Reviewerly/exHarmony huggingface dataset:
Statistical analysis of mineral evolution and mineral ecology: The current state and a vision for the future

Mineral evolution, the study of the diversity and temporal distribution of Earth’s crystalline phases, posits that mineral species have diversified over the past 4.5 billion years as a result of varied physical, chemical, and biological processes. The complementary field of mineral ecology is the study of the diversity and spatial distribution of mineral species on Earth and other terrestrial planets. Research in mineral evolution and mineral ecology contributes to questions related to life’s origins and evolution, with the premise that the emergence of life required a sequence of chemical reactions. Is the origin of life deterministic or rather an improbable event? How did Earth form? Are we and Earth unique? And to what extent is the distribution of mineral species in Earth’s near-surface environment a consequence of both deterministic factors (common to any Earth-like planet) and chance events (frozen accidents)? Previous research has showed that the distribution of Earth’s mineral species conforms to a Large Number of Rare Events (LNRE) distribution: most of Earth’s mineral species are rare, known from only a few localities. In this paper, we describe recent computational aspects in mineral evolution and mineral ecology that attempt to shed light on some of the questions provided above. In particular, we focus on the statistical analysis of deep time events (billions of years ago) that relate to the co-evolution of the geosphere and biosphere. A new approach to mineralogy is to represent the distribution of minerals in rocks and ore deposits by mineral network diagrams. New ideas for the statistical analysis of mineral network data, will be given.
Hard sample mining makes person re-identification more efficient and accurate

In recent years, the field of person re-identification has made significant advances riding on the wave of deep learning. However, owing to the fact that there are much more easy examples than those meaningful hard examples in a dataset, the training tends to stagnate quickly and the model may suffer from over-fitting, which leads to some error matching of models especially for some hard samples during the test process. Therefore, the hard sample mining method is fateful to optimize the model and improve the learning efficiency. In this paper, an Adaptive Hard Sample Mining algorithm is proposed for training a robust person re-identification model. No need for hand-picking the images in the batch or designing the loss function for both positive and negative pairs, we can briefly calculate the hard level by comparing the prediction result with the true label of the sample. Meanwhile, taking into account the change in the number of samples required for the model during training process, an adaptive threshold of hard level can make the algorithm not only stay in step with training process harmoniously but also alleviate the under-fitting and over-fitting problem simultaneously. Besides, the designed network to implement the approach is very efficient and has good generalization performance that can be combined with various existing models readily. Experimental results on Market-1501, DukeMTMC-reID and CUHK03 datasets clearly demonstrate the effectiveness of the proposed algorithm.
A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning.

Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains. While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity. No work yet exists that examines few-shot learning across different imaging methods seen in real world scenarios, such as aerial and medical imaging. In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods. This includes natural images, such as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning. The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. Performance gains previously observed with methods specialized for cross-domain few-shot learning vanish in this more challenging benchmark. Finally, accuracy of all methods tend to correlate with dataset similarity to natural images, verifying the value of the benchmark to better represent the diversity of data seen in practice and guiding future research.
Black-Box Adversarial Attack with Transferable Model-based Embedding

We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to attack an unknown target network. The method produces adversarial perturbations with high level semantic patterns that are easily transferable. We show that this approach can greatly improve the query efficiency of black-box adversarial attack across different target network architectures. We evaluate our approach on MNIST, ImageNet and Google Cloud Vision API, resulting in a significant reduction on the number of queries. We also attack adversarially defended networks on CIFAR10 and ImageNet, where our method not only reduces the number of queries, but also improves the attack success rate.
CONAN: Complementary Pattern Augmentation for Rare Disease Detection

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved .96 precision recall area under the curve (PR-AUC) and 50.1% relative improvement over the best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22 PR-AUC with 41.3% relative improvement over the best baseline.
Conditional Flow Variational Autoencoders for Structured Sequence Prediction

Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world. Prior work for structured sequence prediction based on latent variable models imposes a uni-modal standard Gaussian prior on the latent variables. This induces a strong model bias which makes it challenging to fully capture the multi-modality of the distribution of the future states. In this work, we introduce Conditional Flow Variational Autoencoders (CF-VAE) using our novel conditional normalizing flow based prior to capture complex multi-modal conditional distributions for effective structured sequence prediction. Moreover, we propose two novel regularization schemes which stabilizes training and deals with posterior collapse for stable training and better fit to the target data distribution. Our experiments on three multi-modal structured sequence prediction datasets -- MNIST Sequences, Stanford Drone and HighD -- show that the proposed method obtains state of art results across different evaluation metrics.
Interactive Dual Generative Adversarial Networks for Image Captioning

