Reviewerly/exHarmony huggingface dataset:
Web News Mining Using New Features: A Comparative Study

Web-based applications are a well-known platform to exchange information between Internetusers.However, in this modern world, the processing of huge information or Big-Data such as web news or web advertisement of product information through users is the main challenge.In another side, such web applications are the most accessible media for users to get up-to-date information.Equally, these applications need huge computation in terms of spaces and times as well as they drain the battery power of the users' mobile devices.Therefore, one of the solutions to mitigate these challenges is to mine or extract specific information based on specific features.Furthermore, the features will be the users' behavior or retrieved information from different sources.This article aims at designing and carrying out a web application to extract news information using new features such as geolocation and time information as well as showing a comparative study on three different mining techniques.The application can run on different devices including Laptops, Smartphones, and Tablets.Moreover, the application can retrieve information features accordingly.Then, the obtained information could be used as a basis for starting or as input for the data-mining techniques, including K-Nearest-Neighbor (k-NN), decision tree and deep-learning recurrent neural network (such as Long Short-Term Memory 'LSTM').These techniques are separately implemented and they are compared in terms of time/space complexity and classification accuracy.The obtained results show that the mining accuracy via k-NN is the worst one (∼85%) and takes much more time, while the mining accuracy through using LSTM is the best one and its accuracy is around (∼94%), when location information is used.
Variational-based latent generalized Dirichlet allocation model in the collapsed space and applications

In topic modeling framework, many Dirichlet-based models performances have been hindered by the limitations of the conjugate prior. It led to models with more flexible priors, such as the generalized Dirichlet distribution, that tend to capture semantic relationships between topics (topic correlation). Now these extensions also suffer from incomplete generative processes that complicate performances in traditional inferences such as VB (Variational Bayes) and CGS (Collaspsed Gibbs Sampling). As a result, the new approach, the CVB-LGDA (Collapsed Variational Bayesian inference for the Latent Generalized Dirichlet Allocation) presents a scheme that integrates a complete generative process to a robust inference technique for topic correlation and codebook analysis. Its performance in image classification, facial expression recognition, 3D objects categorization, and action recognition in videos shows its merits.
Profit optimization in service-oriented data market: A Stackelberg game approach

Deep learning has drawn a lot of attention recently. It is successful in a variety of applications from natural language processing to autonomous vehicle control. A main difference from traditional learning is that deep learning learns the representations of the data, i.e., the features, via greedy layer-wise pre-training. The characteristics of deep learning promotes it as a power tool to mine the data from Internet of Vehicles (IoV). The paper focuses on the economic aspect of IoV and investigates deep learning enabled IoV market for data trading and processing. The economic model of IoV consists of three side: the data provider, the service provider, and the user. The data provider collects the data for the user. The user buys the raw data. The data is further processed by the service provider, who provides the learned features for the user to obtain some profit. To optimize the profit of three-sided participators, a Stackelberg game is proposed to model the interactions among them. We derive the equilibrium pricing mechanism of the providers and corresponding demands of the users. The existence and the uniqueness of the equilibrium strategies are proved. Our analysis reveals that the strategy of each participator is related to the utility of the user and the data/service provider’s cost. To the best of our knowledge, this is the first time that the data provider and the service provider directly interact with the user in the data market.
Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods

Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS); and a physically based model, Yang’s hybrid model (YHM). Daily meteorological variables, including air temperature (T), relative humidity (RH), surface pressure (SP), and sunshine duration (SD) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square (RMSE) of 1.989 MJ m−2day−1, and the highest correlation coefficient (R = 0.956), respectively. Then, monthly and annual mean DHI during 1960–2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of −0.019 MJ m−2during 1960–2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan−Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze River Basin. The possible causes for spatiotemporal variation of DHI across China were investigated by discussing cloud and aerosol loading.
Salient object detection of dairy goats in farm image based on background and foreground priors

In order to achieve object detection of dairy goats in farm image efficiently and accurately, this paper proposes a novel salient object detection approach based on background and foreground priors. First, we improve the FastMBD algorithm to generate background-prior-based saliency map, which eliminate background interference initially. Second, the approach of seed selection is optimized to determine the foreground seeds more accurately, and then saliency map is generated based on manifold ranking to further eliminate the background interference. Finally, two saliency maps are fused, and then combined with post-processing to achieve saliency object detection of farm image. Subsequently, we also use the threshold segmentation based on K-Means algorithm to separate the objects and background. Moreover, the horizontal scanning method and contour extraction are employed to realize the extraction and counting of dairy goats. Experimental results show that the proposed approach based on background and foreground priors has promising performance, and the accuracy of the proposed segmentation method was up to 89.503%.
Design Methodologies for Human-Artificial Systems Design: An Automotive AR-HUD Design Case Study

With the development of society, technical products become more complex and intelligent. Interaction design of intelligent products is meeting new challenges. It requires practitioners to wade into the whole product development process and grasp basic interdisciplinary knowledge. This paper focuses on design methodologies for Human-Artificial Systems design with interdisciplinary knowledge. In the Interaction Design Method Framework (IDMF) we proposed, design runs through the whole product development process to help practitioners propose emerging technology-based designs. The IDMF framework is composed of 102 design methods and six phases. We deconstructed each phase of the IDMF according to three dimensions, design, information, and business. This paper offers a practical guide to using the IDMF framework by providing a high-level summary of the automotive AR-HUD design. It presented how the IDMF framework help practitioners effectively guide the design team and propose emerging technology-based designs.
Topology-aware multi-objective virtual machine dynamic consolidation for cloud datacenter

With help of virtualization technology, cloud datacenters provide affordable computation power to numerous enterprises and business users. Virtual machine dynamic consolidation (VMDC) plays an important role in the energy consumption management of datacenters. Previous solutions on VMDC either fail to consider impact resulting from the interaction between datacenter network topology and communication topology, or handle this issue in coarse granular level. In this paper, we propose a new approach to coordinate datacenter network topology and communication topology. The proposed VMDC approach adopts a multi-objective genetic algorithm to simultaneously minimize energy consumption and communication traffic causing performance bottlenecks, while meeting SLA constraint on migration time. In this approach, both SLA constraint and objective function on communication traffic are formulated with topology awareness in switch link level. A comprehensive simulation study shows that the proposed approach can significantly save energy consumption of datacenters and provide SLA assurance for different types of datacenter topologies. Simulation results demonstrate that the proposed approach outperforms prior approaches in terms of reducing energy consumption, bottleneck ratio and SLA violation ratio.
Dynamic Background Subtraction Using Histograms Based on Fuzzy C-Means Clustering and Fuzzy Nearness Degree

Background subtraction has been widely used in the detection of a moving object from a still scene. Due to the uncertainty in the classification of the pixels in the foreground and background, we propose a novel fuzzy approach for background subtraction using fuzzy histograms based on fuzzy c-means clustering and the fuzzy nearness degree, called FCFN. In this method, the temporal characteristics of the pixels are described by a fuzzy histogram using the fuzzy c-means algorithm. The segmentation threshold is adaptively calculated according to the distribution of the fuzzy nearness degree of the individual pixel. Fuzzy adaptive background maintenance is adopted in the background update framework. The performance of the FCFN is evaluated against several state-of-the-art methods in the complex dynamic scenes. The experimental results demonstrate that the proposed method doubles the improvements in performance than the classic fuzzy background modeling methods and outperforms most state-of-the-art methods.
Classification by multi-semantic meta path and active weight learning in heterogeneous information networks

Heterogeneous information network (HIN) is a kind of large-scale network which contains different types of objects and complex links. It is distinguished from a homogenous network for its heterogeneity of objects represented as nodes and complexity of links, which also makes the object classification more difficult. A meta-path can denote the relationship between nodes in HINs, and the path information can be enriched by extracting jump-paths. Based on this idea, the problem of data sparseness can be alleviated effectively. As multiple meta-paths represent different semantics, we propose an active weight learning method for each type, which aims to maximize the weight of meta-path with strong correlation and lower the weight if the correlation is weak. The feature matrix based on the meta-path is constructed and the Random Forest classifier is trained to implement the node classification in HINs. The experimental results show that our method achieves better performance in the complex network by using the fewer labeled data. The active learning strategy is effective for identifying objects to label for training.
HARSAM: A Hybrid Model for Recommendation Supported by Self-Attention Mechanism

Collaborative filtering is one of the most commonly used methods in recommendation systems. However, the sparsity of the rating matrix, cold start-up, and most recommendation algorithms only consider the users while neglecting the relationship between the products, all of what limit the effectiveness of the recommendation algorithms. In this paper, based on the self-attention mechanism, a deep learning model, named HARSAM, is proposed for modeling user interaction data and learning the user’s latent preference expression. HARSAM partitions the user’s latent feedback data in different time granularity and employs the self-attention mechanism to extract the correlation among the data in each partition. Moreover, the model learns the user’s latent preferences through the deep neural network. Simultaneously, the model learns the item latent representation by making use of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stacked denoising autoencoder</i> to model the item’s rating data. As the result, the model recommends items to users according to the similarities between user’s preference and items. Experiments conducted on the public data demonstrate the effectiveness of the proposed model.
Enhanced recommender system using predictive network approach

Recommender systems have a unique role in on-line trading companies due to building relationships among users and items to reduce big information load. There exist several successful algorithms in the recommender systems like collaborative filtering (CF), although most of them suffer from the sparsity problem. Here, we propose a novel integrated recommendation approach based on the tools of network science to mitigate the sparsity problem. The link prediction approach is used to extract hidden structures among users, and diffusion of information is applied to enhance the rating matrix in our proposed framework. Not only, the sparsity problem is alleviated through a more efficient way, but the proposed approach also can be applied in a hybrid way with the well-known algorithms. The proposed approach is examined on several datasets via standard evaluation criteria. The experimental results show that the proposed approach outperforms the earlier methods.
Correct filtering for subgraph isomorphism search in compressed vertex-labeled graphs

We present a revised filtering process that corrects an error in our previous work on subgraph isomorphism search over compressed graphs. We provide additional experiments to test the contribution of the compression itself and the candidate filtering to the performance of four representative backtracking-based subgraph isomorphism search algorithms.
Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

Dynamic optimization has become an increasingly important aspect of chemical processes in the past few decades. To solve such chemical dynamic optimization problems (DOPs) effectively, we put forward a modified differential evolution algorithm named XADE in this paper, which integrates the self-adaptive principle and multiple mutation strategies. In XADE, four mutation strategies with different characteristics are introduced instead of using a single strategy. Meanwhile, the mutation strategies and DE’s two control parameters are gradually adjusted adaptively based on the knowledge learned from the previous searches in generating improved solutions. The advantageous performance of XADE is validated by comparisons with several state-of-the-art adaptive DE variants on 24 complex test instances. Experimental results show that XADE is an effective approach to solving global numerical optimization problems. Moreover, the effectiveness of XADE is validated by applying the approach to 4 real-world complex DOPs with different characteristic in the chemical engineering field.
Security validation of VP-based SoCs using dynamic information flow tracking

Abstract Modern System-on-Chips (SoCs) are notoriously insecure. Hence, the fundamental security feature of IP isolation is heavily used, e. g., secured Memory Mapped IOs (MMIOs), or secured address ranges in case of memories, are marked as non-accessible. One way to provide strong assurance of security is to define isolation as information flow policy in hardware using the notion of non-interference. Since, an insecure hardware opens up the door for attacks across the entire system stack (from software down to hardware), the security validation process should start as early as possible in the SoC design cycle, i. e. at Electronic System Level (ESL). Hence, in this paper we propose the first dynamic information flow analysis at ESL. Our approach allows to validate the run-time behavior of a given SoC implemented using Virtual Prototypes (VPs) against security threat models, such as information leakage (confidentiality) and unauthorized access to data in a memory (integrity). Experiments show the applicability and efficacy of the proposed method on various VPs including a real-world system.
A multilevel sampling strategy based memetic differential evolution for multimodal optimization

