Glasgow: The City Centre Opportunity for Change

After a brief introduction, this thesis begins by exploring the nature of the centre of Glasgow in both functional and physical terms in order to assess its prospects for change. This assessment is fairly general and includes a review of recent planning policies of Glasgow District Council, in its attempts to counteract the disastrous policies of the 1950's and 1960's a period where Glasgow was struggling to cope with slums clearance. This part of the thesis concludes that Glasgow city centra has been trying to respond to the decline of its residential population and the loss of much of its original heavy industries. This resulted in changes to the form of the centre which has been identified mainly in respect to its component parts, namely the inner core of the city in which are concentrated the major economic activites as well as the physically structured fabric and the decaying and derelict fringes. We then turn to one of the main reasons for the rapid loss of identity of many cities namely the failure to adopt and implement visual policies. A brief summary is then given to the Glasgow Action report and the work of Gordon Cullen. This study shows how powerly influential is the combination of an economic strategy to a physical process of visual manipulation in the effort to revitalise the centre of a city. The concluding chapter looks at the opportunities necessary to achieve an inclusive guided future development of the city centre. It focusses mainly on ideas and principles rather than on detailed proposal We believe that the principles and proposals emitted throughout this thesis can be relevant in any other city where opportunities are given in bringing a new identity to its centre

LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning

Transformer-based models, such as BERT, have revolutionized various language tasks, but still struggle with large file classification due to their input limit (e.g., 512 tokens). Despite several attempts to alleviate this limitation, no method consistently excels across all benchmark datasets, primarily because they can only extract partial essential information from the input file. Additionally, they fail to adapt to the varied properties of different types of large files. In this work, we tackle this problem from the perspective of correlated multiple instance learning. The proposed approach, LaFiCMIL, serves as a versatile framework applicable to various large file classification tasks covering binary, multi-class, and multi-label classification tasks, spanning various domains including Natural Language Processing, Programming Language Processing, and Android Analysis. To evaluate its effectiveness, we employ eight benchmark datasets pertaining to Long Document Classification, Code Defect Detection, and Android Malware Detection. Leveraging BERT-family models as feature extractors, our experimental results demonstrate that LaFiCMIL achieves new state-of-the-art performance across all benchmark datasets. This is largely attributable to its capability of scaling BERT up to nearly 20K tokens, running on a single Tesla V-100 GPU with 32G of memory.Comment: 12 pages; update results; manuscript revisio

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have

Idiopathic encapsulating peritonitis revealed by an acute bowel occlusion in a young patient

La péritonite encapsulante est une péritonite chronique aboutissant à une membrane fibreuse épaisse, blanc nacré. C’est une affection rare dont la physiopathologie reste mal expliquée et le diagnostic est souvent porté en peropératoire ; elle peut être la cause d’une urgence chirurgicale, le caractère idiopathique est exceptionnel, retrouvé chez l’adolescent provenant des régions tropicales et subtropicales, jamais dans le Maghreb. Nous rapportons l’observation d’une jeune patiente marocaine de 18 ans, opérée pour une occlusion intestinale, chez qui le diagnostic d’une péritonite encapsulante a été posé en peropératoire.Encapsulating peritonitis is a chronic peritonitis leading to the constitution of a thick pearly-white fibrosis membrane. It is a rare affection, which physiopathology is poorly elucidated. Diagnosis is usually assessed during surgery; the idiopathic character is exceptional, occurring in teenagers coming from the tropical and subtropical countries, never in Maghreb. We report an unpublished case of an 18-year-old patient, admitted for bowel obstruction; diagnosis was made during surgery revealing an encapsulating peritonitis

Verneuil’s disease: case report (107 patients)

La maladie de Verneuil est une suppuration chronique fistulisante, sclérosante d’évolution cicatricielle. Son diagnostic est souvent méconnu et confondu avec une autre pathologie suppurative. Cette affection est caractérisée par une évolution longue, un traitement essentiellement chirurgical souvent en plusieurs temps avec un délai de cicatrisation long. C’est une maladie très invalidante au plan social et familial, pouvant entraîner un handicap réel pour le patient. À travers l’analyse de 107 observations de la maladie de Verneuil, nous avons étudié les caractéristiques épidémiologiques et cliniques de cette pathologie.Verneuil's disease is a chronic suppuration fistulizing, sclerosing and scarring. Its diagnosis is often misunderstood and therefore wrongly taken for another fistulizing disease. Verneuil's disease is characterized by its long evolution; treatment is mainly surgical sometimes requiring several operations, and a long healing time. This very invalidating disease may cause real handicap for the patient along with a familial and social impact. Through the analysis of 107 cases of Verneuil's disease, we studied the epidemiological and clinical characteristics of this disease

Implementation of an Affordable Method for MPS Diagnosis from Urine Screening to Enzymatic Confirmation: Results of a Pilot Study in Morocco

