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A Web user today has his/her data and information distributed in a number of services that operate in silos. Computer wizards already know how to control their personal data to some extent. It is now becoming possible for everyone to do the same, and there are many advantages to doing so. Everyone should now be in a position to manage his/her personal information. Furthermore, we will argue that we...
As an emerging area, data science is facing great opportunities as well as challenges. Often arguments exist: What is data science? Why data science? We have information science already, why do we need data science? Do we need analytics science? Is analytics new? What is the difference between statistics and data analytics? What makes a data scientist? We believe that a special session on Trends and...
The goal of exploratory data analysis or data mining is making sense of data. We develop theory and algorithms that help us understand our data, with the goal that this helps formulating better hypotheses. The role of statisticians is to provide methods that give detailed insight in how data is structured: characterising distributions in easily understandable terms, showing the most informative patterns,...
As more and more enterprises are looking forward to leveraging the connected network of Facebook to capture inputs and feedback on their brands, it is becoming increasingly important to mine the unstructured information from Facebook. The recent advances in open source software technologies in the map reducing paradigm opens up a whole new opportunity for such advanced analytics. This paper illustrates...
Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network — Long Short-Term Memory (DBN-LSTM)...
Researchers have been devoted to using context to extract implicit features. However, little concerns have been given to the situation that not all the contexts are meaningful. To solve this problem, we present a new method to evaluate the contribution of the contexts for extracting. We build an improved Co-occurrence matrix that containing the distance between an opinion word and different contexts...
Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution...
Algebraic structures are well studied mathematical structures in abstract algebra with applications in many fields of computer security such as cryptography and authentication. Generating such structures is computationally very expensive because of the huge number of permutations. Also, many of these permutations are redundant as they are symmetrically equivalent. The symmetry breaking (finding symmetrically...
With the exponential growth of time-stamped data from social media, e-commerce and sensor systems, time series data analysis is of growing interests for extracting useful insights. In many real-world applications, there is usually a large amount of unlabeled data but limited labeled data, which can be difficult to obtain. In this paper, we present a graph-based semi-supervised learning framework which...
The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and...
The multi-armed bandit is a model of exploration and exploitation, where one must select, within a finite set of arms, the one which maximizes the cumulative reward up to the time horizon T. For the adversarial multi-armed bandit problem, where the sequence of rewards is chosen by an oblivious adversary, the notion of best arm during the time horizon is too restrictive for applications such as ad-serving,...
The Map Matching Problem (MMP) aims to find a real optimal region from a map repository, which is the most similar map to the ideal sample. Though there is a significant difference between MMP and the general Image Matching Problem. The former prefers approximate match and ignores details of edge. Both of them must solve variances of scale, translation and rotation. However, the Scale Invariant Feature...
The complexity and continuous evolution of enterprise systems is making it increasingly difficult to maintain the predictability of the system. The system managers are often unaware of the impact of an action across various layers of enterprise IT ranging from business functions, to applications, to IT infrastructure. Due to lack of this transparency, many risks go unnoticed leading to business outages...
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