Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.     

Real time 3D face and facial feature tracking

Detecting and tracking human faces in video sequences is useful in a number of applications such as gesture recognition and human-machine interaction. In this paper, we show that online appearance models (holistic approaches) can be used for simultaneously tracking the head, the lips, the eyebrows, and the eyelids in monocular video sequences. Unlike previous approaches to eyelid tracking, we show that the online appearance models can be used for this purpose. Neither color information nor intensity edges are used by our proposed approach. More precisely, we show how the classical appearance-based trackers can be upgraded in order to deal with fast eyelid movements. The proposed eyelid tracking is made robust by avoiding eye feature extraction. Experiments on real videos show the usefulness of the proposed tracking schemes as well as their enhancement to our previous approach.

Ear recognition with feed-forward artificial neural networks

Biometrics has become one of the most important techniques in recognizing a person’s identity. A person’s face, iris and fingerprint are mostly used in biometrics today. It has been established that there are no two ears exactly alike, even in the cases of identical twins. In this paper, we define a 7-element ear feature set and design and train a feed-forward artificial neural network to recognize a human ear. We train and test the network with 51 ear pictures from 51 different persons. Simulation experiments with various networks with various number of layers and number of neurons per layer and with and without noise are conducted. Results indicate that a 95 % ear recognition accuracy is achieved with a simple 3-layer feed-forward neural network with only a total of 18 neurons even in the presence of some noise. This design outstands previous work in simplicity and implementation cost.     

Texture-independent recognition of facial expressions in image snapshots and videos

This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.     

Optimized Hexagon-Based Deployment for Large-Scale Ubiquitous Sensor Networks

Ubiquitous Sensor Network describes an application platform comprised of intelligently networked sensors deployed over a large area, supporting multiple application scenarios. On one hand, at the user-end, storing and managing the large amount of heterogeneous data generated by the network is a daunting task. On the other hand, at the network-end, ensuring network connectivity and longevity in a dynamically changing network environment, while trying to provide context-aware application data to the end-users are very challenging for the resource constrained sensor network. While cloud computing offers a cost-effective solution for storage of the large volume of data generated by the underlying heterogeneous network, an equally elegant solution does not exist on the network interface to provide application-aware data. In this paper, we propose the use of cognitive nodes (CNs) in the underlying sensor network to provide intelligent information processing and knowledge-based services to the end-users. We identify tools and techniques to implement the cognitive functionality and formulate a strategy for the deployment of CNs in the underlying sensor network to ensure a high probability of successful data reception among communicating nodes. From Matlab simulations, we were able to verify that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to deliver application-aware data that satisfies the end-user’s quality of information requirements, even at high application payloads.     

The potential of wind energy via an intelligent IoT-oriented assessment

The Journal of Supercomputing - In contemporary times, renewable energy reliability has been an important field of research that is combined with the Internet of Things (IoT) including the...     

Convolutional neural networks for the classification of chest X-rays in the IoT era

Multimedia Tools and Applications - Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is...     

Embedding new observations via sparse-coding for non-linear manifold learning

Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data—the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance.     

Poly(butylene succinate‐co‐butylene adipate)/polyethylene oxide blends for controlled release materials: A morphological study

Varying the formulation and processing conditions of polymer blends allows the design of materials with a large range of morphologies. Active materials embedding active compounds in a devoted phase are promising applications of such blends, offering possible various transport properties. In this study, 13 poly(butylene succinate‐co‐butylene adipate) (PBSA)/polyethylene oxide (PEO) blends were extruded in a slit die. Their morphologies were characterized by water extraction (selective PEO dissolution), FTIR spectroscopy, and differential scanning calorimetry. Transport properties were assessed by water vapor permeation and fluorescein release as model migrant. Indeed, the desorption in water of fluorescein (previously entrapped in PEO) was monitored to preliminary investigate the release properties of these materials: two morphologies were obtained (i) pseudo multilayer films made of PEO‐rich layer/PBSA‐rich layer/PEO‐rich layer and (ii) PEO nodules dispersed in the PBSA‐rich matrix for the highest PBSA contents. The first systems were erodible ones with an uncontrolled fast delivery by PEO dissolution whereas the second ones showed a controlled release by permeation through the PBSA matrix from PEO nodules. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42874.