Abstract The EP (Enterprise-Participant) data model is a language homomorphic to and semantically equivalent to a class of total recursive functions. It takes a unique position in the fields of programming languages and database management. Mathematically, it enables a programming language to achieve the greatest possible ease of use in software development and maintenance. (This assumes that a programming language incorporated... The EP (Enterprise-Participant) data model is a language homomorphic to and semantically equivalent to a class of total recursive functions. It takes a unique position in the fields of programming languages and database management. Mathematically, it enables a programming language to achieve the greatest possible ease of use in software development and maintenance. (This assumes that a programming language incorporated with a higher expressive data model was easier than another one with a lower expressive data model, or no data model at all.) Practically, Froglingo -- a programming language incorporated with the EP data model is a monolith that consolidates multiple software components of traditional software architecture. In addition, Froglingo is untyped, i.e., programmers write application programs without a necessity of user-defined types. Finally, Froglingo is a novel approach to many challenges facing traditional technologies, including feature scalability, user interface flexibility, and similarity. In this paper, we show with further clarity the concept of ease of use by proposing a mathematical definition for the concept of data models and by relating Froglingo with other programming language through the analysis of types and higher-order functions. Read More Read Less

Abstract Data mining is a part of a process called KDDKnowledge Discovery in Databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation [1, 2]. There may be thousands or millions of records that have to be read and to extract the rules for, but the... Data mining is a part of a process called KDDKnowledge Discovery in Databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation [1, 2]. There may be thousands or millions of records that have to be read and to extract the rules for, but the question is what will happen if there is new data, or there is a need to modify or delete some or all the existing set of data during the process of data mining. Also real-world databases are highly susceptible to noise, missing, and inconsistent data due to their typically huge size, often several gigabytes or more. The questions here are; how can the data be preprocessed in order to help improve the quality of the data, and consequently the mining results? How can the new updated data be preprocessed in order to help improve the quality of the data, efficiency, and simplify of the mining process? Therefore the purpose of this study is to find solutions (Item-summation) for dynamic data mining process that is able to take into considerations all updates (insert, update, and delete problems) into account. Read More Read Less

Abstract An ad hoc network is a multi-hop wireless network formed by a collection of mobile nodes without the intervention of fixed infrastructure. Limited bandwidth and a high degree of mobility require that routing protocols for ad hoc networks be robust and simple. In mobile ad hoc networks (MANETs), node mobility causes network topologies to change dynamically over time, which complicates... An ad hoc network is a multi-hop wireless network formed by a collection of mobile nodes without the intervention of fixed infrastructure. Limited bandwidth and a high degree of mobility require that routing protocols for ad hoc networks be robust and simple. In mobile ad hoc networks (MANETs), node mobility causes network topologies to change dynamically over time, which complicates the important tasks such as route discovery and maintenance. Most of the routing algorithms usually focus on how to discover a stable route, but rarely considers the adaptability of constructed route to the change of node’s motion. This paper proposes a new ad hoc route maintenance protocol called Route Maintenance using Link State Prediction (RM-LSP). RM-LSP utilizes node locality to enhance resilience against mobility and hence the throughput. RM-LSP reduces the overhead of route failure recovery in source end and attempts to improve route efficiency and network throughput. Our simulation results show that RM-LSP delivers packets efficiently while substantially reducing control overhead in various environments. Simulation study of the algorithm proves it to offer significant benefits in dense scenarios. Read More Read Less

Abstract The internet is an important part of our world. Offering a large and increasing amount of information, people can use it for learning, teaching, etc. Automatic tools for learning information retrieval based on semantic tags have not been effective yet. We propose here a model which aims at automatically annotating texts with semantic metadata. These metadata would allow us to... The internet is an important part of our world. Offering a large and increasing amount of information, people can use it for learning, teaching, etc. Automatic tools for learning information retrieval based on semantic tags have not been effective yet. We propose here a model which aims at automatically annotating texts with semantic metadata. These metadata would allow us to index and extract learning objects from texts. This model is composed of two parts. While the first part consists of a semantic annotation of learning objects according to their categories (definition, example, exercise, etc.), the second one uses automatic semantic annotation. Generated by the first part, the latter aims at creating a semantic inverted index able to find relevant learning objects for queries. To sort the results according to their relevance, we apply the Rocchio’s classification technique to the learning objects. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness. Read More Read Less

Abstract For centuries, humans fantasized about the idea of controlling objects only by thoughts. In the last decade, this idea has shifted from fantasy or science fiction to reality. Brain Computer Interfaces (BCI) have made this a reality. A BCI is a communication channel between the brain and a computer program or application. It allows the development of new applications that... For centuries, humans fantasized about the idea of controlling objects only by thoughts. In the last decade, this idea has shifted from fantasy or science fiction to reality. Brain Computer Interfaces (BCI) have made this a reality. A BCI is a communication channel between the brain and a computer program or application. It allows the development of new applications that provide assistance to people with disabilities in regular everyday activities, making them accessible and easy to perform. We present in this paper a simple yet efficient email client which enables severely motor-disabled people to open, compose, send, reply and forward emails. It performs a real-time classification of user’s brain activity signals using the P300 technique and performs the appropriate corresponding actions. Read More Read Less

Abstract Association rule mining perhaps the most widely described technique among the minding paradigms. The temporal association rule mining in the association rule mining tries to find relations among items in datasets. The temporal association mining has strength in detecting the dynamic nature of databases. Unfortunately the current mining methods ignore the consideration of database content updates. In the current research... Association rule mining perhaps the most widely described technique among the minding paradigms. The temporal association rule mining in the association rule mining tries to find relations among items in datasets. The temporal association mining has strength in detecting the dynamic nature of databases. Unfortunately the current mining methods ignore the consideration of database content updates. In the current research we have introduced the Incremental Standing method for Segment Progressive Filter (ISPF). The proposed technique can support the database update and mine updated datasets. We prove that the proposed algorithm is an optimal way of mining. We have applied the scan reduction technique to generate all candidate k-item sets to form 2-candidate item sets directly. We also bring good experimentation to validate and document clearly the algorithm with good examples. Read More Read Less