Miracle-dz/UniFAR-FTUs huggingface dataset:
J ul 2 02 0 UNIFIED LAGRANGIAN-HAMILTONIAN FORMALISM FOR CONTACT SYSTEMS

We present a unified geometric framework for describing both the Lagrangian and Hamiltonian formalisms of contact autonomous mechanical systems, which is based on the approach of the pionnering work of R. Skinner and R. Rusk. This framework permits to skip the second order differential equation problem, which is obtained as a part of the constraint algorithm (for singular or regular Lagrangians), and is specially useful to describe singular Lagrangian systems. Some examples are also discussed to illustrate the method.
Internship Proposal Knowledge Compilation and Enumeration for Efficient Query Evaluation Algorithms

General background. Over the past decades, the database and knowledge representation communities have converged to expressive formalisms to describe queries and reasoning. They have studied how to evaluate complex queries on large databases [2] and perform reasoning at scale in the context of ontology-mediated query answering [5, 8, 10]. However, even if these approaches are very efficient in the size of the input data, the set of query answers to compute can be prohibitively large. For this reason, the database theory community has focused in recent years on efficient enumeration of query results. In this approach, the database is first preprocessed to compute a compact representation of all solutions. Second, the solutions can be enumerated one after the other, with a small delay between answers. This fine study of complexity makes it possible to design algorithms that can return a first answer, or the first few answers, as efficiently as possible. In 2017, we proposed [3] a framework for efficient enumeration algorithms, via the field of knowledge compilation. This area studies families of Boolean and arithmetic circuits under different syntactic and semantic restrictions, to ensure the tractability of basic operations on these circuits. We showed that enumeration algorithms could be designed in a modular fashion. First, express the set of all solutions as a circuit in a tractable knowledge compilation formalism. Second, design general-purpose enumeration algorithms on them. The point is that circuits serve as an intermediate representation that neatly separate the algorithmic problems of enumeration from the logical problems of reasoning and query evaluation. Further, circuits can also serve for other tasks, e.g., explanation of query results [7, 4], counting, etc.
FUGIO: Automatic Exploit Generation for PHP Object Injection Vulnerabilities

A PHP object injection (POI) vulnerability is a security-critical bug that allows the remote code execution of class methods existing in a vulnerable PHP application. Exploiting this vulnerability often requires sophisticated property-oriented programming to shape an injection object. Existing off-the-shelf tools focus only on identifying potential POI vulnerabilities without confirming the presence of any exploit objects. To this end, we propose FUGIO, the first automatic exploit generation (AEG) tool for POI vulnerabilities. FUGIO conducts coarse-grained static and dynamic program analyses to generate a list of gadget chains that serve as blueprints for exploit objects. FUGIO then runs fuzzing campaigns using these identified chains and produces exploit objects. FUGIO generated 68 exploit objects from 30 applications containing known POI vulnerabilities with zero false positives. FUGIO also found two previously unreported POI vulnerabilities with five exploits, demonstrating its efficacy in generating functional exploits.
Establishing quantitative indicators for measuring the partnering performance of construction projects in Hong Kong

Research into Key Performance Indicators (KPIs) for partnering projects in construction becomes vital because an increasing trend of client organizations has been observed to introduce a partnering approach to their building and construction works internationally during the last decade. A Partnering Performance Index (PPI) has been developed for construction projects in Hong Kong. The PPI can assist in developing a benchmark for measuring the performance of their partnering projects. However, it is worth noting that assessors may have their own semantic interpretations on each KPI. The aim of this paper is to establish suitable quantitative indicators (QIs) and reasonable quantitative ranges (QRs) for each KPI in order to avoid any possible discrepancies in interpreting the meaning of each KPI and provide objective evaluation results based on quantitative evidence. By conducting five structured face‐to‐face interviews and two rounds of a Delphi questionnaire survey in Hong Kong, a set of QIs were developed to measure the seven most important KPIs, including: (1) time performance; (2) cost performance; (3) top management commitment performance; (4) quality performance; (5) trust and respect performance; (6) effective communications performance; and (7) innovation and improvement performance. The identified QIs and QRs could assess and compare different partnering projects on a common basis objectively, thus helping to set a benchmark for measuring the performance level of partnering projects in Hong Kong. Construction senior executives and project managers can thus apply the QIs and QRs to measure, evaluate and improve the existing performance of their partnering projects in order to strive for construction excellence.
IMPACT OF GST ON SELECTED COMPANIES OF AUTOMOBILE SECTOR IN NSE