Background: Rapid and accurate diagnosis of mucopolysaccharidoses (MPS) is still a challenge due to poor access to screening and diagnostic methods and to their extensive clinical heterogeneity. The aim of this work is to perform laboratory biochemical testing for confirming the diagnosis of mucopolysaccharidosis (MPS) for the first time in Morocco. Methods: Over a period of twelve months, 88 patients suspected of having Mucopolysaccharidosis (MPS) were referred to our laboratory. Quantitative and qualitative urine glycosaminoglycan (GAG) analyses were performed, and enzyme activity was assayed on dried blood spots (DBS) using fluorogenic substrates. Enzyme activity was measured as normal, low, or undetectable. Results: Of the 88 patients studied, 26 were confirmed to have MPS; 19 MPS I (Hurler syndrome; OMIM #607014/Hurler-Scheie syndrome; OMIM #607015), 2 MPS II (Hunter syndrome; OMIM #309900), 2 MPS IIIA (Sanfilippo syndrome; OMIM #252900), 1 MPS IIIB (Sanfilippo syndrome; OMIM #252920) and 2 MPS VI (Maroteaux-Lamy syndrome; OMIM #253200). Parental consanguinity was present in 80.76% of cases. Qualitative urinary glycosaminoglycan (uGAGs) assays showed abnormal profiles in 31 cases, and further quantitative urinary GAG evaluation and Thin Layer Chromatography (TLC) provided important additional information about the likely MPS diagnosis. The final diagnosis was confirmed by specific enzyme activity analysis in the DBS samples. Conclusions: The present study shows that the adoption of combined urinary substrate analysis and enzyme assays using dried blood spots can facilitate such diagnosis, offer an important tool for an appropriate supporting care, and a specific therapy, when available

REVISITING AND BOOSTING STATE-OF-THE-ART ML-BASED ANDROID MALWARE DETECTORS

Android offers plenty of services to mobile users and has gained significant popularity worldwide. The success of Android has resulted in attracting more mobile users but also malware authors. Indeed, attackers target Android markets to spread their malicious apps and infect users’ devices. The consequences vary from displaying annoying ads to gaining financial benefits from users. To overcome the threat posed by Android malware, Machine Learning has been leveraged as a promising technique to automatically detect malware. The literature on Android malware detection lavishes with a huge variety of ML-based approaches that are designed to discriminate malware from legitimate samples. These techniques generally rely on manually engineered features that are extracted from the apps’ artefacts. Reported to be highly effective, Android malware detection approaches seem to be the magical solution to stop the proliferation of malware. Unfortunately, the gap between the promised and the actual detection performance is far from negligible. Despite the rosy excellent detection performance painted in the literature, the detection reports show that Android malware is still spreading and infecting mobile users. In this thesis, we investigate the reasons that impede state-of-the-art Android malware detection approaches to surround the spread of Android malware and propose solutions and directions to boost their detection performance. In the first part of this thesis, we focus on revisiting the state of the art in Android malware detection. Specifically, we conduct a comprehensive study to assess the reproducibility of state-of-the-art Android malware detectors. We consider research papers published at 16 major venues over a period of ten years and report our reproduction outcome. We also discuss the different obstacles to reproducibility and how they can be overcome. Then, we perform an exploratory analysis on a state-of-the-art malware detector, DREBIN, to gain an in-depth understanding of its inner working. Our study provides insights into the quality of DREBIN’s features and their effectiveness in discriminating Android malware. In the second part of this thesis, we investigate novel features for Android malware detection that do not involve manual engineering. Specifically, we propose an Android malware detection approach, DexRay, that relies on features extracted automatically from the apps. We convert the raw bytecode of the app DEX files into an image and train a 1-dimensional convolutional neural network to automatically learn the relevant features. Our approach stands out for the simplicity of its design choices and its high detection performance, which make it a foundational framework for further developing this domain. In the third part, we attempt to push the frontier of Android malware detection via enhancing the detection performance of the state of the art. We show through a large-scale evaluation of four state-of-the-art malware detectors that their detection performance is highly dependent on the experimental dataset. To solve this issue, we investigate the added value of combining their features and predictions using 22 combination methods. While it does not improve the detection performance reported by individual approaches, the combination of features and predictions maintains the highest detection performance independently of the dataset. We further propose a novel technique, Guided Retraining, that boosts the detection performance of state-of-the-art Android malware detectors. Guided Retraining uses contrastive learning to learn a better representation of the difficult samples to improve their prediction

Assessment of the implementation of action plan against cutaneous leishmaniasis in Morocco: case of Essaouira focus

Background & objective. Leishmaniases are a group of infectious diseases transmitted by phlebotomine sandflies, and their distribution depends on the presence of vectors, parasites, reservoirs and susceptible hosts in the same environment. Essaouira province is one of the active focus of Leishmania tropica cutaneous leishmaniasis (CL) in Morocco, where these diseases present a major health problem. Moroccan Ministry of Health has launched a national Program for Surveillance and Control of Leishmaniasis (Programme national de lutte contre les leishmanioses PNLL), in order to diagnosing and treating human cases and controlling the transmission of the disease.Methods. A cross-sectional observational study was conducted in Essaouira province in order to describe the state of the implementation of 2013-2016 action plan of PNLL in this CL focus.Results. The results highlight the lack and the instability of human resources in the study area which negatively affect the installation and the implementation of control actions. According to the program components, local staff is well involved in screening, diagnosing and treating human cases; while vector control actions are limited.Conclusion. Our recommendations place greater emphasis on the specific training, information of the staff and the strengthening of vector control activities.