A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance

Comprehensive evaluation of window-integrated semi-transparent PV for building daylight performance

© 2019 Elsevier Ltd Building-integrated semi-transparent photovoltaic windows (PV windows) have been considered as a potential candidate to replace conventional windows to improve building energy efficiency and hence reduce carbon emissions. With the integration of PV windows, the indoor luminous environment may be significantly affected. The presence of solar cells may cause undesirable shading, low illuminance levels and affect colour quality of the transmitted daylight. Therefore, it is important to comprehensively assess daylight performance of PV windows to ensure a comfortable luminous environment. In this study, the daylight performance of Cadmium telluride (CdTe) PV window with four different transparencies (i.e. 20%, 30%, 40% and 50%) applied to a cellular office space has been assessed in terms of daylight quantity and daylight quality. RADIANCE was selected to predict the annual daylight performance through advanced dynamic metrics including Useful Daylight Illuminance (UDI), simplified Daylight Glare Probability (DGPs) and Illuminance Uniformity (Uo). Correlated Colour Temperature (CCT) and Colour Rendering Index (CRI), which are two attributes to characterise the colour quality of transmitted daylight were used to evaluate performance of the selected PV windows. CCT and CRI were calculated under three CIE standard daylight scenarios (CCT of 4000 K, 6500 K and 25000 K respectively). It is found that CdTe PV windows can significantly improve the homogeneity of daylight distribution on a task area located close to the window and reduce the risk of daylight glare when compared with the performance of a conventional double glazing. Moreover, the recommended CCT (i.e. 3000–7500 K) can be achieved with the employment of CdTe PV windows under 4000 K and 6500 K daylight scenarios. All of the CdTe PV windows examined were able to maintain CRI at a comfortable level i.e. above 90 under the three daylight scenarios

Optical aspects and energy performance of switchable ethylene-tetrafluoroethylene (ETFE) foil cushions

A pneumatic multilayer foil construction with a kinetic shading mechanism has the potential to be an effective response to dynamic climatic factors, such as solar radiation, and therefore moderate the energy consumption of buildings. A parametric study was carried out on a switchable ethylene-tetrafluoroethylene (ETFE) foil cushion with the purpose of investigating the optical performance of an adaptive building envelope and its impact on building energy performance regarding heating, cooling and lighting. Ray-tracing techniques were used to investigate the effects of surface curvature, frit layout and frit properties, on the optical performance of the cushion in open and closed mode. A range of incidence angles for solar radiation were simulated. The results of the simulation showed an angle dependent optical behaviour for both modes. The influence of the dynamic shading mechanism on building energy performance was further evaluated by integrating the optical data obtained for the ETFE foil cushions in a comprehensive energy simulation of a generic atrium building using EnergyPlus. Results suggested that switchable ETFE foil cushions have a higher potential to reduce cooling and heating loads in different climatic regions, compared to conventional glazing solutions (i.e. uncoated double-glazing and reflective double-glazing), while providing good conditions of natural daylighting. Annual energy savings of up to 44.9% were predicted for the switchable ETFE foil cushion in comparison to reflective double glazing. As such, this study provides additional insight into the optical behaviour of multilayer foil constructions and the factors of design and environment that potentially have a major impact on buildings energy performance

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM))

A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

This article aims to describe (a) Teacher's Book Encourages Information Literacy and (b) Teacher's book Encourages the Improvement of Creative Critical Thinking Ability. The teacher's book contains a variety of information, from the procedures for using the book, the substance of learning materials, to comprehensive literacy content. In addition, the book must also be able to guide the teacher to be able to select and apply learning materials and strategies according to the times and needs of students. In addition, the book must also be able to guide teachers in order to grow and improve students' critical thinking skills and attitudes.Keywords: literacy information, teacher’s book, creative-critical thinking ability

A revisit to social network-based recommender systems

With the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based rec-ommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users ’ ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimen-tal study demonstrates that our techniques significantly im-prove the accuracy of SNRS

A framework for efficient spatial web object retrieval

The conventional Internet is acquiring a geospatial dimension. Web documents are being geo-tagged and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables new kinds of queries that take into account both location proximity and text relevancy. This paper proposes a new indexing framework for top-k spatial text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within this framework. The framework encompasses algorithms that utilize the proposed indexes for computing location-aware as well as region-aware top-k text retrieval queries, thus taking into account both text relevancy and spatial proximity to prune the search space. Results of empirical studies with an implementation of the framework demonstrate that the paper’s proposal is capable of excellent performance

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Recommending High Utility Queries via Query-Reformulation Graph

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries