Reviewerly/exHarmony huggingface dataset:
Cloud-Enabled Ubiquitous Collaboration on Large Models Using Augmented Reality

Abstract A cloud-based augmented reality system is presented which is designed to enhance the real-time collaboration of domain experts involved in modeling large reservoirs. An evaluation of traditional techniques is compared with this new approach. Work-from-home (WFH) scenarios are becoming more important and, in some cases, critical. There is a need to untether the geologist and other domain experts from high end office-bound workstations. Reservoir models in recent years have become increasingly large. The volume of seismic, well data and modeling data structures presents a challenge in both management of the data and making it accessible to domain experts and others. The traditional practice of siloing data in various disconnected data repositories in various corporate data centers is becoming increasingly untenable. What is needed is a comprehensive approach that scales to the largest models and is accessible anywhere for time critical analysis and collaboration. The authors have created a prototype of a potential solution to this problem, where the model resides in the cloud and can be visualized in augmented reality either as a hologram through a head mounted display or through the AR capabilities of tablets and smartphones. This technology is ideal for WFH scenarios: it is light weight, inexpensive and does not require broadband speeds in excess of what most home users can access. What we have found is that AR based approaches to modeling large reservoirs can rival traditional workstation approaches. Furthermore, AR based approaches are superior for close collaboration among domain experts. Collaboration on 3D models has not changed significantly in a generation. For co-located personnel the approach is to gather around a 2D screen. For remote personnel the approach is sharing a model through a 2D screen along with video chat. Over the years various attempts have been tried to enhance the collaboration experience and have all fallen short. Virtual reality (VR) has been proposed as a solution. However, we have found that augmented reality (AR) is a much better solution for many reasons which are explored in the paper. The cloud has been transformative for nearly every industry. The oil and gas industry has been slow to adopt cloud technologies for many reasons that have largely been overcome. AR has already acquired an impressive track record in various industries. AR will have applications in nearly all industries. For various historical reasons, the uptake for AR is much faster in some industries than others. It is too early to tell whether the use of cloud-based augmented reality for modeling large reservoirs will be transformative, however the results of this initial work are promising.
Fraud detection via deep neural variational autoencoder oblique random forest

Fraud detection is critical problem of many financial company that has been researched both in academy organizes and industry. The objective of this paper is fraud detection for transaction level in credit card and e-commerce. The new machine learning model for fraud detection is proposed that deep neural variational autoencoder oblique random forest. Variational autoencoder is strong by regarding the distribution of latent variables and by the most optimal connecting weights between latent variables and visual variables are successful by maximizing log likelihood of observed variables. The proposed method is combined the advantage of variational autoencoder and oblique random forest for fraud detection problem. The proposed method is applied for two data sets being credit card data set and e-commerce data set. The experimental results show that the proposed method attains the better performance than the single or the other methods which used the same data sets.
A Refinement Calculus for Requirements Engineering Based on Argumentation Theory

The Requirements Engineering (RE) process starts with initial requirements elicited from stakeholders – however conflicting, unattainable, incomplete and ambiguous – and iteratively refines them into a specification that is consistent, complete, valid and unambiguous. We propose a novel RE process in the form of a calculus where the process is envisioned as an iterative application of refinement operators, with each operator removing a defect from the current requirements. Our proposal is motivated by the dialectic and incremental nature of RE activities. The calculus, which we call CaRE, casts the RE problem as an iterative argument between stakeholders, who point out defects (ambiguity, incompleteness, etc.) of existing requirements, and then propose refinements to address those defects, thus leading to the construction of a refinement graph. This graph is then a conceptual model of an RE process enactment. The semantics of these models is provided by Argumentation Theory, where a requirement may be attacked for having a defect, which in turn may be eliminated by a refinement.
Formal Description Approach for Agent-Based Mobile Computing

Aiming at the problems of architecture design and semantic feature verification during the construction of mobile cloud computing system, a formal semantic description model for agent-based mobile cloud computing system was proposed on the basis of typed category theory. Agent was depicted as the object node in category theory, and the interaction and dependency between agents are used as morphisms. The whole cloud service system is described as a typed category diagram. On this basis, agent interaction and cooperation, and architecture design and refinement, as well as agent migration and agent substitutability and application request satisfiability, were all analyzed and discussed. Application research shows that the formal description approach enhances the semantic description ability of the system architecture, and helps to analyze and verify the specifications of mobile cloud service system.
A Study On Information Security Management