Multimodal optimization, aiming to locate multiple optima in parallel, is a challenging task. In this paper, a multilevel sampling strategy based memetic differential evolution algorithm is proposed to tackle the problem. In the proposed algorithm, a multilevel sampling strategy is devised to sample a subpopulation for evolution at each generation. In this strategy, the entire population is dynamically divided into multiple levels according to the fitness of individuals at each generation. A subpopulation is then adaptively sampled from the individuals at different levels to undergo a niching based evolution for identifying multiple optima in the search space. Further, a crossover-based local search scheme is designed to fine-tune the seed solutions of niches in the population during evolution. We evaluate the proposed method on 20 benchmark multimodal problems and compare it with state-of-the-art multimodal optimization algorithms. The results show that our proposed algorithm can effectively and accurately locate multiple optima, outperforming related methods to be compared.
Meta-Path-Based Weapon-Target Recommendation in Heterogeneous Combat Network

The information period has significantly changed in the use of warfare patterns, and modern operations require rapid operational planning and decision making under different combat conditions. Weapon-target recommendation is essential for the automation of military command and control, which is of tremendous military significance for the eventual outcome of warfare. In this paper, we study the weapon-target recommendation problem in heterogeneous combat networks (HCNs) and propose a united framework named meta-path-based HCN weapon-target recommendation (MPWTR). The MPWTR not only considers the topological information of an HCN, but also explores the semantic similarity hidden in the various meta-paths. Specifically, first, the weapon-target-related meta-paths in an HCN are extracted and defined to calculate the similarities in the weapon-target pairs. Subsequently, a weapon-target recommendation model is presented by assigning each meta-path with different preferences and combining all the meta-path similarities by an optimal process. Finally, we conduct considerable experiments on an HCN case to demonstrate the feasibility and effectiveness of the proposed MPWTR. This paper compares several baseline methods and it shows that the MPWTR can achieve very good performance, which is superior to the baselines. The results provide useful insight into the operation guidance and optimal planning and scheduling of the allocated weapon resources.
A new approach for effective retrieval and indexing of medical images

The use of medical imaging has increased manifold in past decade. The medical images have become more and more informative about the patient’s anatomy. Thus, the medical images are now not only used for diagnosis purpose but also stored for R&D purpose in order to have better understanding and deeper insights into the cause and cure of various diseases. For real-time retrieval of medical images from such storage repositories there is a grave need of an effective and efficient biomedical image indexing and retrieval approach. In this quest, this paper presents a new approach for the retrieval of CT and MR images using orthogonal Fourier-Mellin moments (OFMMs). OFMMs have excellent information representation capability that enables them to pack the entire image information in very less number of coefficients. This property makes the proposed approach not only effective but also computationally very efficient and most favorable among all the existing approaches. The proposed approach has been tested and compared with numerous existing, state-of-the-art as well as recently published biomedical indexing and retrieval approaches on four standard databases namely, Emphysema CT, NEMA CT, NEMA MRI and OASIS MRI. Additional experiments have been conducted to analyze the noise robustness ability of the proposed and all the compared approaches. The reported results show a significant increase of about 7% and 20% (average) in the retrieval rate of the proposed approach over all the existing approaches on noise free and noisy images, respectively, of all four test medical databases.
Alice in DIY wonderland or: Instructing novice users on how to use tools in DIY projects

We present the interactive assistant Robert that provides situation-adaptive support in the realisation of do-it-yourself (DIY) home improvement projects. Robert assists its users by providing comprehensive step-by-step instructions for completing the DIY project. Each instruction is illustrated wi th detailed graphics, written and spoken text, as well as with videos. They explain how the steps of the project have to be prepared and assembled and give precise instructions on how to operate the required electric devices. The step-by-step instructions are generated by a hierarchical planner, which enables Robert to adapt to a multitude of environments easily. Parts of the underlying model are derived from an ontology storing information about the available devices and resources. A dialogue manager capable of natural language interaction is responsible for hands-free interaction. We explain the required background technology and present preliminary results of an empirical evaluation.