Reviewerly/exHarmony huggingface dataset:
Prioritizing software refactoring opportunities using multi-objective optimization

Software refactoring aims to improve software quality and developer productivity. It has gained significant attention from both academia and software industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization approach to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we use Multi-objective Spotted Hyena Optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results demonstrate that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.
Practice in Caption Generation with Keras

Hard-attention model has been proposed for automatically generating captions for images. Currently this model is implemented in many ways. For instance, its original implementation is written in Theano and most other implementations are written using Tensorflow library. In this paper, we use Keras, a modular framework for constructing deep neural networks, to implement this model. We also propose two attention models, called Attention-L and Attention-C, are slightly modified from the original attention model. The benchmark dataset Flickr30 is used to compare these three attention models, and the results demonstrate Attention-C model is more likely to obtain the better scores than that of other two models.
Sparse representation based medical ultrasound images denoising with reshaped-RED

Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noise removal is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, many regularization methods have been proved effective. This paper introduces an approach which collaborate both sparse dictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary by an efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recovered image. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling and texture-preserving of medical ultrasound images compared to some popular methods.
Desktop Artillery Simulation Using Augmented Reality

Augmented reality (AR) is a popular subject that is commonly associated with entertainment purposes. However, it can be practically useful in many other areas, from medicine to military technologies. Creating an interactive 3D ballistics simulation on a table is one of those purposes. In this paper, we present the work we carried out for designing and developing an augmented reality military game. The game is an artillery simulation where the player controls cannons and tries to take successful shots. The key feature of the game is that it utilizes AR technologies to place the game environment in the player's real life environment through smart AR glasses. The terrain where all the action takes place, is perceived to be on the table or floor in player's room. A specific 2D image is used to track the position and orientation of the surface where the game environment is placed. A general research about the background of AR technologies and game development, and detailed walk-through of the project's development are provided in the following sections.
A Scene-Based Augmented Reality Framework for Exhibits

This paper presents a novel augmented reality (AR) framework specifically targeting scene-based exhibits. Unlike traditional AR libraries that rely on specific image targets, the proposed framework utilizes a bag-of-words model for scene recognition, which enables recognition and subsequent launch of AR experiences under challenging environments. Moreover, the proposed framework utilizes relocalization capabilities of the popular ORB-SLAM algorithm to track the user’s movement after recognition of a particular exhibit. We demonstrate the efficacy of the proposed framework through a complete mobile app designed for a local museum, where artifacts are enhanced through AR.
Research on lightning radiation electromagnetic field of induced thunder on the ground of perfect conductor

Abstract The influence of lightning on smart substation mainly includes three aspects, one of which is lightning striking the grounding body of adjacent smart substation and affecting smart components through space electromagnetic induction. When the lightning strike point of induction lightning is in the station, its radiation field is closely related to the location of the lightning strike point. It is necessary to verify whether it meets the electromagnetic compatibility level of intelligent components of lightning radiation field according to the actual situation. Firstly, the simulation model of intelligent component under the influence of induction mine is studied, and the steps of simulation modelling are given. On this basis, through derivation, the calculation method and corresponding formula of lightning current radiation field on the ground of perfect conductor are given in detail.
A Shape Retrieval Technique Using Isothetic Cover of Digital Objects

This work presents a novel approachAman, Abdul Aziz Al to classify digital objects based on outer isothetic cover (OIC). The descriptor vectors of the isothetic cover are used as the shape descriptors of a particular digital object. Two objects may be of similar kind of shape if their shape descriptors are analogous. To find a match between two descriptors, common substringsSarkar, Apurba are found out and based on their length of the common substringBiswas, Arindam a decision is made. Experimental results show encouraging results.
Behavior Analysis of College Students on Using Microblog in Big Data Environment

