Miracle-dz/UniFAR-FTUs huggingface dataset:
DroidSF - A framework for security analysis of mobile applications (cid:63)

Mobile devices, specially smart-phones, are an increasingly valuable target for bad actors as they often hold important personal information, that can potentially be exploited against its user. With the growing number of mobile devices connected to the internet, it’s imperative that we develop tools and document how to perform an in-depth analysis of mobile applications. We believe this knowledge will help software developers, and even users, to be more conscious about security and implement better code following the recommended practices. This thesis will cover techniques and software one can use to analyse how an Android application was built and gain insight to what it does in background. To be able to evaluate the security of an Android mobile application it’s imperative to understand how they are developed, assembled and how they operate on devices at runtime. With this in mind, we will provide details about the inner-workings of the Android platform, with special attention to its security features. A framework was produced along side this thesis, that aggregates frequently used tools to facilitate the security analysis process. Our framework was designed to be a fully automated, easy to use and extendable. These characteristics seek to promote a good starting point for anyone that wants to analyse the behaviour of mobile applications and develop systematic tests to help assert their overall security level. We’ll focus on methodologies that allow us to inspect critical components of the application as described by the Open Web Application Security Project (OWASP) Top 10 Security Risks.
Event Identification in STRING Extended Abstract

Event identification in texts is an important task in Natural Language Processing (NLP), as it allows for the extraction of information in a structured way, which can have multiple applications in automatic summarization an event reconnaissance. The work described in this document focused on the identification of seven different types of events, i.e. Crime, Trial, Prision, Location Static, Location Visit, Public and Ephemerid. Theseevents were incorporated in the processing chain STRING (Statistical and Rule-Based Natural Language Processing), developed at Laboratory for Spoken Language Systems (L2F) of the Institute of Systems Engineering and Computers Research and Development in Lisbon (INESC-ID), in an effort of improving the system’s ability of event identification.
Classification with Markov Logic Networks in the presence of domain knowledge

In this work, we study the problem of learning to classify a set of instances based on an available training set and domain knowledge represented through a taxonomy. Classification is a family of supervised machine learning algorithms that assigns the input as belonging to one of several pre-defined classes. Traditionally, classification approaches tended to fall into two largely separate strands: one focused on logical representations, and one focused on statistical ones. Logical approaches deal better with the complexity of the real world while the statistical approaches excel on dealing with the uncertainty that is present in any real applications. As the real world is complex and uncertain, we think is important to explore methods that can have both characteristics. Markov Logic Networks (MLNs) combine logic and probability by attaching weights to first-order clauses. By combining first-order logic (FOL) and probability we can use the advantages of both. MLNs have gained traction in the AI community in recent years because of this ability to combine the expressiveness of FOL with the robustness of probabilistic representations. We propose an approach to add domain knowledge, represented in taxonomies, in classification problems using MLNs to improve the performance of the algorithms.
Tree Based Opinion Mining in Tamil for Product Recommendations using R

- Sentiment analysis, the automated extraction of expressions of positive or negative attitudes from text has received considerable attention from researchers during the past decade. In addition, the popularity of internet users has been growing fast parallel to emerging technologies; that actively use online review sites, social networks and personal blogs to express their opinions. In this paper we discuss some of the challenges in sentiment extraction especially in Tamil language and some of the approaches that have been taken to address these challenges and our approach analyses sentiments from Twitter social media. Tamil, a Dravidian language has a very rich morphological structure which is agglutinative. Tamil words are made up of lexical roots followed by one or more affixes, mostly suffixes. Tamil is also a post positional inflectional language. We have developed a Parts of speech tagging system to handle nouns and verbs. So finding a word in a language like Tamil is very complex. We try to resolve this complexity by identifying the categorical ambiguities and developing decision tree classification techniques at word grammatical category and grammatical feature level. These techniques were used to annotate the corpora and trained using the R Tool. The results obtained in each level were encouraging.
Klassifikation subaxialer Halswirbelsäulenverletzungen

Zusammenfassung Im klinischen Alltag wurden Verletzungen der unteren Halswirbelsäule lange Zeit analog zu den Brust- und Lendenwirbelsäulenverletzungen gemäß der AO-Klassifikation beschrieben. Im Rahmen dieser Übersicht wird nun die neue AOSpine-Klassifikation für Verletzungen der subaxialen Halswirbelsäule vorgestellt.
Natural Language Generation for Open Domain Human-Robot Interaction

