Reviewerly/exHarmony huggingface dataset:
Enhanced Grasshopper optimization Algorithm for Modeling Multivariable Systems with SVR

This paper proposes an enhanced grasshopper optimization algorithm (EGOA) for modelling a multivariable system with SVR. In the EGOA, the coevolution strategy and Gaussian mutation operator are employed to improve the search ability. The results with some test functions show the good performance of EOGA. The EGOA is adopted to model the binary distillation column using SVR. The simulation results demonstrate that the proposed modeling method has small modeling error and can reflect the dynamic characteristics of the multivariable plant.
An approach to generalizing the handling of preferences in argumentation-based decision-making systems

As a practical mechanism for formalizing commonsense reasoning, argumentation has shown its potential for applications in diverse areas, many related to decision-making in knowledge-based systems. Following this line, and for helping users in making a better and informed decision, different recommender systems proposals have been developed in the argumentation literature. We will use recommender systems as a good example where to exercise our proposal. In particular, the role of preference criterion in argumentation-based recommender systems which is used to compare competing arguments is central to the user’s query answering process where if the criterion does not adjust to the represented domain, the system could fail by being undecided too often. Therefore, having tools that allow to select and change the argument comparison mechanism has to be used become a central issue. Argumentation-based recommender systems that offer these tools provide an interesting ability that can be used for improving the reasoning capabilities in this type of systems. This work introduces an approach to handle multiple argument preference criteria in argumentation-based recommender systems and general knowledge-based decision support systems. More precisely, the proposal allows changing the information that a criterion can use in the argument comparison process and specify how several criteria can be simultaneously used in such process as well; to achieve that goal, a set of operators to combine several criteria is presented. The knowledge representation and reasoning is performed in Defeasible Logic Programming, a defeasible argumentation formalism based on logic programming.
Vehicle pose estimation via regression of semantic points of interest

In this paper we address the problem of extracting vehicle 3D pose from 2D RGB images. An accurate methodology is presented that is capable of locating 3D coordinates of 20 pre-defined semantic vehicle points of interest or keypoints from 2D information. The presented two-step pipeline provides a straightforward way of extracting three-dimensional information from planar images and avoiding also the usage of other sensor that would lead to a more expensive and hard to manage system. The main contribution of this work is the presented dedicated network architectures that are able to locate simultaneously occluded and visible semantic points of interest to convert these 2D points into 3D space in a simple but efficient way. The presented method uses a robust network based on Stack-Hourglass architecture for precise prediction of semantic 2D keypoints from vehicles even if they are occluded. Furthermore, in the second step another dedicated network converts the 2D points into 3D world coordinates and therefore, the 3D pose of the vehicle can be automatically extracted, outperforming state-of-the-art techniques in terms of accuracy.
On the Relationship Between Coupling and Refactoring: An Empirical Viewpoint

Background: Refactoring has matured over the past twenty years to become part of a developer's toolkit. However, many fundamental research questions still remain largely unexplored. Aim: The goal of this paper is to investigate the highest and lowest quartile of refactoring-based data using two coupling metrics - the Coupling between Objects metric and the more recent Conceptual Coupling between Classes metric to answer this question. Can refactoring trends and patterns be identified based on the level of class coupling? Method: In this paper, we analyze over six thousand refactoring operations drawn from releases of three open-source systems to address one such question. Results: Results showed no meaningful difference in the types of refactoring applied across either lower or upper quartile of coupling for both metrics; refactorings usually associated with coupling removal were actually more numerous in the lower quartile in some cases. A lack of inheritance-related refactorings across all systems was also noted. Conclusions: The emerging message (and a perplexing one) is that developers seem to be largely indifferent to classes with high coupling when it comes to refactoring types - they treat classes with relatively low coupling in almost the same way.
Augmented reality tracking registration and process visualization method for large spacecraft cable assembly

Abstract The new generation of spacecraft has the characteristics of large-scale structure and complicated cable assembly process. In order to meet the needs of the augmented reality process visualization technology in its further application exploration, the two aspects of augmented reality tracking registration and process visualization are now studied. In the large-scale spacecraft cable assembly application scenario, the augmented reality process guidance technology puts more stringent requirements on the continuous tracking range and virtual and real registration accuracy of large space. The 3D process visualization method for cable assembly is optimized to improve the accuracy, quality and efficiency of the operator in process interpretation and implementation.
Higher Dimension Quantum Entanglement Generators