Abstract With the development of information technology in big data environment, the “closed” campus has gradually turned into an open campus, which brings a series of new opportunities and challenges for the student management of college. Social platforms such as Microblog can provide students with a broader space for learning and communication, students are no longer limited to being managed, and they are increasingly inclined to their own opinions. In this paper, a distributed Microblog spider platform is studied and implemented for the behavior of student users. First, the data support can be provided for behavior analysis by the crawling of user information and content of Microblog. Secondly, the combing, integrating, analyzing and mining of the crawled data can make the useful value behind the information fully utilized in the student management work.
Global optimization of arrival picking for microseismic events

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Global optimization of arrival picking for microseismic eventsAuthors: Zhichao YuZhonghua MaoYuyang TanGuiting HouChuan HeZhichao YuPeking UniversitySearch for more papers by this author, Zhonghua MaoShengli Branch, Geophysical Company, SINOPECSearch for more papers by this author, Yuyang TanUniversity of Science and Technology of ChinaSearch for more papers by this author, Guiting HouPeking UniversitySearch for more papers by this author, and Chuan HePeking UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215483.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractArrival-time picking is a fundamental step in processing of downhole microseismic events. Due to different signal-to-noise ratios of the events recorded in different time sections, conventional picking workflow may result in inconsistent problems. In order to eliminate the inconsistency caused by S/N ratios, we present a global optimization method based on waveform correlation method to refine arrival picks for microseismic events that were initially picked. Considering the similarity of induced events that clustering in time and space, the proposed method regards the arrival data as a whole and calculate the residual time difference among different traces. We evaluate the performance of the proposed method on a field dataset, and the result shows that the consistency of the arrivals is significantly improved compared to the original picks and the adjusted picks obtained from single event based processing.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 3:05 PMLocation: 302BPresentation Type: OralKeywords: microseismic, traveltime, processing, optimization, crosscorrelationPermalink: https://doi.org/10.1190/segam2019-3215483.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 10 Aug 2019 CITATION INFORMATION Zhichao Yu, Zhonghua Mao, Yuyang Tan, Guiting Hou, and Chuan He, (2019), "Global optimization of arrival picking for microseismic events," SEG Technical Program Expanded Abstracts : 2094-2098. https://doi.org/10.1190/segam2019-3215483.1 Plain-Language Summary KeywordsmicroseismictraveltimeprocessingoptimizationcrosscorrelationPDF DownloadLoading ...
Into the melting pot: A celebration of the career of Michael Brown

Journal of Metamorphic GeologyVolume 37, Issue 7 p. 889-897 EDITORIAL Into the melting pot: A celebration of the career of Michael Brown Tim E. Johnson, Corresponding Author Tim E. Johnson tim.johnson@curtin.edu.au School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, Australia State Key Laboratory for Geological Processes and Mineral Resources and Center for Global Tectonics, School of Earth Sciences, China University of Geosciences, Wuhan, China Correspondence Tim E. Johnson, School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, Australia. Email: tim.johnson@curtin.edu.auSearch for more papers by this authorRichard W. White, Richard W. White School of Earth & Environmental Sciences, University of St Andrews, St Andrews, UKSearch for more papers by this authorChris Clark, Chris Clark School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, AustraliaSearch for more papers by this author Tim E. Johnson, Corresponding Author Tim E. Johnson tim.johnson@curtin.edu.au School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, Australia State Key Laboratory for Geological Processes and Mineral Resources and Center for Global Tectonics, School of Earth Sciences, China University of Geosciences, Wuhan, China Correspondence Tim E. Johnson, School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, Australia. Email: tim.johnson@curtin.edu.auSearch for more papers by this authorRichard W. White, Richard W. White School of Earth & Environmental Sciences, University of St Andrews, St Andrews, UKSearch for more papers by this authorChris Clark, Chris Clark School of Earth and Planetary Sciences, The Institute for Geoscience Research, Curtin University, Perth, WA, AustraliaSearch for more papers by this author First published: 08 August 2019 https://doi.org/10.1111/jmg.12505Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume37, Issue7Special Issue: Special Issue on: Into the melting pot: a celebration of the career of Michael BrownSeptember 2019Pages 889-897 RelatedInformation
New Benchmark Datasets and a Character Identification System on TV Series

