Reviewerly/exHarmony huggingface dataset:
Features Extraction and Classification of Wood Defect Based on Hu Invariant Moment and Wavelet Moment and BP Neural Network

Wood defect will reduce wood properties, wood quality and use value, so it is of great practical significance to detect wood defect[1]. The key to feature extraction of target defect image is target recognition and classification. Moment feature is a common feature descriptor in defect extraction algorithm. Aiming at the problem that the seven feature components of Hu moments differ greatly in magnitude and are affected by scale factor, based on the principle and characteristics of invariant moments and wavelet energy, a feature extraction algorithm based on wavelet moments is proposed and applied to the feature extraction of wood defects. Finally, the experiment collects the actual wood defect image, decomposes the preprocessed image into three sub-images by wavelet transform, calculates the modified Hu moment invariants for the sub-images, takes the moment invariants as the feature variables, and obtains the recognition results by using the minimum neighborhood distance classification. The experimental results show that the feature extracted by this method has the invariance of translation, rotation and scale, and can reflect the important and original attributes of the target image. Compared with the traditional Hu moment, the recognition rate is significantly improved, and the expected goal is achieved.
Better Supporting Workers in ML Workplaces

This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It's our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in Machine Learning (ML) business applications, understanding workers in these work contexts have received little attention. As this category experiences dramatic growth, it's important to better understand the role that workers play, both individually and collaboratively, in a workplace where the output of prediction and machine learning is becoming pervasive. There is a closing window of opportunity to investigate this topic as it proceeds toward ubiquity. CSCW and HCI offer concepts, tools and methodologies to better understand and build for this future.
Dynamic Occlusion Handling for Real-Time AR Applications

Augmented reality (AR) allows computer generated graphics to be overlaid in images or video captured by a camera in real time. This technology is often used to enhance perception by providing extra information or simply by enriching the experience of the user. AR offers a significant potential in many applications such as industrial, medical, education and entertainment. However, for AR to achieve the maximum potential and become fully accepted, the real and virtual objects within the user’s environment must become seamlessly integrated. Three main types of problems arise when we try to achieve this effect: illumination issues, tracking difficulties and occlusion troubles. In this work we present an algorithm to handle AR occlusions in real time. Our approach uses raw depth information of the scene to realize a rough foreground / background segmentation. We use this information, as well as details from color data to estimate a blending coefficient and combine the virtual objects with the real objects into a single image. After experimenting with different scenes we show that our approach is able to produce consistent and aesthetically pleasing occlusions between virtual and real objects, with a low computational cost. Furthermore, we explore different alternatives to improving the quality of the final results while overcoming limitations of previous methods.
IoT Based Sign Language Interpretation System

Abstract In India about 31% of peoples are deaf or mute. One third of them are both deaf and mute. Gesture is a simple and important mode of interpersonal communication, which can simulate language and behaviour to express particular feelings and words. In this paper we have proposed a method that gives a method for improving Sign Language Interpretation system. In this method, we will use flex sensors along with pressure sensor which are incorporated on a glove to detect the gestures and convert it to text with in an Android Smart Phone. The text-to-speech module is also added in android application to improve the way of communication for deaf and mute peoples.
HarpGBDT: Optimizing Gradient Boosting Decision Tree for Parallel Efficiency

Gradient Boosting Decision Tree (GBDT) is a widely used machine learning algorithm, whose training involves both irregular computation and random memory access and is challenging for system optimizations. In this paper, we conduct a comprehensive performance analysis of two state-of-the-art systems, XGBoost and LightGBM. They represent two typical parallel implementations for GBDT; one is data parallel and the other one is parallel over features. Substantial thread synchronization overhead, as well as the inefficiency of random memory access, is identified. We propose HarpGBDT, a new GBDT system designed from the perspective of parallel efficiency optimization. Firstly, we adopt a new tree growth method that selects the top K candidates of tree nodes to enable the use of more levels of parallelism without sacrificing the algorithm's accuracy. Secondly, we organize the training data and model data in blocks and propose a block-wise approach as a general model that enables the exploration of various parallelism options. Thirdly, we propose a mixed mode to utilize the advantages of a different mode of parallelism in different phases of training. By changing the configuration of the block size and parallel mode, HarpGBDT is able to attain better parallel efficiency. By extensive experiments on four datasets with different statistical characteristics on the Intel(R) Xeon(R) E5-2699 server, HarpGBDT on average performs 8x faster than XGBoost and 2.6x faster than LightGBM.
Geometric consistency of principal component scores for high‐dimensional mixture models and its application

