Reviewerly/exHarmony huggingface dataset:
Evaluation of artificial neural networks for the prediction of deep reservoir temperatures using the gas-phase composition of geothermal fluids

Three-layer artificial neural networks were used for the multivariate analysis of the gas-phase composition of fluids, and the prediction of geothermal reservoir temperatures. The major gas-phase composition of geothermal fluids (CO2, H2S, CH4, and H2) was defined as input variables whereas the measured bottomhole temperatures were used as output. Multivariate statistical analysis and log-ratio transformations were used for the normalization of input variables. These data sets were randomly divided into training (80%), validation (10%) and testing (10%). Matlab script algorithms based on a learning process of artificial neural networks (ANN) were programmed. The ANN architectures were determined by using up to five input neurons with thirteen different combinations of the input normalized variables for the input layer, a variable number of neurons for the hidden layer ranging from 1 to 35, and one neuron for the output layer. The Levenberg-Marquardt algorithm, the hyperbolic tangent sigmoid, and linear transfer functions were used for the training of the neural networks with tuned learning rates after using some initialization values (ranging from 0.01 to 0.00001). A sensitivity analysis to evaluate the relative importance of input variables on the output was also performed. Six ANN architectures were selected as the best from a comprehensive geochemometric analysis based on a statistical comparison and linear regressions between simulated and measured data. From an additional external validation (using a different geochemical database containing 13 samples from the Olkaria, Kenya geothermal field), the most efficient ANN made possible to predict geothermal reservoir temperatures with an acceptable accuracy (with mean error differences between simulated and measured data ranging from 2 to 11%). The best neural network was given by the ANN-33 architecture [3-8-1] which was characterized by three variables in the input layer [ln(H2S/CO2), ln(CH4/CO2), and ln(H2/CO2)], 8 neurons for the hidden layer and one neuron for the output layer. These results support, for the first time, the use of ANN as a gas geothermometry tool to predict geothermal reservoir temperatures.
Long short-term memory-based recurrent neural networks for nonlinear target tracking

Nonlinear target tracking is essentially to estimate the target state from observations where the system model or observation model undergoes nonlinearities. Among the traditional methods, the particle filter can handle this problem well, though its statistical effectiveness and computational efficiency should be balanced in implementation. In this paper, deep neural network-based methods are proposed to resolve this problem because of their strong capabilities of fitting any mapping as long as the training data is sufficient. Specifically, the long short-term memory-based recurrent neural networks are proposed to take in the observations and output the true states in a sequential manner. Simulation results show that the proposed networks can obtain better estimation accuracy with shorter computational time compared to traditional methods. The newly proposed structure is shown to be able to provide an estimate of the uncertainty related to the target state online and automatically. Besides, nearly the same estimation accuracy can be provided by the proposed methods even when the exact initial prior of the target state is considered unknown.
The Appropriation of Blockchain for Small and Medium-sized Enterprises

This article aims to provide a conceptual framework for small and medium-sized enterprises (SMEs) to evaluate the appropriation of blockchain technology for their business needs and challenges. This conceptual framework aims to respond to the problem of increasing speculations surrounding the blockchain that is considered to be an absolute and innovative solution to many business-related problems. However, this argument might not be realistic for SMEs despite this assumption. The main argument of the article is that SMEs should be skeptical while evaluating the appropriation of this technology for their business needs and they should follow a tailored approach whilst adopting it. Otherwise, the likelihood is high that a short-term and unstructured knowledge management process to adopt blockchain will end up with a waste of resources. The present paper conceptualizes an appropriation framework within nine factors namely, reduction of costs, internalization, digital representation of assets, unalterable data recording, network size, transparent and synchronized ledger, scalability, fair trade, and financing. Given the field’s infancy state of development, the paper relies on a qualitative document analysis involving SME literature and extant literature and white papers of blockchain applications.
ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images

You Only Look Once (YOLO) deep network can detect objects quickly with high precision and has been successfully applied in many detection problems. The main shortcoming of YOLO network is that YOLO network usually cannot achieve high precision when dealing with small-size object detection in high resolution images. To overcome this problem, we propose an effective region proposal extraction method for YOLO network to constitute an entire detection structure named ACF-PR-YOLO, and take the cyclist detection problem to show our methods. Instead of directly using the generated region proposals for classification or regression like most region proposal methods do, we generate large-size potential regions containing objects for the following deep network. The proposed ACF-PR-YOLO structure includes three main parts. Firstly, a region proposal extraction method based on aggregated channel feature (ACF) is proposed, called ACF based region proposal (ACF-PR) method. In ACF-PR, ACF is firstly utilized to fast extract candidates and then a bounding boxes merging and extending method is designed to merge the bounding boxes into correct region proposals for the following YOLO net. Secondly, we design suitable YOLO net for fine detection in the region proposals generated by ACF-PR. Lastly, we design a post-processing step, in which the results of YOLO net are mapped into the original image outputting the detection and localization results. Experiments performed on the Tsinghua-Daimler Cyclist Benchmark with high resolution images and complex scenes show that the proposed method outperforms the other tested representative detection methods in average precision, and that it outperforms YOLOv3 by 13.69 % average precision and outperforms SSD by 25.27 % average precision.
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks

Currently, no large-scale training data is available for the task of scientific paper summarization.In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences.We hypothesize that such talks constitute a coherent and concise description of the papers' content, and can form the basis for good summaries.We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries.A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually.In addition, we validated the quality of our summaries by human experts.
Participatory Research through Gastronomy Design: A designerly move towards more playful gastronomy

Socialization, eating and play are core activities that make us human. While they are often brought together, play theory suggests that their combination has unexplored potential in the context of gastronomy. Our research also indicates that a chef’s desire to control the meal may be a key impediment to developing dining experiences in which the diner’s playful engagement impacts taste, texture and flavour combination. We investigate if combining participatory research through design and play theory might better situate chefs to diversify their approach to playful gastronomy. Using experimental design methods, we interviewed a chef, a maître d’, a professional gastronomist, two food enthusiasts and a novice, to identify overlooked opportunities to extend play in gastronomy. We then conducted a series of dinners – designed with and for experts, enthusiasts and novices – to explore these opportunities, and tested the resulting method through a workshop with student chefs and game designers. We present the method: Participatory Research through Gastronomy Design (PRGD), using the case of its development to explicate its characteristics. Our research suggests that PRGD supports the design of playful gastronomic experiences that appeal to a range of diners, affords exploration of play’s impact on social dynamics and can productively inform concrete design choices. It also – crucially – supports chefs to partially transfer control of how a meal unfolds, without diluting their sense of controlling the overall experience. PRGD thus addresses a key impediment to extending play in gastronomy. Gastronomy that responds to diners’ needs and desires for play are currently limited. We propose PRGD as an exciting – and viable – approach to address this limitation.
Isoform function prediction based on bi-random walks on a heterogeneous network

Abstract Motivation Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level. Results We propose IsoFun to accomplish Isoform Function prediction based on bi-random walks on a heterogeneous network. IsoFun firstly constructs an isoform functional association network based on the expression profiles of isoforms derived from multiple RNA-seq datasets. Next, IsoFun uses the available Gene Ontology annotations of genes, gene–gene interactions and the relations between genes and isoforms to construct a heterogeneous network. After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the association between GO terms and isoforms, thus accomplishing the prediction of GO annotations of isoforms. Experimental results show that IsoFun significantly outperforms the state-of-the-art algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respectively. We further validated the performance of IsoFun on the genes ADAM15 and BCL2L1. IsoFun accurately differentiates the functions of respective isoforms of these two genes. Availability and implementation The code of IsoFun is available at http://mlda.swu.edu.cn/codes.php? name=IsoFun. Supplementary information Supplementary data are available at Bioinformatics online.
What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.
Double-Domain Imaging and Adaption for Person Re-Identification

Person re-identification (re-ID) performance has been significantly boosted in recent works, but re-ID model trained on one dataset usually cannot work effectively on another one. To address this problem, we proposed a novel framework-double-domain translation generative adversarial network (DTGAN) that can train images between two domains and generalize the model trained on one domain well to another domain. We divide this paper into two steps. First, the source images are translated in an unsupervised manner, and the translated images retain the style of target images and the ID labels in the source domain. Second, the translated images are used as the training data for supervised feature learning. Besides, with the purpose of moderating the influence of noise, we employ the strategy of label smoothing regularization (LSR). In our experiments, we observe that the images generated by the DTGAN are of high quality and more appropriate for domain adaption. In addition, the re-ID accuracy of the DTGAN is competitive to the state-of-the-art methods on Market-1501 and DukeMTMC-reID.
Sharing Attention Weights for Fast Transformer

Recently, the Transformer machine translation system has shown strong results by stacking attention layers on both the source and target-language sides. But the inference of this model is slow due to the heavy use of dot-product attention in auto-regressive decoding. In this paper we speed up Transformer via a fast and lightweight attention model. More specifically, we share attention weights in adjacent layers and enable the efficient re-use of hidden states in a vertical manner. Moreover, the sharing policy can be jointly learned with the MT model. We test our approach on ten WMT and NIST OpenMT tasks. Experimental results show that it yields an average of 1.3X speed-up (with almost no decrease in BLEU) on top of a state-of-the-art implementation that has already adopted a cache for fast inference. Also, our approach obtains a 1.8X speed-up when it works with the AAN model. This is even 16 times faster than the baseline with no use of the attention cache.
Is M-Learning a Challenge?

