Reviewerly/exHarmony huggingface dataset:
Why Measure 17O? Historical Perspective, Triple-Isotope Systematics and Selected Applications

Research Article| January 01, 2021 Why Measure 17O? Historical Perspective, Triple-Isotope Systematics and Selected Applications Martin F. Miller; Martin F. Miller Planetary and Space Sciences, School of Physical Sciences, The Open University, Walton Hall, Milton Keynes MK7 6AA, UK m.f.miller@open.ac.uk Search for other works by this author on: GSW Google Scholar Andreas Pack Andreas Pack Planetary and Space Sciences, School of Physical Sciences, The Open University, Walton Hall, Milton Keynes MK7 6AA, UK apack@uni-goettingen.de Search for other works by this author on: GSW Google Scholar Reviews in Mineralogy and Geochemistry (2021) 86 (1): 1–34. https://doi.org/10.2138/rmg.2021.86.01 Article history first online: 04 Jan 2021 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Martin F. Miller, Andreas Pack; Why Measure 17O? Historical Perspective, Triple-Isotope Systematics and Selected Applications. Reviews in Mineralogy and Geochemistry 2021;; 86 (1): 1–34. doi: https://doi.org/10.2138/rmg.2021.86.01 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyReviews in Mineralogy and Geochemistry Search Advanced Search For many years, it was considered that measurements of the least abundant stable isotope of oxygen, 17O, would not provide any information additional to that obtainable from determinations of the 18O/16O abundance ratio, which, by being a factor of ~5.2 larger than 17O/16O, can be measured more easily. Here, we summarize significant events in the historical development of oxygen stable isotope ratio measurements and their application to Earth and planetary sciences, leading to a consideration of the potential information to be gained from high precision measurements of the 'third isotope'. This is followed... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
An agglomerative hierarchical clustering algorithm for linear ordinal rankings

This paper mainly proposes a new method for clustering linear ordinal ranking (LOR) information by agglomerative hierarchical clustering (AHC) algorithm. Considering that the cores of the AHC algorithm for LOR clustering are the difference measure among different LORs and the aggregation of the individual LORs, we firstly systematically analyze the existing studies for LOR distance measure, based on which we extend the method to depict LORs. Subsequently, the corresponding new distance measure is proposed starting from the perspective of utilizing the rankings’ position information and relationship information together. In addition, we simplify the dominating index and dominated index-based aggregation method for LORs fusion. Further, we present a numerical case on online financial product recommendation to illustrate the usage of the algorithm and also try to provide a feasible way for online financial product recommendation. Then, we make some discussions on the proposed distance measure and the aggregation method under the framework of the AHC algorithm to show the features of the algorithm proposed in this paper.
Yoga Pose Detection and Classification Using Deep Learning

Yoga is an ancient science and discipline originated in India 5000 years ago. It is used to bring harmony to both body and mind with the help of asana, meditation and various other breathing techniques It bring peace to the mind. Due to increase of stress in the modern lifestyle, yoga has become popular throughout the world. There are various ways through which one can learn yoga. Yoga can be learnt by attending classes at a yoga centre or through home tutoring. It can also be self-learnt with the help of books and videos. Most people prefer self-learning but it is hard for them to find incorrect parts of their yoga poses by themselves. Using the system, the user can select the pose that he/she wishes to practice. He/she can then upload a photo of themselves doing the pose. The pose of the user is compared with the pose of the expert and difference in angles of various body joints is calculated. Based on thisdifference of angles feedback is provided to the user so that he/she can improve the pose.
Managing the Hybrid Organization: How Can Agile and Traditional Project Management Coexist?

Overview: Agile project management aims to increase the flexibility of product development processes through increased interaction among project stakeholders. Since its introduction in the software industry 20 years ago, Agile project management has progressively spread to other contexts, even though large-scale organizations seem to struggle to switch toward Agile-only practices. In these contexts, we see an integration of the traditional Stage-Gate project management, focused on planning and validation, with Agile, focused on responsiveness and flexibility. Although it’s effective, the Agile–Stage-Gate hybrid model is not easy to adopt, as it requires a clear alignment between the project team, the organizational objectives, and the project implementation. We collected interview data from eight Agile coaches experienced in Agile implementation in non-software industries. We identified practical actions that practitioners can use to manage the challenges connected to the implementation of hybrid models. These corrective actions can be grouped at three levels: integration aspects, cultural change, and perceptions of the Agile methodology.
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations

We present a new white-box backdoor attack that exploits a vulnerability of convolutional neural networks (CNNs). In particular, we examine the application of facial recognition. Deep learning techniques are at the top of the game for facial recognition, which means they have now been implemented in many production-level systems. Alarmingly, unlike other commercial technologies such as operating systems and network devices, deep learning-based facial recognition algorithms are not presently designed with security requirements or audited for security vulnerabilities before deployment. Given how young the technology is and how abstract many of the internal workings of these algorithms are, neural network-based facial recognition systems are prime targets for security breaches. As more and more of our personal information begins to be guarded by facial recognition (e.g., the iPhone X), exploring the security vulnerabilities of these systems from a penetration testing standpoint is crucial. Along these lines, we describe a general methodology for backdooring CNNs via targeted weight perturbations. Using a five-layer CNN and ResNet-50 as case studies, we show that an attacker is able to significantly increase the chance that inputs they supply will be falsely accepted by a CNN while simultaneously preserving the error rates for legitimate enrolled classes.
Screening of Alzheimer’s disease by facial complexion using artificial intelligence

Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could distinguish between the faces of people with cognitive impairment and those without dementia.121 patients with cognitive impairment and 117 cognitively sound participants were recruited for the study. 5 deep learning models with 2 optimizers were tested. The binary differentiation of dementia / non-dementia facial image was expressed as a AI score. Xception with Adam was the model that showed the best performance. Overall sensitivity, specificity, and accuracy by the Xception AI system and AUC of the ROC curve were 87.31%, 94.57%, 92.56%, and 0.9717, respectively. Close and significant correlations were found between Face AI score and MMSE (r = -0.599, p < 0.0001). Significant correlation between Face AI score and chronological age was also found (r = 0.321, p < 0.0001). However, MMSE score showed significantly stronger correlation with Face AI score than chronological age (p < 0.0001). The study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia, paving the way for future studies into the development of a facial biomarker for dementia.
The hidden costs of connectivity: nature and effects of scholars’ online harassment

A growing body of research reveals that some scholars face online harassment and that such harassment leads to a wide variety of adverse impacts. Drawing on data collected from an online survey of 182 scholars, we report on the factors and triggers involved in scholars' experiences of online harassment; the environments where said experiences take place, and; the consequences it has for personal and professional relationships. We find that online harassment is heavily entwined with the work, identity, and in some cases, the requirements of being a scholar. The online harassment scholars experience is often compounded by other factors, such as gender and physical appearance. We build on prior research in this area to further argue that universities ought to widen their scope of what constitutes workplace harassment and workplace safety to include online spaces.
A New Image Encryption Algorithm with Cantor Diagonal Scrambling Based on the PUMCML System

In this paper, a new spatiotemporal chaotic Parameter Uncertainty Mixed Coupled Map Lattice (PUMCML) is proposed. The Cantor diagonal matrix is generated from the Cantor set, and the ordered rotation scrambling strategy for this matrix is used to generate the scrambled image. Cantor set is a fractal system, so the Cantor set has a good effect on chaotic image encryption. The dynamic behavior of the PUMCML system is analyzed. The system has good chaotic property, so it is very suitable for chaotic image encryption. Using the PUMCML system, a diffusion strategy based on the mixture of Arnold and Logistic is proposed. Compared with other algorithms, the encryption effect of the proposed method is better and more secure.
Automating just-in-time comment updating

Code comments are valuable for program comprehension and software maintenance, and also require maintenance with code evolution. However, when changing code, developers sometimes neglect updating the related comments, bringing in inconsistent or obsolete comments (aka., bad comments). Such comments are detrimental since they may mislead developers and lead to future bugs. Therefore, it is necessary to fix and avoid bad comments. In this work, we argue that bad comments can be reduced and even avoided by automatically performing comment updates with code changes. We refer to this task as "Just-In-Time (JIT) Comment Updating" and propose an approach named CUP (<u>C</u>omment <u>UP</u>dater) to automate this task. CUP can be used to assist developers in updating comments during code changes and can consequently help avoid the introduction of bad comments. Specifically, CUP leverages a novel neural sequence-to-sequence model to learn comment update patterns from extant code-comment co-changes and can automatically generate a new comment based on its corresponding old comment and code change. Several customized enhancements, such as a special tokenizer and a novel co-attention mechanism, are introduced in CUP by us to handle the characteristics of this task. We build a dataset with over 108K comment-code co-change samples and evaluate CUP on it. The evaluation results show that CUP outperforms an information-retrieval-based and a rule-based baselines by substantial margins, and can reduce developers' edits required for JIT comment updating. In addition, the comments generated by our approach are identical to those updated by developers in 1612 (16.7%) test samples, 7 times more than the best-performing baseline.
An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm

It is the most challenging and difficult task to segment a satellite image because of its complete randomness, multiple regions of interest, weak correlation with pixels, and regions of ambiguity. There are several Nature-inspired algorithms available, which are used to overcome these difficulties and those are more efficient to generate the best threshold value for the segmentation of satellite images. Though various modern methodologies opt for better results but methods have some drawbacks too like techniques are computationally expensive and time-consuming. In this paper, we have proposed a more effective satellite image segmentation approach using a new adaptive cuckoo search (ACS) algorithm. The result obtained from the projected technique is compared with CSMcCulloch incorporating McCulloch’s method for levy flight generation in Cuckoo Search (CS) algorithm by using two different objective functions namely Otsu’s method and Tsallis entropy function. The measurement techniques such as PSNR, MSE, FSIM, SSIM, UIQI, and computational time in term of CPU running time have been considered for validating and evaluating the proposed method. This proposed algorithm technique has resulted in improve segmentation quality of satellite images and reduced computational time. The analysis of the convergence rate proves that ACS is superior to the CSMcCulloch algorithm for reaching the global convergence rate. These experimental outcomes help to encourage researchers in different domains such as computer vision, application of medical image analysis, machine learning as well as deep learning.
Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.
Classical simulation of lossy boson sampling using matrix product operators

Characterizing the computational advantage from noisy intermediate-scale quantum (NISQ) devices is an important task from theoretical and practical perspectives. Here, we numerically investigate the computational power of NISQ devices focusing on boson sampling, one of the well-known promising problems which can exhibit quantum supremacy. We study hardness of lossy boson sampling using matrix product operator (MPO) simulation to address the effect of photon-loss on classical simulability using MPO entanglement entropy (EE), which characterizes a running time of an MPO algorithm. An advantage of MPO simulation over other classical algorithms proposed to date is that its simulation accuracy can be efficiently controlled by increasing an MPO's bond dimension. Notably, we show by simulating lossy boson sampling using an MPO that as an input photon number grows, its computational cost, or MPO EE, behaves differently depending on a loss-scaling, exhibiting a different feature from that of lossless boson sampling. Especially when an output photon number scales faster than the square root of an input photon number, our study shows an exponential scaling of time complexity for MPO simulation. On the contrary, when an output photon number scales slower than the square root of an input photon number, MPO EE may decrease, indicating that an exponential time complexity might not be necessary.
Automatic Generation of Two-Level Hierarchical Tutorials from Instructional Makeup Videos

We present a multi-modal approach for automatically generating hierarchical tutorials from instructional makeup videos. Our approach is inspired by prior research in cognitive psychology, which suggests that people mentally segment procedural tasks into event hierarchies, where coarse-grained events focus on objects while fine-grained events focus on actions. In the instructional makeup domain, we find that objects correspond to facial parts while fine-grained steps correspond to actions on those facial parts. Given an input instructional makeup video, we apply a set of heuristics that combine computer vision techniques with transcript text analysis to automatically identify the fine-level action steps and group these steps by facial part to form the coarse-level events. We provide a voice-enabled, mixed-media UI to visualize the resulting hierarchy and allow users to efficiently navigate the tutorial (e.g., skip ahead, return to previous steps) at their own pace. Users can navigate the hierarchy at both the facial-part and action-step levels using click-based interactions and voice commands. We demonstrate the effectiveness of segmentation algorithms and the resulting mixed-media UI on a variety of input makeup videos. A user study shows that users prefer following instructional makeup videos in our mixed-media format to the standard video UI and that they find our format much easier to navigate.
Soft-sensing of Wastewater Treatment Process via Deep Belief Network with Event-triggered Learning

