An Approach to Parallel Processing

Parallel processing offers enhanced speed of execution to the user and facilitated by different approaches like data parallelism and control parallelism. Graphic Processing Units provide faster execution due to dedicated hardware and tools. This paper presents two popular approaches and techniques for distributed computing and GPU computing, to assist a novice in parallel computing technique. The paper discusses environment needs to be setup for both the above approaches and as a case study demonstrate matrix multiplication algorithm using SIMD architecture

Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function

AbstractIn Information Security, intrusion detection is the act of detecting actions that attempt to compromise the security goals. One of the primary challenges to intrusion detection is the problem of misjudgment, misdetection and lack of real time response to the attack. Various data mining techniques as clustering, classification and association rule discovery are being used for intrusion detection. The proposed hybrid technique combines data mining approaches like K Means clustering algorithm and RBF kernel function of Support Vector Machine as a classification module. The main purpose of proposed technique is to decrease the number of attributes associated with each data point. So, the proposed technique can perform better in terms of Detection Rate and Accuracy when applied to KDDCUP’99 Data Set

Data Partitioning for Semantic Web

Semantic web database is an RDF database. Tremendous increase can be seen in semantic web data, as real life applications of semantic web are using this data. Efficient management of this data at a larger scale, and efficient query performance are the two major concerns. This work aims at analyzing query performance issues in terms of execution time and scalability using data partitioning techniques. An experiment is devised to show effect of data partitioning technique on query performance. It demonstrates the query performance analysis for partitioning techniques applied. Vertical partitioning, hybrid partitioning and property table was used to store the RDF data and query execution time is analyzed. The experiment was carried out on a very small dummy data and now it will be scaled up using Barton library catalogue

Garlic improves insulin sensitivity and associated metabolic syndromes in fructose fed rats

<p>Abstract</p> <p>Background</p> <p>Type 2 diabetes mellitus, characterized by peripheral insulin resistance, is a major lifestyle disorder of the 21^stCentury. Raw garlic homogenate has been reported to reduce plasma glucose levels in animal models of type 1 diabetes mellitus. However, no specific studies have been conducted to evaluate the effect of raw garlic on insulin resistance or type 2 diabetes mellitus. This study was designed to investigate the effect of raw garlic on fructose induced insulin resistance, associated metabolic syndrome and oxidative stress in diabetic rats.</p> <p>Methods</p> <p>Male Sprague Dawley rats weighing 200-250 gm body weight were divided into 3 groups (n = 7 per group) and fed diet containing 65% cornstarch (Control group) and 65% fructose (Diabetic group) for 8 weeks. The third group (Dia+Garl group) was fed both 65% fructose and raw garlic homogenate (250 mg/kg/day) for 8 weeks. Whole garlic cloves were homogenized with water to make a fresh paste each day.</p> <p>Results</p> <p>At the end of 8 weeks, serum glucose, insulin, triglyceride and uric acid levels, as well as insulin resistance, as measured by glucose tolerance test, were significantly (p < 0.01) increased in fructose fed rats (Diabetic group) when compared to the cornstarch fed (Control) rats. Administration of raw garlic to fructose fed rats (Dia+Garl group) significantly (p < 0.05) reduced serum glucose, insulin, triglyceride and uric acid levels, as well as insulin resistance when compared with fructose fed rats. Garlic also normalised the increased serum levels of nitric oxide (NO) and decreased levels of hydrogen sulphide (H₂S) after fructose feeding. Although body weight gain and serum glycated haemoglobin levels of fructose fed rats (Diabetic group) were not significantly different from control rats, significant (p < 0.05) reduction of these parameters was observed in fructose fed rats after garlic administration (Dia+Garl group). Significant (p < 0.05) increase in TBARS and decrease in GSH was observed in diabetic liver. Catalase was not significantly affected in any of the groups. Administration of raw garlic homogenate normalised both hepatic TBARS and GSH levels.</p> <p>Conclusions</p> <p>Our study demonstrates that raw garlic homogenate is effective in improving insulin sensitivity while attenuating metabolic syndrome and oxidative stress in fructose-fed rats.</p

Opinion Mining Using Twitter Feeds for Political Analysis

Sentiment analysis deals with identifying and understanding opinions and sentiments expressed in a particular text. The masses give their opinion regarding various subjects on social media platforms using tweets, status updates and blogs. By analyzing this very data, we can gain better insight of the public opinion on any subject in specific. On performing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters allowed in Twitter is 140. In this paper, we try to analyze the twitter posts about government issues and political reforms. The proposed framework uses Twitter as the platform to analyze the emotions of the users using Sentiment Analysis. The system will use the opinions of the users, analyze the reaction and then map it to the appropriate region

Hvorfor tar det tid å fremstille en vaksine mot SARS-CoV-2 på markedet?

Den 11. mars 2020 ble utbruddet av Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) erklært som en global pandemi. Viruset spredde seg raskt, og har rammet 213 land, områder og territorier. Det fører til luftveisinfeksjonen (covid-19), og for utsatte pasientgrupper kan dette være en livstruende tilstand. Helsepersonell er også i utsatte posisjoner med mye pasientkontakt og andre mulige smittebærere. Det beste forsvaret mot SARS-CoV-2 vil være en vaksine, som ikke vil være klar før om tidligst 12 måneder. Denne oppgaven bygger på et minimum av 10 forskningsartikler, og problemstillingen tar utgangspunkt i coronavirusene sin oppbygging og hvordan dette utfordrer utviklingen av en vaksine mot SARS-CoV-2. Søkemotorer som PubMed og MedLine har vært hovedkilden til informasjon. Det blir lagt vekt på ulike aspekter som kompliserer utviklingen av en velfungerende vaksine mot SARS-CoV-2. Deriblant virusets evne til mutasjon da det allerede er påvist tre mutasjonsvarianter av SARS-CoV-2. Det blir gjennomgått hva man frem til nå vet om det nyoppdagede SARS-CoV-2, blant annet dets oppbygning med en helikal symmetrisk nukleokapsid, som ikke er spesielt vanlig blant positiv-sense RNA virus. Videre presenteres smittemåter og inkubasjonstid. Det blir gjennomgått ulike typer vaksinemodeller, og en sammenligning av tidsbruken for ordinær og fremskyndet vaksineutvikling; hvordan en vaksine kan fremstilles på 12 måneder, fremfor de vanlige 10 årene. Innføring av vaksiner i Norge baseres på globale retningslinjer for et standardisert, transparent og kunnskapsbasert system. Til slutt belyses også etiske aspekter ved en for rask vaksineutvikling, og blir satt i sammenheng med tidligere erfaringer fra verdensomspennende epidemier og pandemier og økt vaksinemotstand. Forskning for å forstå det nye SARS-CoV-2 er tidkrevende, samtidig som prosessen med vaksineutviklingen vil kreve tid. Balansegangen mellom en rask nok og trygg nok vaksineutvikling er et dilemma. Samtidig skal samfunnet ha tillit til at den er trygg å ta. Alle disse faktorene er avgjørende for hvorfor en vaksine må ta tid

Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data

Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns

Need for design patterns: Interoperability issues and modelling challenges for observational data

Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns