Reviewerly/exHarmony huggingface dataset:
Plane-Constrained Efficient Large-scale Stereo Disparity Estimation Algorithm

As a light-weight and efficient stereo disparity estimation algorithm, ELAS (Efficient Large-scale Stereo) has gained a lot of attention in the community of vision-based robotics. However, ELAS directly generate the disparity map from the Delaunay triangulation mesh of detected support points, which ignores the fact that these points may reside on different physical planes and consequently results in big errors on planar surfaces. In this paper we introduce the plane constraint into ELAS for better performance and robustness. We first cluster the selected support points using mean shift segmentation before triangulation, and then use the classified point sets belonging to different planes to triangulate separately, which avoids the errors caused by the cross-plane triangulation neglecting possible depth discontinuities in ELAS algorithm. Moreover, our algorithm also gets parametric plane models by fitting the large flat planes to rectify some mismatched support points. We evaluated our algorithm on both the Middlebury dataset and our own captured indoor scenes, and the results show that our approach behaves better in occlusion and weak texture areas with faster speed.
Interface Design for Boccia Robot Considering Operation Characteristic

When designing a product, there are many possibilities of modeling construction. And it is influenced by tacit knowledge such as experiences of the designer and sense of aesthetics. Therefore, sometimes things made with the designer’s design model do not conform to the user’s mental model, so problems such as unknown usage, unsuited experience to use may occur.
Feasibility Assessment of Cloud SaaS Enabled Collaboration and Information Confidentiality for the Public Accounting Industry

In recent years, the growing use of Software as a Service (SaaS) presents both new opportunities and challenges for organizations. Specifically, building trust in SaaS as an alternative to traditional methods of conducting business presents unique challenges for the CPA industry, who deals with sensitive financial client information and strict regulations. This study seeks to understand the attitudes and behavior of CPA industry staff members facing changing technology in the workspace. It investigates the usability of cloud SaaS collaboration platform, utilizing Microsoft Office 365 based solution and Microsoft SharePoint Online technology. It examines whether CPA staff members can overcome preconceptions about trust in SaaS tools. Specifically, can these tools, which store client information in the cloud, be used to safely exchange sensitive information between two parties while allowing CPA firms and clients to effectively collaborate on projects. The study design included a walk-through experiment as well as a semi-structured interview survey consisting of both open and closed-ended questions. The study participants were asked to use a pre-configured SharePoint collaboration portal and execute several predefined financial common tasks, following prescribed testing script, as well as fill an online survey regarding their experience. The study results demonstrate that CPA team members felt the software provides a better platform for communicating with clients than the currently used tools. However, participants’ comfort level managing specific sensitive client information on the new online portal was lower, pointing to the potential need for follow-up training and improved education for CPA team members on the SaaS platform capabilities.
Class-Based Variational Representation Learning For Robust Image Retrieval

Supervised learning for Content-based Information Retrieval allows for obtaining discriminative representations that often excel within the training domain. However, recent evidence suggests that these representations can actually harm the retrieval precision for queries that do not belong to the domain of the training set compared to other, less discriminative representations. To avoid this behavior, we propose to learn discriminative representations which also encode the latent generative factors for each class. In this way, the proposed method is capable of maintaining (part of) the in-class variance, as well as being able to represent data that belong to classes that were not seen during the training by better learning the structure of the input space. The proposed method is evaluated under different in-domain and out-of-domain setups, significantly outperforming existing supervised and unsupervised representation learning approaches.
ShadowHunter: Facilitating Children’s Outdoor Exploration with Shadows

Currently, HCI researchers focus on novel solutions that combine technology into children’s outdoor activities to address the modest decline in such experience. This research presents a novel AR game in which shadows from the outdoor environment are used as AR markers, to facilitate children’ motivation and engagement in outdoor exploration experience. Our prototype uses physical shadows as clues with a treasure hunting game mechanism to make a connection with children and their familiar outdoor environment. To evaluate our approach, we designed and implemented the game and then conducted a qualitative user study with children (n = 10) with our initial prototype in a kindergarten. Our striking findings suggest that the use of shadows from the outdoor environment as an AR maker has the potential to expand the approach to facilitate children’ engagement and motivation through their outdoor interaction experience.
Technological Foundations of Cloud Computing

