Static Taint Analysis via Type-checking in TypeScript

With the widespread use of web applications across the globe, and the ad- vancements in web technologies in recent years, these applications have grown more ubiquitous and sophisticated than ever before. Modern web applications face the constant threat of numerous web security risks given their presence on the internet and the massive influx of data from external sources. This paper presents a novel method for analyzing taint through type-checking and applies it to web applications in the context of preventing online security threats. The taint analysis technique is implemented in TypeScript using its built-in type-checking features, and then integrated into a web application developed using the React web framework. This web application is then validated against different types of injection attacks. The results of the validation show that taint analysis is an effective means to prevent pervasive online attacks, such as eval injection, cross-site scripting (XSS), and SQL injection in web applications. Considering that our proposed taint analysis technique can be implemented using existing type-checking features of TypeScript, it can be quickly adopted by developers to add taint analysis into their applications with no performance overhead. With the large number of web applications developed in TypeScript, the widespread adoption of our technique can help prevent cyberattacks and protect the online community from potential harm. By combining taint analysis with other secure web practices, such as input validation, application developers can strengthen the overall security of web applications

Genetic Programming Based Approach Towards Understanding the Dynamics of Urban Rainfall-runoff Process

AbstractGenetic Programming (GP) is an evolutionary-algorithm based methodology that is the best suited to model non-linear dynamic systems. The potential of GP has not been exploited to the fullest extent in the field of hydrology to understand the complex dynamics involved. The state of the art applications of GP in hydrological modelling involve the use of GP as a short-term prediction and forecast tool rather than as a framework for the development of a better model that can handle current challenges. In today's scenario, with increasing monitoring programmes and computational power, the techniques like GP can be employed for the development and evaluation of hydrological models, balancing, prior information, model complexity, and parameter and output uncertainty. In this study, GP based data driven model in a single and multi-objective framework is trained to capture the dynamics of the urban rainfall-runoff process using a series of tanks, where each tank is a storage unit in a watershed that corresponds to varying depths below the surface. The hydro-meteorological data employed in this study belongs to the Kent Ridge catchment of National University Singapore, a small urban catchment (8.5 hectares) that receives a mean annual rainfall of 2500 mm and consists of all the major land uses of Singapore

Identification of causative pathogen and its antibiotic sensitivity in cases of preterm premature rupture of membranes

Background: Pre-labor rupture of membranes is defined as amniotic membrane rupture before the onset of labor contractions, and if it happens before 37 weeks, it is called preterm premature rupture of membranes (PPROM). Several organisms commonly present in the vaginal tract are E.coli, Group-B streptococci, staphylococcus aureus, chlamydia trachomatis, Gardnerella vaginalis and Enterococcus faecalis which secrete proteases that degrade collagen thereby weakening  the fetal membranes leading to PPROM. Appropriate antibiotic therapy has a significant role in the prevention and treatment of maternal and neonatal complications.Methods: This was a prospective observational study done in the department of obstetrics and gynaecology, Narayana medical college, Nellore. Selectively 100 patients with complaint of PPROM admitted to labor room were included in the study. Diagnosis of membrane rupture was established by speculum examination, and high vaginal swabs are taken and sent to laboratory for identifying bacteria using gram staining and cultured in aerobic and anaerobic methods. Antimicrobial susceptibility testing of the organisms was performed by disk diffusion method by Kirby and Bauer.Results: Out of 100, high vaginal swabs had growth in 82 patients, and 18 were sterile. The repeatedly isolated organism in patients with PPROM is E.coli amounting 32%, followed by candidal species 20%. Staphylococci are scoring 11% and enterococci 8%. However, organisms like gardenella vaginalis and Group B streptococcus are least common with a score of 6% and 5% respectively. In this study, E.coli is highly sensitive to tigecycline, colistin 100% each and highly resistant to gentamycin and amikacin.Conclusions: In this study, E.coli is related to the maximum number of cases with preterm premature rupture of membranes. Appropriate use of antibiotics significantly lowers maternal morbidity and neonatal mortality

Induction Of Governing Differential Equations From Hydrologic Time Series Data Using Genetic Programming

Induction of Governing Differential Equations from Hydrologic Time Series Data using Genetic Programming Jayashree Chadalawada and Vladan Babovic This contribution describes an evolutionary method for identifying causal model from the observed time-series data. In the present case, we use a system of ordinary differential equations (ODEs) as the causal model. Usefulness of the approach is demonstrated on real-world time series of hydrologic processes and the unknown function of governing factors are determined. To explore the evolutionary search space more effectively, the right hand sides of ODEs are inferred by genetic programming (GP). The importance of different fitness criteria, as well as introduction of background knowledge about underlying processes are also being discussed and assessed. The method is applied on several cases and empirically demonstrated how successfully GP infers the systems of ODEs

