Recognition of Actions of Medical Workers on the Basis of Accelerometer Readings with the Use of a Deep Belief Network

This paper analyzes a real set of large-volume medico-statistical data used for the recognition of actions of medical workers on the basis of accelerometer readings at definite moments of time. During the recognition process, a deep belief network was applied to unlabeled data, and then it was supervisedly learned using the error backpropagation method. The obtained results demonstrate higher recognition accuracy in comparison with basic methods. A considerable improvement in medical staff productivity is also achieved.     

3D Semantic Scene Completion: A Survey

International Journal of Computer Vision - Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years...     

Molecular and genetic features of fragile X syndrome

This study explored the claim that superior disembedding performance in autism reflects "less capture by meaning" and/or reduced "central coherence" [Shah & Frith, Journal of Child Psychology & Psychiatry, 24, 613-620 (1983); Shah & Frith, Journal of Child Psychology & Psychiatry, 34, 1351-1364 (1993)]. Meaningless as well as meaningful disembedding contexts were used, and memory for contextual information was examined. Neither qualitative (search strategy) nor quantitative (RT or accuracy) data indicated that high-functioning individuals with autism/PDD were superior to younger, developmentally matched controls. For both groups, disembedding was slowest from meaningful contexts, which generally were remembered best. No evidence was provided for "less capture by meaning" or reduced "central coherence" in autism/PDD, raising the possibility that earlier findings reflect a developmental, rather than a stable autism-specific, phenomenon.     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

Use of center of gravity with the common vector approach in isolated word recognition

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.     

Usability evaluation methods for the web: A systematic mapping study

Context

In recent years, many usability evaluation methods (UEMs) have been employed to evaluate Web applications. However, many of these applications still do not meet most customers’ usability expectations and many companies have folded as a result of not considering Web usability issues. No studies currently exist with regard to either the use of usability evaluation methods for the Web or the benefits they bring.

Objective

The objective of this paper is to summarize the current knowledge that is available as regards the usability evaluation methods (UEMs) that have been employed to evaluate Web applications over the last 14 years.

Method

A systematic mapping study was performed to assess the UEMs that have been used by researchers to evaluate Web applications and their relation to the Web development process. Systematic mapping studies are useful for categorizing and summarizing the existing information concerning a research question in an unbiased manner.

Results

The results show that around 39% of the papers reviewed reported the use of evaluation methods that had been specifically crafted for the Web. The results also show that the type of method most widely used was that of User Testing. The results identify several research gaps, such as the fact that around 90% of the studies applied evaluations during the implementation phase of the Web application development, which is the most costly phase in which to perform changes. A list of the UEMs that were found is also provided in order to guide novice usability practitioners.

Conclusions

From an initial set of 2703 papers, a total of 206 research papers were selected for the mapping study. The results obtained allowed us to reach conclusions concerning the state-of-the-art of UEMs for evaluating Web applications. This allowed us to identify several research gaps, which subsequently provided us with a framework in which new research activities can be more appropriately positioned, and from which useful information for novice usability practitioners can be extracted.     

A DSS for integrated distribution of empty and full containers

A DSS integrating empty and full containers transshipment operations is presented, addressing the typically unbalanced export/import containers trading problem. The problem is modeled as a network, where nodes represent customers, leasing companies, harbors and warehouses, while arcs represent transportation routes. The underlying mathematical model operates in stages, first prioritizing and adjusting full containers demands considering available empty containers supplies, and then statically optimizing costs. Transportation routes are registered and dynamically controlled, cyclically, for a given time horizon. The DSS is flexible, allowing several parameters to be configured. Experimental examples using randomly generated parameters were conducted to evaluate the effectiveness of the system.     

The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler’s choice of mode and destination and the shipper’s choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.     

Resilient overlay network for real-time interactive multimedia sessions in corporate networks

Real-time interactive multimedia communications are becoming increasingly useful for education, business, e-commerce and e-government, providing an enriched user experience in teleconferencing, e-meetings, distance training and product demonstrations. Large corporations are usually located at several sites, so real-time multipoint sessions within corporations are especially difficult. IP multicast is available or feasible within each site of an organization. Thus, corporate networks can be considered as various multicast-capable networks interconnected through a wide area network without multicast connectivity. This paper proposes a resilient self-managed overlay network to support real-time multipoint interactive sessions within corporate networks. The proposed overlay takes advantage of the configuration of corporate networks to self-organize and provide an efficient media delivery service, making use of multicast communications wherever available. Various self-healing techniques are implemented allowing for the continuity of ongoing sessions in spite of network disruptions and entity failures. Extensive simulations and tests have been carried out to assess the performance and resilience of the overlay facing several types of disruptions.