Automatic character identification has been extensively studied in the past decades. However, most related works find characters only depending on their faces. One important reason is no dataset containing labels with all the occurrences of characters. Towards this end, we propose 3 datasets for character identification, which consist of 15 episodes (573 minutes). We label all the frames in which specified characters can be identified manually, regardless of whether faces or persons are detected. To the best of our knowledge, they are the first and largest datasets with all the occurrences of specified characters labelled for automatic character identification on TV material. Based on these datasets, we propose an automatic character identification system. Given a query image with a clear frontal face of a character, we can effectively utilize identified frames as new queries to explore remaining ones. In experiments, we show the average precision is substantially boosted in many cases.
Intention Understanding Model Inspired by CBC Loops

Accurate intention understanding of the user inputs is the key to human-computer interaction (HCI). At present, more and more studies just focus on the improvement of algorithm efficiency and ignore the nature exploration of intention understanding. In humans, working memory is regarded as a cognitive system for handling a range of neuro-cognitive tasks. Because the intention understanding is a kind of human cognitive ability, in this paper we will explore the human cognitive execution mechanism and try to apply it to improve the machines' intention understanding level. First, we demonstrated a cognitive learning model called Cortico-Basal ganglia-Cerebella (CBC) loops plays an important role in the process of working memory. Then, based on the full understanding of the loops operation mechanism, we put forward a new model of intension understanding. Finally, we applied this model on speech data and compared it with other two methods. The results showed that the new model could help to get task-specific vectors and offer further gains in performance on intention understanding.
Clustering Facial Expressions with Transductive Learning

By initiating the process without guidance from labels, transductive learning techniques allow the discovery of the internal, natural structures existing in datasets. In parallel, various paradigms have been proposed to explore the human facial dynamics while showing an expression. Highly used paradigms are the Facial Action Coding System (FACS) and respectively, the intensity-valence encoding. In this paper, we are using standard clustering techniques over state of the art facial landmarking to investigate the natural preference of a paradigm. The surprising conclusion is that, although clustering is constructed upon a database build to illustrate the FACS system, face expression images do cluster along arousal and valence.
Improving multilabel classification via heterogeneous ensemble methods

We consider the task of multilabel classification, where each instance may belong to multiple labels simultaneously. We propose a new method, called multilabel super learner (MLSL), that is built upon the problem transformation approach using the one-vs-all binary relevance method. MLSL is an ensemble model that predicts multilabel responses by integrating the strength of multiple base classifiers, and therefore it is likely to outperform each base learner. The weights in the ensemble classifier are determined by optimization of a loss function via V-fold cross-validation. Several loss functions are considered and evaluated numerically. The performance of various realizations of MLSL is compared to existing problem transformation algorithms using three real data sets, spanning applications in biology, music, and image labeling. The numerical results suggest that MLSL outperforms existing methods most of the time evaluated by the commonly used performance metrics in multilabel classification.
Short Text based Cooperative Classification for Multiple Platforms

With the popularity of electronic commerce, there are increasing requirements on product comparison services, which collect the similar products information on different platforms for a user reference. Since there are a large quantity of products on each platform, it is necessary to classify the products based on their short descriptions and to learn the relationships between the different categories on multiple platforms. In this paper, we propose the Rectified Topic Classification model to classify products into hierarchical categories based on their short text descriptions. We adopt the topic model to capture the latent features of products from the noisy short descriptions generated by merchants. To reduce the uncertainty of the inferring topic features of a new product, we invoke the topic model several times to get a set of probabilistic feature results and adopt the convolutional neural network for classification. To learn the correlations between two platform categories, the mapping matrix is learned by using a set of seed products. We crawled several real datasets from popular e-commerce platforms and perform experiments to verify our methods. The results show that our method outperforms the related methods.
Automatic Memory-Efficient Scheduling of CNNs