Abstract In this article, we consider clustering based on principal component analysis (PCA) for high‐dimensional mixture models. We present theoretical reasons why PCA is effective for clustering high‐dimensional data. First, we derive a geometric representation of high‐dimension, low‐sample‐size (HDLSS) data taken from a two‐class mixture model. With the help of the geometric representation, we give geometric consistency properties of sample principal component scores in the HDLSS context. We develop ideas of the geometric representation and provide geometric consistency properties for multiclass mixture models. We show that PCA can cluster HDLSS data under certain conditions in a surprisingly explicit way. Finally, we demonstrate the performance of the clustering using gene expression datasets.
APDP: Attack-Proof Personalized Differential Privacy Model for a Smart Home

The proliferation of smart devices in recent years has led to novel smart home applications that upgrade traditional home appliances to intelligent units and automatically adapt their services without human assistance. In a smart home system, a central gateway is required to coordinate the functions of various smart home devices and allow bidirectional communications. However, the gateway may cause leakage of sensitive information unless proper privacy protections are applied. In this work, we first introduce a smart home model based on fog computing and secured by differential privacy. Then, we apply a personalized differential privacy scheme to provide privacy protection. Furthermore, we consider a collusion attack and propose our differential privacy model called APDP based on a modified Laplace mechanism and a Markov process to strengthen privacy protection, thus resisting the attack. Lastly, we perform extensive experiments based on the real-world datasets to evaluate the proposed APDP model.
New Insights of Geomorphologic and Lithologic Features on Wudalianchi Volcanoes in the Northeastern China from the ASTER Multispectral Data

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imaging system onboard NASA’s (National Aeronautics and Space Administration’s) Terra satellite is capable of measuring multispectral reflectance of the earth’s surface targets in visible and infrared (VNIR) to shortwave infrared (SWIR) (until 2006) as well as multispectral thermal infrared (TIR) regions. ASTER VNIR stereo imaging technique can provide high-resolution digital elevation models (DEMs) data. The DEMs data, three-dimensional (3D) perspective, and ratio images produced from the ASTER multispectral data are employed to analyze the geomorphologic and lithologic features of Wudalianchi volcanoes in the northeastern China. Our results indicate that the 14 major conical volcanic craters of Wudalianchi volcanoes are arranged as three sub-parallel zones, extending in a NE (Northeast) direction, which is similar to the direction of regional fault system based on the ASTER DEMs data. Among the 14 volcanic craters in Wudalianchi, the Laoheishan, and Huoshaoshan lavas flows, after the historic eruptions, pouring down from the crater, partially blocked the Baihe River, which forms the Five Large Connected Pools, known as the Wudalianchi Lake. Lithologic mapping shows that ASTER multispectral ratio imagery, particularly, the Lava Flow Index (LFI) (LFI = B10/B12) imagery, can clearly distinguish different lava flow units, and at least four stages of volcanic eruptions are revealed in the Wudalianchi Quaternary volcano cluster. Thus, ASTER multispectral TIR data can be used to determine relative dating of Quaternary volcanoes in the semi-arid region. Moreover, ASTER 3D perspective image can present an excellent view for tracking the flow directions of different lavas of Wudalianchi Holocene volcanoes.
Efficient in-network aggregation mechanism for data block repairing in data centers

Many distributed storage systems in data centers, e.g., Google Colossus SF, Facebook HDFS, and Microsoft Azure, adopt erasure codes to improve storage reliability and reduce the storage space overhead. Accordingly, when a block of data fails, multiple involved blocks are sent to a new node to regenerate the failed block. Furthermore, repairing those failed data blocks brings a large amount of bandwidth consumption. Meanwhile, prior repairing methods only focus on how to aggregate data blocks on involved storage nodes, which fails to efficiently save the network bandwidth. By contrast, our observations show that the aggregation is not limited to those storage nodes. Specifically, servers and switches can all be utilized to aggregate those data blocks during the transmission phase. Therefore, in this paper, we propose an efficient in-network aggregation mechanism, called AggreTree, which can effectively leverage the intermediate nodes along the transmission paths to aggregate data blocks, thus repairing failed blocks. Compared with existing methods, the results of experiments show that AggreTree can efficiently improve the repairing speed of failed blocks by up to 4.06 and 2.86 times, and further reduce the transmission cost by 80.86% and 81.68% in Fat-tree and BCube data centers, respectively.
Accelerating recommender system training 15x with RAPIDS

