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It is very crucial for news aggregator websites which are recent in the market to actively engage its existing users. A recommendation system would help to tackle such a problem. However, due to the lack of sufficient amount of data, most of the state-of-the-art methods perform poorly in terms of recommending relevant news items to the users. In this paper, we propose a novel approach for Item-based...
Trading strategies basing on both financial analysis and machine learning techniques are becoming increasingly popular due to their ability to capture micro market price movements and leverage big data. An important class of works are focusing on exploiting the structural relationships between companies for accurate stock price prediction. In this paper we develop an algorithm for learning the parameters...
Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
IP Addresses are a central part of packet- and flow-based network data. However, visualization and similarity computation of IP Addresses are challenging to due the missing natural order. This paper presents a novel similarity measure IP2Vec for IP Addresses that builds on ideas from Word2Vec, a popular approach in text mining. The key idea is to learn similarities by extracting available context...
Due to the advances of wireless sensor networks, radiofrequency identification (RFID) and Web-based services, large volume of devices have been interconnected to the Internet of Things (IoT). In addition, the tremendous number of IoT services provided by service providers arises an urgent need to propose effective recommendation methods to discover suitable services to users. In this paper, we propose...
The recent progress of motion sensor system enables to the personal identification from the human behavior observed from the sensor. Kinect is a motion sensing input device developed by Microsoft for Xbox 360 and Xbox One. The personal identification using the Microsoft Kinect sensor, shortly Kinect, is presented in this study. The use of the Kinect estimates the pedestrian's body size and walk behavior...
The worldwide market for luxury and fashion goods is dominated today by a handful of multinational corporations (MNCs). The way MNCs access foreign markets and organize distribution, however, remains unclear. In this paper, based on an analysis of foreign trade statistics, we take the example of watches and provide a model to highlight the most important flows as well as regional hubs in this global...
Data-driven analytics and decision-making have been essential for numerous applications in our society. To transform the data into a source of rich intelligence and support decision-making, data-driven analytics often need to aggregate intelligence from multiple sources and disaggregate signals into significant constituents. Though many existing approaches perform these two tasks respectively, there...
Information networks such as social networks, publication networks, and the World Wide Web are ubiquitous in the real world. Traditionally, adjacency matrices are used to represent the networks. However, adjacency matrices are too sparse and too high dimensional when the scale of the networks is large. Network embedding, which aims to learn low-dimensional continuous representations for nodes, has...
Networks naturally capture a host of real-world interactions, from social interactions and email communication to brain activity. However, graphs are not always directly observed, especially in scientific domains, such as neuroscience, where monitored brain activity is often captured as time series. How can we efficiently infer networks from time series data (e.g., model the functional organization...
Graph data management and mining in HPC environments has been a widely discussed issue in recent times. In this talk I will describe the use of Partitioned Global Address Space languages for graph data mining and management. I will first discuss the rationale behind X10 based graph libraries and graph database benchmarks using ScaleGraph and XGDBench as examples. Next, I will take Acacia which is...
Given a stream of heterogeneous edges, comprising different types of nodes and edges, which arrive in an interleaved fashion to multiple different graphs evolving simultaneously, how can we spot the anomalous graphs in real-time using only constant memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. In this...
Caregiving is the act of providing assistance to an individual unable to perfom some daily living activities. Caregiving can be either paid or unpaid. An informal caregiver is an unpaid caregiver to an older, sick, or disabled family member or friend on a daily basis. Informal caregiving is associated with increased physical, mental, and emotional stressors contributing to poor health outcomes, caregiver...
Domain generation algorithms (DGAs) automatically generate large numbers of domain names in DNS domain fluxing for the purpose of command-and-control (C&C) communication. DGAs are immune to static prevention methods like blacklisting and sinkholing. Detection of DGAs in a live stream of queries in a DNS server is referred to as inline detection. Most of the previous approaches in the literature...
In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model...
This paper presents a novel approach for activity recognition from accelerometer data. Existing approaches usually extract hand-crafted features that are used as input for classifiers. However, hand-crafted features are data dependent and could not be generalized for different application domains. To overcome these limitations, our approach relies on matrix factorization for dimensionality reduction...
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis...
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as...
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