Accessing large external DRAM is costly, and poses a challenge to efficiently evaluate data-intensive convolutional neural networks (CNNs) on embedded devices. These external memory accesses can be minimized by exploiting data reuse in on-chip memory. Selecting the combination of code transformations that minimize the external DRAM accesses is however an extremely complex task. In this work a mathematical model is presented to quickly and very precisely evaluate combinations of code transformations on CNNs. An accompanying open source tool is developed which leverages this model to perform automated design space exploration and code generation for CNNs. The correctness of the developed model is demonstrated by measurement of seven neural networks. Results show the transformations selected by the tool can reduce external memory accesses by over an order of magnitude.
Centroid vectoring control using aerial manipulator: Experimental results

This paper addresses the issues of aerial manipulation and its dynamic center of mass variations by deriving a control principle capable of exploiting this disturbance as a means to stabilize the Unmanned Aerial Vehicle (UAV). A complete mathematical model of an aerial robot consisting of rotorcraft UAV body and a generic multiple degree of freedom manipulator is derived and presented. Previously developed control scheme utilizing both the rotor speed control and centroid vectoring through the mid-ranging approach is augmented with a standard position control loop. The proposed control approach is tested and verified in manual and autonomous flight experiments. We present the results of centroid vectoring control on a trajectory tracking example alongside which we provide comparison with state-of the-art approaches.
An Interactive Scene Generation Using Natural Language

Scene generation is an important step of robotic drawing. Recent works have shown success in scene generation conditioned on text using a variety of approaches, with which the generated scenes cannot be revised after its generation. To allow modification on generated scenes, we model the scene generation process as a discrete event system. Instead of training text-to-pixel mappings using large datasets, the proposed approach uses object instances retrieved from the Internet to synthesize scenes. Evaluated on 128 experiments using MSCOCO evaluation dataset, the result shows the scene generation performance has been increased by 197%, 22.3%, and 55.7% compared with the state of the art approach on three standard metrics (CIDEr, ROUGH-L, METEOR), respectively. Human evaluation conducted on Amazon Mechanical Turk shows over 80% of generated scenes are considered to have higher recognizability and better alignment with natural language descriptions than baseline works.
Some New Array Information Formulations of the UD-based Kalman Filter

The paper addresses the UD factorization based Kalman filtering (KF) implementation methods. We propose two new numerically favored and convenient array information formulations of the UD-based KF: the UD-based array Information Filter (algorithm UD-IF) and the extended UD-based array Information Filter (algorithm eUD-IF). To confirm the correctness of our results, we have proved that the newly constructed UD based array computational schemes are algebraically equivalent to the “straight” (conventional) information filter. Although all these information-type algorithms are theoretically equivalent, their computational properties are different. The newly proposed algorithms are numerically robust to machine round-off errors due to the numerically stable orthogonal transformations applied on each iteration. Additionally, algorithm eUD-IF has the extended array form, i. e., it allows updating absolutely all required filter quantities with the use of the numerically stable modified weighted Gram-Schmidt orthogonalization procedure. So, our results extend the existing class of numerically efficient KF implementation methods and can be used in practical applications.
Hierarchical Bayesian Modelling for Wireless Cellular Networks

With the recent advances in wireless technologies, base stations are becoming more sophisticated. Network operators are also able to collect more data to improve network performance and user experience. In this paper we concentrate on modeling performance of wireless cells using hierarchical Bayesian modeling framework. This framework provides a principled way to navigate the space between the option of creating one model to represent all cells in a network and the option of creating separate models at each cell. The former option ignores the variations between cells (complete pooling) whereas the latter is overly noisy and ignores the common patterns in cells (no pooling). Hierarchical Bayesian modeling strikes a trade-off between these two extreme cases and enables us to do partial pooling of the data from all cells. This is done by estimating a parametric population distribution and assuming that each cell is a sample from this distribution. Because this model is fully Bayesian, it provides uncertainty intervals around each estimated parameter which can be used by network operators making network management decisions. We examine the performance of this method on a synthetic dataset and a real dataset collected from a cellular network.