Reviewerly/exHarmony huggingface dataset:
ℓ<sub> <i>q</i> </sub>-Norm Sample-Adaptive Multiple Kernel Learning

Existing multiple kernel learning (MKL) algorithms indiscriminately apply the same set of kernel combination weights to all samples by pre-specifying a group of base kernels. Sample-adaptive MKL learning (SAMKL) overcomes this limitation by adaptively switching on/off the base kernels with respect to each sample. However, it restricts to solving MKL problems with pre-specified kernels. And, the formulation of existing SAMKL falls to an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm MKL which is not flexible. To allow for robust kernel mixtures that generalize well in practical applications, we extend SAMKL to the arbitrary norm and apply it to image classification. In this paper, we formulate a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL, and derive an efficient ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> -norm (q ≥ 1 and denoting the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> -norm of kernel weights) SAMKL algorithm. The cutting plane method is used to solve this margin maximization problem. Besides, we propose a framework for solving MKL problems in image classification. Experimental results on multiple data sets show the promising performance of the proposed solution compared with other competitive methods.
A Method of Planar Multi-Segment Five-Degree Polynomial Trajectory Planning for Simplified Obstacles

It is proposed that under the prescribed conditions, the obstacle spree is simplified by the minimum outer ball method, and the robot end ensures that the speed acceleration reaches the five target points in the working surface under the continuous conditions of the operating conditions, and compares the shortest distance of the track line to the track point of the simplified post-center point to avoid touching, And change the parameters when intersecting the obstacle to re-plan the trajectory.
Code Convention Adherence in Research Data Infrastructure Software: An Exploratory Study

Science is rapidly evolving, incorporating technology like autonomous vehicles, high-throughput scientific instruments, high-fidelity numerical models, and sensor networks, all generating data with increasing frequency, variety, and volume. Scientists committed to open science are interested in sharing this data, which requires research data infrastructure (RDI). The software underlying RDI is often created and/or deployed by people who have not received formal training in software engineering, or at organizations with primary mandates that do not include software development. Our understanding of software engineering as a field and practice does not universally translate to this software. As RDI software is pushed to handle larger data sets, and used to share data more widely, it is important to understand the maintainability, the resilience of the development community, and other indicators of long-term software project health. While there is a body of research on scientific software, and on free and open source software, it is not known if existing approaches to assessing these properties are effective for RDI software. In this exploratory study, we calculate one proxy measure for maintainability (code convention adherence) for a popular ocean data management system, and compare the results with four open source projects, and with the apparent experience of users as captured in public mailing lists and an issue tracker. The results advance our limited understanding of this type of software, and inform hypothesis generation and future research design.
Random Sampling Deep Learning Mechanism for Discovering Unique Property of No Specific Local Feature Images

A unique and innovative deep learning mechanism is devised and investigated to discover the underline global property of an image or group of images of no any specific local features. The images of tea, rice, and coffee, etc., are some examples. Further extend and generalize the concept, this paper makes a conjecture that every image or a group of images has its own unique global property which can be used as an ID of that image or group of images. As such, if some properties of the physical product and therefore the property in its corresponding image are altered, one should be able to detect it. This paper makes the first research attempt to address and investigate this issue. Some initial experiments by random sampling deep learning algorithm are devised to explore this conjecture. Based on the devised mechanism, a real time tea authentication application can be built allowing farmers to trustfully establish their own product brand name which is pervasively promoted by enabling customers' real time tea authentication during the purchasing. This real time authentication can be friendly achieved simply by extracting the unique global tea image property captured by customer's mobile phone.
Visualization System for Evolutionary Neural Networks for Deep Learning

Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network architecture and other hyper-parameters (at each layer). Evolving architectures and hyper-parameters using a genetic algorithm is one current approach to search the huge space of all possible configurations to find those more optimal for the problem. However, examining an evolutionary process and tuning the genetic algorithm are challenging, pushing most users to treat the process as a black box. To address this challenge, we propose a visualization system for evolutionary neural networks for deep learning. The key feature of our visualization system is to provide a visual analytics environment for evaluating a genetic algorithm in order to improve the underlying operations to reduce time to find good solutions. Our system is able to not only visualize how a genetic algorithm traverses its search space but also allows users to examine evolving networks in-depth to get insights to improve performance through interactive visualization components.
Review of Inpainting Techniques for UAV Images