Image captioning is usually built on either generation-based or retrieval-based approaches. Both ways have certain strengths but suffer from their own limitations. In this paper, we propose an Interactive Dual Generative Adversarial Network (IDGAN) for image captioning, which mutually combines the retrieval-based and generation-based methods to learn a better image captioning ensemble. IDGAN consists of two generators and two discriminators, where the generation- and retrieval-based generators mutually benefit from each other's complementary targets that are learned from two dual adversarial discriminators. Specifically, the generation- and retrieval-based generators provide improved synthetic and retrieved candidate captions with informative feedback signals from the two respective discriminators that are trained to distinguish the generated captions from the true captions and assign top rankings to true captions respectively, thus featuring the merits of both retrieval-based and generation-based approaches. Extensive experiments on MSCOCO dataset demonstrate that the proposed IDGAN model significantly outperforms the compared methods for image captioning.
Multipartite nonlocality in the Lipkin-Meshkov-Glick model

Lipkin-Meshkov-Glick (LMG) models describe a set of $N$ qubits which are located at the corners of an $N$-dimensional simplex and embedded in an external magnetic field. Because of its high coordinate number, quantum correlations in the models are quite nontrivial. For instance, in the large-$N$ limit, two-site entanglement concurrence vanishes for any magnetic field. Multisite global entanglement and generalized global entanglement also tend to vanish in some quantum regions. We characterize the quantum correlations in the LMG model with multipartite nonlocality. In LMG models with anisotropy in the $x\ensuremath{-}y$ plane, for any fixed magnetic field, the ground-state nonlocality (denoted by $\mathcal{S}$) is found to scale as ${log}_{10}\mathcal{S}\ensuremath{\sim}aN+b$, with $a$ and $b$ the fitting parameters. For LMG models which are isotropic in the $x\ensuremath{-}y$ plane, nevertheless, the nonlocality for each Dicke state scales as ${log}_{10}\mathcal{S}\ensuremath{\sim}a\frac{1}{N}+b$, with $b&gt;0$. Signals for the quantum phase transitions of the models will also be discussed. These results indicate that multipartite nonlocality captures some key ingredient of the ground-state quantum correlations in the models. In addition to the ground states, we also study multipartite nonlocality in the LMG models at finite temperatures. In the anisotropic models, the thermal stability of the nonlocality at low temperatures is determined by the energy gap. For the isotropic models, nevertheless, as the temperature increases, the nonlocality presents a sharp valley which is followed by a round peak. The behavior is far beyond the above-mentioned ``thermal stability vs energy gap'' picture. The underlying mechanics is the ``contribution inversion'' of low-lying energy states in the thermal-state nonlocality $\mathcal{S}({\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\rho}}}_{T})$; that is, in a low-temperature region, the first-excited state ``defeats'' the ground state and plays a dominant role in thermal-state nonlocality.
The effects of tourism websites' attributes on e-satisfaction and e-loyalty: a case of American travellers' to Jordan

The paper aims to examine the effects of websites attributes (user friendliness, information quality, entertainment, and security and privacy) on tourists' e-satisfaction, and to examine the effects of tourists' e-satisfaction on attitudinal e-loyalty and the effects of e-attitudinal loyalty on e-behavioural loyalty. Based on a sample of 258 US tourists visiting Jordan, exploratory and confirmatory factor analyses were performed to assess the validity and composite reliability of the research constructs. Structural equation modelling was used to test the proposed model and hypothesised relationships. The findings show that user friendliness, information quality, and security and privacy have positive effects on tourists' e-satisfaction. Tourists' e-satisfaction exerts a strong positive effect on e-attitudinal loyalty, which in turn affects e-behavioural loyalty positively. Website user friendliness and information quality are the strongest drivers of e-satisfaction, which determines tourists' e-attitudinal and e-behavioural loyalty. Results discussion, implications, limitations and future research were outlined.
Corrections to “STAT: Spatial-Temporal Attention Mechanism for Video Captioning”

Presents corrections to affiliations in the above named paper.
Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks

The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g., 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts' support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.
A real-time big data sentiment analysis for iraqi tweets using spark streaming

The scale of data streaming in social networks, such as Twitter, is increasing exponentially. Twitter is one of the most important and suitable big data sources for machine learning research in terms of analysis, prediction, extract knowledge, and opinions. People use Twitter platform daily to express their opinion which is a fundamental fact that influence their behaviors. In recent years, the flow of Iraqi dialect has been increased, especially on the Twitter platform. Sentiment analysis for different dialects and opinion mining has become a hot topic in data science researches. In this paper, we will attempt to develop a real-time analytic model for sentiment analysis and opinion mining to Iraqi tweets using spark streaming, also create a dataset for researcher in this field. The Twitter handle Bassam AlRawi is the case study here. The new method is more suitable in the current day machine learning applications and fast online prediction.
RETRACTED: Evaluation Model of College English Teaching Quality Based on Big Data Analysis

