAM techniques and SGX, while mitigating individual disadvantages of these technologies. To the best of our knowledge, ZeroTrace represents the first oblivious memory primitives running on a real secure hardware platform. ZeroTrace simultaneously enables a dramatic speedup over pure cryptography and protection from software-based side-channel attacks. The core of our design is an efficient and flexible block-level memory controller that provides oblivious execution against any active software adversary, and across asynchronous SGX enclave terminations. Performance-wise, the memory controller can service requests for 4 Byte blocks in 1.2 ms and 1 KB blocks in 3.4 ms (given a 10 GB dataset). On top of our memory controller, we evaluate Set/Dictionary/List interfaces which can all perform basic operations (e.g., get/put/insert) in 1-5 ms for a 4-8 Byte block size. ZeroTrace enables secure remote computations at substantially lower overheads than other comparable stateof-the-art techniques.
Statistical classification in high-dimensional scenarios with a focus on microarray data sets

High-dimensional data analysis characterises many contemporary problems in statistics and arise in many application areas. This thesis focuses on very high-dimensional problems in which the input predictor variables are gene expression measurements in microarray studies. Accurate analysis of microarray data sets can provide new insight into cancer diagnosis using gene expression profiles and can result in breakthroughs in medical research. K-nearest neighbours (KNN), fastKNN, linear discriminant analysis (and variants thereof), nearest shrunken centroids (NSC) and support vector machines (SVMs) are investigated in this thesis as binary (and multi-class) classification procedures on microarray data sets. The important problem of eliminating redundant input variables before implementing classification procedures in high-dimensional data sets is addressed in this thesis. Several variable selection and dimension reduction procedures suitable for microarray data sets are discussed, with the focus on implementing sure independence techniques, NSC and fastKNN feature engineering in the empirical study. Principal component analysis and supervised principal component analysis are implemented as the two main dimension reduction techniques in this thesis. The performance of the classification procedures is evaluated on three real and three synthetic high-dimensional microarray data sets. The comparison of the different classification methods in the empirical study led to the conclusion that SVMs prove to be the most accurate procedure on the binary data sets considered, whilst NSC is the most accurate procedure on the multi-class data set.
Unsupervised Identification of the User's Query Intent in Web Search

This doctoral work focuses on identifying and understanding the intents that motivate a user to perform a search on the Web. To this end, we apply machine learning models that do not require more information than the one provided by the very needs of the users, which in this work are represented by their queries. The knowledge and interpretation of this invaluable information can help search engines to obtain resources especially relevant to users, and thus improve their satisfaction.By means of unsupervised learning techniques, which have been selected according to the context of the problem being solved, we show that is not only possible to identify the user’s intents, but that this process can be conducted automatically. The research conducted in this thesis has involved an evolutionary process that starts from the manual analysis of different sets of real user queries from a search engine. The work passes through the proposition of a new classification of user’s query intents; the application of different unsupervised learning techniques to identify those intents; up to determine that the user’s intents, rather than being considered as an uni–dimensional problem, should be conceived as a composition of several aspects, or dimensions (i.e., as a multi–dimensional problem), that contribute to clarify and to establish what the user’s intents are. Furthermore, from this last proposal, we have configured a framework for the on–line identification of the user’s query intent. Overall, the results from this research have shown to be effective for the problem of identifying user’s query intent.
Language learner perspectives on the functionality and use of electronic language dictionaries

Abstract This paper investigates the extent of electronic dictionary use by language learners in an Australian university. All students in the study are formally enrolled in language courses across ten languages at first, second or third year level. The study places a particular emphasis on gauging student perceptions of the beneficial aspects of electronic dictionaries as judged by learners themselves in circumstances where they are able to act independently. As these benefits are often described in terms of usability and functionality, these particular terms are defined and introduced in the literature review, and then later they are employed to help structure and describe the results. The arguments for the discussion are supported by the use of empirical data taken from a large-scale survey conducted in 2011 (n=587) where comments from students were obtained on why and how dictionary-type resources were accessed and used (see also Steel & Levy, 2013). The paper restricts itself to the quantitative and qualitative data gathered on mobile phones, translators, dictionaries and web conjugators and related items (e.g. discussion forums). The particular functions that students use and the ways in which they use them are described and categorised, with the discussion supported by student comments. The data exhibits a remarkable range of resources available to students to look up unknown words or to see translations and, consequently, our understanding of what exactly an electronic dictionary might comprise is challenged. Many students’ comments demonstrate a sophistication and knowledge about the effective use of these dictionary tools together with a keen awareness of their limitations.
Contextual boundness and discourse patterns revisited

Two issues relevant to discourse description and analysis are discussed, namely which property of the sentence structure reflects its discourse anchoring, and how to combine the ‘dynamic’ (communication based) view of language and discourse with the description of (underlying) sentence syntax. To this aim, our approach to the information structure of the sentence (of its topic-focus articulation, TFA) is briefly summarized, introducing then the notion of the hierarchy of activation of the elements of the stock of knowledge assumed by the speaker to be shared by him and the hearer and proposing some heuristics guiding the development of the activation degrees. These heuristics are applied to a piece of text discussing certain implications of the proposed analysis and some related problems, and briefly sketching a possibility of how to test the proposed approach to text analysis on a large annotated corpus of texts.
Inter Project Validation for Change Proneness Prediction using Object Oriented Metrics

Developing prediction models for determining change prone classes of a software is a significant upcoming research area. Such models help usin effective and efficient resource utilization during maintenance phase. A crucial step in developing these prediction models is the use of training data of that project. Thus training data of a project dictates its prediction model. This dependency leads to difficulties in applying the prediction model to other projects. The training data of a project, consists of metrics data as well as change statistics. Although computation of metrics data is easy, but collection of change statistics is complex. In this paper we at- tempt to reuse the generated prediction model of one project and validate it on another project. For the purpose of our evaluation, we have used two open source projects written in Java language. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results of our study indicate that we can suc- cessfully apply the training sets for inter project validation. These results help us in optimizing time and effort required to generate training set of each project. It leads to efficient utilization of constraint resources and time.
The Role of Mailing Groups in the Development of the Library Profession in Pakistan: the case of plagpk

