Reviewerly/exHarmony huggingface dataset:
Long Term Memory Assistance for Evolutionary Algorithms

Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.
High Resolution SAR Image Synthesis with Hierarchical Generative Adversarial Networks

Generative adversarial network (GAN) is an artificial neural network based on unsupervised learning method. Due to its powerful model representation capabilities, GAN has been introduced to synthesize synthetic aperture radar (SAR) image data, for the real sample is difficult to acquire. Large-scale, high-resolution SAR images play an important role in promoting SAR applications, such as automatic target recognition and image interpretation. However, on account of the difficult training problem of GAN network, especially for SAR images with speckle noise, it is difficult to obtain high-resolution SAR images by simply transfer the net from optical image. Recent studies in other image fields have shown that hierarchical structure is an effective and useful way to decompose a generation task into several smaller subtasks. How to obtain more high-resolution SAR images from limited original samples through GAN is the target of our research. Therefore, in this paper, we introduce a hierarchical GAN network model to generate SAR images, through the multi-stage network, gradually improve the quality of the generated image, and finally obtain high-resolution images. The type and aspect of generated images are determined by the input of condition vectors in the last two stages. In addition, we introduce the triple loss, in which the background loss is used to imitating background clutter noise of SAR image, the condition loss is to make the generated images' type and aspect become controllable, and the global loss for getting higher image generation quality. The generated images show high similarity with the real samples.
Learning a local symmetry with neural networks

We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns: the gauge symmetry ${Z}_{2}$. This symmetry is present in physical problems from topological transitions to quantum chromodynamics, and controls the computational hardness of instances of spin-glasses. Here, we show how to design a neural network, and a dataset, able to learn this symmetry and to find compressed latent representations of the gauge orbits. Our method pays special attention to system-wrapping loops, the so-called Polyakov loops, known to be particularly relevant for computational complexity.
A cross-domain hierarchical recurrent model for personalized session-based recommendations

Recently, much attention has been paid to personalized session-based recommendations, where detailed user information is available due to users’ automatic or active login. Nevertheless, most methods focus on a single-domain scenario, assuming that users are only active in a single domain. Consequently, they always suffer from lack of data due to ignoring the fact that users’ behaviors are scattered across domains. Therefore, we propose a novel model, called Cross-Domain Hierarchical Recurrent Model (CDHRM), to incorporate cross-domain sequential information by exploring correlations among users’ cross-domain behaviors. Specifically, we devise a cross-domain user-level recurrent neural network (RNN) to systematically depict users’ global interests by capturing the cross-domain inter-session dynamics. To separately capture intra-session dynamics of different domains, two domain-specific session-level RNNs, which can preserve behavioral differences, are constructed. Meanwhile, for achieving synchrony of interactions among domains, the user-level RNN exchanges information with different session-level RNNs in a chronological order. Moreover, fusion layers with different integration strategies are introduced to further capture behavioral differences. Finally, cross-domain user-level and session-level information are jointly exploited to predict users’ future behaviors. Empirical results show CDHRM outperforms the state-of-the-art methods on three cross-domain datasets and can work well even with non-overlapping and sparse item information across domains.
A Public Domain Dataset for Human Activity Recognition in Free-Living Conditions

In Human Activity Recognition (HAR), supervised Machine Learning methods are predominantly used, making availability of datasets a major issue for research in the field. In particular, the majority of available datasets are collected under controlled conditions. Consequently, models trained under similar circumstances, generally exhibit a significant decrease in recognition accuracy when they are moved to final deployment in the wild, within unconstrained settings. This paper presents a new dataset for HAR, collected in free-living and unconstrained conditions. 10 subjects were recruited for the purpose of data collection. Data was recorded over a 6 week period using a smartphone app, and a wristband activity monitor. During the first and last week of observation, participants also wore an ActivPAL" activity logger. The data collected have been partially self-labeled by participants, by means of the mobile app provided for data collection. The dataset collected can be used to evaluate HAR algorithm and models in real-world unconstrained settings. Together with the description of the dataset, this work presents some preliminary results, obtained cross-validating a model trained on the publicly available Extrasensory dataset, and testing its performance on our newly collected dataset. Results obtained highlighted high cross-subject variability when testing on new subjects, with a balanced accuracy varying between 53.33% and 90.01%, with an average balanced accuracy of 71.73%.
A Hybrid Ontology-Based Recommendation System in e-Commerce