Quantum information processing and communication techniques rely heavily upon entangled quantum states, and this dependence motivates the development of methods and systems to generate entanglement. Much research has been dedicated to state preparation for radix-2 qubits, and due to the pursuit of entangled states, the Bell state generator and its generalized forms where the number of entangled qubits is greater than two have been defined. In this work, we move beyond radix-2 and propose techniques for quantum state entanglement in high-dimensional systems through the generalization of the binary bipartite entanglement states. These higher-radix quantum informatic systems are composed of n quantum digits, or qudits, that are each mathematically characterized as elements of an r -dimensioned Hilbert vector space where r &gt; 2. Consequently, the wave function is a time-dependent state vector of dimension r n . The generalization of the binary controlled-NOT to the controlled-modulo-addition gate, the concept of partial versus maximal entanglement, and architectures for generating higher-radix entangled states for the partial and maximal case are all presented.
An Optimal Motion Planning Method of 7-DOF Robotic Arm for Upper Limb Movement Assistance

Assistive robotic arm is crucial alternative resource for people disabled or injured in the upper limbs, which enable them to complete basic living tasks independently. Thus, an extremely accurate motion planning for robotic arm needs to be applied to improve assistive performance. Rapidly-Exploring Random Tree Star (RRT*) is one of the most representative methods, however, this method has great limitations due to the tedious iteration process while planning. In this study, the potentials guide sampling based-on RRT* (PGS-RRT*) approach is introduced through combination with artificial potential fields (APF) as an expansion of RRT* algorithm. A revision of repulsive potential force's formula in APF has been applied into sampling process of RRT*. The samples during motion planning is gathered through the optimization of potentials formulations. Specifically, the basic potential function give each sample an offset oriented to goal. Experiments in 2D and 3D environments and simulations on KUKA LBR iiwa 7 prove that the PGS-RRT* method is able to find an optimal path in a short time, which highlights the application prospect on robots with a number of degree of freedom (DOF) in patient's daily life assistance.
An extensive evaluation of deep featuresof convolutional neural networks for saliency prediction of human visual attention

Based on transfer learning, feature maps of deep convolutional neural networks (DCNNs) have been used to predict human visual attention. In this paper, we conduct extensive comparisons to investigate effects of feature maps on the predictions of the human visual attention using a deep features based saliency model framework. The feature maps of seven pretrained DCNNs are investigated using classical and class activation maps approaches. The performances of various saliency implementations are evaluated over four datasets using three metrics. The results demonstrate that deep feature maps of the pretrained DCNNs can be used to create saliency maps for the prediction of human visual attention. The incorporation of multiple levels of blurred and multi-scale feature maps improves the extraction of salient regions. Moreover, DCNNs pretrained using the Places dataset provide more localized objects that can be beneficial to the top-down saliency maps.
A Novel Intrusion Detection System based on Extreme Machine Learning and Multi-Voting Technology

With the fast development of networking technology, billions of devices have been developed with network function. When the scale of a network traffic grows by an order of magnitude, traditional intrusion detection system (IDS) are no longer effective to detect malicious network intrusions. In our work, we propose a novel network intrusion detection framework based on extreme learning machine (ELM) and multi-voting technology (MVT). Due to the real time feature of ELM, several independent ELM networks can be trained simultaneously. The final results are obtained by MVT strategy. The standard UNSW-NB15 data set has been used to evaluate the performance of the proposed method. The experimental result illustrated that the high accuracy can be achieved by using the proposed method.
Bidirectional GRU-Based Attention Model for Kid-Specific URL Classification

The Gen Z kids highly rely on internet for various purposes like entertainment, sports, and school projects. There is a demand for parental control systems to monitor the children during their surfing time. Current web page classification approaches are not effective as handcrafted features are extracted from the web content and machine learning techniques are used that need domain knowledge. Hence, a deep learning approach is proposed to perform URL-based web page classification. As the URL is a short text, the model should learn to understand where the important information is present in the URL. The proposed system integrates the strength of attention mechanism with recurrent convolutional neural network for effective learning of context-aware URL features. This enhanced architecture improves the design of kids-relevant URL classification. By conducting various experiments on the benchmark collection Open Directory Project, it is shown that an accuracy of 0.8251 was achieved.
An Automatic Detection and Recognition Method for Pointer-type Meters in Natural Gas Stations

