The Needleman-Wunsch algorithm is considered the benchmark for global alignment, this work presents a new framework for the parallel NW algorithm by focusing on the filling process(the most time consuming phase of the algorthim) and only filling a percentage of the matrix and neglecting unnecessary cells to reduce the computational power needed to achieve the desired results. The framework applies this concept and implements it on the GPU to further reduce its execution time, the framework has also been implemented on a grid of GPUs to speed up the alignment of large DNA datasets. Experiments with the proposed framework shows its effectiveness and efficiency.
Software engineering principles are brought into practice by Information Technology companies all around the world. Software can be developed by local teams which members have different cultural backgrounds, as well as by teams distributed across countries. To save costs and be close to markets and customers, companies offshore or outsource the personnel. Although developing software in distributed teams offers multiple benefits, there are also stiff challenges that engineers and managers have to deal with, e.g. communication and collaboration may get affected because of geographic distance, different time zones and distinct cultural backgrounds among team members. If not addressed on time and effectively, these challenges generate misunderstanding and conflict among the team which eventually may impact the projects deadlines and quality of the software. This paper presents the most common software engineering practices, challenges and tools in global teams, as well as practical cases in the industrial and academic realms.
As A Relatively New Framework Suggested For Core Problems Of Software Development, One Important Issue For Essence Framework (Ef) Is Mapping Software Development Practices To The Ef’S Conceptual Domain. There Are Several Works Describing Systematic Procedures, However, A Review Of Literature Cannot Suggest A Study Using Formal Method(S). In This Paper, A Software Practice Mapping Method Is Proposed, Which Adopts And Employs Concept Algebra Principles In A Scrum Case. The Results Are Promising, However, More Empirical Evidences Are Needed To Support The Solution.
Nlp And Text Mining Is Mainly Involved In The Task Of Automatic Summarization. The Text Is Reduced In The Shortest Way By Preserving The Properties. The Ability To Post Opinionated Reviews Is A Service That Is Provided By Many Reviews Sites Where Customers Can Post Their Opinions As Free Text. Identification Of Aspects From Free Text Is An Interesting Task In Sentiment Analysis. Aspect-Based Opinion Summarization Includes Two Main Areas Under Opinion Min-Ing; I.E. Aspect Identification And Opinion Summarization. Novel Al-Gorithm Is Proposed For Extracting Aspects Using Dependency Rela-Tions And Linguistic Features From Customer Reviews. Thereafter, Principal Component Analysis Is Used For Generation Of Aspect Based Summary. The Results Are Carried Out On Dissimilar Datasets Consisting Of Numerous Opinions And Comparison With Previous Based Approaches Demonstrates The Success Of The Work. The Accura-Cy Results Are Reported With F-Scores As 0.14197 (Rouge-1) And 0.03021 (Rouge-2) For Extractive Based Summarization On Opinosis Dataset. The Three Random Individuals Are Contacted For Reference Summaries In Order To Compare The System Generated Gold Summaries On The Real Dataset For Conducting Subjective Evaluation.
The large number of geo referenced data sets provided by Open Data portals, social media networks and created by volunteers within citizen science projects (Volunteered Geographical Information) is pushing analysts to define and develop novel framework for analysing these multisource heterogeneous data sets in order to derive new data sets that generate social value. For analysts, such an activity is becoming a common practice for studying, predicting and planning social dynamics. The convergence of various technologies related with data representation formats, database management and GIS (Geographical Information Systems) can enable analysts to perform such complex integration and transformation processes. JSON has become the de-facto standard for representing (possibly geo-referenced) data sets to share; NoSQL databases (and MongoDB in particular) are able to natively deal with collections of JSON objects; the GIS community has defined the GeoJSON standard, a JSON format for representing georeferenced information layers, and has extended GIS software to support it.
However, all these technologies have been separately developed, and consequently, there is actually a gap that shall be filled to easily manipulate GeoJSON objects by performing spatial operations. In this paper we pursue the objective of defining both a unifying view of several NoSQL databases and a query language that is independent of specific database platforms to easily integrate and transform collections of GeoJSON objects. In the paper, we motivate the need for such a framework, named J-CO, able to execute novel high-level queries, written in the J-CO-QL language, for JSON objects and will show its possible use for generating open data sets by integrating various collections of geo-referenced JSON objects stored in different databases.
The aim of the research presented in this paper is to support the hypothesis that augmented reality is a good educational tool compared to the traditional ways of learning. In order to understand the level of knowledge, attitude and preferences towards the use of AR in education is in the Republic of Macedonia, we have conducted a survey where we designed several questions and examples that helped us to collect initial insights on the topic. The second part of the study was a practical experiment that showed that regardless of the environment, children prefer this visual way of studying compared to the traditional one. The AR applications used in this experiment were Skyview, Augment and Anatomy 4D, introduced to two classes of 6-8 and 10-12 years old. Additionally, AR applications were used by two children with dyslexia, 9 and 12 years old.
We Study The Problem Generating Top K Diverse Ranking, A Constrainedbi-Objective Optimization Problem, And Propose An Algorithm To Generatediverse Rankings Given A Corpus Of Partially Labelled Data. To The Best Ofour Knowledge, Our Algorithm Is The ﬁRst Attempt To Generate Diverse Rank-Ing In The Transductive Semi-Supervised Settings, Whereas Earlier Attemptswere On Diversifying An Already Available Ranked List. Our Experimentalstudy Shows That Our Approach Compares Superior To The State-Of-The-Art‘Learning To Rank’ Algorithms On Several Benchmark Datasets
Cluster Analysis And Anomaly Detection Are The Primary Methods For Database Mining. However, Most Of The Data In Today’S World, Generated From Multifarious Sources, Don’T Adhere To The Assumption Of Single Distribution As Their Source — Hence The Problem Of Finding Clusters In The Data Becomes Arduous As Clusters Are Of Widely Differing Sizes, Densities And Shapes, Along With The Presence Of Noise And Outliers.Thus We Propose A Relative Knn Kernel Density Based Clustering Algorithm. The Un-Clustered (Noise) Points Are Further Classified As Anomaly Or Non-Anomaly Using A Weighted Rank Based Anomaly Detection Method. This Method Works Particularly Well When The Clusters Are Of Varying Variability And Shape, In These Cases Our Algorithm Can Not Only Find The “Dense” Clusters That Other Clustering Algorithms Find, It Also Finds Low-Density Clusters That These Approaches Fail To Identify. This More Accurate Clustering In Turn Helps Reduce The Noise Points And Makes The Anomaly Detection More Accurate.