Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent data-driven reverse engineering of software design patterns

Recognising implemented instances of Design Patterns (DPs) in software design discloses and recovers a wealth of information about the intention of the original designers and the rationale of their design decisions. Because it is often the case that the documentation available for software systems, if any, is poor and/or obsolete, recovering such information can be of great help and importance for maintenance tasks. Since DPs are abstractly and vaguely defined, a set of software classes with exactly the same relationships as expected for a DP instance may actually be only accidentally similar. On the other hand, a set of classes with relationships that are to an extent different from the typically expected can still be a true DP instance. The deciding factor is mainly whether or not the set of classes is actually intended to solve the design problem addressed by the DP, which makes the intent a fundamental and defining characteristic of DPs. Discerning the intent of potential instances requires building complex models that cannot be built using the information known about DPs. So, a paradigm shift in DP recognition to fully machine learning based approaches is required. The problem is that there exists no accurate and sufficiently large DP datasets and it is difficult to manually construct one. Also, there is a lack of research on the feature set that should be used in DP recognition. The main aim of this thesis is to enable the paradigm shift by laying down an accurate, comprehensive and information-rich foundation of feature and data sets. To achieve this aim, a large set of features is developed to cover a wide range of design aspects, with a particular focus on the design intent. This set serves as a global feature set from which different subsets can be objectively selected for different DPs. A new and feasible approach for DP dataset construction is designed and used to construct training datasets. The feature and data sets are then used experimentally to build and train DP classifiers. The results demonstrate the accuracy and utility of the sets introduced, and show that fully machine learning based approaches do provide the appropriate and well-equipped solutions to the problem of DP recognition.
A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition

The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum produces images that have improved face recognition performance under varying lighting conditions. This is because long-wave infrared images are the result of emitted, rather than reflected, light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images have also improved face recognition under varying pose and lighting. The opacity of glass to long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces the recognition performance. This thesis presents the design and performance evaluation of a novel camera system which is capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth video images via a common optical path requiring no spatial registration between sensors beyond scaling for differences in sensor sizes. Experiments using a range of established face recognition methods and multi-class SVM classifiers show that the fused output from our camera system not only outperforms the single modality images for face recognition, but that the adaptive fusion methods used produce consistent increases in recognition accuracy under varying pose, lighting and with the presence of eyeglasses.
A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture.
Applications of single-pixel imaging

In this body of work, several single-pixel imaging applications are presented, based on structured light manipulation via a Digital Micromirror Device (DMD) and a single element photodetector (PD). This is commonly known as computational single-pixel imaging, and is achieved by using the measurements made by the PD to weight a series of projected structured light-fields. This indicates the strength of correlation between each light-field, and some object or scene placed in its propagation path. After many iterations the ensemble average of the weighted structured light-field converges to the object. Historically, computational single-pixel imaging has suffered from long image acquisition times and low resolution. Inhibiting the ability of physical systems from competing with conventional imaging in any form. Advances in computer and DMD technology have opened new avenues of research for this novel imaging technique. These advances have been utilised in this work by creating fast acquisition demonstrator systems, which have real world applications, such as multi-wavelength, polarisation, and long-range imaging. Several PDs were added to allow for simultaneous measurement of multiple images in the desired application. For multi-wavelength, RGB and white light illumination was spectrally filtered on three detectors to create full-colour images. While conversely the same multi-detector approach allowed for simultaneous measurement of orthogonal linear polarisation states essential to Stokes' parameter image reconstruction. Differential projection of the structured light-fields further allowed for the single-pixel camera to compensate from some sources of real world noise, such as background illumination. This work demonstrates an evolution of the single-pixel camera. From a system capable of only imaging simple, binary transmissive objects twice per hour and constrained to an optical bench, to a semi portable camera, capable of multiple frames per second 2D reconstructions of 3D scenes over a range of 20 kilometres. These improvements in capability cement the idea that the single-pixel camera is now a viable alternate imaging technology.
Inferring user tasks in pedestrian navigation from eye movement data in real-world environments

ABSTRACT Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.
Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images. (Learning and exploiting semantic representations for image classification and retrieval)

Dans cette these nous etudions differentes questions relatives a la mise en pratique de modeles d'apprentissage profond. En effet malgre les avancees prometteuses de ces algorithmes en vision par ordinateur, leur emploi dans certains cas d'usage reels reste difficile. Une premiere difficulte est, pour des tâches de classification d'images, de rassembler pour des milliers de categories suffisamment de donnees d'entrainement pour chacune des classes. C'est pourquoi nous proposons deux nouvelles approches adaptees a ce scenario d'apprentissage, appele >. L'utilisation d'information semantique pour modeliser les classes permet de definir les modeles par description, par opposition a une modelisation a partir d'un ensemble d'exemples, et rend possible la modelisation sans donnee de reference. L'idee fondamentale du premier chapitre est d'obtenir une distribution d'attributs optimale grâce a l'apprentissage d'une metrique, capable a la fois de selectionner et de transformer la distribution des donnees originales. Dans le chapitre suivant, contrairement aux approches standards de la litterature qui reposent sur l'apprentissage d'un espace d'integration commun, nous proposons de generer des caracteristiques visuelles a partir d'un generateur conditionnel. Une fois generes ces exemples artificiels peuvent etre utilises conjointement avec des donnees reelles pour l'apprentissage d'un classifieur discriminant. Dans une seconde partie de ce manuscrit, nous abordons la question de l'intelligibilite des calculs pour les tâches de vision par ordinateur. En raison des nombreuses et complexes transformations des algorithmes profonds, il est difficile pour un utilisateur d'interpreter le resultat retourne. Notre proposition est d'introduire un > dans le processus de traitement. La representation de l'image est exprimee entierement en langage naturel, tout en conservant l'efficacite des representations numeriques. L'intelligibilite de la representation permet a un utilisateur d'examiner sur quelle base l'inference a ete realisee et ainsi d'accepter ou de rejeter la decision suivant sa connaissance et son experience humaine.
Going beyond the sentence: Contextual Machine Translation of Dialogue. (Au-delà de la phrase: traduction automatique de dialogue en contexte)

While huge progress has been made in machine translation (MT) in recent years, the majority of MT systems still rely on the assumption that sentences can be translated in isolation. The result is that these MT models only have access to context within the current sentence; context from other sentences in the same text and information relevant to the scenario in which they are produced remain out of reach. The aim of contextual MT is to overcome this limitation by providing ways of integrating extra-sentential context into the translation process. Context, concerning the other sentences in the text (linguistic context) and the scenario in which the text is produced (extra-linguistic context), is important for a variety of cases, such as discourse-level and other referential phenomena. Successfully taking context into account in translation is challenging. Evaluating such strategies on their capacity to exploit context is also a challenge, standard evaluation metrics being inadequate and even misleading when it comes to assessing such improvement in contextual MT. In this thesis, we propose a range of strategies to integrate both extra-linguistic and linguistic context into the translation process. We accompany our experiments with specifically designed evaluation methods, including new test sets and corpora. Our contextual strategies include pre-processing strategies designed to disambiguate the data on which MT models are trained, post-processing strategies to integrate context by post-editing MT outputs and strategies in which context is exploited during translation proper. We cover a range of different context-dependent phenomena, including anaphoric pronoun translation, lexical disambiguation, lexical cohesion and adaptation to properties of the scenario such as speaker gender and age. Our experiments for both phrase-based statistical MT and neural MT are applied in particular to the translation of English to French and focus specifically on the translation of informal written dialogues.
Dynamic user profiling for search personalisation

The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests. A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document. In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests. To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines.
Interfaces tangíveis e o design de ambientes educacionais para co-construção de narrativas

Contar histórias é uma atividade autêntica e pessoalmente relevante, que tem mostrado ter benefícios para as crianças, como o desenvolvimento da criatividade, a imaginação, a manipulação de conceitos abstratos e, em alguns casos, o desenvolvimento do pensamento lógico. Esta tese apresenta uma contribuição às áreas da Interação Humano-Computador (IHC) e da Informática aplicada à Educação, especialmente no campo do Design da Interação, uma vez que apresenta um modelo para a criação de ambientes computacionais para a criação de narrativas de forma colaborativa (Figura 1a), instanciados em tecnologias tangíveis contemporâneas (Figura 1b). Este modelo é chamado CPES (Collaborative Programmable Environment for Storytelling).
Pemanfaatan Google Scholar Dan Citation Dalam Memenuhi Kebutuhan Pembuatan Skripsi Mahasiswa Pada Perguruan Tinggi

Pada perkembangan teknologi saat ini, menulis dokumen secara digital sudah banyak dilakukan oleh para mahasiswa. Namun, mencari sumber referensi yang sah dan mengetahui apakah tulisan tersebut mengandung unsur plagiarism atau tidak masih mengalami banyak kendala. Selama ini terkadang para mahasiswa masih banyak yang mencari referensi dari sumber yang belum terpercaya, padahal hal tersebut dapat menjadi hal yang sangat fatal untuk penulisan laporan Tugas Akhir (TA) dan Skripsi. Oleh karena itu Google mensupport dengan adanya sistem google scholar sebagai sumber informasi dan referensi yang sah. Selain google scholar, Google pun mensupport dengan pembuatan Citation (Daftar Pustaka),yang k ita ketahui selama ini membuat daftar pustaka dilakukan dengan cara manual. Karena perkembangan jaman Google kembali mensupport pembuatan data menggunakan citation dengan terkomputerisasi oleh sistem. Pada penelitian ini menggunakan metode mind mapping dan analisa SWOT. Diharapkan dengan adanya penelitian ini mahasiswa mampu membuat laporan Tugas Akhir (TA) dan Skripsi menemukan referensi yang lebih luas dan dapat memudahkan para penguji sidang menemukan plagiarism pada laporan Tugas Akhir dan Skripsi yang dibuat oleh mahasiswa. Kata kunci : Skripsi, Google Scholar, Citation On development of current technologies, writing documents digitally has been done by the students. However, finding a legitimate reference source and find out whether the text contains elements of palgiarism or not still encountered many constraints. During this time the students sometimes still mecari references from sources that have not been reliable, but it can be very fatal for the final project report writing (TA) and Thesis. Therefore Google support by having the system google scholar as a source of information and reference. In addition to google scholar, Google any support with the creation of Citation (bibliography), which we know as long as it mak es the references done by manual. Because of the changing times Google returns support creation of data using the citation with a computerized system. On the research method using mind mapping and SWOT analysis. It is hoped this research with the students are able to make the final project reports (TA) and Thesis find a broader reference and can facilitate the trial examiners find final project reports on plagiarism and Thesis made by students. Keywords : Thesis, Google Scholar, Citation
A Comparative Study of Feature Vector-Based Topic Detection Schemes for Text Streams

Topic detection is an important subject when voluminous text data is sent continuously to a user. We examine a method to detect topics in text data using feature vectors. Feature vectors represent the main distribution of data and they are obtained by various data analysis methods. This paper examines three methods: Singular Value Decomposition (SVD), clustering, and Independent Component Analysis (ICA). SVD and clustering are popular existing methods. Clustering, especially, is applied to many topic detection methods. ICA was recently developed in signal processing research. In applications related to text data, however, ICA has not been compared with SVD and clustering, nor has its relationship with them been explored. This paper reports comparative experiments for these three methods and then shows properties as they apply to text data.
Updating relational databases through views (complements, mappings, select, project, join)

We consider the problem of updating databases through views composed of selections, projections, and joins applied to Boyce-Codd Normal Form relations. The updates are expressed against the view and they must be translated into updates expressed against the database. We present and justify five criteria that these translations must satisfy. For each type of view update (insert, delete, replace), we provide a list of templates for translation into database updates that satisfy the five criteria. We show that there cannot be any other translations that satisfy the five criteria. A translator can be chosen at view definition time by asking the definer of the view a series of questions based on the view definition and database schema. We conclude that it is practical to update relational databases through views for a large class of views by choosing a view update translator at view definition time.
Parallel implementations of evolutionary algorithms

1 I n t r o d u c t i o n In this paper we describe the implementat ion of a s tandard Genetic Algori thm [1,2] on a number of different multiprocessor machines. After the discussion of the da ta dependencies in a straight forward implementat ion of a GA, the parallelization s t rategy on each machine is discussed and speedup measurements are presented. No a t tempt is made to optimize the algorithm for each specific architecture, instead we tried to stay as closely as possible to the original and parallelize in a way which seems natural for an application programmer on such a machine. This includes the use of s tandard libraries like Pthreads if available and e.g. recommended by the vendor as the preferred method of parallelization. 2 G e n e t i c A l g o r i t h m s In this section we shortly outline the GA we have implemented on the different machines. Selection The fitness of each individual in the population is evaluated. The fitness of each individual relative to the mean value of all other individuals gives the probabil i ty with which this individual is reproduced in the next generation. Therefore the frequency h~ of an individual in the next generation is given by * This work has been supported by the Deutsche Forschungsgemeinschaft (DFG) under grant Ma 1150/8-1.
Business Process Modeling Notation

The Business Process Modeling Notation (BPMN) specification provides a graphical notation for expressing business processes in a Business Process Diagram (BPD). The objective of BPMN is to support business process management by both technical users and business users by providing a notation that is intuitive to business users yet able to represent complex process semantics. The BPMN specification also provides a mapping between the graphics of the notation to the underlying constructs of execution languages, particularly BPEL4WS. Status of this Document This document is version 1.0 of the BPMN specification submitted by members of the BPMI initiative on May 3, 2004. It supersedes any previous version. It has been produced based on the work of the members of the BPMI Notation Working Group. Comments on this document and discussions of this document should be sent to BPMN-PublicReview@bpmi.org. It is expected that as experience is gained with BPMN there will be feedback about this relatively young specification, particularly the mapping from the notation to BPEL4WS. Thus, this document may be updated, replaced, or made obsolete by other documents at any time.