In this paper we present the novel aspects of our 15th place solution to the RecSys Challenge 2019 which are focused on the acceleration of feature generation and model training time. In our final solution we sped up training of our model by a factor of 15.6x, from a workflow of 891.8s (14m52s) to 57.2s, through a combination of the RAPIDS.AI cuDF library for preprocessing, a custom batch dataloader, LAMB and extreme batch sizes, and an update to the kernel responsible for calculating the embedding gradient in PyTorch. Using cuDF we also accelerated our feature generation by a factor of 9.7x by performing the computations on the GPU, reducing the time taken to generate the features used in our model from 51 minutes to 5. We demonstrate these optimizations on the fastai tabular model which we relied on extensively in our final ensemble. With training time so drastically reduced the iteration involved in generating new features and training new models is much more fluid, allowing for the rapid prototyping of deep learning based recommender systems in hours as opposed to days.
Relevance Feedback using Genetic Algorithm on Information Retrieval for Indonesian Language Documents

Background: The Rapid growth of technological developments in Indonesia had resulted in a growing amount of information. Therefore, a new information retrieval environment is necessary for finding documents that are in accordance with the user’s information needs.Objective: The purpose of this study is to uncover the differences between using Relevance Feedback (RF) with genetic algorithm and standard information retrieval systems without relevance feedback for the Indonesian language documents.Methods: The standard Information Retrieval (IR) System uses Sastrawi stemmer and Vector Space Model, while Genetic Algorithm-based (GA-based) relevance feedback uses Roulette-wheel selection and crossover recombination. The evaluation metrics are Mean Average Precision (MAP) and average recall based on user judgments.Results: By using two Indonesian language document datasets, namely abstract thesis and news dataset, the results show 15.2% and 28.6% increase in the corresponding MAP values for both datasets as opposed to the standard Information Retrieval System. A respective 7.1% and 10.5% improvement on the recall value at 10th position was also observed for both datasets. The best obtained genetic algorithm parameters for abstract thesis datasets were a population size of 20 with 0.7 crossover probability and 0.2 mutation probability, while for news dataset, the best obtained genetic algorithm parameters were a population size of 10 with 0.5 crossover probability and 0.2 mutation probability.Conclusion: Genetic Algorithm-based relevance feedback increases both values of MAP and average recall at 10th position of retrieved document. Generally, the best genetic algorithm parameters are as follows, mutation probability is 0.2, whereas the size of population size and crossover probability depends on the size of dataset and length of the query.Keywords: Genetic Algorithm, Information Retrieval, Indonesian language document, Mean Average Precision, Relevance Feedback
Object Detection Algorithm Based on Deformable Convolutional Networks for Underwater Images

The accuracy is low and the speed is slow of existing underwater images object detection algorithms, especially in variable scale forms on the same or different object (such as sea urchins, starfish, etc.). To address this problem, this paper propose an effective object detection algorithms based on deformable convolutional networks for underwater images. Firstly, we preprocess underwater images using convolutional neural networks and white balance to remove color deviation and increase the contrast, as well as improving the resolution. Then, in order to obtain a score map of the class information that has more representation, we rebuild ResNet-101 feature extraction sub-network using deformable convolution model to improve the feature extraction ability, and construct a deformable position-sensitive ROI pooling process with RPN. Finally, we apply softmax to regression and classification for feature information of each object in order to accurately output the classes and position. On testing dataset, the subjective and objective experimental results show better detection effect, and the mean average precision (mAP) is 90.3%, which is higher than the comparison algorithm. The experimental results show that the proposed algorithm has better detection accuracy and effect for object detection of underwater images.
Smooth Surface Visual Imaging Method for Eliminating High Reflection Disturbance

At present, visual imaging is widely applied for surface defects such as bumps and scratches in the manufacture of precise parts with a highly reflective surface. However, the high light reflection and halo disturbance as a result of the illumination in visual imaging exert a direct influence on the accuracy of defect detection. In this regard, the present paper develops an adaptive illumination method based on space-time modulation for a visual imaging system. Furthermore, a digital micro-mirror device (DMD) is employed to realize the pixel-level spatiotemporal modulation of illumination. Then, in combination with the illumination intensity feedback of charge coupled device (CCD), the time-space ratio is adjusted automatically to achieve adaptive uniform illumination and effectively suppress the high light reflection and halo disturbance of highly reflective surfaces. The experimental results show that, in terms of restraining high light disturbance, the visibility and accuracy of visual imaging are improved.
Design and Implementation of Various Payment System for Product Transaction in Mobile Application

The development of technology and information has spread to various sectors, especially the product marketing sector and product transactions. The presence of e-commerce brings new changes in the world of business production and marketing. Therefore, changes to the payment method must also follow the scheme of e-commerce, namely payments connected to the internet. The e-payment makes it easy for buyers to transact non-cash and provides convenience for buyers in terms of time and also the ease of purchasing goods because e-payment is very important for e-commerce. the Indonesian market has different characteristics and is quite unique, because in fact the use of credit cards has not been so popularly used. Transactions via ATM transfers are the most dominant payment method used followed by e-money or e-wallet payment methods. In this research, a payment system was tested on local e-commerce applications, the system was built on mobile applications and web services with the REST API, mobile applications as clients and web services as servers. The test was carried out by evaluating the performance of the payment system for thirty users in seven days, the results of this research showed that the performance of the payment system and the authentication process of payment system.
Pixelated polarizing system for dynamic interferometry events employing a temporal phase unwrapping approach

