Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases LSTM model with minimum error is chosen for predicting daily and weekly cases It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots Preventive measures to reduce the spread in respective zones are also suggested A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners This study can be applied by other countries for predicting COVID-19 cases at the state or national level

Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India

It is believed that transactions belong to key services of componentbased middleware. However, surprisingly, there is no general agreement on how the component-based middleware support for transactions should look like. In this paper we exploit our experiences with Jironde [4], a flexible framework that extends the Fractal component model [1, 2] with transactions via a set of transactional controllers that manage transactions on behalf of a component. Then we identify several key architectural and technical issues related to enhancing component-based middleware with transactions. We investigate how current technologies address theses issues, and the suitability of current standards to support transaction processing in component-based middleware. 1 Transactions and Components: Architectural Issues Different component models deal with component’s participation in a transaction differently. In the explicit transaction participation, the scenario of involving a component C to a transaction t essentially consists of the three steps as follows: 1) C is registered to t. 2) A client invokes various operations on C. 3) At the time of t’s validation, the transaction manager invokes specific methods of the registered C’s interfaces. These (callback) methods must be implemented by the transactional components. With the implicit transaction participation, components are not obliged to implement any functionality related to transactions. Any time C is visited by a transaction t, the transaction manager of the container keeps all necessary information to manage atomicity, concurrency control, and recovery. Different component standards deal with component participation in transactions differently. CCM use the explicit transaction participation, COM+ uses the implicit one, while EJB mix both. Component-unaware transactions manipulate data without any knowledge on whether they are organized or related to components. For component-aware transactions, components are the data they manipulate with. We believe that all the CCM, EJB, and COM+ transactions are component-unaware. A component is transaction-unaware if its code does not use any transactional primitives and is not therefore in any way dependent on any transactional standard, while a transactionaware component is the opposite. Component’s awareness of transactions reflects the implicit/explicit transaction participation but includes also some hidden expectations of the component design. Transaction-unaware container does not deal with On Enhancing Component-Based Middleware with Transactions 2 transactions, which are managed at the application level instead. Transaction-aware container provides some transaction management features, such as containerdemarcation, transaction propagation, concurrency control, etc. EJB, CCM and COM+ are examples that provide such containers. 2 Transactions and Components: Technical Issues The technical issues related to transactional component include concurrency control, recovery, and transaction context propagation. As for concurrency control, the current technologies either use a simple read/write model or do not allow any concurrent access to component instances at all. It would be beneficial to exploit the concurrency potential of components by e.g. the use of conflict matrixes defined on all the methods of all implemented interfaces. Both concurrency control and recovery of components reflect the architectural patterns presented in the previous section. Another important issue is the transaction context propagation. There are several options of how to specify a transaction propagation policy, as well as whether to define it either during the component’s development or during its deployment. A last issue is to support the definition of new propagation policies, like JOTDF [5] does.

On The Move to meaningful Internet systems 2003 : OTM 2003 Workshops : OTM Confederated International Workshops : HCI-SWWA, IPW, JTRES, WORM, WMS, and WRSM 2003, Catania, Sicily, Italy, November 3-7, 2003 : proceedings

A wireless medical device is disclosed. The wireless medical device comprises a processor, a memory, a sensor for detecting a physiological signal, a radio and a proximity detector to measure a distance of the wireless medical device relative to a second wireless medical device. The proximity detector includes a ranging functionality. A wireless communication channel is created when a distance between the wireless medical device and the second wireless medical device is within a first predetermined threshold. The distance is greater than zero.

Medical devices with proximity detection

Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single user as well as group recommendation. Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users' satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.

Personalized hybrid recommendation for group of users

Analysis of results of sport matches has always been a hot topic for both data analysts and general public. Modern methods for processing data, in combination with strong computational power of computers, allow us to predict the results of the next matches by applying machine learning algorithms on collected data. This paper suggests innovative approach of processing input data to predict results from input features based on players of the match with limited information about match history itself. Such a model could be used to find optimal players based on their attributes for the specific match or team.

Football Match Prediction Using Players Attributes

Many of software engineering tools and systems are focused to monitoring source code quality and optimizing software development. Many of them use similar source code metrics to solve different kinds of problems. This inspired us to propose an environment for platform independent code monitoring, which supports employment of multiple software development monitoring tools and sharing of information among them to reduce redundant calculations. In this paper we present design of an architecture of the environment, whose main contribution is employing (acquiring, generating and processing) information tags - descriptive metadata that indirectly refer source code artifacts, project documentations and developers activity via document models and user models. Information tags represent novel concept unifying traditional content based software metrics with recently developed activity-based metrics. We also describe prototype realization of the environment within project PerConIK (Personalized Conveying Information and Knowledge), which proves feasibility and usability of the proposed environment.

/pdf/platform-independent-software-development-monitoring-design-3yjfw14exb.pdf

Platform Independent Software Development Monitoring: Design of an Architecture

Diacritics restoration problem is defined as correct insertion of diacritic marks into a sentence without changing the context of a particular sentence. Changing a letter in a text can cause the meaning of the word to change, and therefore the meaning of the whole sentence. This kind of problem is common among languages that use Latin alphabet such as the Slovak language. Our goal is to develop an artificial neural network that can restore diacritic errors made by a human or a computer. For this purpose, we chose state of the art architecture of recurrent neural network. Our results prove that neural network is able to restore diacritics with 97 % accuracy on given Slovak corpus. The accuracy test on a text from a different genre, however, has shown a lower accuracy of 76%.

Diacritics Restoration Using Deep Neural Networks

Simulation of human behavior in various situations is nowadays heavily used in miscellaneous research fields (e.g. emergency exits design, psychology, riot or humanitarian help simulation). The Holy Grail is to identify key elements of human beings that drive our behavior and be able to sufficiently simulate them and replicate in artificial computer environment. This work is focused on riot simulation in city conditions with multiple defense components. Behavior of agents/protesters is driven by PECS (Physical Conditions, Emotional State, Cognitive Capabilities and Social Status) psychological model and movement is adjusted by sophisticated collision avoidance system RVO2 (Reciprocal Collision Avoidance for RealTime Multi-Agent Simulation). Created simulation proposes several scenarios that resemble real demonstrations and can be parameterized for further studies.

http://ieeexplore.ieee.org/iel7/6700566/6705159/06705246.pdf

Riot simulation in urban areas

Ensemble learning is one of the machine learning approaches, which can be described as the process of combining diverse models to solve a particular computational intelligence problem. We can find the analogy to this approach in human behavior (e.g. consulting more experts before taking an important decision). Ensemble learning is advantageously used for improving the performance of classification or prediction models. The whole process strongly depends on the process of determining the weights of base methods. In this paper we investigate different weighting schemes of predictive base models including biologically inspired genetic algorithm (GA) and particle swarm optimization (PSO) in the domain of electricity consumption. We were particularly interested in their ability to improve the performance of ensemble learning in the presence of different types of concept drift that naturally occur in electricity load measurements. The PSO proves to have the best ability to adapt to the sudden changes.

Application of Biologically Inspired Methods to Improve Adaptive Ensemble Learning

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