Development of Carbon Emission Assessment Tool Towards Promoting Sustainability in Cal State LA

The great demand for the burning of fossil fuels has greatly increased greenhouse gases (GHG) concentrations in the atmosphere. An increase in the atmospheric concentrations of greenhouse gases produces a positive climate forcing or warming effect [EPA, Climate Change Indicators]. Therefore, mitigation of GHG concentrations is important to prevent long-term impacts on the environment. On April 4, 2016, California State University, Los Angeles signed the most comprehensive of Second Nature’s three Climate Leadership Commitments, the Climate Commitment. Following this commitment, California State University, Los Angeles, set the ambitious goal of operational carbon neutrality by the year 2040. To assist California State University, Los Angeles in moving effectively toward this goal, we developed an energy dashboard that can bring access, awareness, and education to campus about campus carbon footprint and promote energy-efficient behaviors. The developed energy dashboard is an interactive web application that works based on an energy model that is composed of various energy-consuming and GHG producing units such as Heating, Ventilation and Air Conditioning (HVAC), Heated Potable Water (HPW), Electricity, and Campus-Related Commutes. This energy dashboard enables individuals to analyze the campus’s energy consumption and carbon footprint. Our research showed that campus-related commute was the first largest contributor to Cal State LA’s carbon footprint in 2018 and accounted for 71.5% of carbon emissions. Electricity and heated potable water accounted for 20%, and 8.5% of the total campus carbon emissions, respectively. * Our developed energy dashboard is currently accessible at the following link [Khodayari, Arezoo et. al.]: https://cysun.org/espc-researchlab/EnergyDashboard

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

The purpose of this review is to establish and classify the diverse ways in which evolutionary computation (EC) techniques have been employed in water demand modelling and to identify important research challenges and future directions. This review also investigates the potentials of conventional EC techniques in influencing water demand management policies beyond an advisory role while recommending strategies for their use by policy-makers with the sustainable development goals (SDGs) in perspective. This review ultimately proposes a novel integrated water demand and management modelling framework (IWDMMF) that enables water policy-makers to assess the wider impact of water demand management decisions through the principles of egalitarianism, utilitarianism, libertarianism and sufficientarianism. This is necessary to ensure that water policy decisions incorporate equity and justice. Environmental science; Applied computing; Computing methodology; Civil engineering; Process modeling; Hydrology; evolutionary computation; water justice; water demand; Artificial intelligence; water equity; Sustainable development goal

DATA DRIVEN MODELLING AND KNOWLEDGE DISCOVERY IN WATER RESOURCES ENGINEERING

Ph.DDOCTOR OF PHILOSOPH

SAR image content retrieval by speckle robust compression based methods

This paper presents a study of content based image retrieval using compression based methods with original and despeckled TerraSAR-X images. This study aims at analysing the behaviour of our method regarding speckle noise. Our method is based on Lempel-Ziv-Welch compression algorithm for feature extraction and fast compression distance as similarity metric. From the experimental results can be observed that the method is independent of the speckle noise

Assessment of earth observation data content based on data compression - applications to settlement understanding

Urban areas around the world are rapidly changing in an unregulated manner and remote sensing is the most effective option for their monitoring and planning. The wide variability in the organization of cities all over the world makes it difficult to make a global and accurate model for urban areas. Good modeling of urban areas means reliable translation of the scene semantics into an algorithmic language

Real Time Detection and Recognition of Construction Vehicles : Using Deep Learning Methods

Background. The driving conditions of construction vehicles and their surrounding environment is different from the traditional transportation vehicles. As a result,they face unique challenges while operating in the construction/evacuation sites.Therefore, there needs to be research carried-out to address these challenges while implementing autonomous driving, although the learning approach for construction vehicles is the same as for traditional transportation vehicles such as cars. Objectives. The following objectives have been identified to fulfil the aim of this thesis work. To identify suitable and highly efficient CNN models for real-time object recognition and tracking of construction vehicles. Evaluate the classification performance of these CNN models. Compare the results among one another and present the results. Methods. To answer the research questions, Literature review and Experiment have been identified as the appropriate research methodologies. Literature review has been performed to identify suitable object detection models for real-time object recognition and tracking. Following this, experiments have been conducted to evaluate the performance of the selected object detection models. Results. Faster R-CNN model, YOLOv3 and Tiny-YOLOv3 have been identified from the literature review as the most suitable and efficient algorithms for detecting and tracking scaled construction vehicles in real-time. The classification performance of these algorithms has been calculated and compared with each other. The results have been presented. Conclusions. The F1 score and accuracy of YOLOv3 has been found to be better amongst the algorithms, followed by Faster R-CNN. Therefore, it has been concluded that YOLOv3 is the best algorithm in the real-time detection and tracking of scaled construction vehicles. The results are similar to the classification performance comparison of these three algorithms provided in the literature