The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.
Resume Information Extraction with A Novel Text Block Segmentation Algorithm

In recent years, we have witnessed the rapid development of deep neural networks and distributed representations in natural language processing.However, the applications of neural networks in resume parsing lack systematic investigation.In this study, we proposed an end-to-end pipeline for resume parsing based on neural networks-based classifiers and distributed embeddings.This pipeline leverages the position-wise line information and integrated meanings of each text block.The coordinated line classification by both line type classifier and line label classifier effectively segment a resume into predefined text blocks.Our proposed pipeline joints the text block segmentation with the identification of resume facts in which various sequence labelling classifiers perform named entity recognition within labelled text blocks.Comparative evaluation of four sequence labelling classifiers confirmed BLSTM-CNNs-CRF's superiority in named entity recognition task.Further comparison among three publicized resume parsers also determined the effectiveness of our text block classification method.
Creating sustainable performance in the fourth industrial revolution era: The effect of employee’s work well-being on job performance

In the beginning of the fourth industrial revolution, competition in acquiring and retaining best talents in concert with talents' unfamiliarity of what they will face and obtain in the course of working at a company are two major problems.It is essential for a company to make its employees satisfied and pleased with their jobs.These very satisfaction and pleasure are hoped to serve as a key for motivating employees to perform and contribute their best for the company.Numerous research studies have proven the positive effect of work well-being on job performance, but the findings came with inconsistencies and controversies.This fact has caused reluctance in a good many companies to invest in their employees' work well-being.A survey of 509 millennial employees in the Indonesian startup digital industry was conducted in this research.The results show that employee's work well-being had a significant, positive effect on job performance.Theoretically, this research has contributed in responding to inconsistencies in literature.It is hoped that this research will also offer practical contribution to individual employees as well as human resources department and increase company executives' confidence in making organization-related strategic decisions to attain sustainable performance.Finally, we propose a number of intervention suggestions for performance improvement through work well-being.
Interrogating the Explanatory Power of Attention in Neural Machine Translation

Attention models have become a crucial component in neural machine translation (NMT).They are often implicitly or explicitly used to justify the model's decision in generating a specific token but it has not yet been rigorously established to what extent attention is a reliable source of information in NMT.To evaluate the explanatory power of attention for NMT, we examine the possibility of yielding the same prediction but with counterfactual attention models that modify crucial aspects of the trained attention model.Using these counterfactual attention mechanisms we assess the extent to which they still preserve the generation of function and content words in the translation process.Compared to a state of the art attention model, our counterfactual attention models produce 68% of function words and 21% of content words in our German-English dataset.Our experiments demonstrate that attention models by themselves cannot reliably explain the decisions made by a NMT model. 1
Intraoperative frozen section consultation by remote whole-slide imaging analysis –validation and comparison to robotic remote microscopy

Digital pathology including whole slide image (WSI) acquisition is a promising tool for histopathologic teleconsultation. To test and validate the use of WSI in comparison with robotic microscopy for intraoperative frozen section consultation of peripheral hospitals serviced by our department, we compared the VENTANA DP 200 slide scanner with an established remote-controlled digital microscope. Thirty cases were retrospectively analysed. In comparison with a median specimen handling time of 19 min using remote-controlled microscopy, the WSI handling was significantly shorter (11 min, p=0.0089) and offered better image quality, for example, allowing to detect a positive resection margin by a malignant melanoma that had been missed using the former system. Prospectively assessed on 12 cases, the median handling time was 6 min. Here, we demonstrate the applicability and the advantages of WSI for intraoperative frozen section teleconsultation. WSI-based telepathology prooves to be an efficient and reliable tool providing superior turn-around time and image resolution.
Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.
Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step

Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets.We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset.Our baseline BiLSTM model achieves higher accuracy than any previously published tagger not taking advantage of a morphological lexicon.When we extend the model by incorporating such data, we outperform previous state-of-theart results by a significant margin.We also report on work in progress that attempts to address the problem of data sparsity inherent in morphologically detailed, finegrained tagsets.We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input for the main model.This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results.Finally, we train and test our tagger on a new gold standard for Icelandic.
Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate

In this paper, measured data of solar radiation was applied to develop forty-three (43) empirical models for estimation of monthly average diffuse solar radiation using clearness index, sunshine duration and a combination of them as predictors. The data covered a period of two years from May 2015 to April 2017 and was measured at Mehran University of Engineering and Technology, Hyderabad, Pakistan. Through a comprehensive statistical performance analysis, 43 dimensional models developed were tested for constructing the most accurate regression model to predict the monthly mean daily diffuse solar radiation in Hyderabad, Pakistan. On the whole, the model 42 – a hybrid of sunshine duration and clearness index predictors of diffuse fraction outperformed the remaining models proposed in this study. The best model (model 42) was then compared with 5 models and 5 measured data of diffuse solar radiation available in the literature and the NASA database by applying statistical indicators such as MBE, MPE, RMSE, RRMSE, R 2 and GPI. Through the analysis, the hybrid of sunshine duration and clearness index predictors of diffuse fraction model (model 42) was selected as the most appropriate model. The study concluded that the proposed hybrid model can serve as a baseline for the design of photovoltaic systems and estimate the monthly mean daily diffuse solar radiation on the horizontal surface for Hyderabad, Pakistan and other locations with similar local climate conditions. Citation: Nwokolo, S.C. and Otse, C.Q. (2019). Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate. Trends in Renewable Energy, 5, 307-332. DOI: 10.17737/tre.2019.5.3.00107
Vehicle global 6-DoF pose estimation under traffic surveillance camera

Accurately sensing the global position and posture of vehicles in traffic surveillance videos is a challenging but valuable issue for future intelligent transportation systems. Although in recent years, deep learning has brought about major breakthroughs in the six degrees of freedom (6-DoF) pose estimation of objects from monocular images, accurate estimation of the geographic 6-DoF poses of vehicles using images from traffic surveillance cameras remains challenging. We present an architecture that computes continuous global 6-DoF poses throughout joint 2D landmark estimation and 3D pose reconstruction. The architecture infers the 6-DoF pose of a vehicle from the appearance of the image of the vehicle and 3D information. The architecture, which does not rely on intrinsic camera parameters, can be applied to all surveillance cameras by a pre-trained model. Also, with the help of 3D information from the point clouds and the 3D model itself, the architecture can predict landmarks with few and/or blurred textures. Moreover, because of the lack of public training datasets, we release a large-scale dataset, ADFSC, that contains 120 K groups of data with random viewing angles. Regarding both 2D and 3D metrics, our architecture outperforms existing state-of-the-art algorithms in vehicle 6-DoF estimation.
Chinese Named Entity Recognition with Bert

As a basic task of NLP, named entity recognition has always been the focus of researchers. At the same time, the word vector representation which is a necessary part of many named entity recognition neural network models has been more and more important. Recently, the emergence of a new type word representation, BERT, has greatly promoted many NLP tasks. In this paper, we will use Bert to train Chinese character embedding and connect it with Chinese radical-level representations, and put it into the BGRU-CRF model. We have achieved good results in Chinese data set through a series of experiments.
Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.
Single image dehazing via a dual-fusion method

Single image dehazing is a challenging task because of the hue and brightness distortion problems. In this paper, we propose a dual-fusion method for single image dehazing. By a segmentation method creating two divided regions, the sky and non-sky regions can be obtained. To properly optimize the transmission, a multi-region fusion method is proposed for single image smooth. An exposure fusion method is constructed by the brightness transform function to effectively remove the haze from a single image. Experimental results show that this method outperforms state-of-the-art dehazing methods in terms of both efficiency and the dehazing effect.
Presenting TWITTIRÒ-UD: An Italian Twitter Treebank in Universal Dependencies

In this paper we describe the early stage application of the Universal Dependencies to an Italian corpus from social media developed for shared tasks related to irony and stance detection.The development of this novel resource (TWITTIR Ò-UD) serves a twofold goal: it enriches the scenario of treebanks for social media and for Italian, and it paves the way for a more reliable extraction of a larger variety of morphological and syntactic features to be used by sentiment analysis tools.On the one hand, social media texts are especially hard to parse and the limited amount of resources for training and testing NLP tools further damages the situation.On the other hand, we thought that adding the Universal Dependencies format to the fine-grained annotation for irony, that was previously applied on TWITTIR Ò, might meaningfully help in the investigation of possible relationships between syntax and semantics of the uses of figurative language, irony in particular.