Reviewerly/exHarmony huggingface dataset:
Human action recognition based on low- and high-level data from wearable inertial sensors

Human action recognition supported by highly accurate specialized systems, ambulatory systems, or wireless sensor networks has a tremendous potential in the areas of healthcare or wellbeing monitoring. Recently, several studies carried out focused on the recognition of actions using wearable inertial sensors, in which raw sensor data are used to build classification models, and in a few of them high-level representations are obtained which are directly related to anatomical characteristics of the human body. This research focuses on classifying a set of activities of daily living, such as functional mobility, and instrumental activities of daily living, such as preparing meals, performed by test subjects in their homes in naturalistic conditions. The joint angles of upper and lower limbs are estimated using information from five wearable inertial sensors placed on the body of five test subjects. A set of features related to human limb motions is extracted from the orientation signals (high-level data) and another set from the acceleration raw signals (low-level data) and both are used to build classifiers using four inference algorithms. The proposed features in this work are the number of movements and the average duration of consecutive movements. The classifiers are capable of successfully classifying the set of actions using raw data with up to 77.8% and 93.3% from high-level data. This study allowed comparing the use of two data levels to classify a set of actions performed in daily environments using an inertial sensor network.
Deterministic remote preparation of arbitrary single-qubit state via one intermediate node in noisy environment

We propose a remote state preparation scheme working in noisy environment via one intermediate node. Firstly, we report the remote preparation of an arbitrary single-qubit state with one intermediate node in ideal environment. Afterwards, we investigate the influence of four kinds of representative noises that are commonly encountered in practical applications of quantum communication protocols. Detailed analysis indicates that the fidelities of the output state rely on the coefficients of the prepared state and the decoherence rate. The turning point is λ=0.5 in both the bit-flip and phase-flip noisy channels. The fidelity in the depolarizing channel is related with the decoherence rate, and independent with the phase parameters.
Challenges of Annotating a Code-Switching Treebank

This paper presents challenges and observations on creating a code-switching treebank based on ongoing annotation efforts of a Turkish-German spoken corpus following the Universal Dependencies annotation scheme.We present and discuss a number of issues that arise because of the need for consistent multilingual annotation within a single treebank, as well as the informal language which is where code-switching is observed most.Besides proposing solutions to these issues, our aim in this paper is to stimulate discussion and facilitate consistency over upcoming code-switching annotation projects.
Sentiment Analysis of Reviews Based on Deep Learning Model

As known as opinion mining, sentiment analysis is a work by using the "natural language processing" method to find out the author's attitude, emotion or evaluation on certain topics. This paper using a dataset by Mass et al from its original Stanford AI Repository, and a commonly pre-processing method--word embedding, and establish a deep learning model for sentiment analysis. From the perspective of data analysis, learn about the movie preferences and cultural characteristics of audiences from domestic and foreign. In the experiment, we compared the performance of RNN, LSTM and GRU in natural language processing, and improve the efficiency and accuracy of sentiment analysis by a fusion model which integrating recurrent neural networks variant at the output of convolutional neural network.
Dynamic Knowledge Routing Network for Target-Guided Open-Domain Conversation

Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations. Existing methods mainly rely on single-turn data-driven learning and simple target-guided strategy without considering semantic or factual knowledge relations among candidate topics/keywords. This results in poor transition smoothness and low success rate. In this work, we adopt a structured approach that controls the intended content of system responses by introducing coarse-grained keywords, attains smooth conversation transition through turn-level supervised learning and knowledge relations between candidate keywords, and drives an conversation towards an specified target with discourse-level guiding strategy. Specially, we propose a novel dynamic knowledge routing network (DRKN) which considers semantic knowledge relations among candidate keywords for accurate next topic prediction of next discourse. With the help of more accurate keyword prediction, our keyword-augmented response retrieval module can achieve better retrieval performance and more meaningful conversations. Besides, we also propose a novel dual discourse-level target-guided strategy to guide conversations to reach their goals smoothly with higher success rate. Furthermore, to push the research boundary of target-guided open-domain conversation to match real-world scenarios better, we introduce a new large-scale Chinese target-guided open-domain conversation dataset (more than 900K conversations) crawled from Sina Weibo. Quantitative and human evaluations show our method can produce meaningful and effective target-guided conversations, significantly improving over other state-of-the-art methods by more than 20% in success rate and more than 0.6 in average smoothness score.
Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification

Feature augmentation, which manipulates the feature space by integrating the label information, is one of the most popular strategies for solving Multi-Dimensional Classification (MDC) problems. However, the vanilla feature augmentation approaches fail to consider the intra-class exclusiveness, and may achieve degenerated performance. To fill this gap, a novel neural network based model is proposed which seamlessly integrates the Label Embedding and Feature Augmentation (LEFA) techniques to learn label correlations. Specifically, based on attentional factorization machine, a cross correlation aware network is introduced to learn a low-dimensional label representation that simultaneously depicts the inter-class correlations and the intra-class exclusiveness. Then the learned latent label vector can be used to augment the original feature space. Extensive experiments on seven real-world datasets demonstrate the superiority of LEFA over state-of-the-art MDC approaches.
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process

The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.
Multi-Scale Anisotropic Gaussian Kernels for Image Edge Detection

In this paper, a new edge detection method is proposed where multi-scale anisotropic Gaussian kernels (AGKs) are used to obtain an edge map from an input image.The main advantage of the proposed method is that high edge detection accuracy and edge resolution are attained while maintaining good noise robustness.The proposed method consists of three aspects: First, anisotropic Gaussian directional derivatives (AGDDs) are derived from the AGKs which are used to acquire local intensity variation from an input image with multiple scales.Second, multi-scale AGDD based edge strength maps (ESMs) are fused into a new ESM with high edge resolution and little edge stretch effect which has the ability to solve the contradiction issue between noise robustness and accurate edge extraction.Third, the fused ESM is embedded into the framework of Canny detection for obtaining edge contours.Finally, the criteria on precision-recall curve, detection accuracy, and noise robustness are used to evaluate the proposed detector against four state-of-the-art methods.The experimental results show that our proposed detector outperforms all the other tested edge detection methods.
Unsupervised EA-Based Fuzzy Clustering for Image Segmentation

This paper presents an unsupervised fuzzy clustering based on evolutionary algorithm for image segmentation. It needs no prior information about exact numbers of segments. Local and nonlocal spatial information derived from observed images are incorporated into fuzzy clustering process. It consists of three major procedures. First, a multi-objective evolutionary sampling is proposed to locate image pixels with a variety of image information. Secondly, optimizing fuzzy compactness and fuzzy separation, a multi-objective evolutionary fuzzy clustering with spatial information is performed on sampling pixels. The particular numbers of segments and balances of spatial information can be obtained. Then fuzzy clustering segmentation on whole image is carried out by two fuzzy clustering approaches, which are depended on fuzzy c-means and evolutionary algorithm respectively. To enhance qualities of final segmentation results, a label correction based on entropy and local spatial information is introduced. Experiments on different types of images demonstrate the effectiveness of our approach for image segmentation.
Cost-Accuracy Aware Adaptive Labeling for Active Learning

Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for given classifier under a budget constraint. However, in many real settings, different labelers have different labeling costs and can yield different labeling accuracies. Moreover, a given labeler may exhibit different labeling accuracies for different instances. This setting can be referred to as active learning with diverse labelers with varying costs and accuracies, and it arises in many important real settings. It is therefore beneficial to understand how to effectively trade-off between labeling accuracy for different instances, labeling costs, as well as the informativeness of training instances, so as to achieve the best generalization performance at the lowest labeling cost. In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset.
An Integrated Enhancement Solution for 24-Hour Colorful Imaging