Occupying dead pixels, removing uninterested objects and shadows are often desired in the applications of an UAV to extract the natural and man-made feature boundaries. Image inpainting provides a mean to reconstruct the image. The basic idea behind inpainting methods is to naturally fill in absent or lacking portion of an image by using information from the surrounding area. Applications of this technique include the rebuilding of imperfect photographs and films, elimination of superimposed text, removal/replacement of unwanted objects, redeye correction, image coding. This paper reviews various image inpainting methods like PDE based image inpainting, wavelet-based inpainting, structural inpainting, exemplar-based image inpainting and textural inpainting with their variations. Image inpainting can also be used indirectly in squeezing image where some percentage of the original image is transmitted, and the whole image can be reconstructed on the other end using a pre-trained neural network. The critical reviews of each of these traditional methods along with the latest CNN based techniques are compared and suitability of these techniques for examining or repairing the UAV image is analyzed. In this paper, some of the existing quality assessment metrics like PSNR, MSE, ASVS, BorSal etc.related to image inpainting are also discussed.
An improved neural network for domain adaptive Chinese word segmentation

Various methods have been proposed recently to solve the problem of weak domain adaptability of Chinese word segmentation (CWS) models based on neural networks. However, although some of these improved models achieve high segmentation accuracy in a specific domain, they need to be retrained when applied to another. After rethinking the domain adaptability, two criteria, including the segmentation accuracy and the universality, are suggested for measuring it. Taking the above two criteria into consideration, an improved neural-based CWS model is proposed, which incorporates the common lexicon and unlabeled data into BERT. To make the most use of lexicon, a new method is proposed to construct the lexicon-based feature vector. In addition, the domain-specific words can be effectively extracted by pre-training a language model on the unlabeled data. Finally, a GRU-like gate structure is used to integrate the lexicon-based feature vector and language model into BERT. Experiments on five different domains reveal that the domain adaptability of this model is very strong.
MobileGAN: Compact Network Architecture for Generative Adversarial Network

In this paper we introduce a compact neural network architecture for a generator of generative adversarial network (GAN), which reduces the number of weight parameters so that mobile devices can afford to download these parameters via cellular networks. Network architecture of GAN generator usually consists of a fully-connected layer on top and succeeding convolutional layers with upscaling which extend picture characteristics to a larger image. Consequently, the GAN generator network is highly enlarged, and the size of its weight parameters becomes tens or hundreds of MBs, which is not preferable for transmission over cellular network or running on mobile or embedded devices. Our approach named MobileGAN is based on layer decomposition technique like MobileNets. At a generator side, all convolutional layers except the bottom layer to output an RGB image are decomposed into pixelwise and depthwise convolutional layers to reduce the number of weight parameters. Also, the first fully-connected layer is decomposed into a couple of 1-dimensional convolution layers for the similar purpose. On the other hand, our approach does not modify any layers in discriminator network in order to maintain picture quality of output images generated by the decomposed generator network. We evaluated the performance of MobileGAN generator network by Inception Score and Fréchet Inception Distance. As a result, we confirmed that MobileGAN can reduce the size of weight parameter into up to 20.5% of generator network of ResNet GAN with slight score degradation.
Multimedia Teaching Model of College English Based on the Network

The traditional foreign language teaching mode can no longer meet the realistic needs of the foreign language education in China and the strategic goal of the leapfrog development of our education. How to cultivate a large number of the talents with the high English application ability, especially listening and speaking abilities, which are suitable for the needs of the international exchanges, is an important task for the higher education in China. Therefore, it is necessary to innovate the foreign language education, that is, to innovate the educational viewpoints and modes. Therefore, actively exploring the computer and the network teaching modes is an important way of the college English teaching reform.
Improving Grapheme-to-Phoneme Conversion by Investigating Copying Mechanism in Recurrent Architectures

Attention driven encoder-decoder architectures have become highly successful in various sequence-to-sequence learning tasks. We propose copy-augmented Bi-directional Long Short-Term Memory based Encoder-Decoder architecture for the Grapheme-to-Phoneme conversion. In Grapheme-to-Phoneme task, a number of character units in words possess high degree of similarity with some phoneme unit(s). Thus, we make an attempt to capture this characteristic using copy-augmented architecture. Our proposed model automatically learns to generate phoneme sequences during inference by copying source token embeddings to the decoder's output in a controlled manner. To our knowledge, this is the first time the copy-augmentation is being investigated for Grapheme-to-Phoneme conversion task. We validate our experiments over accented and non-accented publicly available CMU-Dict datasets and achieve State-of-The-Art performances in terms of both phoneme and word error rates. Further, we verify the applicability of our proposed approach on Hindi Lexicon and show that our model outperforms all recent State-of-The-Art results.
Software Startup ESSENCE: How Should Software Startups Work?

Software startups need to work in a systematic fashion just like mature organizations. However, existing software engineering methods and practices are not aimed at software startups. They do not account for the business aspect of startups and may not be well suited for software startups in general. The Lean Startup Methodology on the other hand contains some useful practices for software startups but is nonetheless impractical, offering little in the way of telling you what to do. Software startups are thus required to tailor their own method. Currently, many software startups simply work ad hoc or use various Agile methods and practices. In terms of Agile methods and practices, little consensus exists between startups. In this chapter, we discuss methods and method tailoring. We give guidelines on how to create your own way of working and recommend a tangible tool for doing so: the Essence Theory of Software Engineering.
Mapping vegetation on ferruginous substrates using ASTER and gamma-spectrometry images in the Iron Quadrangle, Minas Gerais

ABSTRACT The Iron Quadrangle (IQ) region in Minas Gerais is remarkably geobiodiverse, despite a long history of anthropogenic pressures such as mining and urbanization, but still lacks detailed studies on the distribution of its remaining native vegetation in different substrates. In this study, we utilized Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, besides Gamma-spectrometry (Gamma) survey data associated with existing geological mapping (GM) and extensive fieldwork, to discriminate and quantify remnants of vegetation on ferruginous substrates in the IQ. The Maximum Likelihood (ML) algorithm was used to classify the vegetation types, thus named: open Rupestrian Field, shrubby Rupestrian Field, Capão Forest, Cerrado stricto sensu, Cerrado Field, Seasonal Forests, Pastures and Reforestation (the latter three regardless of substrate type) associated with the predominant substrates (ferruginous ironstone, phyllites, and quartzite). The use of ASTER images alone did not allow a reliable separation of ferruginous and non-ferruginous substrates, but the integration of all different data (ASTER-ML + Gamma + GM) allowed the provisional mapping of the vegetation associated with ferruginous substrates, potentially ferruginous and non-ferruginous substrates. The resulting map shows that the vegetation on ferruginous and potentially ferruginous substrates cover 8.7% and 6.9% of the IQ, respectively. The detailed analysis of the distribution and fragmentation of phytophysiognomies on ferruginous substrates is of great importance for developing strategies to conserve the geobiodiversity of the IQ, and need to be further refined by checking and field mapping by novel approaches.
Object Detection and Recognition Using Local Quadrant Pattern

Object detection and recognition is one of the important techniques in computer vision for searching and scanning and identifying an object in images or videos. Object detection and recognition enters into many important fields where one of the uses of object detection and recognition is to detect region of injury and determine the type of injury. This paper suggested a new effective method called Local Quadrant Pattern (LQP). The proposed method uses a window and passes it on all pixels of the image and uses the pixel direction to arrange the adjacent pixels. It also uses four code values to encode and then produce a texture feature matrix which is used to detect objects as well as extract features based on magnitude of pixels for image classification. The experiments were conducted on the infected regions in the skin and the results showed the ability of the method to detect regions of infection as well as the high accuracy in the classification of those regions.
Model-based Empowerment Computation for Dynamical Agents

Empowerment is defined as the channel capacity between action sequences and sensor information and has been studied as a kind of intrinsic rewards for survival. It is based on the mutual information conditioned by a current state, but generally calculating it needs heavy computation. This paper points out weak points of the previous methods to compute empowerment and proposes an improved method for sensorimotor environments, where their states are observed as continuous values, under the model-based setting. Then, we introduce a planning problem, whose goal is to maximize cumulative empowerment value on the articulated robots, and the behaviors after training are discussed while referring to other theories of intrinsic rewards and the control theory.
An Insight into Decisive Factors in Cloud Provider Selection with a Focus on Security

