Reviewerly/exHarmony huggingface dataset:
Linkages between hydrothermal alteration, natural fractures, and permeability: Integration of borehole data for reservoir characterization at the Fallon FORGE EGS site, Nevada, USA

Between 2015 and 2018, the Fallon FORGE EGS (Engineered or Enhanced Geothermal System) site in Churchill County, Nevada, USA, underwent detailed geoscientific characterization to evaluate its suitability to operate as a field demonstration site to test and develop EGS technologies. This included the compilation, synthesis and acquisition of geophysical, geological, geochemical, hydrological, down-hole log, and mineralogy datasets. Site activities culminated in the drilling of a 2481 m-deep EGS observation well (21−31) to further verify the geology and rock properties of the proposed EGS reservoir formations, which are older than the Miocene and include felsic intrusions, felsic volcanics, and meta-sedimentary lithologies. In this paper, we integrate and evaluate borehole data from well 21−31, including drill cuttings and sidewall cores (petrographic, XRD, hyperspectral), wireline geophysical logs (e.g., gamma, neutron porosity, resistivity, sonic scanner), borehole imagers (FMI, BHTV), and pressure and temperature logs. Hydrothermal alteration and veining is found throughout the entire pre-Miocene interval of well 21−31, and includes epidote mineral assemblages that suggest past temperatures >250 °C. This is higher than the hottest measured equilibrated temperature at similar depths in other wells on the FORGE site today, and suggests that the system was higher temperature in the past. Overall lithologic variability is high, with volcanic, intrusive, metamorphic, and sedimentary rocks present in the bottom 700 m of the well. Natural fractures are abundant throughout the pre-Miocene section, but are more frequent in the felsic intrusive and felsic volcanic lithologies. The dominant orientation of these natural fractures observed in FMI logs is NNE-SSW with an average strike of 010° ± 40°, subparallel to the interpreted SHmax orientation of 009° ± 14° that is indicated by drilling-induced structures in the wellbore in the BHTV log. Therefore, natural fractures appear optimally oriented for reactivation via hydraulic stimulation. Mud loss locations and a non-equilibrated temperature log suggest that there are some zones with relatively-higher permeability and/or open fractures. However injection test results indicate an injectivity index of 0.025 L/min/kPa, which is an order of magnitude lower than a typical threshold for commercial wells. Analytical modeling of injection test results indicate that the majority of the well is low permeability (< 10−16 m²), with some discrete, localized zones of higher permeability (∼ 10 -12 m², less than 0.5 percent of the pre-Miocene (deep) section intersected by the well).
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Matching clothing images from customers and online shopping stores has rich applications in e-commerce. Existing algorithms mostly encode an image as a global feature vector and perform retrieval via global representation matching. However, distinctive local information on clothing is immersed in this global representation, resulting in sub-optimized performance. To address this issue, we propose a novel graph reasoning network (GRNet) on a similarity pyramid, which learns similarities between a query and a gallery cloth by using both initial pairwise multi-scale feature representations and matching propagation for unaligned representations. The query local representations at each scale are aligned with those of the gallery via an adaptive window pooling module. The similarity pyramid is represented by a similarity graph, where nodes represent similarities between clothing components at different scales, and the final matching score is obtained by message propagation along edges. In GRNet, graph reasoning is solved by training a graph convolutional network, enabling the alignment of salient clothing components to improve clothing retrieval. To facilitate future research, we introduce a new benchmark, i.e. FindFashion, containing rich annotations of bounding boxes, views, occlusions, and cropping. Extensive experiments show that GRNet obtains new state-of-the-art results on three challenging benchmarks, e.g. pushing the accuracy of top-1, top-20, and top-50 on DeepFashion to 27, 66, and 75 percent (i.e. 6, 12, and 10 percent absolute improvements), outperforming competitors with large margins. On FindFashion, GRNet achieves considerable improvements on all empirical settings.
Data security sharing method based on CP-ABE and blockchain

Although the data trading platform has accelerated the flow of data, the current data trading platform still has many problems. According to the characteristics of the blockchain technology, from the aspects of the attack behavior in the blockchain and the security application of the blockchain technology in power transactions, this paper studies the security of the blockchain. Moreover, this article focuses on the privacy protection of the ciphertext strategy in the CP-ABE scheme, and protects the privacy information of the access strategy by designing appropriate ciphertext and key structures and access structure forms. In addition, the system efficiency is improved by computing outsourcing, and solutions to problems in outsourcing computing are proposed. Meanwhile, two efficient and flexible support policy hidden multi-authorization center access control schemes are constructed. Finally, this study analyzes the performance of the model through controlled experiments. The research results show that this scheme has excellent performance.
On the Plausibility of Virtual Body Animation Features in Virtual Reality