Goods and Service Tax is an iconic Indirect Tax framework intended to assist and improve the economic development of India. It has been one of the most discussed subject area since its initial talks about implementing it in India. The study focuses on the impact of the announcement and implementation of GST in Indian Financial System on selected stocks of NSE and to analyze the sectorial implications in Automobile sector, also testing the semi-strong form of the Indian Stock Market. The efficiency of the market and its reactions provides us a response from the economy. The reactions of the investors is presumed to alter the stock prices IndexTerms GST, NSE, Event Study, Abnormal Return, CAR, AAR, CAAR ________________________________________________________________________________________________________
Dynamics of Land Form Features in Coastal Tract of Odisha from Mahanadi to Dhamra River Estuary Using Remote Sensing and Geospatial Technology

The Coastal land use and land cover features are dynamically regulated due to marine and terrestrial processes and controlling by natural and anthropogenic activities. The detail knowledge of extent and impact of past changes and their implications for future management is needed to understand the challenges in coastal tract. Worldwide the coastal zone occupy about 8% of earth’s terrestrial surface But contribute 37% and 44% of world’s population (as of 1994) within 100k.m and 150k.m of a coast line respectively(Cohen et.al, 1997). Thus, coastal zone ecosystem (Estuaries, Wetlands, Coral reefs etc), which are rich in natural resources are in extreme pressure. There has been a tremendous pressure on the resources and habitats of Odisha coast in the last few decades. Rapid industrialization in Paradeep and Dhamra port, increase of aquaculture activities, denudation of coastal sands, rapid degradation of coastal vegetation, salinity ingress in the agricultural land, natural and artificial disturbance on the habitats of sea turtles etc. Mangroves, which act as a barrier for the coast from any natural disasters is now degrading in a rapid rate which threatens the ecosystem and coastal environment.Multidate Satellite images were used to delineate changes in Land use features. Arc Map and ERDAS Imagine softwares were used in the interpretation and analysis land use features. The main objectives of the current study changes in Land features and other thematic layers such as infractures, drainage etc to understand the impact of Land use changes in the coastal region and their socio-economic impact on the Coastal region. The coastal resources and their apparent changes and threats suggest that the existing conservation measures and protection plans are inadequate to reclaim the Coastal natural resources. The community participation is essential for the protection of the brittle coastal environment. But more rigid laws must be effectively enforced. In developing countries like India, public participation, education programme, awareness initiatives are the foundation of success of any Coastal Management Programme. Thus, in order to achieve a sustainable development in the delicate coastal ecosystem, involvement of local community at all levels is essential to protect the environmentally sensitive Coastal tract of Odisha. The findings of the study can be useful for the Coastal managers and decision making authority for proper implementation of Coastal Sustainable goals. Index Terms -LULC, GIS, Geo-Spatial technology, MNP
Decision Making Process for Personnel Selection Under Fuzzy Environment

As it is in all the selection problems, personnel selection is also a kind of a decision making problem. It requires the usage of the imprecise and unclear data in the decision-making process and it is an important subject for the organizations. In this study, the personnel selection problem of a firm which is active in the logistics sector was dealt and an algorithm based on the Fuzzy Vikor method, oriented towards the solution of the problem. In the assessment of the alternatives on the basis of the factors, linguistic variables were used and the refinement process of the fuzzy weights were realized with Best Nonfuzzy Performance Value (BNP) method. The study showed that the Fuzzy Vikor method could be used in personnel selection as an efficient method.
Control mechanisms for ITS cooperative safety applications