Currently, pointer-type meters in natural gas stations are read manually, which causes a waste of human resources. Researchers have proposed lots of methods to realize automatic reading of such meters. However, these methods need high quality source image rigorously so that they cannot perform well in real environments. To solve this problem, an automatic detection and recognition method for pointer-type meters is proposed, which consists of R-FCNs (region-based fully convolutional networks), an improved local threshold segmentation method and a probability-circle method. The experimental results show that the method performs well when the source image is blurred, the illumination is complex or the camera's angle is tilted. It has good robustness and accuracy to detect and recognize the pointer-type meters automatically.
A Viewpoint Control Method for 360° Media Using Helmet Touch Interface

We have developed a helmet touch interface for the viewpoint control of a 360° media. The user of this interface can control the camera in 360° media by touching the surface of the helmet. To detect touch, two micro-controllers and 54 capacitive touch sensor points mounted on the interface surface are used.
RHNet: Lightweight Dilated Convolutional Networks for Dense Objects Counting

In this paper, we propose a lightweight network called RHNet to achieve a faster counting method for dense objects counting. RHNet is composed of two major components. The first part contains four convolution layers and two max-pooing layers, which is designed to extract features mainly. The following second part is a structure named dilated special pyramid pooling, which is aimed at understanding the multiscale information. Compared to other published excellent networks, RHNet is a lightweight network because it has only 0.03 million parameters. Importantly, we demonstrate RHNet a decent accuracy on famous ShanghaiTech crowd dataset, WorldExpo'10 crowd dataset, UCF-QNRF dataset and an extended dataset with more kinds of dense objects.
Study on Super-Resolution of Images Obtained by Micro Satellite with CMOS Sensor

This paper proposes a new image reconstruction algorithm to improve the resolution of images obtained by micro satellite with CMOS sensor. Satellite with CMOS Sensor can obtain images of same ground object in consecutive integration times. Because of the low attitude stability of micro satellite, there are sub-pixel displacements for same target between these images. In order to measure the displacements, three image registration algorithms, which are Maximum Mutual Information, SURF (Speeded Up Robust Features) and Keren, are compared through simulation experiment. Then, an image super-resolution reconstruction algorithm named Weighted Average Interpolation is proposed. Based on two sequential low-resolution images, one high-resolution image is acquired through Weighed Average Interpolation. Simulation result proved that PSNR in synthesized image is enhanced by 1. 44dB compared with that in bilinear interpolated image. Test chart imaging experiment showed that Average Gradient, Edge Intensity and Space Frequency of synthesized image are all obviously greater than of bilinear interpolated image. The resolution of synthesized image is effectively improved by Weighed Average Interpolation.
Towards Agent-Based Model Specification of Smart Grid: A Cognitive Agent-Based Computing Approach

Information technologies have become pervasive in various organizational functions.However, their usage impacts organizational performance in various manner, and it depends on the intensity of information technology usage.Human resources information systems (HRIS) are widely used in various organizations.Previous research investigated impact of HRIS to organizational performance, but on the country level and from the adoption perspective.In this work, we focus on the intensity of HRIS usage in organizations and their impact on organizational performance.In order to investigate this relationship, we develop several regression models, using the global dataset with large number of companies worldwide.Research results revealed that the strongest impact to organizational performance is attained trough the intensity of HRIS usage, measured by the number of different functionalities available in the software systems.However, this could also be the result of overall development of organizational management and its impact on organizational performance.
DEVELOPMENT OF TEACHING MATERIALS FOR SASAK ALUS LANGUAGE AS CULTURAL PRESERVATION AND LEARNING RESOURCES

Purpose: This study aims to develop language teaching materials as a learning resource for fourth-grade students in elementary school. Methodology: The approach used in this study is library research that is operationalized by the development method. The procedure of development research according to Gall and Borg consists of ten steps, namely; (1) research and information collecting), (2) planning, (3) developing preliminary form of product), (4) preliminary field testing, (5) main product revision, (6) main field testing, (7) operational product revision, (8) operational field testing, (9) final product revision, (10) dissemination and implementation. Main Findings: The results showed that the instructional materials developed were suitable to be used as learning resources with the results of content expert validation 84.5%, media expert validation 77.2%, instructional design expert validation 83.2%. The results of trials one to one by learner 80.8%, 85.4% small group, and 85.8% field trial. Implications: Based on the results of the test it was concluded that the teaching materials developed were suitable to be used as learning resources. The products of this study can be used to apply the concept of resource-based learning and improve the quality of local subject learning in the Sasak Alus language in elementary school.
Visual place recognition based on multilevel descriptors for the visually impaired people