We present two experiments to assess the relative impact of different levels of body animation fidelity on plausibility illusion (Psi). The first experiment presents a virtual character that is not controlled by the user ( n=13), while the second experiment presents a user-controlled virtual avatar ( n=24, all male). Psi concerns how realistic and coherent the events in a virtual environment look and feel and is part of Slater's proposition of two orthogonal components of presence in virtual reality (VR). In the experiments, the face, hands, upper and lower bodies of the character or self-avatar were manipulated to present different degrees of animation fidelity, such as no animation, procedural animation, and motion captured animation. Participants started the experiment experiencing the best animation configuration. Then, animation features were reduced to limit the amount of captured information made available to the system. Participants had to move from this basic animation configuration towards a more complete one, and declare when the avatar animation realism felt equivalent to the initial and most complete configuration, which could happen before all animation features were maxed out. Participants in the self-avatar experiment were also asked to rate how each animation feature affected their sense of control of the virtual body. We found that a virtual body with upper and lower body animated using eight tracked rigid bodies and inverse kinematics (IK) was often perceived as equivalent to a professional capture pipeline relying on 53 markers. Compared to what standard VR kits in the market are offering, i.e., a tracked headset and two hand controllers, we found that foot tracking, followed by mouth animation and finger tracking, were the features that added the most to the sense of control of a self-representing avatar. In addition, these features were often among the first to be improved in both experiments.
An Electricity Cross-Chain Platform Based on Sidechain Relay

Abstract Based on sidechain relay strategy, a cross-chain platform BitXhub-PI is implemented to realize the interoperability between the heterogeneous blockchains for electric power industry. The platform is composed of three roles: relay chain, application chain and cross-chain pier, and it has three core functional characteristics of universal cross-chain transmission protocol, heterogeneous transaction verification engine, and multi-level routing to ensure the security, flexibility and reliability of cross-chain transactions of electricity business. The platform provides a unified cross-chain contract template for homogeneous and heterogeneous electricity application chains, and the relay chain contains a dynamically upgradeable verification engine. The experiments show that the platform guarantees asynchronous distributed electricity business transactions between heterogeneous blockchains, achieving high performance with high throughput, low latency, high scalability, and low overhead.
On Machine Learning Models for Heart Disease Diagnosis

Convolutional Neural Networks (CNNs) have different architecture than regular Neural Networks (NNs) and are both applied extensively in many application fields. In this article, we used both of the two machine learning models in the heart disease diagnosis problems. We implemented the algorithms, tuned the parameters, and conducted a series of experiments. We aim to compare the prediction accuracy of the two models under different parameters settings. We used the Cleveland database which is took from UCI learning dataset repository for diagnosis heart disease. From the experimental results, we found that NNs outperform CNNs in prediction accuracy in most of the cases.
Verified Linear Session-Typed Concurrent Programming

We present a system of linear session types that integrates several features aimed at verification of different properties of concurrent programs, specifically types indexed with arithmetic expressions, linear constraints and quantification. We prove the standard type safety properties of session fidelity and deadlock freedom. In order to control the verbosity of programs we introduce implicit syntax and an algorithm for reconstruction, which is complete under some mild assumptions on the structure of types. We then illustrate the expressive power of our language (called Rast) with a variety of examples, including normalization for the linear λ-calculus, balanced ternary arithmetic, binary counters and tries.
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition

State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks. We improve and extend both aspects. First, we systematically yield enhanced receptive fields for complementary feature extraction via coarse-to-fine decomposition of input imagery along the spatial and temporal dimensions, and adaptively focus on training important feature pathways using a reparameterized fully connected layer. Second, we develop a ‘use when needed’ scheme with a ‘coarse-exit’ strategy that allows selective use of expensive high-resolution processing in a data-dependent fashion to retain accuracy while reducing computation cost. Our C2F learning approach builds ensemble networks that outperform most competing methods in terms of both reduced computation cost and improved accuracy on the Something-Something V1, V2, and Jester datasets, while also remaining competitive on the Kinetics-400 dataset. Uniquely, our C2F ensemble networks can operate at varying computation budget constraints.
Security and Privacy Analysis of Wearable Health Device

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.
Towards understanding emotional response to requirements changes in agile teams

Agile software development welcomes requirements changes even late in the process. However, it is unclear how agile teams respond to these changes. Therefore, as the starting point of a planned extensive study on handling requirements changes in agile projects, we examined the emotional responses to requirements changes by agile teams. In this paper, we (i) introduce a novel combined approach of using Grounded Theory and Sentiment Analysis (through SentiStrength and Job Emotion Scale), (ii) present three distinct phases of emotional responses, a summary of emotions, and variation in emotions and sentiment polarity (positivity, negativity, and neutrality) at each stage, and (iii) emphasize the necessity of taking emotional responses of agile teams into consideration when applying agile principles and practices to deal with changes.
Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method

Estimates of project costs in the early stages of a construction project have a significant impact on the operator’s decision-making in essential matters, such as the site’s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR’s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one.
Incentive-Driven Computation Offloading in Blockchain-Enabled E-Commerce