Abstract With the implementation of internationalization, the quality of English education has become more and more important. How to assess English Teaching Quality of contemporary college students has become one of the research hot spots. The widespread application of big data analysis is to solve the current English Teaching of college students. Evaluation issues provide new directions for resolution. Construction of College English TQA System Based on Big Data Analysis. It is based on the construction of big data of college students ‘English teaching quality, by constructing a model, visually presenting the college English teaching quality trajectory, so as to understand the students’ learning situation more intuitively. Aiming at the complex changes in the quality of English teaching, this paper designs a big data analysis model of English teaching quality for college students in order to obtain high-precision evaluation results of English teaching quality. This paper uses big data analysis technology to build a college English TQA model, and experimentally verifies the reliability of the model established in this paper. After experimental verification, it is found that the big data analysis of the college English TQA model proposed by this paper has an evaluation accuracy of 90.22% This fully demonstrates the reliability of the model and provides a good reference value for the evaluation of teaching quality.
Fast Privacy-Preserving Keyword Search on Encrypted Outsourced Data

Cloud providers offer storage as a service to the data owners to store emails and files on the cloud server. However, sensitive data should be encrypted before storing on the cloud server to avoid privacy concerns. With the encryption of documents, it is not feasible for data owners to retrieve documents based on keyword search as they can do with plain text documents. Hence, it is desirable to perform a multi-keyword search on encrypted data. To achieve this goal, we present a fast privacy-preserving model for keyword search on encrypted outsourced data in this paper. Specifically, the model first performs a keyword search on encrypted data and checks its support for dynamic operations. Based on keyword search results, it then sorts all the relevant data documents using the number of keywords matched for a given query. To evaluate its performance of our model, we applied the standard metrics like precision and recall. The results show the effectiveness of our privacy-preserving keyword search on encrypted outsourced data.
Low‐cost digital image correlation and strain measurement for geotechnical applications

Abstract Particle image velocimetry (PIV), or digital image correlation (DIC), is a widely used technique to measure soil displacements and strains in small‐scale geotechnical models. Arrays of single‐board computers (SBCs) produced by Raspberry Pi, and their associated 8‐MP cameras, are being used at the University of Cambridge to capture the images required for DIC analysis. This alternative to more expensive camera set‐ups has numerous advantages. A single expensive and large camera can be replaced—at low cost—by multiple cameras, adding flexibility and affordability to any experimental set‐up. Traditionally, the alignment of multiple cameras to each other and the referencing to a known coordinate system required painted or machined markers to be located on the observation windows through which the experiments are viewed. This can obstruct localised soil grain displacement measurements in those areas of the model where such markers are placed. To complement the Raspberry Pi camera system, a markerless calibration method was used during image acquisition. This paper outlines the set‐up of four of these small computers and associated cameras, provides an overview of the use of the markerless referencing system and reviews two different experimental apparatus used to measure soil displacement and strain. When the cost of additional cabling, connectors and mounting hardware is considered for this system, the total cost to implement was approximately $125 USD per camera plus one‐time costs of $175 USD for system peripherals, which represents outstanding value and enables practically all geotechnical laboratories to develop similar capabilities.
The role of abusive supervision and organizational commitment on employees' information security policy noncompliance intention

Purpose The purpose of this paper is to investigate the association between abusive supervision and employees' information security policy (ISP) noncompliance intention, building on affective commitment, normative commitment and continuance commitment. The study also examines the moderating effect of perceived certainty and severity of sanctions on the relationship between the three dimensions of organizational commitment and ISP noncompliance intention. Design/methodology/approach Survey methodology was used for data collection through a well-designed online questionnaire. Data was analyzed using the structural equation model with Amos v. 22.0 software. Findings This study demonstrates that abusive supervision has a significant, negative impact on affective, normative and continuance commitment, and the three dimensions of organizational commitment are negatively associated with employees' ISP noncompliance intention. Results also indicate that the moderating effect of perceived severity of sanctions is significant, and perceived certainty of sanctions plays a positive moderating role in the relationship between affective commitment and employees' ISP noncompliance intention. Practical implications Findings of this research are beneficial for organizational management in the relationships between supervisors and employees. These results provide significant evidence that avoiding abusive supervision is important in controlling employees' ISP noncompliance behavior. Originality/value This research fills an important gap in examining employees' ISP noncompliance intentions from the perspective of abusive supervision and the impact of affective, normative and continuance commitment on ISP noncompliance. The study is also of great value for information systems research to examine the moderating role of perceived certainty and severity of sanctions.
Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.