The Visually Impaired People (VIP) have the difficulty in perceiving the accurate localization in their daily life. Developing an efficient algorithm to address the localization issues of the VIP is crucial. Visual Place Recognition (VPR) refers to using the image retrieval algorithms to determine the location of a query image in the database, which is promising to help the VIP solve their localization problems. However, the accuracy of VPR is directly affected by the changes of scene appearances such as illumination, seasons and viewpoints. Therefore, finding a method to extract robust image descriptors under the changes of scene appearance is one of the most critical tasks in current VPR research. In this paper, we propose a VPR approach to assist the localization and navigation of visually impaired pedestrians. The core of our proposal is a combination of multi-level descriptors by using appropriate descriptors: the whole image, local regions and key-points, aimed to enhance the robustness of VPR. The matching procedure between query images and database images includes three steps. Firstly, we obtain the Convolutional Neural Networks (CNN) features of the whole images from a pre-trained GoogLeNet, and the Euclidean distances between the query images and the database images are computed to determine the top 10 good matches. Secondly, local salient regions are detected from the top-10 best-matched images with Non-Maximum Suppression (NMS) to control the number of bounding boxes. Thirdly, we detect the SIFT key-points and extract the geodesc descriptors of the key-points, from the local salient region, and determine the top 1 among the top 10 good matches. In order to verify our approach, a comprehensive set of experiments has been conducted on dataset with challenging environmental changes, such as the GardensPointWalking dataset.
Minority–Majority Mix mean Oversampling Technique: An Efficient Technique to Improve Classification of Imbalanced Data Sets

The challenges related to handling of the gigantic imbalanced data volumes are incredible and has set a new trail for its efficient processing. The inventive prospects contained by these huge imbalanced data sets have posed a priority of concern in recent research avenues. The several applications handling imbalanced Big Data sets have noted significance for precise classification while determining unidentified values from these data sets. Traditional classifiers are not able to discourse the imbalance of class distribution among the data samples. A class having fewer samples indicates difficulty in learning, whereas it points to a notable drop in the performance. Recent studies demonstrate that the classifier independent set of oversampling techniques are more capable to efficiently handle the issues raised in imbalanced data sets. An enhanced oversampling technique, viz., Minority–Majority Mix mean Oversampling Technique (MMMmOT), improving classification performance is discussed in detail in this paper. An appropriate consideration of majority as well as minority samples is planned to generate the synthetic samples. The proposed technique is investigated encircling data sets mainly from the UCI repository over Apache Hadoop. Furthermore, the stimulus of maintaining the imbalance ratio with better oversampling instances from the generated pool is analyzed. The results of classification performance are recognized using standard parameters like F-Measure and area under the curve. The achieved experimental outcomes clearly exhibit the preeminence of the presented technique over the traditional techniques.
Neural Gender Prediction from News Browsing Data

Online news platforms have attracted massive users to read digital news online. The demographic information of these users such as gender is critical for these platforms to provide personalized services such as news recommendation and targeted advertising. However, the gender information of many users in online news platforms is not available. Fortunately, male and female users usually have different pattern in reading online news. Thus, the news browsing data of users can provide useful clues for inferring their genders. In this paper, we propose a neural gender prediction approach based on the news browsing data of users. Usually a news article has different kinds of information such as title, body and categories. However, the characteristics of these components are very different, and they should be processed differently. Thus, we propose to learn unified user representations for gender prediction by incorporating different components of browsed news as different views of users. In each view, we use a hierarchical framework to first learn news representations and then learn user representations from news representations. In addition, since different words in news titles and bodies usually have different informativeness for learning news representations, we use attention mechanisms to select important words. Besides, since different news articles may also have different informativeness for gender prediction, we use news-level attentions to attend to important news articles for learning informative user representations. Extensive experiments on a real-world dataset validate the effectiveness of our approach.
Recognizing sentences concerning future research from the full text of JASIST

ABSTRACT In the conclusion part of an academic paper, sentences concerning future research provide an outlook about future focus of research and reflect the latest development trend in a specific topic or field. These sentences are of high value for further investigation. However, very little has been done on sentences talking about future research in academic full texts. Here, papers in JASIST were studied with respect to these sentences. Deep learning model was trained on articles containing sentences annotated as relating to “future research” by BERT model. The model capable of automatic extraction of such sentences from academic full texts was obtained. Later this model was verified on the unannotated corpus, which demonstrated good performance. Finally, clustering was performed for the extracted sentences. It was found by cluster analysis that the sentences concerning future research were divided into four types. Our findings provide support for classification of sentences concerning future research.