Reviewerly/exHarmony huggingface dataset:
Carrying Capacity of Low Carbon Tourism Environment in Coastal Areas from the Perspective of Ecological Efficiency

Han, J., 2018. Carrying capacity of low carbon tourism environment in coastal areas from the perspective of ecological efficiency. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 199–203. Coconut Creek (Florida), ISSN 0749-0208.The carrying capacity of tourism environment integrates the comprehensive factors such as social economy, resource ecology, etc., and becomes an important index to measure the quality of tourism environment and the sustainable development of tourism. Based on the ecological perspective, the carrying capacity of low-carbon tourism environment in coastal areas is studies in this paper. The state space method is used to describe and measure the carrying capacity and bearing status of low-carbon tourism environment. A model of carrying capacity of low-carbon tourism environment is constructed and the low-carbon tourism environment is quantified. The idea of “Economic benefit of tourism scale” is as the objective function, and the limitation of resources and eco-environmental factors is as the restraint to establish a linear programming model for carrying capacity of low-carbon tourism environment, and fulfill the research of carrying capacity of low-carbon tourism environment in coastal areas from the perspective of ecological efficiency. Taking Shandong Peninsula Group and Sanya City as an example, the carrying capacity of low-carbon tourism environment in coastal areas is studied.
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.
Recent developments on XRF spectra evaluation

Since the advent of X-Ray fluorescence (XRF) spectrum analysis technology, with its wide range of elemental analysis, rapid, nondestructive analysis characteristics, and better detection accuracy, the workload of semi-quantitative analysis has taken half of the total analysis in contemporary research institutes and university laboratories engaged in material research. In addition, XRF has been widely and efficiently used in analytical chemistry, geological and mineral survey, ecological and environmental monitoring, food and drug detection, archeology and other fields. XRF spectrum processing technology is based on the application of mathematical principles, using reasonable algorithms to optimize the processing of spectrum data to contribute more accurate quantitative analysis of key elements. Over the years, XRF spectrum processing technology has formed a relatively complete set of processes, in each step of spectrum processing, plenty of research work and paper reports have been implemented and developed. This paper mainly reviews the representative algorithms and their progress in spectrum smoothing, background subtraction and overlapping peak decomposition of XRF spectrum analysis technology, analyzes their applications in related fields, summarizes and proposes the development direction of some potential algorithms.
Implementing a Chaotic Cryptosystem by Performing Parallel Computing on Embedded Systems with Multiprocessors

Profiling and parallel computing techniques in a cluster of six embedded systems with multiprocessors are introduced herein to implement a chaotic cryptosystem for digital color images. The proposed encryption method is based on stream encryption using a pseudo-random number generator with high-precision arithmetic and data processing in parallel with collective communication. The profiling and parallel computing techniques allow discovery of the optimal number of processors that are necessary to improve the efficiency of the cryptosystem. That is, the processing speed improves the time for generating chaotic sequences and execution of the encryption algorithm. In addition, the high numerical precision reduces the digital degradation in a chaotic system and increases the security levels of the cryptosystem. The security analysis confirms that the proposed cryptosystem is secure and robust against different attacks that have been widely reported in the literature. Accordingly, we highlight that the proposed encryption method is potentially feasible to be implemented in practical applications, such as modern telecommunication devices employing multiprocessors, e.g., smart phones, tablets, and in any embedded system with multi-core hardware.
Permissioned blockchains and distributed databases: A performance study

Summary Blockchains are increasingly studied in the context of new applications. Permissioned blockchains promise to deal with the issue of complete removal of trust, a notion that is currently the hallmark of the developed society. Before the idea is adopted in contexts where resource efficiency and fast operation is a requirement, one could legitimately ask the question: can permissioned blockchains match the performance of traditional large‐scale databases? This paper compares two popular frameworks, Hyperledger Fabric and Apache Cassandra, as representatives of permissioned blockchains and distributed databases, respectively. We compare their latency for varying workloads and network sizes. The results show that, for small systems, blockchains can start to compete with traditional databases, but also that the difference in consistency models and differences in setup can have a large impact on the resulting performance.
Modulation Classification of Underwater Communication with Deep Learning Network

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.
Better Word Embeddings by Disentangling Contextual n-Gram Information

Prakhar Gupta, Matteo Pagliardini, Martin Jaggi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Time-sensitive clinical concept embeddings learned from large electronic health records

Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient’s records, which may lead to incorrect selection of contexts. To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications.
RoSeq: Robust Sequence Labeling

In this paper, we mainly investigate two issues for sequence labeling, namely, label imbalance and noisy data that are commonly seen in the scenario of named entity recognition (NER) and are largely ignored in the existing works. To address these two issues, a new method termed robust sequence labeling (RoSeq) is proposed. Specifically, to handle the label imbalance issue, we first incorporate label statistics in a novel conditional random field (CRF) loss. In addition, we design an additional loss to reduce the weights of overwhelming easy tokens for augmenting the CRF loss. To address the noisy training data, we adopt an adversarial training strategy to improve model generalization. In experiments, the proposed RoSeq achieves the state-of-the-art performances on CoNLL and English Twitter NER—88.07% on CoNLL-2002 Dutch, 87.33% on CoNLL-2002 Spanish, 52.94% on WNUT-2016 Twitter, and 43.03% on WNUT-2017 Twitter without using the additional data.
Experimental investigation of the nonlocal advantage of quantum coherence

Nonlocal advantage of quantum coherence (NAQC) based on coherence complementarity relations is generally viewed as a stronger nonclassical correlation than Bell nonlocality. An arbitrary two-qubit state with NAQC must be an entangled state, which demonstrates that the criterion of NAQC can also be regarded as an entanglement witness. In this paper, we experimentally investigate the NAQC for Bell-diagonal states with high fidelity in an optics-based platform. We perform local measurements on a subsystem in three mutually unbiased bases and reconstruct the density matrices of the measured states by a quantum state tomography process. By analyzing characteristics of the ${l}_{1}$ norm, relative entropy, and skew information of coherence with parameters of quantum states, NAQC for the quantum states is accurately captured, and it shows that our experimental results are well compatible with the theoretical predictions. Our results have shown that NAQC could be a new kind of entanglement witness and a useful physical resource for quantum information processing.
A hybrid sentiment analysis method for Turkish

This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the experimental results show that it improves the accuracy by 7 % on average.
Complex Question Decomposition for Semantic Parsing

In this work, we focus on complex question semantic parsing and propose a novel Hierarchical Semantic Parsing (HSP) method, which utilizes the decompositionality of complex questions for semantic parsing.Our model is designed within a three-stage parsing architecture based on the idea of decompositionintegration.In the first stage, we propose a question decomposer which decomposes a complex question into a sequence of subquestions.In the second stage, we design an information extractor to derive the type and predicate information of these questions.In the last stage, we integrate the generated information from previous stages and generate a logical form for the complex question.We conduct experiments on COMPLEXWE-BQUESTIONS which is a large scale complex question semantic parsing dataset, results show that our model achieves significant improvement compared to state-of-the-art methods.
A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Leveraging power consumption models in software systems can achieve easy deployment of low-cost, high-availability power monitoring in cloud datacenters that are usually large-scale, heterogeneous and frequently scaling up. However, traditional regression-based power consumption models generally have two drawbacks. First, their mathematical forms are usually fixed and determined a priori. This may cause unacceptable increase of error or over-fitting as the power signatures of cloud servers are usually uncertain. Second, the characteristic of workload dispatched to cloud servers is constantly changing while regression-based models can hardly generalize to a wide range of servers and workload types. As a novel solution, we in this paper propose a server power consumption model based on Elman Neural Network (PCM-ENN), aiming to allow accurate and flexible power estimation. PCM-ENN is an end-to-end black box model capable of learning the temporal relation between samples in a time series of power consumption. We trained and evaluated PCM-ENN on two power sequence datasets collected from heterogeneous hardware and operating systems running quasi-production benchmarks like CloudSuite. Experimental result shows that PCM-ENN generated accurate estimates on server power consumption with only small errors, outperforming widely-used linear regression model and NARX model in terms of accuracy.
Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports

This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning problem is formulated in this paper as a Traveling Salesman Problem (TSP). Different from the conventional solutions to the TSP, in which the Euclidean distance between two sites is used as the metric, a high-dimensional metric including the factor of pedestrians’ feelings is applied in this work. To obtain the new metric, a novel potential function is firstly proposed to model the relationship between the robot, luggage trolleys, obstacles, and pedestrians. The Social Force Model (SFM) is utilized so that the pedestrians can bring extra influence on the potential field, different from ordinary obstacles. Directed by the attractive and repulsive force generated from the potential field, a number of paths connecting the robot and the luggage trolley, or two luggage trolleys, can be obtained. The length of the generated path is considered as the new metric. The Self-Organizing Map (SOM) satisfies the job of finding a final path to connect all luggage trolleys and the robot located in the potential field, as it can find the intrinsic connection in the high dimensional space. Therefore, while incorporating the new metric, the SOM is used to find the optimal path in which the robot can collect the assigned luggage trolleys in sequence. As a demonstration, the proposed path planning method is implemented in simulation experiments, showing an increase of efficiency and efficacy.
The mediating effect of job happiness on the relationship between job satisfaction and employee performance and turnover intentions: A case study on the oil and gas industry in the United Arab Emirates

This study aims to examine the mediating effect of job happiness factor on the relationship between job satisfaction and both employees' performance and turnover intentions in oil and gas industry in the United Arab Emirates (UAE).The study utilized a total of 722 usable questionnaires that were administered to respondents in oil and gas industry in the United Arab Emirates.This study adopted structural equation modelling (SEM) approach to analyze these relationships.The Analysis of Data revealed that job satisfaction factor has a significant direct positive relationship with both factors the employees' performance and the job happiness, while it has a negative insignificant relationship with employees' turnover intention.On the other hand, the Job happiness displays a significant positive direct effect on job performance, but it records a significant negative effect on employee turnover intention.Results show that job happiness plays a mediating role between job satisfaction and employee performance and turnover intention.These findings may guide the implementation of policies by human resources or other organization managements in the UAE Oil and Gas industry.For instance, they may use job happiness (mental well-being) as a predictor of employee behaviors and then formulate recruitment policies that will help maintain employee happiness and satisfaction, thereby helping in employee retention.
Validation of mitotic cell quantification via microscopy and multiple whole-slide scanners

The establishment of whole-slide imaging (WSI) as a medical diagnostic device allows that pathologists may evaluate mitotic activity with this new technology. Furthermore, the image digitalization provides an opportunity to develop algorithms for automatic quantifications, ideally leading to improved reproducibility as compared to the naked eye examination by pathologists. In order to implement them effectively, accuracy of mitotic figure detection using WSI should be investigated. In this study, we aimed to measure pathologist performance in detecting mitotic figures (MFs) using multiple platforms (multiple scanners) and compare the results with those obtained using a brightfield microscope. Four slides of canine oral melanoma were prepared and digitized using 4 WSI scanners. In these slides, 40 regions of interest (ROIs) were demarcated, and five observers identified the MFs using different viewing modes: microscopy and WSI. We evaluated the inter- and intra-observer agreements between modes with Cohen’s Kappa and determined “true” MFs with a consensus panel. We then assessed the accuracy (agreement with truth) using the average of sensitivity and specificity. In the 40 ROIs, 155 candidate MFs were detected by five pathologists; 74 of them were determined to be true MFs. Inter- and intra-observer agreement was mostly “substantial” or greater (Kappa = 0.594–0.939). Accuracy was between 0.632 and 0.843 across all readers and modes. After averaging over readers for each modality, we found that mitosis detection accuracy for 3 of the 4 WSI scanners was significantly less than that of the microscope (p = 0.002, 0.012, and 0.001). This study is the first to compare WSIs and microscopy in detecting MFs at the level of individual cells. Our results suggest that WSI can be used for mitotic cell detection and offers similar reproducibility to the microscope, with slightly less accuracy.
VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.
Monocular Depth Estimation With Augmented Ordinal Depth Relationships

Most existing algorithms for depth estimation from single monocular images need large quantities of metric ground-truth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos are sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new “relative depth in stereo” (RDIS) dataset densely labeled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then, we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve the state-of-the-art performance.
Neural Machine Translation of Text from Non-Native Speakers

Antonios Anastasopoulos, Alison Lui, Toan Q. Nguyen, David Chiang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Gyroscope-Aided Motion Deblurring with Deep Networks

We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is used to overcome the limitations of gyro-based blur estimation. To train our network, we also introduce a novel way of generating realistic training data using the gyroscope. The evaluation shows a clear improvement in visual quality over the state-of-the-art while achieving real-time performance. Furthermore, the method is shown to improve the performance of existing feature detectors and descriptors against the motion blur.