A Novel Approach to Data Extraction on Hyperlinked Webpages

The World Wide Web has an enormous amount of useful data presented as HTML tables. These tables are often linked to other web pages, providing further detailed information to certain attribute values. Extracting schema of such relational tables is a challenge due to the non-existence of a standard format and a lack of published algorithms. We downloaded 15,000 web pages using our in-house developed web-crawler, from various web sites. Tables from the HTML code were extracted and table rows were labeled with appropriate class labels. Conditional random fields (CRF) were used for the classification of table rows, and a nondeterministic finite automaton (NFA) algorithm was designed to identify simple, complex, hyperlinked, or non-linked tables. A simple schema for non-linked tables was extracted and for the linked-tables, relational schema in the form of primary and foreign-keys (PK and FK) were developed. Child tables were concatenated with the parent table’s attribute value (PK), serving as foreign keys (FKs). Resultantly, these tables could assist with performing better and stronger queries using the join operation. A manual checking of the linked web table results revealed a 99% precision and 68% recall values. Our 15,000-strong downloadable corpus and a novel algorithm will provide the basis for further research in this field.publishedVersio

Effect of Temperature on Friction Behavior of Epoxy Resin Composite Coatings for Sliding Contact

Epoxy composite coatings are used to reduce friction in mechanical components. The epoxy composites at high temperatures cause serious frictional effects due to poor surface characteristics. Hence, there is a need to find a substitute to improve their frictional behaviour in high-temperature conditions. Adding fillers and lubrication are key solutions to improve tribological properties. In this research work, diglycidyl ether of bisphenol A (DGEBA) epoxy resin was coated on steel, and silica powder (5% by weight) was used as filler. The experiments were conducted under dry and lubricated conditions with and without filler. The coefficient of friction (COF) was examined at different temperatures. The results show that the COF increases with an increase in temperature. In addition, the minimum average COF was observed in the case of epoxy coating with the filler under lubrication conditions. For that case, up to 94.4% decrease in COF was observed as compared to epoxy coating without filler under dry conditions, a 48.33% decrease in COF to epoxy coating without filler under lubricated conditions and a 17.98% decrease in COF than epoxy coating with the filler under dry condition was perceived. Scanning Electron Microscopy (SEM) and surface roughness analyses were also conducted to examine the surface of the specimen after the experiment. A worn and rougher surface was observed in the case of epoxy without filler under dry conditions. The more coefficient of friction at high temperatures is due to the thermal degradation of the epoxy matrix at high temperatures, which results in a rougher surface

Hospitalization for heart disease, stroke, and diabetes mellitus among Indian-born persons: a small area analysis

BACKGROUND: We set out to describe the risk of hospitalization from heart disease, stroke, and diabetes among persons born in India, all foreign-born persons, and U.S.-born persons residing in New York City. METHODS: We examined billing records of 1,083,817 persons hospitalized in New York City during the year 2000. The zip code of each patient's residence was linked to corresponding data from the 2000 U.S. Census to obtain covariates not present in the billing records. Using logistic models, we evaluated the risk of hospitalization for heart disease, stroke and diabetes by country of origin. RESULTS: After controlling for covariates, Indian-born persons are at similar risk of hospitalization for heart disease (RR = 1.02, 95% confidence interval 1.02, 1.03), stroke (RR = 1.00, 95% confidence interval, 0.99, 1.01), and diabetes mellitus (RR = 0.96 95% confidence interval 0.94, 0.97) as native-born persons. However, Indian-born persons are more likely to be hospitalized for these diseases than other foreign-born persons. For instance, the risk of hospitalization for heart disease among foreign-born persons is 0.70 (95% confidence interval 0.67, 0.72) and the risk of hospitalization for diabetes is 0.39 (95% confidence interval 0.37, 0.42) relative to native-born persons. CONCLUSIONS: South Asians have considerably lower rates of hospitalization in New York than reported in countries with national health systems. Access may play a role. Clinicians working in immigrant settings should nonetheless maintain a higher vigilance for these conditions among Indian-born persons than among other foreign-born populations

A study of Leslie model under stochastic environments

The prediction and analysis of changes in the numbers of biological populations rest on mathematical formulations of demographic events (births and deaths) classified by the age of individuals. The development of demographic theory when birth and death rates vary statistically over time is the central theme of this work. A study of the standard Leslie model for the demographic dynamics of populations in variable environments is made. At each time interval a Leslie matrix of survival rates and fertilities of a population is chosen according to a Markov process and the population numbers in different age classes are computed. Analytical bounds are developed for the logarithmic growth rate and the age-structure of a population after long times. For a two dimensional case, it is shown analytically that a uniform distribution results for the age-structure if the survival rate from the first to the second age-class is a uniformly distributed random quantity with no serial auto correlation. Numerical studies are made which lead to similar conclusions when the survival rate obeys other distributions. It is found that the variance in the survival parameter is linearly related to the variance in the age structure. An efficient algorithm is developed for numerical simulations on a computer by considering a time sequence of births rather than whole populations. The algorithm is then applied to an example in three dimensions to calculate a sequence of births when the survival rate from the first to the second age-class is a random parameter. Numerical values for the logarithmic growth rate and the logarithmic variance for a population and the probability of extinction are obtained and then compared to the analytical results reported here and elsewhere

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity

Writing Instructions at a University and Identity Issues: A Systemic Functional Linguistics Perspective

In this paper, we explore the discoursal identity in the academic writing of a postgraduate student from the University of Pakistan where English is the medium of instruction as well as taught as a foreign language. The study aims to find out the extent and the specific ways dominant conventions and practices enable and constrain meaning-making. It also helps to identify the role of social and institutional goals in shaping the discoursal identity of students. To achieve our objectives, we have conducted a linguistic analysis of the student’s academic texts by using Systemic Functional Linguistics. The findings from the linguistic analysis of academic texts are quite significant because the lexico-grammatical and discoursal choices in the academic texts reflect their writer’s desired disposition and their orientation within academia and their socio-cultural setting. Thus they reveal the writer’s discoursal identity and his positioning and affiliation with the academic community. The findings of the study provide significant implications for the reconceptualization of writing instructions at universities, also they point to the need to employ emerging technologies in the writing instructions program while not ignoring the students’ identity issues

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases

Government Expenditure impact on the Economic Growth of Pakistan

The primary goal of this study is to inspect the Government Expenditure consequence on the Pakistan’s economy growth. For this intention study used data of annual time series from 1980 to 2020. The research-work utilized ADF Unit-Root Test that verify stationary data. And applied (OLS) technique to estimation the connection among the GDP and Govt Expenditure, Inflation, and GDP per capita. The estimation of the OLS method shows there is a positive and significant impact of Govt expenditure and GDP per capita on GDP. While; Inflation has a significantly negative influence on GDP of the country. This study propose that Fiscal Policy Expansionary can be utilized by the Govt to motivate the economic situation during the time of downturn