Over the last few years, students' learning methods have changed considerably from traditional techniques to e-learning and m-learning. Indeed, mobile learning (m-learning) is a technology that has advanced quickly without creating any limitations on time and place, to deliver electronic learning (e-learning) with the use of personal electronics. Studies that emphasize the use of m-learning in educational institutions are surfacing. This study looks at the advanced techniques of m-learning and examines students' attitudes toward the use and implementation of m-learning techniques for the sustainability of learning. The results are based on a survey conducted with 253 students at various universities in terms of their attitudes toward and perceptions of m-learning techniques as a supplement to traditional learning methods. This study followed and checked the academic details of each student to ascertain the impact of m-learning techniques. The findings suggest that it is essential to design m-learning so that the material to be taught inside and outside the classroom is known.
Evaluation of maxout activations in deep learning across several big data domains

This study investigates the effectiveness of multiple maxout activation function variants on 18 datasets using Convolutional Neural Networks. A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions. However, it is not clear if the activation function itself or the increase in the number of trainable parameters is responsible in yielding the best performance for different entity recognition tasks. This paper investigates if an increase in the number of convolutional filters on traditional activation functions performs equal-to or better-than maxout networks. Our experiments compare the Rectified Linear Unit, Leaky Rectified Linear Unit, Scaled Exponential Linear Unit, and Hyperbolic Tangent activations to four maxout function variants. We observe that maxout networks train relatively slower than networks with traditional activation functions, e.g. Rectified Linear Unit. In addition, we found that on average, across all datasets, the Rectified Linear Unit activation function performs better than any maxout activation when the number of convolutional filters is increased. Furthermore, adding more filters enhances the classification accuracy of the Rectified Linear Unit networks, without adversely affecting their advantage over maxout activations with respect to network-training speed.
Fraudulent Transaction Detection in Credit Card by Applying Ensemble Machine Learning techniques

In the present day scenario, fraudulent activities associated with financial transactions, particularly while using credit card, are observed to be occurring in a fast rate. Hence, a fraud detection system involving various detection techniques is very much essential for the financial institutions to sustain the goodwill from the customers. Several fraud detection techniques have been proposed by researchers as well as practitioners with application of various algorithms to find the pattern of fraud. In this study, the application of various classification models are proposed by implementing machine learning techniques to find out the accuracy and other performance parameters to identify the fraudulent transaction. Classification algorithms such as K-Nearest Neighbor (K-NN), Extreme Learning Machine (ELM), Random Forest (RF), Multilayer Perceptron (MLP) and Bagging classifier have been implemented to critically assessed their performances and the performances are evaluated. We have proposed a predictive classification model by ensemble of five individual algorithms, as it provides a better predictive performance.
Reconstructing Capsule Networks for Zero-shot Intent Classification

Intent classification is an important building block of dialogue systems.With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zeroshot intent classification.Nevertheless, research on this problem is still in the incipient stage and few methods are available.A recently proposed zero-shot intent classification method, IntentCapsNet, has been shown to achieve state-of-the-art performance.However, it has two unaddressed limitations: (1) it cannot deal with polysemy when extracting semantic capsules; (2) it hardly recognizes the utterances of unseen intents in the generalized zero-shot intent classification setting.To overcome these limitations, we propose to reconstruct capsule networks for zeroshot intent classification.First, we introduce a dimensional attention mechanism to fight against polysemy.Second, we reconstruct the transformation matrices for unseen intents by utilizing abundant latent information of the labeled utterances, which significantly improves the model generalization ability.Experimental results on two task-oriented dialogue datasets in different languages show that our proposed method outperforms IntentCapsNet and other strong baselines.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data.With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it.We also introduce two other simple but effective semisupervised methods to compare with.To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.
An Edge Computing Platform for Intelligent Operational Monitoring in Internet Data Centers

The increasing demand for cloud-based services, such as big data analytics and online e-commerce, leads to rapid growth of large-scale internet data centers. In order to provide highly reliable, cost effective, and high quality cloud services, data centers are equipped with sensors to monitor the operational states of infrastructure hardware, such as servers, storage arrays, networking devices, and computer room air conditioning systems. However, such coarse grained monitoring cannot provide fine grained real time information for resource multiplexing and job scheduling. Moreover, the monitoring of node level power consumption plays an important role in the optimization of workload placement and energy efficiency in data centers. In this paper, we propose an edge computing platform for intelligent operational monitoring in data centers. The platform integrates wireless sensors and on-board built-in sensors to collect data during the operation and maintenance of data centers. Using logical functions, we divide the data center clusters into grids, and then deploy wireless sensors and edge servers in each grid. As such, data processing on edge servers can reduce the latency in data transmission to central clouds and thereby enhance the real time resource mapping decisions in data centers. In addition, the proposed platform also provides predictions of resource utilization, workload characteristics, and hardware health trends in data centers.