Many people wonder how cloud computing is capable of providing resource sharing with elasticity using the same physical hardware infrastructure for different users who deal with different kinds of computing tasks. What is it that facilitates the offering of infrastructure or a platform or software to many users in a multi-tenancy environment? What are the key technologies that enable the cloud to take its solid shape? The objective of this chapter is to present answers to these questions. By the end of this chapter the reader should have an understanding of the key technological foundations of cloud computing.
Consonance as a Stylistic Feature for Authorship Attribution of Historical Texts

We present an investigation of the usefulness of consonance as a stylistic feature for author attribution of historical texts. We describe an algorithm for extracting consonance from written text and a set of experiments using different classifiers to explore the accuracy of consonance-based attribution on a set of 18th-century documents and a collection of 19th-century literary works.
—Multiple Cluster-Android lock Patterns (MALPs) for Smart Phone Authentication‖

This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.
Multimodal Point Distribution Model for Anthropological Landmark Detection

While current landmark detection algorithms offer a good approximation of the landmark locations, they are often unsuitable for the use in biological research. We present multi-modal landmark detection approach, based on Point distribution model that detects a larger number of anthropologically relevant landmarks than the current landmark detection algorithms. At the same time we show that improving detection accuracy of initial vertices, using image information, to which the Point distribution model is fitted, increases both the overall accuracy and the stability of the detected landmarks.We show results on data from the public FIDENTIS Database, created for the anthropological research, and compare them to the state-of-the-art landmark detection algorithms that are based on statistical shape models.
On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning

In machine learning and numerical optimization, there has been an ongoing debate about properties of local optima and the impact of these properties on generalization. In this paper, we make a first attempt to address this question for case-based reasoning systems, more specifically for instance-based learning as it takes place in the retain phase. In so doing, we cast case learning as an optimization problem, develop a notion of local optima, propose a measure for the flatness or sharpness of these optima and empirically evaluate the relation between sharp minima and the generalization performance of the corresponding learned case base.
Deep Face Verification for Spherical Images

Over the years, several problems regarding the analysis of face images have been addressed, including face detection, recognition, identification, and verification. The advent of Convolutional Neural Networks (CNNs) gave rise to a drastic improvement on state-of-the-art performances for these problems. With the increasing popularity of 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> cameras, the demand for models to extract relevant information from spherical images has also emerged. However, traditional CNNs, originally designed for planar images, are typically not suitable for spherical images, as it is necessary to project these spherical images onto a plane, leading to severe distortions. This work presents a method for face verification on spherical images that relies upon CNNs to extract features for training a binary classifier, as well as two new face datasets with spherical images. The effectiveness of our method is assessed through a comparative analysis with relevant planar and spherical CNNs.
Block Randomized Optimization for Adaptive Hypergraph Learning

The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big matrices fast. Here, block randomized Singular Value Decomposition (SVD) via subspace iteration is integrated within adaptive hypergraph weight estimation for image tagging, as a first approach. Specifically, creating low-rank submatrices along the main diagonal by tessellation permits fast matrix inversions via randomized SVD. Moreover, a second approach is proposed for solving the linear system in the optimization problem of hypergraph learning by employing the conjugate gradient method. Both proposed approaches achieve high accuracy in image tagging measured by F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score and succeed to reduce the computational requirements of adaptive hypergraph weight estimation.
The TransBank Aligner: Cross-Sentence Alignment with Deep Neural Networks

Sentence-aligned parallel bilingual corpora are the main and sometimes the only required resource for training Statistical and Neural Machine Translation systems. We propose an end-to-end deep neural architecture for sentence alignment. In addition to one-to-one alignment, our aligner can perform cross alignment as well. We used three language pairs from Europarl corpus and an English-Persian corpus to generate an alignment dataset. Using this dataset, we tested our system both in isolation and in an SMT system. In both settings, we obtained significantly better results compared to two competitive baselines.
Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems

In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-Resnet and Mobilenet-V2, which can effectively reduce parameters and delay while maintaining the accuracy of inference. We deploy our L-Mobilenet model to GPU and ZYNQ embedded platform for fully evaluating the performance of our design. By measuring with cifar10 and cifar100 datasets, L-Mobilenet model is able to gain 3× speed up and 3.7× fewer parameters than MobileNet-V2 while maintaining a similar accuracy. It also can obtain 2× speed up and 1.5× fewer parameters than Shufflenet-V2 while maintaining the same accuracy. Experiments show that our network model can obtain better performance because of the special considerations for hardware accelerating and software-hardware co-design strategies in our L-Mobilenet bottleneck architecture.
Introduction to Cloud Computing

The objective of this chapter is to introduce the concept of cloud computing by presenting a quick refresher of traditional computing models—such as monolithic computing, client–server computing, distributed computing, cluster computing, and grid computing—as well as comparing and contrasting it with other computing models. By the end of the chapter the reader should understand the primary purposes for which cloud computing came into existence.
Risk and Uncertainty

The chapter goes into more detail about risk and uncertainty, arguing that we focus too much on the former and not enough on the latter. The chapter clarifies concepts we’ll need for subsequent chapters. These include decision trees, psychology, and social risk. The chapter also highlights decision making, which is tightly entwined with risk and uncertainty.
Review of consensus protocols used in Blockchain technologies

The categories of popular consensus punctures are reviewed: Proof of Work and its hybrids, Proof of Stake (includes LPoS, Proof of Importance) and hybrids, DAGs and its varieties. The article describes their algorithms, characteristics, features, as well as disadvantages and advantages. Vulnerabilities and attacks are subject to. Also provides a list of the use of each protocol in cryptocurrencies and other systems.
Scrum Project Management Dynamics Simulation

The paper presents dynamic aspects of software development project management. Particularly, a client influence or intervention risks, staff promotions, and their influences to software development project management with Agile/Scrum approaches and processes in terms of project performance dynamics and also economic results are considered. During the software development life cycle, the development team is expected to understand customer needs, but it is challenged by many constraints, e.g. narrow project schedule, customers’ interventions and changes, short budget, or staff promotions. For this analysis a simulation model of software project management with application of continuous simulation by System Dynamics (SD) approach is developed. The paper shows dynamics of software project management caused by project risks and disturbances, particularly client interference or intervention, and also draws some conclusions regarding client intervention risk management during software contractor project performance, based on simulation experiments. The paper constitutes an original proposal of PM archetype simulation model and a research of client intervention risk influence to software project management due to application of Agile/Scrum elements within classical PMI/PRINCE2 methods management environments.
Food Self-sufficiency Decision Support Model Based on Provinces in Indonesia Using the Clustering Method

In Indonesia rice is a staple food as a major source of carbohydrates. Rice is also a staple food of the people of most countries in Asia and even most of the population in the world. Food security for Indonesia is closely linked to the adequacy of rice provision. The indicators of national food security are (1) the availability of food every time; (2) the ability or purchasing power of the people towards food; (3) guarantee of distribution and supply of food; and (4) the ability to import in urgent conditions. Rice as a rice-producing crop is a very important commodity for Indonesia, besides being the main food producer, rice commodity is also the main source of income from millions of farmers. The availability of rice at affordable prices to communities is an important factor for national security, security and stability. Indonesia has a large population, the fourth largest in the world after China, America and India. Dependence on rice imports has a high risk not only of food dependency on other countries, but also limited availability of rice in the international market. In 1977 Indonesia imported 2 million tons of rice and accounted for one third of the rice available on the international market, affecting the price of rice. Increased rice production can be achieved through increased productivity with the application of technology varieties and better cultivation and expansion of planting areas with increased intensity of planting and printing or opening of new rice cultivation area. Based on the above explanation, the researcher uses Data Mining Clustering with K-Means to get the assessment result of all rice production provinces. With this method the results of the research can be used as a model of decision makers for the government to give priority attention to areas that have the potential to increase rice production.
Estimation of Intrinsic Dimension using Supervised Parameter Selection Method