The effectiveness of ITS cooperative safety applications depends widely on the efficient exchange of two main types of information. The periodic “awareness” corresponding to the one-hop location information of surrounding environment and the multi-hop event-driven information generated at the detection of a safety situation. Due to the large scale characteristic of ITS, this information is expected to be subject to severe congestion which might impact its reliable reception. The goal of this thesis is to focus on the reliable and robust control of safety-related information by controlling the channel congestion and at the same time taking into account the requirements of safety applications. Due to its demanding constraints, we address first the event-driven safety information. We propose to adapt the multi-hop dissemination benchmark “CBF ” (Contention-Based Forwarding) to the ITS challenging environment by first considering the non-uniform vehicular topology and the high fading channels. Second, we take into account the dissemination capabilities of the relays’ antenna. Even though that our multi-hop policy showed to improve the dissemination of the event-driven information and provide 40% reduction in overhead compared to CBF, it remains strongly sensitive to the channel load resulting from periodic awareness transmissions. On the other hand, the effective transmission of event-driven information depends primarily on the accurate detection of safety events and accordingly on the accuracy of awareness. Thus, we provide an efficient awareness control mechanism. Based on a bio-inspired prediction algorithm, our approach is to adapt the transmission of awareness messages and conceptually make it aperiodic in order to limit the channel congestion. Moreover, our aperiodic awareness system is capable of accurately detecting brutal awareness change. Simulations conducted with iTETRIS platform1 show how our approach is capable of answering the trade-off between channel congestion and cooperative safety requirements. Observing the fact that the transmissions related to safety events depend mainly on the global context perceived by the vehicles, we demonstrate the need for a global entity to handle efficiently the transmissions of safety-related information. We propose an information-centric dissemination framework that should be developed at higher layers in the protocol stack. iTETRIS simulation platform http://www.ict-itetris.eu/10-10-10-community/
Pumped Storage as a complement for renewables

This work aims at evaluating if Pumped Hydro Storage (PHS) can be used to complement wind and solar generation in mainland Portugal. The 2018 energy Portuguese landscape was studied, and it was concluded that there is no substantial amount of excess energy. The PHS operation was simulated, and the results confirmed that when increasing generation by identified factors, installing PHS is viable and much needed to complement the renewable sources and satisfy 100% of demand. Possible locations to install PHS were identified. Future work should include the study of how increasing renewable sources capacity translates to increased generation.
Summary: The present work proposes a Chebyshev-collocation Fourier-Galerkin pseudospectral method for simulating unsteady, three-dimensional, fluid flows in cylindrical geometries with pressure-driven flow

The present work proposes a Chebyshev-collocation Fourier-Galerkin pseudospectral method for simulating unsteady, three-dimensional, fluid flows in cylindrical geometries with pressure-driven flow through permeable boundaries. Such systems occur in diverse applications and are challenging to simulate due to an additional velocity-pressure coupling on the permeable walls through Darcy’s law. The present work extends the projection method of I. Raspo et al. [Comput. Fluids 31, No. 4–7, 745–767 (2002; Zbl 1028.76036)] to assure Darcy’s law is satisfied exactly. A multidomain solver allows the efficient treatment of open boundary conditions that necessitate permeability buffers and a sponge layer. The method is spectrally convergent, and we demonstrate that pressure-prediction is necessary to obtain second-order temporal accuracy. The ability of the method to simulate complicated physical systems is demonstrated by simulating subcritical and supercritical flows in rotating filtration in Taylor-Couette cells. For subcritical cases, numerical results show excellent agreement with analytical solutions. For supercritical cases, the numerical method accurately resolves convectively and absolutely unstable flows with traveling toroidal and helical vortical structures that are in good agreement with a local linear stability analysis and experimental observations.
Knowledge is reward: Learning optimal exploration by predictive reward cashing

There is a strong link between the general concept of intelligence and the ability to collect and use information. The theory of Bayes-adaptive exploration offers an attractive optimality framework for training machines to perform complex information gathering tasks. However, the computational complexity of the resulting optimal control problem has limited the diffusion of the theory to mainstream deep AI research. In this paper we exploit the inherent mathematical structure of Bayes-adaptive problems in order to dramatically simplify the problem by making the reward structure denser while simultaneously decoupling the learning of exploitation and exploration policies. The key to this simplification comes from the novel concept of cross-value (i.e. the value of being in an environment while acting optimally according to another), which we use to quantify the value of currently available information. This results in a new denser reward structure that"cashes in"all future rewards that can be predicted from the current information state. In a set of experiments we show that the approach makes it possible to learn challenging information gathering tasks without the use of shaping and heuristic bonuses in situations where the standard RL algorithms fail.
Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.
Machine Learning from a Bayesian Perspective