Information and communication technologies (ICTs) have made it possible for members of any profession to contact other professionals or communicate on a group level that was not possible in the past. It is due to the availability of ICT-based social networking software. LIS professionals in Pakistan are not behind in adopting modern technologies. They have been using mailing groups since December 2000. Pakistan Library Automation Group (PakLAG), a group of volunteer LIS professionals, launched the first ever mailing group in Pakistan known as plagpk. The impact of the plagpk mailing group is very positive. It provides a useful forum to professionals to share their experiences and solve their problems. It is helping in the placement of fresh graduates by posting jobs. It helped a lot in the revival of a dead national library association. The group has created awareness among Pakistani LIS professionals in using new ICT-based social networking software. Following its example, many other mailing groups have also been launched by LIS professionals. This paper presents the results of content analysis of 5216 e-mail messages posted on plagpk till December 2008. The results of a survey of users about the usefulness of this group are also presented.
The use of performance feedback and reward for optimization of motor learning and neurorehabilitation of motor functions

x Based on this evidence, we conducted an fMRI study with healthy young subjects, relating striatal activity to performance feedback with or without monetary consequences during the training of a repetitive arc-tracking task (Chapter 3). The task required subjects to perform wrist movements to steer a cursor on a computer screen through a semicircular channel while undergoing fMRI. Our results demonstrate an influence of the feedback modality on motor skill learning. Adding a monetary reward after good performance led to better consolidation and higher ventral striatal activation than knowledge of performance alone. In conclusion, rewarding strategies that increase ventral striatal response during the training of a motor skill may be utilized to improve skill consolidation. In stroke survivors, activity of this dopaminergic pathway may not only be reduced because rewards are small, but also because, after stroke, rewarding feedback might not have the same capacity to increase dopaminergic activity as in healthy subjects. This has been demonstrated for cognitive tasks, and the hypothesis for the study presented in Chapter 4 was, that this also happens in motor tasks. To test this hypothesis, we applied a similar arc-tracking task, modified as motor MID task and using fMRI to measure striatal activity linked to performance dependent monetary reward. Results of nine stroke patients and nine age-matched healthy individuals show a tendency for reduced responsiveness of ventral parts of the striatum in stroke patients. This is of particular interest as in the study described above ventral striatal activation was found to be the key factor for successful overnight consolidation. We have learned from animal studies that proper functioning of the dopaminergic reward system is necessary for successful motor skill learning. Thus, a reduced responsiveness of the ventral striatum to a motor performance derived reward, be it extrinsic or intrinsic, could be an implication for a blunted motor learning ability in patients after stroke. The ability to learn, however, is supposed to support motor recovery. After stroke, about 50% of all survivors remain with functional impairments of their upper limb. As we were able to show that training with rewarding feedback improves motor learning in humans, we hypothesize that rehabilitative arm training could also be enhanced by rewarding feedback. This amplification of reward during rehabilitative training might be a means to overcome a potentially deficient response to task inherent feedback in order to stimulate the dopaminergic system to improve recovery after stroke. Therefore, a further achievement of this thesis is the development of a clinical trial protocol, investigating rewards in the form of performance feedback and monetary gains as ways to improve effectiveness of rehabilitative training (Chapter 5). This trial will be the first to directly evaluate
Patent stimuli search and its influence on ideation outcomes

Prior studies on design ideation have demonstrated the efficacy of using patents as stimuli for concept generation. However, the following questions remain: (a) From which part of the large patent database can designers identify stimuli? (b) What are their implications on ideation outcomes? This research aims to answer these questions through a design experiment of searching and identifying patent stimuli to generate new concepts of spherical rolling robots. We position the identified patent stimuli in the home, near and far fields defined in the network of patent technology classes, according to the network’s community structure and the knowledge proximity of the stimuli to the spherical rolling robot design. Significant findings are: designers are most likely to find patent stimuli in the home field, whereas most patent stimuli are identified in the near field; near-field patents stimulate the most concepts, which exhibit a higher average novelty; combined home- and far-field stimuli are most beneficial for high concept quality. These findings offer insights on designers’ preferences in search for patent stimuli and the influence of stimulation distance on ideation outcomes. The findings will also help guide the development of a computational tool for the search of patents for design inspiration.
Chapter 7: Computer-assisted Interpreting: Challenges and Future Perspectives

During the last decades, information technology has played a central role in the language services industry. Translators and technical writers take advantage of dedicated software to reuse already translated texts, to adhere to a customer-specific corporate language, to grant terminology consistency, and so forth. The final goal is to increase quality and productivity. Even if information technology did not have the same impact on conference interpreting, also the profession is undergoing some changes. Computer-assisted interpreting (CAI) tools have entered the profession only in recent years, but other, more general resources had already influenced the way interpreters work. This is not only challenging the way interpreting is performed, but it may have an impact on the cognitive processes underlying the interpreting task, even on some basic assumptions and theories of interpreting, for example the cognitive load distribution between different tasks during simultaneous interpreting. Yet, the academic debate is starting to take notice of these changes and their implications only now. As a consequence, it almost failed to shed light on and address the challenges that lay ahead: there have been relatively few empirical investigations on the impact of CAI tools; interpreting models have not been adapted accordingly; the didactics of interpreting has received almost no new technologies in their curricula and no proposal has been advanced to increase the quality of CAI tools and to meet interpreters’ real needs.