A neural network based intelligent system for tile prefetching in web map services

Web mapping has become a popular way of distributing interactive digital maps over the internet. Instead of dynamically generating map images on the fly, those can be pre-generated and served from a server-side cache for faster retrieval. However, these caches can grow unmanageably in size when the cartography covers mid to large areas for multiple rendering scales. This forces modest organizations to use partial caches containing just a subset of the total tiles, and makes their services less attractive than other mapping services like Google Maps or Microsoft Bing Maps. This work proposes a neural-network-based intelligent system that predicts which areas are likely to be requested in the future from a catalog of geographic features and a short history of past requests. These priority regions can be used by a tile prefetching policy to achieve an optimal population of the cache. Neural networks are trained and validated using supervised learning with real data-sets from a public nation-wide web map service. Trace-driven simulations demonstrate that accurate long-term predictions, up to 90% in terms of cache-hit ratio, can be obtained with the proposed model by prefetching a low fraction, only the 20% of the total tiles, and with a short training period.     

Search based constrained test case selection using execution effort

Software testing is essential to guarantee high quality products. However, it is a very expensive activity, particularly when manually performed. One way to cut down costs is by reducing the input test suites, which are usually large in order to fully satisfy the test goals. Yet, since large test suites usually contain redundancies (i.e., two or more test cases (TC) covering the same requirement/piece of code), it is possible to reduce them in order to respect time/people constraints without severely compromising coverage. In this light, we formulated the TC selection problem as a constrained search based optimization task, using requirements coverage as the fitness function to be maximized (quality of the resultant suite), and the execution effort (time) of the selected TCs as a constraint in the search process. Our work is based on the Particle Swarm Optimization (PSO) algorithm, which is simple and efficient when compared to other widespread search techniques. Despite that, besides our previous works, we did not find any other proposals using PSO for TC selection, neither we found solutions treating this task as a constrained optimization problem. We implemented a Binary Constrained PSO (BCPSO) for functional TC selection, and two hybrid algorithms integrating BCPSO with local search mechanisms, in order to refine the solutions provided by BCPSO. These algorithms were evaluated using two different real-world test suites of functional TCs related to the mobile devices domain. In the performed experiments, the BCPSO obtained promising results for the optimization tasks considered. Also, the hybrid algorithms obtained statistically better results than the individual search techniques.     

A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

Software transactional memories for Scala

Transactional memory is an alternative to locks for handling concurrency in multi-threaded environments. Instead of providing critical regions that only one thread can enter at a time, transactional memory records sufficient information to detect and correct for conflicts if they occur. This paper surveys the range of options for implementing software transactional memory in Scala. Where possible, we provide references to implementations that instantiate each technique. As part of this survey, we document for the first time several techniques developed in the implementation of Manchester University Transactions for Scala. We order the implementation techniques on a scale moving from the least to the most invasive in terms of modifications to the compilation and runtime environment. This shows that, while the less invasive options are easier to implement and more common, they are more verbose and invasive in the codes using them, often requiring changes to the syntax and program structure throughout the code.     

Kynoid: Real-time enforcement of fine-grained,user-defined,and data-centric security policies for Android

We introduce Kynoid, a real-time monitoring and enforcement framework for Android. Kynoid is based on user-defined security policies which are defined for data-items. This allows users to define temporal, spatial, and destination constraints which have to hold for single items. We introduce an innovative approach to allow for the real-time tracking and enforcement of such policies. In this way, Kynoid is the first extension for Android which enables the enforcement of security policies of data-items stored in shared resources. We outline Kynoid's architecture, present its operation and discuss it in terms of applicability, and performance. By providing a proof-of-concept implementation we further show the feasibility of our framework.     

Classification of signals by means of Genetic Programming

This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.     

A differential inclusion approach for modeling and analysis of dynamical systems under uncertainty

In this paper we deal with the application of differential inclusions to modeling nonlinear dynamical systems under uncertainty in parameters. In this case, differential inclusions seem to be better suited to modeling practical situations under uncertainty and imprecision than formulations by means of fuzzy differential equations. We develop a practical algorithm to approximate the reachable sets of a class of nonlinear differential inclusion, which eludes the computational problems of a previous set-valued version of the Heun’s method. Our algorithm is based on a complete discretization (time and state space) of the differential inclusion and it suits hardware features, handling the memory used by the method in a controlled fashion during all iterations. As a case of study, we formulate a differential inclusion to model an epidemic outbreak of dengue fever under Cuban conditions. The model takes into account interaction of human and mosquito populations as well as vertical transmission in the mosquito population. It is studied from the theoretical point of view to apply the Practical Algorithm. Also, we estimate the temporal evolution of the different human and mosquito populations given by the model in the Dengue 3 epidemic in Havana 2001, through the computation of the reachable sets using the Practical Algorithm.     

External tools for collaborative medication scheduling

Medication adherence—taking medication as prescribed—is critical for successful self-care, especially among older adults. Adherence depends on developing and implementing plans for taking medications. Age-related cognitive declines that affect adherence may be mitigated using external tools that support patient-provider collaboration needed to develop these adherence plans. We tested the effectiveness of structured collaborative medication tools to support better medication planning and adherence practices. Evidence for benefits of collaborative tools has been mixed in terms of their usefulness for medication-scheduling tasks, perhaps because the tools have not been explicitly designed to support patient-provider collaboration. A total of 144 community-dwelling older adults participated in pairs and performed the role of a patient or provider in a simulated patient-provider medication-scheduling task. Each pair worked with a structured (MedTable^? and e-MedTable) or unstructured (Medcard) scheduling tool and completed four problems (2 simple and 2 complex). Performance was measured using the following: problem-solving (medication schedule) accuracy, problem-solving time, solution (schedule) optimality, tool usability, collaborative effectiveness, and subjective workload involved in creating the medication schedules. Participants using structured tools produced more accurate and optimal schedules. They also rated subjective workload as lower and thought that the structured tools were easier to use, reduced subjective workload associated with creating the schedules. There was also suggestive evidence that participants using the structured tools rated more highly the quality of their collaboration. Structured medication-scheduling tools have the potential to improve medication adherence among older adults because they support collaborative planning and reduce the cognitive load involved in creating these adherence plans.