Cybersecurity is the only defense in one of the longest wars the world has ever known. Battles are fought daily against nation states, organized crime, thieves, terrorists and bored but smart children. This war escalates every day as the battlefield grows. The main focus of the IT group is to ensure efficient and fast processing of information, while the main goal of the information security group is to ensure confidentiality, integrity and availability of information. The paper presents a review of information security management and the main principles in this field.
Context Adaptive Metric Model for Meta-learning

The metric-based meta-learning is effective to solve few-shot problems. Generally, a metric model learns a task-agnostic embedding function, maps instances to a low-dimensional embedding space, then classifies unlabeled examples by similarity comparison. However, different classification tasks have individual discriminative characteristics, and previous approaches are constrained to use a single set of features for all possible tasks. In this work, we introduce a Context Adaptive Metric Model (CAMM), which has adaptive ability to extract key features and can be used for most metric models. Our extension consists of two parts: Context parameter module and Self-evaluation module. The context is interpreted as a task representation that modulates the behavior of feature extractor. CAMM fine-tunes context parameters via Self-evaluation module to generate task-specific embedding functions. We demonstrate that our approach is competitive with recent state-of-the-art systems, improves performance considerably (4%–6% relative) over baselines on mini-imagenet benchmark. Our code is publicly available at https://github.com/Jorewang/CAMM.
Envisaging Bugs by Means of Entropy Measures

There are numerous methods for envisaging bugs in software practices. A widespread method for bug estimation is applying entropy of alterations as recommended by Hassan []. In the existing literature, investigators have recommended and executed a surplus of bug extrapolation methodologies, which change in relation to precision, difficulty and the input files they need, nonetheless actually rarely any of them has anticipated the amount of bugs in the software established on the entropy. A standard for bug estimation is showed, in the way of a widely accessible data set comprising of numerous software techniques, and a huge divergence of the comprehensive and analytical ability of familiar bug estimation methods, collectively with innovative methods is offered. Based on the outcomes, the working and constancy of the methods with regard to our standard are examined and a number of understandings on bug estimation models are determined. In the present communication, we will acquire a method by taking into concern the amount of bugs logged in numerous announcements of Bugzilla software and compute the distinctive measures of entropy, namely Shannon entropy [] and Kapur entropy [] for the variations in several software updates in these time intervals. Eventually, this work has the possibility to shed light on how to utilize simple linear regression to envisage the bugs which are still impending.
WEKE: Learning Word Embeddings for Keyphrase Extraction

Traditional supervised keyphrase extraction models depend on the features of labeled keyphrases while prevailing unsupervised models mainly rely on global structure of the word graph, with nodes representing candidate words and edges/links capturing the co-occurrence between words. However, the local context information of the word graph can not be exploited in existing unsupervised graph-based keyphrase extraction methods and integrating different types of information into a unified model is relatively unexplored. In this paper, we propose a new word embedding model specially for keyphrase extraction task, which can capture local context information and incorporate them with other types of crucial information into the low-dimensional word vector to help better extract keyphrases. Experimental results show that our method consistently outperforms 7 state-of-the-art unsupervised methods on three real datasets in Computer Science area for keyphrase extraction.
Cloud-based Services Approach as Accelerator in Empowering Digital Transformation

Digital Transformation is the process of using digital technologies that impacts and drives significant business and organization improvements and customers' experience. Cloud Computing is one of key digital enablers of Digital Transformation of the products and processes. The main benefits of cloud services in empowering Digital Transformation are impact on cost, work efficiency, elasticity, agility, scalability and security. These benefits encourage and drive businesses and organizations in adapting their strategies to a new digital era.This research provides insights, for organizations and businesses, in terms of how cloud-based services approach empowers Digital Transformation. The approach that we are going to elaborate is based on cloud-based services, increasing capacity and enhancing functionality on demand services, with the aim of giving businesses and organizations an understanding of how to transform digitally. Digital Transformation of the organizations and businesses using the cloud-based computing services approach is the aim of this paper.
2CS: Correlation-Guided Split Candidate Selection in Hoeffding Tree Regressors

