Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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Abstract. We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that, intuitively, document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.

With technological advancements, there has been a vast increase in the number of companies that fight over their market share. In search of a differentiating factor, companies are investing more and more in their products’ emotional designs. This researched work has evaluated the affects that are caused in visually impaired people when using Facebook's features and then compared them with the experiences of sighted users. To do that, these two types of Facebook users were subjected to a questionnaire that was based on the PANAS affect scale. Once the information was collected, statistics were employed so as to evaluate both users’ feelings. The results have shown that there were significant statistical differences between the sighted and the visually impaired users when the “affects” were evaluated by using the PANAS tool. The five “negative affects” that were selected (Irritability, Uselessness, Frustration, Sadness, and Confusion) were largely more relevant for the blind people in most of the evaluated features. This has indicated some serious accessibility problems. However, a high frequency of the five “positive affects” that were considered (Satisfaction, Pleasantness, Surprise, Excitement, Interest, and Determination) were additionally observed for both of these two groups. These results were interpreted as feelings of both social inclusion and social exclusion, indicating the possibility of exploring technological devices that were unavailable not long ago. After analyzing their experiences in their usage of the Facebook features, the findings have also highlighted the many differing emotions that are felt by the visually impaired and the sighted users. The resulting outcomes have indicated that there are some issues that are still open to problems and difficulties. Moreover, these issues involve human-computer interactions. Nevertheless, fortunately, there is light at the end of the tunnel, as will be revealed.

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