I summarize a Bayesian perspective of machine learning. We view Bayes as an optimization problem whose solutions use the information-geometry of the posterior. Using this perspective, we can show that many machine-learning methods have a (more general) Bayesian side to them. I believe this perspective to be essential for bridging the gap between ‘artiﬁcial’ and ‘natural’ learning systems.
Corrosion protection system for construction and rehabilitation of reinforced concrete bridges

Corrosion of metallic structures has a significant impact on the USA economy. In a recent study, the total direct cost of corrosion was determined to be $276 billion per year, of which the costs associated with the infrastructure represent 16.4%, a sizeable portion. The dollar impact of corrosion on highway bridges is considerable. The average annual direct cost of corrosion for highway bridges (including steel) was estimated to be $8.29 billion. Based on the annual cash outflows, the analysis shows that indirect (user) costs can exceed the direct cost by a factor of ten or more. The paper presents details on various methodologies presently used for rehabilitation of reinforced concrete bridges. In addition, the rationale for calculating annual cost of corrosion is discussed. A brief synopsis on various corrosion protection systems for the new construction is also provided.
A Secure and Graphic Authentication Model to Detect and Prevent Shoulder Surfing Attacks

: This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such a choosing wrong passwords and inputted passwords in not secure way are regarded as “the weakest connection” in the authentication chain, Rather than arbitrary alphanumeric character, users tend to select a password either short or his name related for easy memorization. With web site applications and mobile phone apps charging up, peoples can get access to these types of application anytime, anywhere with multiple devices. This evolution brings good convenience but also improves the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect user’s credentials. To overcome this problem, we proposed a novel authentication system Pass Matrix, based on graphical passwords to resist shoulder surfing attacks. Many authentication methods are presented, but users are familiar with textual password method. Textual password methods are vulnerable to shoulder surfing and key loggers. To overcome this problem many other authentication system like token based authentication, biometric based authentication systems, graphical password methods have been proposed. However biometric based authentication systems are costly and graphical password systems are secure and efficient.
Skin Cancer Diagnosis Using Dermoscopic Images and Patient Information

Skin cancer is the most common type of cancer worldwide. Early detection leads to an increased survival rate. CAD systems, which process dermoscopic images, can improve the early detection rates. In recent years, different CAD (Computer-Aided Diagnosis) systems have been developed. However, almost all of these systems ignore additional patient metadata (e.g., age, region of the body, and gender), which is also taken into account by dermatologists when diagnosing the lesions. This work aims to answer the following question: ”Does combining patient information with dermoscopic images for skin lesion diagnosis lead to further improvements over just dermoscopic images? ”. The goal is to understand if there are any performance improvements when incorporating the patient’s clinical information (age, sex, body region) in the decision system. Thus, different strategies based on Deep Neural Networks, that combine these covariates with images, are proposed. These strategies are compared against models trained just with images. Experiments conducted on the ISIC 2019 dataset verified that metadata improves the results, since the strategies that incorporate patient’s metadata reach a higher BACC. The bestevaluated configuration achieved a BACC of 77.76% for the validation set and 56.01% for the test set, and it led to an improvement of 3.14% and 3.79%, respectively, over the model without metadata. In this configuration, the fusion of the image network and the metadata network is performed by multiplying their outputs. Lastly, the relevance of each combination of metadata is explored, and a website application is developed to be used by dermatologists.
Diffractive Optical Element for Noise-reduced Beam Shaping of Multi-array Point Light Source

An arrayed diffractive optical element design for the beam-shaping of a multi-array light source is proposed. This is an essential device for recent optical security and face recognition applications. In practice, we devise a DC noise reduction technique featuring high fabrication error tolerance regarding the multi-array light source diffractive optical elements, as a necessary part of the proposed design method. The spherical diverging illumination leads to DC-conjugate noise spreading. The main idea is tested experimentally, and the multi-array light source diffraction pattern is investigated numerically.