Incremental machine learning algorithms have been effective alternatives to deal with stream data. The Hoeffding Tree framework is one of the most successful solutions for supervised online prediction tasks. Although online regression tasks are present in several forms, and in many real-life problems, most of the research efforts have been devoted to classification. Existing regression tree solutions have strong limitations, mainly regarding their memory usage and running time. Hence, a new algorithm able to address these aspects in Hoeffding Tree Regressors is a relevant research issue. In this paper, we propose 2CS, a correlation-guided strategy to speed up Hoeffding Tree Regressor training. 2CS is conceptually simple and works by avoiding the exhaustive evaluation of all possible features as split candidates, as occurs in the existing solutions. Moreover, 2CS can be easily merged into existing incremental tree solutions and online tree ensembles algorithms, such as bagging and boosting. Throughout an extensive experimental evaluation, we show that the induction of 2CS-based models can be significantly faster than the traditional Hoeffding Tree Regressor algorithms, whereas retaining similar predictive power and memory use.
Classification of Human Blastocyst Quality Using Wavelets and Transfer Learning

Embryo culture and transfer are the procedure of maturation and transmission of the embryo into the uterus. This procedure is one of a stage in the series of in vitro fertilization processes, better known as IVF. The selection of good quality embryos to be implanted presents a problem because of the blastocyst image. Blastocyst image is a very intricate texture to be visually determined, which is good or poor quality. This research aims to implement the pre-trained Inception-v3 network to predict blastocyst quality with add image pre-processing using wavelets. Using only 249 of human blastocyst microscope images, we developed an accurate classifier that can classify blastocyst quality with a transfer learning. The experiment with twenty epochs, the accuracy of training for only raw blastocyst images is 95%, and the best training accuracy uses a pre-processing image with Daubechies 6-tap of 99.29%. Our model was then tested on the 14 of blastocyst images and classified the images of two kinds of grade with the best accuracy of around 64.29%.
Authority and priority signals in automatic summary generation for online reputation management

Abstract Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in‐domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.
Privacy-Aware Sensor Data Upload Management for Securely Receiving Smart Home Services

Recently smart homes equipped with many sensors and IoT devices are widespread. However, when smart home users receive smart home services like elderly monitoring, they need to upload their privacy sensitive data to potentially untrusted cloud servers where the service quality (user's benefit) depends on the amount/frequency of the uploaded data. In this paper, aiming to minimize the risk of privacy leakage and maximize users' benefit obtained through services, we propose a novel privacy-aware data management method that works on a smart-home system composed of smart homes with sensors, edge computing servers, and a cloud server. We formulate a combinatorial optimization problem which determines the best choice of data type (raw or activity label recognized at the edge) and upload frequency in each time slot taking into account the constraints of edge server resources and users' budgets as well as the k-anonymity of activities and users' preferences. Since the target problem is NP-hard, we propose a heuristic algorithm to derive semi-optimal solutions by determining choices with better objective function values in a greedy manner. Through experiments using smart-home open dataset, we confirmed that the proposed method outperforms the conventional methods using only a cloud server.
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping

In this paper, we propose efficient and effective methods for 2D human pose estimation. A new ResBlock is proposed based on depthwise separable convolution and is utilized instead of the original one in Hourglass network. It can be further enhanced by replacing the vanilla depthwise convolution with a mixed depthwise convolution. Based on it, we propose a bottom-up multi-person pose estimation method. A rooted tree is used to represent human pose by introducing person centroid as the root which connects to all body joints directly or hierarchically. Two branches of sub-networks are used to predict the centroids, body joints and their offsets to their parent nodes. Joints are grouped by tracing along their offsets to the closest centroids. Experimental results on the MPII human dataset and the LSP dataset show that both our single-person and multi-person pose estimation methods can achieve competitive accuracies with low computational costs.
Hierarchical Transformer Encoder With Structured Representation for Abstract Reasoning

