Cup-length estimates for leaf-wise intersections

We prove that on a restricted contact type hypersurface the number of leaf-wise intersections is bounded from below by a certain cup-length.Comment: 13 pages, 4 figures; v2: minor modification

A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing

The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy

QoS - Aware content oriented flow routing in optical computer network

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this thesis, one of the most important issues in the field of networks communication is tackled and addressed. This issue is represented by QoS, where the increasing demand on highquality applications together with the fast increase in the rates of Internet users have led to massive traffic being transmitted on the Internet. This thesis proposes new ideas to manage the flow of this huge traffic in a manner that contributes in improving the communication QoS. This can be achieved by replacing the conventional application-insensitive routing schemes by others which take into account the type of applications when making the routing decision. As a first contribution, the effect on the potential development in the quality of experience on the loading of Basra optical network has been investigated. Furthermore, the traffic due to each application was dealt with in different ways according to their delay and loss sensitivities. Load rate distributions over the various links due to the different applications were deployed to investigate the places of possible congestions in the network and the dominant applications that cause such congestions. In addition, OpenFlow and Optica Burst Switching (OBS) techniques were used to provide a wider range of network controllability and management. A centralised routing protocol that takes into account the available bandwidth, delay, and security as three important QoS parameters, when forwarding traffics of different types, was proposed and implemented using OMNeT++ networks simulator. As a novel idea, security has been incorporated in our QoS requirements by incorporating Oyster Optics Technology (OOT) to secure some of the optical links aiming to supply the network with some secure paths for those applications that have high privacy requirements. A particular type of traffic is to be routed according to the importance of these three QoS parameters for such a traffic type. The link utilisation, end to end delays and securities due to the different applications were recorded to prove the feasibility of our proposed system. In order to decrease the amount of traffic overhead, the same QoS constraints were implemented on a distributed Ant colony based routing. The traditional Ant routing protocol was improved by adopting the idea of Red-Green-Blue (RGB) pheromones routing to incorporate these QoS constraints. Improvements of 11% load balancing, and 9% security for private data was achieved compared to the conventional Ant routing techniques. In addition, this Ant based routing was utilised to propose an improved solution for the routing and wavelength assignment problem in the WDM optical computer networks

Secure approximation of edit distance on genomic data

© 2017 The Author(s). Background: Edit distance is a well established metric to quantify how dissimilar two strings are by counting the minimum number of operations required to transform one string into the other. It is utilized in the domain of human genomic sequence similarity as it captures the requirements and leads to a better diagnosis of diseases. However, in addition to the computational complexity due to the large genomic sequence length, the privacy of these sequences are highly important. As these genomic sequences are unique and can identify an individual, these cannot be shared in a plaintext. Methods: In this paper, we propose two different approximation methods to securely compute the edit distance among genomic sequences. We use shingling, private set intersection methods, the banded alignment algorithm, and garbled circuits to implement these methods. We experimentally evaluate these methods and discuss both advantages and limitations. Results: Experimental results show that our first approximation method is fast and achieves similar accuracy compared to existing techniques. However, for longer genomic sequences, both the existing techniques and our proposed first method are unable to achieve a good accuracy. On the other hand, our second approximation method is able to achieve higher accuracy on such datasets. However, the second method is relatively slower than the first proposed method. Conclusion: The proposed algorithms are generally accurate, time-efficient and can be applied individually and jointly as they have complimentary properties (runtime vs. accuracy) on different types of datasets

Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have started acknowledging the need to build a diverse, equitable, inclusive, and accessible (DEIA) transportation system. Traditionally, this has been done through survey-based approaches that are time-consuming and expensive. While there is recent attention on leveraging social media data in transportation, the literature is inconclusive regarding the use of social media data as a viable alternative to traditional sources to identify the latent DEIA indicators based on public reactions and perspectives on social media. This study utilized large-scale Twitter data covering eight counties around the New York City (NYC) area during the initial phase of the Covid-19 lockdown to address this research gap. Natural language processing techniques were used to identify transportation-related major DEIA issues for residents living around NYC by analyzing their relevant tweet conversations. The study revealed that citizens, who had negative sentiments toward the DEIA of their local transportation system, broadly discussed racism, income, unemployment, gender, ride dependency, transportation modes, and dependent groups. Analyzing the socio-demographic information based on census tracts, the study also observed that areas with a higher percentage of low-income, female, Hispanic, and Latino populations share more concerns about transportation DEIA on Twitter

Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those who involved heavily and interacted closely with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters

Secure and efficient multiparty computation on genomic data

© ACM 2016. Large scale biomedical research projects involve analysis of huge amount of genomic data which is owned by different data owners. The collection and storing of genomic data is sometimes beyond the capability of a sole organization. Genomic data sharing is a feasible solution to overcome this problem. These scenarios can be generalized into the problem of aggregating data distributed among multiple databases and owned by different data owners. However, we should guarantee that an adversary cannot learn anything about the data or the individual contribution of each party towards the final output of the computation. In this paper, we propose a practical solution for secure sharing and computation of genomic data. We adopt the Paillier cryptosystem and the order preserving encryption to securely execute the count query and the ranked query. Experimental results demonstrate that the computation time is realistic enough to make our system adoptable in the real world