The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The most significant trend in human creativity is the shift from individual to teams. Great achievements across academic disciplines and industries are increasingly teamwork. Motivated by this, we aim to uncover teamwork in networks, including predicting teams' performance, optimizing teams' compositions and explaining the prediction results and optimization actions.
Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
It is widely acknowledged that the value of a house is the mixture of a large number of characteristics. House price prediction thus presents a unique set of challenges in practice. While a large body of works are dedicated to this task, their performance and applications have been limited by the shortage of long time span of transaction data, the absence of real-world settings and the insufficiency...
In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Such processes are often synthetically characterized using PDEs (partial differential equations) and are observed in a variety of natural phenomena. In this paper, we present ACLIME-ADMM, an efficient two-step algorithm for...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
To improve the effective utilisation of its supercomputing platforms, the New Zealand eScience Infrastructure (NeSI) offers, in addition to user support and the installation of a comprehensive software stack, a consultancy service to some of its users. Here we present lessons learned from this work and how additional improvements can be made to further enhance productivity of researchers on computing...
The design and deployment of networked embedded systems is challenging. In particular, the environment in which the system operates has a severe impact on the final performance. Existing tools trade generality for specificity with arbitrary setups, e.g., in simulation, or specific configurations, e.g., in public testbeds. As a result, the peculiar effect of the target deployment scenario on the system...
Analysis of network traffic behavior and modeling to predict, for network management and security early warning has a very important significance. An improved FOA-ESN method using opposition-based learning (OBL) mechanism for the network traffic prediction with multiple steps is proposed in this paper. Firstly, reconstructing the phase space of the original network flow time series, and then building...
Stochastic optimization is playing an increasingly important in machine learning in the big data era. In this paper, we use forward-backward splitting for the stochastic optimization problems, where the objective is the sum of two functions: one is the expected risk function, another is a regularized term. At each iteration of this method, we just use a single sample to adjust the variables. We prove...
In water flooding oilfield, petroleum production is the most crucial target for production-injection wells system. An effective, informative and accurate production prediction facilitates parameter adjustment, production optimization, fault analysis and decrease in production cost. Some effective Artificial Intelligence (AI) technologies have been widely used in various kinds of industrial fields...
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts...
Anticipatory networking is a recent branch of network optimization that exploits contextual information to improve resource allocation decisions based on prediction. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real-world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
It is non-trivial to optimise computations of chaotic systems since slightly perturbed simulations diverge exponentially over time due to the well-known butterfly effect if bit-reproducible results are not achieved. Therefore, two model setups that show the same quality in the representation of a chaotic system will show uncorrelated behaviour if integrated long enough, hence it is challenging to...
Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and diversity of modern hardware and software. Machine learning is aproven technique for learning such heuristics, but its success is bound by thequality of the features used. These features must be hand crafted by developersthrough a combination of expert domain knowledge and trial and error. This makesthe quality...
Differential evolution (DE) algorithm mainly uses the distance and direction information from the current population to guide search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. It can be used to extract the global information about...
Financial market dynamics forecasting has long been a focus of economic research. A hybridizing functional link artificial neural network (FLANN) and improved particle warm optimization (PSO) based on wavelet mutation (WM), named as IWM-PSO-FLANN, for forecasting the CSI 300 index is proposed in this paper. In the training model, it expands a wider mutation range while apply wavelet theory to the...
When leg/wheel mobile robots are controlled by model predictive control (MPC), the optimization is too complex for an embedded CPU. Then, we proposed a method which reduces computational complexity by sequential optimization for the models partitioned into each leg. However, robustness of the proposed method was not evaluated enough in the disturbing environment. In this paper, we conduct simulations...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.