In the last ten years cloud computing has developed from a buzz word to the new computing paradigm on a global scale. Computing power or storage capacity can be bought and consumed flexibly and on-demand, which opens up new opportunities for cost-saving and data processing. However, it also goes with security concerns as it represents a form of IT outsourcing. We investigate how these concerns manifest as a decisive factor in cloud provider selection by interviews with eight practitioners from German companies. As only a moderate interest is discovered, it is further examined why this is the case. Additionally, we compared the results from a systematic literature survey on cloud security assurance to cloud customers’ verification of their providers’ security measures. This paper provides a qualitative in-depth examination of companies’ attitudes towards security in the cloud. The results of the analysed sample show that security is not necessarily decisive in cloud provider selection. Nevertheless, providers are required to guarantee security and comply. Traditional forms of assurance techniques play a role in assessing cloud providers and verifying their security measures. Moreover, compliance is identified as a strong driver to pursue security and assurance.
Enhancing the Capability of Load Management Techniques in Cloud Using H_FAC Algorithm Optimization

Load balancing is a major research discipline in Cloud computing. The services are provided to users on pay as you go manner. Although a lot of algorithms have been proposed for load balancing, but performance is still an issue. The authors have proposed a new hybrid algorithm H_FAC to optimize the performance in cloud computing. The hybrid technique combines cuckoo search along with the Firefly algorithm of swarm intelligence. The benefit of using hybridization technique is that strength of one algorithm will overcome the shortcomings of other algorithms. Blockchain ID based Signature technique is used to ensure the authenticity of cloud service provider. The experimental results of H_FAC minimize the standard deviation, execution time significantly and improved throughput thereby optimizing the performance. The hybrid algorithm is also compared with other algorithms like ant colony optimization, artificial bee colony, round robin, FCFS and modified throttled. This approach helps the users to get the resources from authentic resource providers with a reduced execution time.
Dual attention module and multi‐label based fully convolutional network for crowd counting

High‐density crowd counting in natural scenes is an extremely difficult and challenging research subject in computer vision. Although the algorithm based on the convolutional neural network has achieved significantly better results than the traditional algorithm, most of them tend to focus on the local features of images, and difficult to obtain the rich global contextual dependencies. To solve this problem, a dual attention module and a multi‐label based fully convolutional network are proposed in this study. Moreover, the authors improve the algorithm by the following multiple perspectives. Firstly, introducing the dual attention module, the global‐context and long‐range dependency are adaptively integrated into both spatial and channel dimensions, which improve the network expression ability. Then, the prediction error is effectively reduced by designing a multi‐label mechanism, so the crowd‐counting task is transformed into foreground and background segmentation tasks to assist in the regression task of the density map. Furthermore, on the basis of the traditional Euclidean distance loss and cross‐entropy loss, the structural similarity index is introduced to further improve the training effect of the model. The test results of the UCF_CC_50, ShanghaiTech, and UCF‐QNRF datasets indicate that the proposed method is superior to the current mainstream algorithm.
Optimization of Click-Through Rate Prediction of an Advertisement

Online advertising has become lucrative for businesses to reach millions of customers. Revenue model for online advertising is different from conventional practice, as advertisers get charged only when users click on their advertisement; this method is referred as “pay-per-click”. Several studies have been carried using multi-criteria regression and kernel prediction algorithms to analyse click patterns. User profile and purchase history helps to identify the ad content to be displayed and the order in which they are displayed. Click-through rating method increases the probability of user watching and clicking on each ad. In the current study, we present a novel approach to improve the accuracy of the frequently clicked advertisements by using firefly algorithm. We have used KDD Cup 2012 datasets collected from popular websites selling US and UK products. The results of our study show 0.99 accuracy confirming our approach to be better than existing methods.
Banknotes Serial Number Coding Recognition

Banknotes serial number coding is the main basis of bank's supervision, currency circulation and authenticity verification, the traditional manual identification method can hardly meet the current needs of currency circulation and security control. This paper solves the problem of banknotes serial number coding recognition based on deep learning algorithm. Our algorithm is composed of text localization and text recognition. The aim of the text localization phase is to get the region of the serial number coding. Based on YOLOv3 model, this paper replaces original convolution module with depthwise separable convolution. The idea of Inception is adopted in subsample stage. K-means algorithm is used to re-cluster width and height of serial number coding region to adjust anchor value, thus, improving the localization accuracy. The aim of the text recognition phase is to get the serial number coding of the banknotes, CNN+LSTM+CTC is taken as the model for text recognition. In this paper, the fourth edition of RMB data set is used for serial number coding recognition, 99.38% accuracy is obtained in experimental results.