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.
Towards Privacy-Preserving Domain Adaptation

This study suggests a new domain adaptation paradigm that can address potential data-privacy issues. Despite promising results of existing domain adaptation methods, they have a strong constraint where the source and target samples are accessible during a training phase. However, direct usage of source samples possibly causes data-privacy issues especially when each label of source domain acts as an individual's identifier such as biometric information. To address data-privacy problems in conventional domain adaptation, we propose privacy-preserving domain adaptation (PPDA). Our main hypothesis is that if we train our target model initialized from a pre-trained source model in a self-learning manner, we can successfully transfer knowledge from a labeled source domain to an unlabeled target domain. In our preliminary study, we observe that target samples with low self-entropy measured from the pre-trained source model achieves sufficiently high accuracy. From this key observation, we first select the reliable samples based on self-entropy and define them as class prototypes. We then assign pseudo labels to the target samples through the similarity between target samples and class prototypes. To further reduce the uncertainty of the pseudo labeling process, we also introduce a sample-level reweighting scheme. Surprisingly, our PPDA model outperforms conventional domain adaptation methods on public datasets even though we do not directly access any source data.
Visual Quality Assessment for Perceptually Encrypted Light Field Images

Perceptual encryption has received widespread attention as a technology for protecting multimedia visual information. In multimedia applications, perceptual encryption only protects a portion of the content without previewing all the information. At present, perceptual encryption is mostly oriented to conventional plain images while few methods aim at visual security measures for perceptually encrypted images. Existing solutions usually adopt well-known quality assessment metrics to measure the visual quality of encrypted images. However, they often exhibit undesired behavior on perceptually encrypted images with low quality. As a typical representation of three-dimensional scenes, light field images record the intensity and direction of light during propagation which is distinct from 2-D images. In this paper, we construct a perceptually encrypted light field image database (PE-LFID) for quality assessment based on 14 reference light field plaintext images. For each scene, we employ four encryption methods, each of which has six levels. Additionally, a novel visual security evaluation method based on PE-LFID is proposed by taking into account the local and global features of the light field images. First, we use a multi-threshold edge detection method to obtain the edge similarity in the spatial domain of the light field image. Afterwards, the epipolar plane image (EPI) generated from the angular domain of the light field image is used to calculate the gradient magnitude similarity, which is expressed as a global feature. Furthermore, the final quality prediction score is calculated by adaptively weighting between local and global features. We conduct extensive experiments on the proposed PE-LFID to assess the performance of classical and state-of-the-art IQA models. The experimental results demonstrate the effectiveness of the proposed method for visual security evaluation of perceptually encrypted light field images, as well as the scalability of PE-LFID.
Explore Video Clip Order With Self-Supervised and Curriculum Learning for Video Applications

We present a self-supervised spatiotemporal learning approach by exploring the temporal coherence of videos. The chronological order of shuffled clips from the video is used as the supervisory signal to guide the 3D Convolutional Neural Networks (CNNs) to learn meaningful visual knowledge. Unlike the existing approaches which use frames, we utilize dynamic video clips to reduce the uncertainty of order. We test three types of representative 3D CNNs, all of which benefit from the proposed approach. The learned 3D CNNs can be used either as a feature extractor or a pre-trained model for further fine-tuning on downstream tasks. We also propose two curriculum learning strategies to make the 3D CNNs easier to train and get the state-of-the-art results in nearest neighbor retrieval and action recognition tasks compared with other self-supervised learning methods. Meanwhile, it is further extended to the field of visual question answering application and has achieved promising results. Besides, comprehensive and extensive experimental results and analyses are provided for readers to better understand the video clip order we explore with self-supervised and curriculum learning for video application.
A Poisson Factorization Topic Model for the Study of Creative Documents (and Their Summaries)

The author proposes a topic model tailored to the study of creative documents (e.g., academic papers, movie scripts), which extends Poisson factorization in two ways. First, the creativity literature emphasizes the importance of novelty in creative industries. Accordingly, this article introduces a set of residual topics that represent the portion of each document that is not explained by a combination of common topics. Second, creative documents are typically accompanied by summaries (e.g., abstracts, synopses). Accordingly, the author jointly models the content of creative documents and their summaries, and captures systematic variations in topic intensities between the documents and their summaries. This article validates and illustrates the model in three domains: marketing academic papers, movie scripts, and TV show closed captions. It illustrates how the joint modeling of documents and summaries provides some insight into how people summarize creative documents and enhances understanding of the significance of each topic. It shows that the model described produces new measures of distinctiveness that can inform the perennial debate on the relation between novelty and success in creative industries. Finally, the author shows how the proposed model may form the basis for decision support tools that assist people in writing summaries of creative documents.
Multilabel Distribution Learning Based on Multioutput Regression and Manifold Learning

Real-world multilabel data are high dimensional, and directly using them for label distribution learning (LDL) will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multioutput regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and LDL, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multioutput regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel datasets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.