Abstract In this paper, we propose a new method for estimating the intrinsic dimension of datasets. The new method uses the local information of different scales of the sample points (by adjacency matrix) to estimate the intrinsic dimension. The only parameter used in the new method is the scaling ratio k , which determines the adjacency matrix of different scales. We propose a parameter selection method based on the difference of estimated dimension and the classification accuracy of projection data. Experiments on real datasets demonstrate the effectiveness of the proposed method.
Product detection based on CNN and transfer learning

With the development of artificial intelligence and the introduction of “new retail” concept, unmanned settlement has gradually become a research hotspot in academia and industry. As an important part of the retail, settlement is important for supermarket and user experience. In the traditional method, bar code based recognition requires a lot of manual assistance, and the salary cost is high; RFID also requires special equipment, and the hardware cost is high. At present, convolutional neural networks (CNNs) exhibit many advantages over traditional methods in various machine vision tasks such as image classification, object detection, instance segmentation, image generation, etc. Based on deep learning, this paper provides a novelty unmanned settlement solution that requires only a few cameras, which can achieve a new experience that is faster, more accurate and lower cost. A very high accuracy rate is achieved on our product dataset. The subsequent paper also demonstrate the effectiveness and the robustness of the algorithm under different conditions through a series of experiments.
An Analysis of Employment Satisfaction of Business English Majors under the background of Belt and Road Initiative

The specialty setting and employment guidance in colleges and universities are usually worked out based on empiricism. Data mining technology is rarely used to guide it. In recent years, under the background of Belt and Road Initiative, many schools have offered foreign the majors related to foreign trade English. What is the setting of these majors and the employment satisfaction of graduates? Both have an impact on social and economic development. Access to this information in a timely manner becomes the key. In this paper, the rough set theory, a data mining technology, is used to solve this problem. First of all, this paper collects the employment statistics of foreign trade English graduates from Guangxi Talent International College. Secondly, based on the rough set theory, the quantitative measurement method of college students' employment satisfaction research is put forward, and the intelligent data analysis model based on rough set is established. The experimental results show that the new method is effective and can provide some references for school employment guidance and the training of foreign trade English majors. Finally, the resolution matrix of rough set is used to reduce the attributes of the evaluation data and reduce the unnecessary evaluation indexes. The experimental results show that the evaluation method after the statute is more efficient.
Modified EM Algorithms for Parameter Estimation in Finite Mixture Models

There’s no doubt that finite mixture models boom as a result of the proposition and development of the expectation-maximization (EM) algorithm. In this article, we propose three modified EM algorithms for parameter estimation in finite mixture models. The new methods replace the mixing proportions with other estimates while calculating the conditional expectations for the hidden labels given observations and the current estimates for parameters in the E step. We also discuss the convergence properties of the new procedures. Numerical studies show that the new acceleration methods perform much better than the classical EM algorithm in convergence rate and estimation accuracy.
Normal Distribution Sampling Convolutional Neural Network for Fine-Grained Image Classification

In this paper, we propose a Normal distribution Sampling Convolutional Neural Network (NS-CNN) for Fine-grained Image Classification. Different from other fine-grained classification networks, which directly use the discriminative feature area for classification, NS-CNN centers on the discriminative feature area, and uses the probability model based on the two-dimensional normal distribution to resample the image pixels, and then recognizes the new image. This method can focus on the discriminative feature area and consider the influence of the surrounding areas of the discriminative feature. At the stage of classification, NS-CNN divides the image into several grids, and each grid is classified as a discriminative feature area. And finally, all the classification results are merged to get the final result. This method omits the complex discriminative feature localization network, so the network parameters are fewer. This helps enable fine-grained classification networks to work on computers with common hardware. We tested this method on CUB-200-2011 dataset, and the experimental result show that NS-CNN can get an outstanding performance on fine-grained classification with a lightweight network architecture.