—In this work, we approach one of the key components of dialogue systems, natural language generation, to study how this component is affected by open domain communication. We rely on statistical methods, namely topic models and deep learning, and approach the traditional generation architecture by optimising the sentence planning and surface realisation, as different tasks. We use documentaries’ subtitles to model domain-speciﬁc aspects and a large vocabulary dataset to account for domain-independent linguistic concerns. Latent Dirichlet Allocation is used for describing the ﬁne-grained relationships in the domain-speciﬁc corpus, while word embeddings (providing geometric semantic relations) are used to represent the large vocabulary. Natural language generation tasks are modelled as deep learning problems. Speciﬁcally, sentence planning is implemented with feedforward and convolutional neural networks. Microplanning and surface realisation are implemented with recurrent neural networks, to account for sequential aspects of language. We evaluate our corpus construction method by analysing different time boundaries in the scene detection algorithm and how those parameters affects the topic models. We evaluate sentence planning using cosine similarity and surface realisation with subjective analysis. Our results suggest that the sentence planner can learn a mapping from the generic domain-independent space into the domain-speciﬁc space. The quality of surface realisation results must be considered preliminary.
The non-adaptive query complexity of testing k-parities

We prove tight bounds of Q(k log k) queries for non-adaptively testing whether a function f :f0; 1g n ! f0; 1g is a k-parity or far from any k-parity. The lower bound combines a recent method of Blais, Brody and Matulef (4) to get lower bounds for testing from communication complexity with an W(k log k) lower bound for the one-way communication complexity of k-disjointness.
Anomalous Origin of Left Main Coronary Artery from the Right Sinus of Valsalva: A Case Series-based Review

Congenital anomalies involving the origin of coronary arteries are rare and the most common anomaly is left circumflex (LCX) arising from the right sinus of Valsalva (RSV). Other anomalies include a single coronary artery from the left sinus of Valsalva, both coronary arteries from RSV and left anterior descending coronary artery (LAD) from RSV. Anomalous origin of left main from RSV carries a high risk of sudden cardiac arrest. A retrospective analysis and literature review of three patients admitted to our medical center with the acute coronary syndrome, who underwent coronary angiography and were found to have left main coronary artery (LMCA) originating from the right coronary cusp (RCA). One patient had non-diseased coronaries with symptoms caused by the variant anatomy with possible compression of the LMCA, whereas the other two patients had 100% occluded RCA with variable stenosis in the left coronary system. Eventual surgical re-implantation with bypass grafting was required in all three patients. LMCA from the RSV is a rare, but often fatal anomaly. Due to a lack of data and inability to predict sudden cardiac death, the latest guidelines recommend surgical intervention (class 1 recommendation) for all patients with LMCA from RSV, regardless of ischemia or ischemic symptoms.
Diaphyseal osteomyelitis of femur with suspected ewing sarcoma in 8 years old children: a case report

Metaphyseal osteomyelitis in children due to direct bone trauma or vascular insufficiency is a frequent problem in orthopaedic surgery. In contrast, diaphyseal osteomyelitis represents a rare entity that almost exclusively affects child with bony infarct in sickle cell anemia. Differentiating neoplasm from musculoskeletal infection can sometimes be very challenging. In particular, Ewing sarcoma can masquerade as osteomyelitis with the presenting symptoms of fever, localized bone pain, and elevated inflammatory markers common to both entities, although osteomyelitis is a totally different type of disease. In this article, we report the case of chronic osteomyelitis of the femur in an immunocompetent and otherwise healthy 8 years old boy with minor inflammation signs and misleading clinical features. The X-ray showed onion skin periosteal reaction. We evacuated about 200 cc of abscess. Biopsy report revealed polimononuclear, mononuclear, and necrotic inflammatory cells. The patient was treated with antibiotic for two weeks and discharged with improved general condition. Six months follow-up shows clinical and radiological improvement. The diagnosis had to be confirmed by surgery which allowed the initiation of a targeted therapy. A case of diaphyseal osteomyelitis of a femur, lacking predisposing factors or trauma, is unique in children and never been reported previously.
Cardiopulmonary Exercise Test in Advanced Heart Failure Among Heart Transplantation Candidates

Cardiopulmonary exercise test has been raised through recent decades as a brilliant prognostic tool in a wide field of diseases and clinical conditions which makes it valuable to be used in prognostic assessment during the current study among chronic heart failure (CHF) patients. This study recruited 71 patients with severe CHF who were candidates for heart transplantation. Bicycle-protocol Cardiopulmonary Exercise Test was done, and several parameters were measured and compared between four groups of patients based on their VO2/kg to show four grades of the disease from G1 to G4. Total 71 CHF subjects from 18 to 46 year of age enrolled in the study. Statistically significant correlation s were determined between HRR, VE/VCO2, VE/VO2, BR and the severity of CHF. VO2max, HRR, VE/VO2, VE/VCO2, BR, and AT were the most prominent factors of CPET which showed their validity and reliability in terms of prognosis of CHF. It seems that combination of CPET and other cardiac prognostic tools like echocardiography and measurement of the pressure in cardiac chambers can improve the prognosis in CHF.
MPTEE: bringing flexible and efficient memory protection to Intel SGX