Due to the complex dynamic behavior of a Wastewater Treatment Process (WWTP), the existing soft-sensing models usually fail to efficiently and accurately predict its effluent water quality. Especially when a lot of practical data is provided and we do not know which data-pair is more valuable, WWTP modeling becomes a time-consuming process. The main reason is that the existing soft-sensing models update their parameters at each data-pair in one iteration, while some update operations are meaningless. To address this thorny problem, this paper proposes a Deep Belief Network with Event-triggered Learning (DBN-EL) to improve the efficiency and accuracy of soft-sensing model in WWTP. First, some events are defined according to different running condition during the process of training DBN-based soft-sensing model. The different running condition is dominated by the fluctuation of error-reduction rate. Second, an event-triggered learning strategy is designed to construct DBN-EL, whose parameters are updated only when a positive event is triggered. Thirdly, we present the convergence analysis of DBN-EL based on the optimization in a Markov process. Finally, the effectiveness of DBN-EL is demonstrated on soft-sensing of total phosphorus concentration in a practical WWTP system. In experiment, DBN-EL is compared with nine different models on soft-sensing of WWTP. The experimental results show that the efficiency of DBN-EL is 27.6%–64.9% higher than that of nine competitive models, which indicates that the proposed model is readily available for industrial deployment.
Blockchain Smart Contract for Scalable Data Sharing in IoT: A Case Study of Smart Agriculture

The emerging Smart Agriculture based on Internet of Things (IoT) is facing major challenges like data sharing, storage, and monitoring, primarily due to the distributed nature of IoT and massive scale. We performed a review of the literature and found that blockchain performance, scalability, cost, and throughput are the major challenges in adopting blockchain for smart agriculture. To overcome these challenges, this paper proposes a scalable and distributed data sharing system integrating access control for smart agriculture. We demonstrate our approach in a smart agriculture setting, which consists of four tiers that are: smart agriculture, smart contract, Interplanetary File System (IPFS) and agriculture stakeholders (remote users). This paper explains in detail the different components of our proposed architecture. Our approach uses anonymous identities to ensure users' privacy. Our approach is fully scalable because a large number of resource owners can use their data sharing smart contracts to create, update or delete data sharing policies. In addition, our approach does not require transaction fees when the smart contract receives a large number of policy evaluation requests. For the sake of simplicity, we publish and test a single data sharing smart contract. However, in practice, multiple smart contracts need to be deployed to allow each resource owner to securely share agriculture data with stakeholders. Finally, we evaluate the performance of our proposed system on the EOS blockchain to show that the resource consumption (in terms of computing power and network bandwidth) introduced by our framework are insignificant compared to its scalability, cost and security benefits.
Unsupervised Cross Domain Person Re-Identification by Multi-Loss Optimization Learning

Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present, most UCD person re-ID methods perform “supervised learning” by assigning pseudo labels to the target domain, which leads to poor re-ID performance due to the pseudo label noise. To address this problem, a multi-loss optimization learning (MLOL) model is proposed for UCD person re-ID. In addition to using the information of clustering pseudo labels from the perspective of supervised learning, two losses are designed from the view of similarity exploration and adversarial learning to optimize the model. Specifically, in order to alleviate the erroneous guidance brought by the clustering error to the model, a ranking-average-based triplet loss learning and a neighbor-consistency-based loss learning are developed. Combining these losses to optimize the model results in a deep exploration of the intra-domain relation within the target domain. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that our model outperforms the state-of-the-art UCD re-ID methods with a clear advantage.
Heterogeneous Signcryption Scheme From IBC to PKI With Equality Test for WBANs

The signcryption protocols supporting the equality test are only for a single system rather than a flexible heterogeneous environment of wireless body area networks (WBANs). In this article, this problem is efficiently addressed through a proposed heterogeneous signcryption scheme from identity-based cryptosystem (IBC) to public key infrastructure (PKI) with an equality test (HSCIP-ET) for WBANs. Our scheme allows the sensors planted or worn by a user in the IBC system to signcrypt sensitive data using the public key of the management center in the PKI system, and then uploading it to the cloud server. Subsequently, the equality test can be executed by the cloud server on diverse ciphertexts to determine whether they contain the identical plaintext and return the corresponding result after receiving a request from an application provider. Furthermore, a rigorous analysis and an experimental comparison demonstrate the security and efficiency of the HSCIP-ET approach, respectively.