Reviewerly/exHarmony huggingface dataset:
Stackelberg Game for Energy-Aware Resource Allocation to Sustain Data Centers Using RES

Smart Grid (SG) has emerged as one of the most powerful technologies of the modern era for an efficient energy management by integrating information and communication technologies (ICT) in the existing infrastructure. Among various ICT, cloud computing (CC) has emerged as one of the leading service providers which uses geo-distributed data centers (DCs) to serve the requests of users in SG. In recent times, with an increase in service requests by end users for various resources, there has been an exponential increase in the number of servers deployed at various DCs. With an increase in the size, the energy consumption of DCs has increased many folds which leads to an increase in overall operational cost of DCs. However, efficient resource allocation among these geo-distributed DCs may play a vital role in reducing the energy consumption of DCs. Moreover, with an increase in harmful emissions, the use of renewable energy sources (RES) can benefit DCs, SG, and society at large. Keeping focus on these points, in this paper, an energy-aware resource allocation scheme is proposed using a Stackelberg game for energy management in cloud-based DCs. For this purpose, a cloud controller is used to receive the requests of users which then distributes these requests among geo-distributed DCs in such a way that the energy consumption of DCs is sustained by RES. However, if energy consumption of DCs is not sustained by RES then the energy is drawn from the grid. The requests of users are routed to the DC which is offered lowest energy tariff from the grid. For this purpose, a Stackelberg game for energy trading is also proposed to select the grid offering lowest energy tariff to DCs. The proposed scheme is evaluated using various performance metrics using Google workload traces. The results obtained show the effectiveness of the proposed scheme.
Using POI functionality and accessibility levels for delivering personalized tourism recommendations

The aim of this paper is to propose a recommendation system that considers user's functionality levels regarding physical or psychological limitations. This paper describes a set of models and algorithms used under a tourism recommendation system based in users and points-of-interest (POI) profiles. Also, this proposal considers a different manner to classify POI including their accessibility levels, mapped with similar physical and psychological issues. In this study, based on the Design Science Research methodology, an architecture is proposed and a touristic recommendation system prototype where users are modelled with new types of information in addition to traditional approaches such as their levels of functionality regarding a set of physical and intellectual issues, is also presented. POIs are also modelled with the same information structure and maintain knowledge on their limitations against some health conditions.
Decision Trees and Random Forests

The capability to model unkown complex interactions between variables made machine learning a pervasive tool in bioinformatics and computational biology. Among others, decision trees and random forests have been used within the field for several successful applications achieveing high levels of performance. Furthermore, decision trees and random forests offer the possibility of inspecting the decision rules and to investigate the relevance of each variable, as well as, the dependencies among them. Here we review the theoretical foundations of both decision trees and random forests and illustrate basic case-studies as well as applications from recent literature.
Dynamic ensemble selection for quantification tasks

Ensembles are among the most effective and successful methods for almost all supervised tasks. Not long ago, an ensemble approach has been proposed for quantification learning. The idea of such method is to exploit the prior knowledge about quantification tasks, building ensembles in which diversity is achieved by training each model with a different distribution. These training samples are generated taking into account the expected drift in class distribution. This paper extends this method proposing three new quantifier selection criteria particularly devised for quantification problems, where two of them are defined for dynamic ensemble selection. The experiments demonstrate that, in many cases, these selection functions outperform straightforward approaches, like averaging all models and using quantification accuracy to prune the ensemble. Moreover, the results show that performance heavily depends on the combination of the base quantification algorithm and the selection measure.
Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies

Cyber Physical Systems and Internet of Things have grown significant attention from industry and academia during the past decade. The main reason behind this interest is the capabilities of such technologies to revolutionize human life since they appear as seamlessly integrating classical networks, networked objects and people to create more efficient environments. However, enhancing these technologies with intelligent skills becomes an even more interesting and enticing scenario. In this paper, we propose and illustrate through a number of case studies how Decisional DNA, a multi-domain knowledge structure based on experience, can be implemented as a comprehensive embedded knowledge representation for Internet of Things and Cyber Physical Systems. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The main advantages of using Decisional DNA are as follows: (i) offers a standardized form of the collected knowledge and experience, (ii) provides versatility and dynamicity of the knowledge structure, (iii) stipulates storage of day-to-day explicit experience in a single configuration, (iv) delivers transportability and shareability of the knowledge, and (v) provides predicting capabilities based on the collected experience. Consequently, test and results of the presented implementation of Decisional DNA case studies support it as a technology that can improve and be applied to the aforementioned technologies enhancing them with intelligence by predicting capabilities and facilitating knowledge engineering processes.
A High Throughput Acceleration for Hybrid Neural Networks With Efficient Resource Management on FPGA

Deep learning is the amazing technology which has promoted the development of artificial intelligence and achieved many amazing successes in intelligent fields. Convolution-based layers (CLs), fully connected layers (FLs) and recurrent layers (RLs) are three types of layers in classic neural networks. Most intelligent tasks are implemented by the hybrid neural networks (hybrid-NNs), which are commonly composed of different layer-blocks (LBs) of CLs, FLs, and RLs. Because the CLs require the most computation in hybrid-NNs, many field-programmable gate array (FPGA)-based accelerators focus on CLs acceleration and have demonstrated great performance. However, the CLs accelerators lead to an underutilization of FPGA resources in the acceleration of the whole hybrid-NN. To fully exploit the logic resources and the memory bandwidth in the acceleration of CLs/FLs/RLs, we propose an FPGA resource efficient mapping mechanism for hybrid-NNs. The mechanism first improves the utilization of DSPs by integrating multiple small bit-width operations on one DSP. Then the LB-level spatial mapping is used to exploit the complementary features between different neural networks in the hybrid-NN. We evaluate the mapping mechanism by implementing four hybrid-NNs on Xilinx Virtex7 690T FPGA. The proposed mechanism achieves a peak performance of 1805.8 giga operations per second (GOPs). With the analysis on resource utilization and throughput, the proposed method exploits more computing power in FPGA and achieves up to $4.13 \times$ higher throughput than the state-of-the-art acceleration.
Correlated Matrix Factorization for Recommendation with Implicit Feedback

As a typical latent factor model, Matrix Factorization (MF) has demonstrated its great effectiveness in recommender systems. Users and items are represented in a shared low-dimensional space so that the user preference can be modeled by linearly combining the item factor vector <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> using the user-specific coefficients <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> . From a generative model perspective, <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> are drawn from two <i>independent</i> Gaussian distributions, which is not so faithful to the reality. Items are produced to maximally meet users’ requirements, which makes <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> strongly correlated. Meanwhile, the linear combination between <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> forces a bijection (one-to-one mapping), which thereby neglects the mutual correlation between the latent factors. In this paper, we address the upper drawbacks, and propose a new model, named Correlated Matrix Factorization (CMF). Technically, we apply Canonical Correlation Analysis (CCA) to map <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> into a new semantic space. Besides achieving the optimal fitting on the rating matrix, one component in each vector ( <inline-formula><tex-math notation="LaTeX">$U$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> ) is also tightly correlated with every single component in the other. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on four public datasets. Experimental results show that our approach achieves competitive performance on both prediction accuracy and efficiency compared with the current state of the art.
Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system's performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications.
Design of Rao–Blackwellized Point-Mass Filter With Application in Terrain Aided Navigation

This paper is devoted to the terrain aided navigation (TAN) systems based on the state estimation algorithms. In particular, the emphasis is laid on the design of the Bayesian Rao–Blackwellized point-mass filter for nonlinear state-space models of a specific structure typically used in the terrain aided navigation. The proposed filter preserves advantages of the point-mass filter, including the high estimation accuracy, robust initialization, deterministic nature of the algorithm, and predictable computational complexity, while the computational complexity is significantly reduced. The performance of the Rao–Blackwellized point-mass filter is illustrated using a simulated numerical example and a TAN system based on the real data.
COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: discover temporal relationship patterns between event locations, i.e., repeated cases when there is a specific temporal relationship (same time, before, or after) between events occurring at two locations. This can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.
Extra-laboratorial usability tests: An empirical comparison of remote and classical field testing with lab testing

The present article examined the effects of using different extra-laboratorial testing procedures in usability testing. Three experiments were conducted using different artefacts (website, computer-simulated mobile phone, fully operational smartphone) to compare different methodological approaches in field testing (synchronous and asynchronous remote testing, classical field testing) to lab-based testing under different operational conditions (dual task demands, poor product usability). Typical outcome variables of usability testing were measured, including task completion time, click rate, perceived usability and workload. Overall, the results showed no differences between field and lab-based testing under favourable operational conditions. However, under difficult operational conditions (i.e. dual task demands, poor product usability) differences between field and lab-based testing emerged (corresponding to small and medium effect sizes). The findings showed a complex pattern of effects, suggesting that there was no general advantage of one testing procedure over another.
Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field

Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems' roadmaps in the coming years.
An exploratory study of waste in software development organizations using agile or lean approaches: A multiple case study at 14 organizations

The principal focus of lean is the identification and elimination of waste from the process with respect to maximizing customer value. Similarly, the purpose of agile is to maximize customer value and minimize unnecessary work and time delays. In both cases the concept of waste is important. Through an empirical study, we explore how waste is approached in agile software development organizations. This paper explores the concept of waste in agile/lean software development organizations and how it is defined, used, prioritized, reduced, or eliminated in practice The data were collected using semi-structured open-interviews. 23 practitioners from 14 embedded software development organizations were interviewed representing two core roles in each organization. Various wastes, categorized in 10 different categories, were identified by the respondents. From the mentioned wastes, not all were necessarily waste per se but could be symptoms caused by wastes. From the seven wastes of lean, Task-switching was ranked as the most important, and Extra-features, as the least important wastes according to the respondents’ opinion. However, most companies do not have their own or use an established definition of waste, more importantly, very few actively identify or try to eliminate waste in their organizations beyond local initiatives on project level. In order to identify, recognize and eliminate waste, a common understanding, and a joint and holistic view of the concept is needed. It is also important to optimize the whole organization and the whole product, as waste on one level can be important on another, thus sub-optimization should be avoided. Furthermore, to achieve a sustainable and effective waste handling, both the short-term and the long-term perspectives need to be considered.
Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg

The authors developed a forecasting model for Luxembourg, able to predict the expected regional PV power up to 72 h ahead. The model works with solar irradiance forecasts, based on numerical weather predictions in hourly resolution. Using a set of physical equations, the algorithm is able to predict the expected hourly power production for PV systems in Luxembourg, as well as for a set of 23 chosen PV-systems which are used as reference systems. Comparing the calculated forecasts for the 23 reference systems to their measured power over a period of 2 years, revealed a comparably high accuracy of the forecast. The mean deviation (bias) of the forecast was 1.1% of the nominal power – a relatively low bias indicating low systemic error. The root mean square error (RMSE), lies around 7.4% - a low value for single site forecasts. Two approaches were tested in order to adapt the short-term forecast, based on the present forecast deviations for the reference systems. Thereby, it was possible to improve the very short term forecast on the time horizon of 1–3 h ahead, specifically for the remaining bias, but also systemic deviations can be identified and partially corrected (e.g. snow cover).
The future scope of large-scale solar in the UK: Site suitability and target analysis

This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).
Effective and Efficient Blind Quality Evaluator for Contrast Distorted Images

This paper mainly focuses on developing a blind quality assessment method that can effectively and efficiently evaluate the quality of contrast distorted images without requiring reference information. Through experiments, we discover and validate that the global intensity change is the main characteristic of contrast distorted images and has a close relationship to the perceptual quality. With these observations, two elements are utilized to quantify this characteristic, i.e., the maximum information entropy of intensity values and the Kullback-Leibler (K-L) divergence between the test image's intensity histogram and the prior one based on the statistical experiment over a great number of high-quality images. To be specific, the entropy represents the valuable information of an image and the K-L divergence reflects the change degree of intensity distribution. In view of these, the proposed method is generated by combining these two elements linearly. Extensive experiments on three publicly available databases demonstrate the superiority of the proposed method. More specifically, it is more consistent with subjective evaluation results than the state-of-the-art image quality assessment methods and requires a lower computational complexity.
A stratified sampling based clustering algorithm for large-scale data

Large-scale data analysis is a challenging and relevant task for present-day research and industry. As a promising data analysis tool, clustering is becoming more important in the era of big data. In large-scale data clustering, sampling is an efficient and most widely used approximation technique. Recently, several sampling-based clustering algorithms have attracted considerable attention in large-scale data analysis owing to their efficiency. However, some of these existing algorithms have low clustering accuracy, whereas others have high computational complexity. To overcome these deficiencies, a stratified sampling based clustering algorithm for large-scale data is proposed in this paper. Its basic steps include: (1) obtaining a number of representative samples from different strata with a stratified sampling scheme, which are formed by locality sensitive hashing technique, (2) partitioning the chosen samples into different clusters using the fuzzy c-means clustering algorithm, (3) assigning the out-of-sample objects into their closest clusters via data labeling technique. The performance of the proposed algorithm is compared with the state-of-the-art sampling-based fuzzy c-means clustering algorithms on several large-scale data sets including synthetic and real ones. The experimental results show that the proposed algorithm outperforms the related algorithms in terms of clustering quality and computational efficiency for large-scale data sets.