Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

X-Ray, Micro-Raman, Optical Absorption/Emission Studies of ErNbO4 Grown by Vapor Transport Equilibration

Vapor transport equilibration (VTE) treatments were performed on a Y-cut bulk Er (1.6 mol%)-doped congruent LiNbO₃ crystal and an X-cut pure congruent crystal, on one surface of which a 40 nm-thick film of erbium metal was coated before the VTE treatment. Scanning electron microscope, powder or single-crystal X-ray diffraction (XRD), polarized infrared absorption/emission of Er³⁺ as well as micro-Raman spectroscopy were used to study the two VTE crystals. The results are discussed in comparison with a corresponding as-grown bulk Er-doped crystal, calcined ErNbO₄ powder, and a locally Er-doped congruent LiNbO₃ crystal prepared by using the standard Er-diffusion technique. The experimental results show that the VTE treatment induces the formation of micrometer-sized ErNbO₄ precipitates with the crystallographic morphology of a flat polyhedron not only on the surfaces of both crystals but also in the bulk of the homogeneously Er-doped one. The optical absorption and emission studies show that the formation of the precipitates results in substantial spectral changes in both the 0.98 and 1.5 μm regions. The micro-Raman studies allow to resolve four additional Raman peaks around 800 cm⁻¹ in the E(TO) spectra of the two VTE crystals. These additional Raman peaks are associated with the characteristic vibrations with respect to the NbO₄³⁻ group. Characteristic XRD, optical absorption, and emission and Raman peaks for identifying the ErNbO₄ phase are proposed. Finally, the formation mechanism and light-scattering effect of the precipitates are discussed.     

Garbage collection of multi-version indexed data on flash memory

Maintaining a multi-version index on flash memory could generate a lot of updates and invalid pages. It is important to have an efficient garbage collection mechanism to ensure the flash memory has sufficient number of free blocks for storing new data versions and their index structures. In this paper, we study the important performance issues in using the purging-range query to reclaim the blocks, which are storing old data versions and invalid index entries, to be free blocks. To reduce the cost for processing the purging-range query, we propose the physical block labeling (PBL) scheme to provide a better estimation on the purging version number to be used for purging old data versions. To further enhance the performance of the garbage collection process, and at the same time to maximize the deadspans of data versions and balance the wear levels of the blocks, we propose two schemes called, the sequential placement (SQ) and frequency-based placement (FBP), for placing new data versions into free pages. As illustrated in the performance studies, both SQ and FBP can effectively balance the wear levels of the blocks. The deadspans of data versions are longer under FBP than both SQ and RR, and the page reallocation cost is also lower under FBP especially when the size of flash memory allocated for the database is limited. The experimental results also illustrate that PBL can effectively minimize the number of invocations of the purging-range query to be one to reclaim the required number of blocks in each garbage collection.     

Journaling deduplication with invalidation scheme for flash storage-based smart systems

Transaction support for filesystems has become a common feature in modern operating systems where data atomicity is achieved by writing transactions to the log region in advance. The logging mechanism is appropriate for flash storage devices due to the inherent nature of flash memory. However, the logging schemes inherently create multiple copies of data, leading to a decrease in the bandwidth of storage systems. In this paper, we present a simple and efficient invalidation scheme for multiple copies of data in a common journaling module. We identify two types of duplications, one in which there is an explicit duplication of the journal region and original region with the same data, and the other in which there is an implicit duplication of transaction commit operations. The invalidation of duplicated data reduces internal write and erase operations and garbage collection overhead for flash devices, which would otherwise increases external I/O bandwidth. Experimental results show that the overall performance improves roughly from 5% to 35% with the invalidation scheme for journal transactions.     

Users’ interest grouping from online reviews based on topic frequency and order

Large volume of online review data can reveal consumers’ major interests on domain product, which attracts great research interests from the academic community. Most of the existing works focus on the problems of review summarization, aspect identification or opinion mining from an item’s point of view such as the quality or popularity of products. Considering the fact that users who generate those review texts draw different attentions to product aspects with respect to their own interests, in this article, we aim to learn K users’ interest groups indicated by their review writings. Such K interest groups’ identification can facilitate better understanding of major and potential consumers’ concerns which are crucial for applications like product improvement on customer-oriented design or diverse marketing strategies. Instead of using a traditional text clustering approach, we treat the groupId/clusterId as a hidden variable and use a permutation-based structural topic model called KMM. Through this model, we infer K interest groups’ distribution by discovering not only the frequency of product aspects (Topic Frequency), but also the occurrence priority of respective aspects (Topic Order). They jointly present an informative summarization on the raw review corpus. Our experiment on several real-world review datasets demonstrates a competitive solution.     

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.     

Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five computationally light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty-eight test points from the sensors. Those location data was fed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty-eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be improved if at least ten position readings from sensors can be collected at small intervals at any location along the moving path. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced. These findings represent a path forward to improve localization accuracy by mixing imperfect data from the sensed environment with supplemental input.     

Conspiracy vs science: A large-scale analysis of online discussion cascades

World Wide Web - With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life....     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.