Most of current urban logistic systems fail to strike a nice trade-off between speed and cost. An express logistic service often implies a high delivery cost. Crowdsourced logistics is a promising solution to alleviating such contradiction. In this art…
Koopman mode decomposition is a flow analysis technique developed by Igor Mezić in 2004, based upon the Koopman operator first proposed by Bernard Koopman in 1931. Via Koopman decomposition any non-chaotic well-sampled dynamic system – li…
Air pollution has become a global challenge, and obtaining real-time air quality information is urgently needed. Although the governments have been trying their best in delivering accurate air quality reports, missing air pollution data remains a key c…
Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). A detection node (i.e., collector) should discover available routes to a collection node for data collection and col…
For a web search engine, it is critical to design a mechanism to promote trustworthy websites and eliminate spam ones in the searching results. In this paper, we propose the OpinionRank algorithm to compute the trustworthiness of a website and identify…
Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from recommender systems. However, most LFA-based recommender systems adopt a standard SGD algorithm, which suffers limited scalability when addressing big data. On the other hand, most existing parallel SGD solvers are either under the memory-sharing framework designed for a bare machine or suffering high communicational costs, which also greatly limits their applications in large-scale systems. To address the above issues, this article proposes a distributed alternative stochastic gradient descent (DASGD) solver for an LFA-based recommender. Its training-dependences among latent features are decoupled via alternatively fixing one-half of the features to learn the other half following the principle of SGD but in parallel. It’s distribution mechanism consists of efficient data partition, allocation and task parallelization strategies, which greatly reduces its communicational cost for high scalability. Experimental results on three large-scale HiDS matrices generated by real-world applications demonstrate that the proposed DASGD algorithm outperforms state-of-the-art distributed SGD solvers for recommender systems in terms of prediction accuracy as well as scalability. Hence, it is highly useful for training LFA-based recommenders on large scale HiDS matrices with the help of cloud computing facilities.
The emerging systems are not only generating huge amounts of data but also expect this data to be analyzed expeditiously to drive online decision-making and control. Thus, identifying the most relevant data and making it available close to the computat…
With the pervasive use of information technologies, the increasing availability of data provides new opportunities for understanding user behaviors. Unearthing anomalies in user behavior is of particular importance as it helps signal harmful incidents …
Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various real-world decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. They are usually large-scale because …
Recently, the capital expenditure of flash-based Solid State Driver (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, all-flash storage systems have started to become more economically viable for large shared storag…