This work investigates how entrepreneurs use social networking sites for business. Through surveys, online discussions and interviews, we have looked at activities, motives for participating on networking sites for business, motives for contributing, and differences between online and offline networking. Our results show that networking, facts finding, and marketing are very common activities while sharing of experience is quite rare. Entrepreneurs connect with new people online rather than reifying offline networks. A novel use of social media is that of small businesses using Facebook as a web hotel. We believe that an important explanation to our results is that social media are still informal and not yet incorporated in traditional work routines.
In this thesis, many machine learning algorithms were applied to electrocardiogram (ECG), spectral analysis, and Field Programmable Gate Arrays (FPGAs). In ECG, QRS complexes are useful for measuring the heart rate and for the segmentation of ECG signals. QRS complexes were detected using WaveletCNN Autoencoder filters and ConvLSTM detectors. The WaveletCNN Autoencoders filters the ECG signals using the wavelet filters, while the ConvLSTM detects the spatial temporal patterns of the QRS complexes. For the spectral analysis topic, the detection of chemical compounds using spectral analysis is useful for identifying unknown substances. However, spectral analysis algorithms require vast amounts of data. To solve this problem, B-spline neural networks were developed for the generation of infrared and ultraviolet/visible spectras. This allowed for the generation of large training datasets from a few experimental measurements. Graphical Processing Units (GPUs) are good for training and testing neural networks. However, using multiple GPUs together is hard because PCIe bus is not suited for scattering operations and reduce operations. FPGAs are more flexible as they can be arranged in a mesh or toroid or hypercube configuration on the PCB. These configurations provide higher data throughput and results in faster computations. A general neural network framework was written in VHDL for Xilinx FPGAs. It allows for any neural network to be trained or tested on FPGAs.
The aim in information filtering is to provide users with a personalised selection of information, based on a description of their interest profile. In some domains, users will want access to such profiles even if they are system generated. We have performed a study of the effects of combining automatic profiling with explicit user involvement. Firstly, we wanted to explore if a machine-learned profile would benefit from being based on an initial explicit user profile. Secondly, we tested if profiles that provided better filtering also were better liked by users. Finally, we tested if users could make improvements to machine-learned profiles. We found that the initial setup of a personal profile was effective, and yielded performance improvements even after feedback training. However, the study showed no correlation between users ratings of profiles and their filtering performance, and neither did user modifications to learned profiles improve filtering performance.
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