We describe a robust system to retrieve the phase using a temporal phase unwrapping approach and a pixelated polarizing camera. By considering the characteristics of the polarizing sensor and its capabilities to acquired simultaneously four phase shifted interferograms in a stable manner, we propose the use of a temporal phase unwrapping technique to simplify the phase retrieval procedure in dynamic events. We also show that by combining the polarizing sensor and temporal phase unwrapping algorithms, the phase unwrapping procedure is not only simplified, but also the computing time is reduced one order of magnitude compared with other algorithms. In the approach taken, each pixel is processed independently obtaining the phase difference for a specific time opening the possibility of real-time analysis. Validation experiments are presented.
Toward scaling hardware security module for emerging cloud services

The hardware security module (HSM) has been used as a root of trust for various key management services. At the same time, rapid innovation in emerging industries, such as container-based microservices, accelerates demands for scaling security services. However, current on-premises HSMs have limitations to afford such demands due to the restricted scalability and high price of deployment. This paper presents ScaleTrust, a framework for scaling security services by utilizing HSMs with SGX-based key management service (KMS) in a collaborative, yet secure manner. Based on a hierarchical model, we design a cryptographic workload distribution between HSMs and KMS enclaves to achieve both the elasticity of cloud software and the hardware-based security of HSM appliances. We demonstrate practical implications of ScaleTrust using two case studies that require secure cryptographic operations with low latency and high scalability.
Dr. Pecker: A Deep Learning-Based Computer-Aided Diagnosis System in Medical Imaging

This chapter intends to demonstrate the clinical applications of Computer-Aided Diagnosis (CAD) systems based on deep learning algorithms while focusing on their IT infrastructure design. In comparison with traditional CAD systems that are mostly standalone applications designed to solve a particular task, we explain design choices of a cloud-based CAD platform that allows for running computational intense deep learning algorithms in a cost-efficient way. It also provides off-the-shelf solutions to collect, store, and secure data anywhere and anytime from various data sources, which is essential for training deep learning algorithms. In the end, we show the superior performance of using such CAD platform for analyzing medical imaging data of various modality before the conclusion.
Beyond EM: A faster Bayesian linear regression algorithm without matrix inversions

The Bayesian linear regression is a useful tool for many scientific communities. This paper presents a novel algorithm for solving the Bayesian linear regression problem with Gaussian priors, which shares the same spirit as the gradient based methods. In addition, the standard scheme for this task, the Expectation Maximization (EM) algorithm, involves matrix inversions but our proposed algorithm is free of. Numerical experiments demonstrate that the proposed algorithm performs as well as the gradient based and EM algorithms in term of precision, but runs significantly faster than the gradient based and EM algorithms. Due to its matrix-inversion-free nature, the algorithm of this paper is a viable alternative to the competing methods available in the literature.
Mexican University Ranking Based on Maximal Clique

The evaluation of individual universities generates university rankings according to a set of indicators. In general, the classical rankings are based on reputation, academic achievements, research levels, material and financial resources, etc. In this chapter, a new methodology for university rankings based on the maximum clique problem in networks is presented. To study the method, the Higher Mexican Educational System was modeled through the complex networks approach, using the available information about the main academic characteristics, influence, and importance in the social networks and educational networks of each university. The numerical results show that the Mexican universities can be classified into at least three groups, where the best universities have better-qualified teachers, offer quality undergraduate and postgraduate programs, and are essential and influential in the exchange and dissemination of knowledge using the information and communication technologies (ICTs) and social networks (SN).
A Generalized S-Transform in Linear Canonical Transform

Abstract The linear canonical S-transform is a generalization of the classical S-transform using linear canonical transform. In the present work, we propose the definition of the linear canonical S-transform and investigate important its properties.
A system for operating energy-aware cloudlets

We present an end-to-end system for operating energy-aware cloudlets with a low-footprint cluster manager and an adaptive client-side load balancing approach. Our system is designed for small-scale high-density compute clusters that host stateless services and have stringent energy resource constraints. It features cluster and service management, runtime monitoring, adaptive load balancing and cluster reconfiguration policies. Furthermore, we present an experimentation and analytics system that allows coordinated execution of complex workload experiments to evaluate different operational strategies.