Intel Software Guard extensions (SGX), a hardware-based Trusted Execution Environment (TEE), has become a promising solution to stopping critical threats such as insider attacks and remote exploits. SGX has recently drawn extensive research in two directions---using it to protect the confidentiality and integrity of sensitive data, and protecting itself from attacks. Both the applications and defense mechanisms of SGX have a fundamental need---flexible memory protection that updates memory-page permissions dynamically and enforces the least-privilege principle. Unfortunately, SGX does not provide such a memory-protection mechanism due to the lack of hardware support and the untrustedness of operating systems. This paper proposes MPTEE, a memory-protection mechanism that provides flexible and efficient enforcement of memory-page permissions in SGX. The enforcement relies on our elastic cross-region bound check technique which uses only three bound registers but provides six memory permissions. To defend MPTEE against potential attacks, we further develop an efficient mechanism that exploits the in-place bound-check technique to ensure the integrity of the memory protection. With MPTEE, developers can enhance the protection for data and code in SGX enclaves and enforce the least-privilege principle such as Execute-no-Read memory readily. We have implemented MPTEE and extensively evaluated its effectiveness, utility, and performance. The results show that MPTEE incurs a performance overhead of only 2%--8%, and is effective in ensuring memory protection and in defending against potential attacks.
ICNIRP Guidelines GUIDELINES ON LIMITS OF EXPOSURE TO STATIC MAGNETIC FIELDS

THE RAPID development of technologies in industry and medicine using static magnetic fields has resulted in an increase in human exposure to these fields and has led to a number of scientific studies of their possible health effects. The World Health Organization (WHO) recently developed a health criteria document on static electric and magnetic fields within the Environmental Health Criteria Program (WHO 2006). The document contains a review of biological effects reported from exposure to static fields and, together with other recent publications [mainly International Commission on Non-Ionizing Radiation Protection (ICNIRP) 2003, McKinlay et al. 2004, and Noble et al. 2005], serves as the scientific database for the development of the rationale for the guidelines described in the current document, which supersede those published by ICNIRP in 1994 (ICNIRP 1994).
Regulatory Impacts on Risk-Return Efficient Credit Portfolios

Efficient credit portfolio management is a key success factor of bank management. Discussions of the new capital adequacy proposals by the Basle Committee on Banking Supervision enlighten the necessity to consider the credit risk management both from the internal and the regulatory point of view. We introduce an optimization approach for the credit portfolio that maximizes expected returns subject to internal and regulatory risk constraints. With a simplified bank portfolio we examine the impact of the regulatory risk limitation rules on the optimal solutions.
Coronary Vasospasm Induced in Transgenic Mouse With Increased Phospholipase C-δ1 Activity

Background—We reported that phospholipase C (PLC)-δ1 activity was enhanced 3-fold in patients with coronary spastic angina. We detected variant PLC-δ1 with replacement of arginine 257 by histidine (R257H) showing increased enzymatic activity. We tested the hypothesis that increased PLC-δ1 activity causes enhanced coronary vasomotility. Methods and Results—We generated transgenic (TG) mice with human R257H variant PLC-δ1 in vascular smooth muscle cells. PLC enzymatic activity in the coronary artery was increased by 2.57 and 1.89 times, respectively, in homozygous and heterozygous TG compared with wild-type (WT) mice. ST elevation after ergometrine occurred in 17 of 18 homozygous TG, 6 of 20 heterozygous TG, and 3 of 22 WT mice (P<0.01, homozygous TG versus WT; P<0.05, homozygous TG versus heterozygous TG; P=NS, heterozygous TG versus WT). ST elevation was associated with bradyarrhythmias in homozygous TG mice. Focal coronary artery narrowing was documented with the microvascular filling technique in 3 of 5.
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction

Convolutional Neural network-based MR reconstruction methods have shown to provide fast and high quality reconstructions. A primary drawback with a CNN-based model is that it lacks flexibility and can effectively operate only for a specific acquisition context limiting practical applicability. By acquisition context, we mean a specific combination of three input settings considered namely, the anatomy under study, undersampling mask pattern and acceleration factor for undersampling. The model could be trained jointly on images combining multiple contexts. However the model does not meet the performance of context specific models nor extensible to contexts unseen at train time. This necessitates a modification to the existing architecture in generating context specific weights so as to incorporate flexibility to multiple contexts. We propose a multiple acquisition context based network, called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition contexts and generalizable to unseen contexts for applicability in real scenarios. The proposed network has an MRI reconstruction module and a dynamic weight prediction (DWP) module. The DWP module takes the corresponding acquisition context information as input and learns the context-specific weights of the reconstruction module which changes dynamically with context at run time. We show that the proposed approach can handle multiple contexts based on cardiac and brain datasets, Gaussian and Cartesian undersampling patterns and five acceleration factors. The proposed network outperforms the naive jointly trained model and gives competitive results with the context-specific models both quantitatively and qualitatively. We also demonstrate the generalizability of our model by testing on contexts unseen at train time.
TDPreﬁxGrowth: an algorithm for mining multiple sequential periodic regularities

The dynamic and evolutionary settings of most domains created a challenge for data analytics’ processes. Therefore, the use of temporal information, in order to disclose interesting evolutionary behaviors, has become one of the most important features in Pattern Mining techniques. In particular, several approaches to Temporal Pattern Mining, such as the Emerging and Cyclic patterns, have been proposed with the aim of disclosing diﬀerent types of evolutionary patterns. However, existing solutions still lack on quality temporal patterns, able to represent interesting dynamic behaviors, and also eﬃcient mining methods able to meet user’s expectations. In this paper, besides deﬁning the problem of mining Cyclic, Converging and Diverging sequential pattern, we propose a new sequential constraint-based approach, named TDPreﬁxGrowth , for solving it. We also propose an optimization to our initial algorithm, together with two additional variations of the original patterns, the Arithmetic and Geometric Progressions. Our experiments on two diﬀerent datasets show the eﬃciency of our algorithm, when compared to other sequential pattern mining approaches, and also its ﬂexibility for dealing with diﬀerent types of temporal patterns and multiple constraints.
Paper FIFTH WARREN K. SINCLAIR KEYNOTE ADDRESS: ISSUES IN QUANTIFYING THE EFFECTS OF LOW-LEVEL RADIATION

Health risks from exposure to high doses of ionizing radiation are well characterized from epidemiological studies. Uncertainty and controversy remain for extension of these risks to the low doses and low dose rates of particular relevance in the workplace, in medical diagnostics and screening, and from background radiations. In order to make such extrapolations, a number of concepts have been developed for radiation protection, partly on the basis of assumed processes in the mechanisms of radiation carcinogenesis. Included amongst these are the assumptions of a linear no-threshold dose response and simple scaling factors for dose rate and radiation quality. With a progressive reduction in recommended dose limits over the past half century, these approaches have had considerable success in protecting humans. But do they go far enough or, conversely, are they overprotective? Four selected underlying aspects are considered. It is concluded that (1) even the lowest dose of radiation has the capability to cause complex DNA damage that can lead to a variety of permanent cellular changes; (2) the unique clustered characteristics of radiation damage, even at very low doses, enable it to stand out above the much larger quantity of endogenous DNA damage; (3) although a chromosome aberration may represent the rate-limiting initiating event for carcinogenesis, as is often assumed, direct evidence is still lacking; and (4) the extensive influence that dicentric aberrations have had on guiding extrapolations for radiation protection may be substantially misleading. Finally, some comments are offered on aspects that lie outside the current paradigm. Health Phys. 97(5):394–406; 2009
Generation of visualizations for Service Architectures

Lately, organizations have been struggling with representing their service architectures due to problems such as lack of communication between the various departments and a common language to represent that architecture. There is also a big problem regarding the size of the architecture that becomes almost incomprehensible to visualize it. In this thesis I will do a research on what has been done regarding this theme, what the discipline of Enterprise Architecture has done to support the visualization of Service Architectures, what the frameworks to model it are, and what tools can be used to help us develop a solution. We provide a solution to what we think it is fit for this problem. This work has the goal of improving service architecture visualization in line with the main Enterprise Architecture frameworks and guidelines. This work was inserted in the context of the organization Serviços Partilhados do Ministério da Saúde.
Two Approaches for the Stabilization of Nonlinear KdV Equation With Boundary Time-Delay Feedback

This paper concerns the nonlinear Korteweg-de Vries equation with boundary time-delay feedback. Under appropriate assumption on the coefficients of the feedbacks (delayed or not), we first prove that this nonlinear infinite dimensional system is well posed for small initial data. The main results of our study are two theorems stating the exponential stability of the nonlinear time-delay system. Two different methods are employed: a Lyapunov function approach (allowing to have an estimation on the decay rate, but with a restrictive assumption on the length of the spatial domain of the KdV equation) and an observability inequality approach, with a contradiction argument (for any noncritical lengths but without estimation on the decay rate). Some numerical simulations are given to illustrate the results.