Abstract reasoning is one of the defining characteristics of human intelligence and can be estimated by visual IQ tests such as Raven's Progressive Matrices. In this paper, we propose using a hierarchical Transformer encoder with structured representation that employs a novel neural network architecture to improve both perception and reasoning in a visual IQ test. For perception, we used object detection models to extract the structured features. For reasoning, we used the Transformer encoder in a hierarchical manner that fits the structure of Raven's Progressive Matrices. Experimental results on the RAVEN dataset, which is one of the major large-scale datasets on Raven's Progressive Matrices, showed that our proposed architecture achieved an overall accuracy of 99.62%, which is an improvement of more than 8% points over CoPINet, the present-day, state-of-the-art neural network model.
GANTOON: Creative Cartoons Using Generative Adversarial Network

We propose a methodology for generating creative cartoon art. The system generates cartoon by looking at various existing images of cartoon characters and learning about their posture/animation style. The proposed system is creative in nature as it generates unique cartoon art by deviating from the existing styles learned by the algorithm. We build over Generative Adversarial Networks (GAN) with unsupervised learning, which have shown the ability to learn to generate novel cartoons by simulating a given distribution. The proposed model exhibits an ability to generate cartoons which are creative and novel in design. We have conducted experiments by considering around 12K Tom’s cartoon images for training purposes. The results show that the increase in number of epochs resulted in better classification accuracy. The proposed system generates the character Tom’s cartoons which are novel and we have validated the same by applying Colton’s creativity benchmark.
Affine-transformation invariant clustering models

Abstract We develop a cluster process which is invariant with respect to unknown affine transformations of the feature space without knowing the number of clusters in advance. Specifically, our proposed method can identify clusters invariant under (I) orthogonal transformations, (II) scaling-coordinate orthogonal transformations, and (III) arbitrary nonsingular linear transformations corresponding to models I, II, and III, respectively and represent clusters with the proposed heatmap of the similarity matrix. The proposed Metropolis-Hasting algorithm leads to an irreducible and aperiodic Markov chain, which is also efficient at identifying clusters reasonably well for various applications. Both the synthetic and real data examples show that the proposed method could be widely applied in many fields, especially for finding the number of clusters and identifying clusters of samples of interest in aerial photography and genomic data.
Quantum Communication—Celebrating the Silver Jubilee of Teleportation

This is an editorial paper presenting the meaning, history, and influence of quantum teleportation.
Optimization of a Robotics Gaze Control System

Gaze control is an important issue in the interaction between a robot and humans. In particular, the decision of who to pay attention to in a multi-party conversation is a way of improving a robot's naturalness in human-robot interaction. A system based on a competitive neural network can decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli are produced. One important aspect in this process is the configuration of the different parameters of such a neural network. The weights of the different stimuli have to be computed in order to achieve behavior similar to humans. This article explains how these weights can be obtained by solving an optimization problem. The experiments carried out and some results are also presented.
Evaluation of the Adoption and Privacy Risks of Google Prompts

Internet services struggle with implementing better techniques for making authentication easier for the end-user by balancing those traits without sacrificing their security or privacy. One very recent such technology is Google Prompts, where users can authenticate by merely tapping a prompt to their mobile phone. In this paper, we attempt to understand how Google Prompts work and the extent to which current users adopt them. To this end, we build a collection system for estimating, using a completely transparent methodology, the fraction of users that have enabled Google Prompts in their accounts. Our collection system can infer the adoption of Google Prompts in the wild. Most importantly, we can use the system for performing a preliminary study of the privacy implications of Google Prompts.
Characterizing In-Air Eyes-Free Typing Movements in VR

We empirically explore fundamental requirements for achieving VR in-air typing by observing the unconstrained eyes-free in-air typing of touch typists. We show that unconstrained typing movements differ substantively from previously observed constrained in-air typing movements and introduce a novel binary categorization of typing strategies: typists who use finger movements alone (FINGER) and those who combine finger movement with gross hand movement (HAND). We examine properties of finger kinematics, correlated movement of fingers, interrelation in consecutive key-strokes, and 3D distribution of key-stroke movements. We report that, compared to constrained typing, unconstrained typing generates shorter (49 mm) and faster (764 mm/s) key-strokes with a high correlation of finger movement and that the HAND strategy group exhibits more dynamic key-strokes. We discuss how these findings can inform the design of future in-air typing systems.