The current industry practice for 24-hour outdoor imaging is to use a silicon camera supplemented with near-infrared (NIR) illumination. This will result in color images with poor contrast at daytime and absence of chrominance at nighttime. For this dilemma, all existing solutions try to capture RGB and NIR images separately. However, they need additional hardware support and suffer from various drawbacks, including short service life, high price, specific usage scenario, etc. In this paper, we propose a novel and integrated enhancement solution that produces clear color images, whether at abundant sunlight daytime or extremely low-light nighttime. Our key idea is to separate the VIS and NIR information from mixed signals, and enhance the VIS signal adaptively with the NIR signal as assistance. To this end, we build an optical system to collect a new VIS-NIR-MIX dataset and present a physically meaningful image processing algorithm based on CNN. Extensive experiments show outstanding results, which demonstrate the effectiveness of our solution.
Sentence Generation for Entity Description with Content-Plan Attention

We study neural data-to-text generation. Specifically, we consider a target entity that is associated with a set of attributes. We aim to generate a sentence to describe the target entity. Previous studies use encoder-decoder frameworks where the encoder treats the input as a linear sequence and uses LSTM to encode the sequence. However, linearizing a set of attributes may not yield the proper order of the attributes, and hence leads the encoder to produce an improper context to generate a description. To handle disordered input, recent studies propose two-stage neural models that use pointer networks to generate a content-plan (i.e., content-planner) and use the content-plan as input for an encoder-decoder model (i.e., text generator). However, in two-stage models, the content-planner may yield an incomplete content-plan, due to missing one or more salient attributes in the generated content-plan. This will in turn cause the text generator to generate an incomplete description. To address these problems, we propose a novel attention model that exploits content-plan to highlight salient attributes in a proper order. The challenge of integrating a content-plan in the attention model of an encoder-decoder framework is to align the content-plan and the generated description. We handle this problem by devising a coverage mechanism to track the extent to which the content-plan is exposed in the previous decoding time-step, and hence it helps our proposed attention model select the attributes to be mentioned in the description in a proper order. Experimental results show that our model outperforms state-of-the-art baselines by up to 3% and 5% in terms of BLEU score on two real-world datasets, respectively.
Low Resource Sequence Tagging with Weak Labels

Current methods for sequence tagging depend on large quantities of domain-specific training data, limiting their use in new, user-defined tasks with few or no annotations. While crowdsourcing can be a cheap source of labels, it often introduces errors that degrade the performance of models trained on such crowdsourced data. Another solution is to use transfer learning to tackle low resource sequence labelling, but current approaches rely heavily on similar high resource datasets in different languages. In this paper, we propose a domain adaptation method using Bayesian sequence combination to exploit pre-trained models and unreliable crowdsourced data that does not require high resource data in a different language. Our method boosts performance by learning the relationship between each labeller and the target task and trains a sequence labeller on the target domain with little or no gold-standard data. We apply our approach to labelling diagnostic classes in medical and educational case studies, showing that the model achieves strong performance though zero-shot transfer learning and is more effective than alternative ensemble methods. Using NER and information extraction tasks, we show how our approach can train a model directly from crowdsourced labels, outperforming pipeline approaches that first aggregate the crowdsourced data, then train on the aggregated labels.
Visual Exploration of Air Quality Data with a Time-correlation-partitioning Tree Based on Information Theory

&lt;?tight?&gt;Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario.
Mobile Assisted Vocabulary Learning: Examining the Effects on Students' Vocabulary Enhancement

The use of mobile phones combined with internet has become so handy in the present world of science, information and communication technologies.The mobile learning strategies have created several new ways to assist learners in the progression of learning a second language.One of the most vital challenges that learners will face during the learning of a second language is knowing the meaning of a vocabulary in order to understand the idea of the sentence.Given the importance of knowing the meaning of vocabularies / words through mobile assisted learning, this study attempts to investigate the effectiveness of using Mobile Phone to search for the meaning of vocabulary compared to the conventional way of using a printed dictionary.Therefore, 50 form 5 arts stream pupils from a public school in a state of Malaysia were selected for an experimental study.A Pre-test and Post-Test of vocabulary have been performed to find out the effectiveness of using the mobile phone technology compared to the printed dictionary.The results of the experimental study clearly show that using mobile phones enhances the pupil's knowledge in learning and knowing the meaning of the vocabulary deeper compared to the printed dictionary.This is because the vocabulary can be found in the form of images and the pronunciation of the word can be listened and seen in the form of an audio or video.The use of mobile phones which combined with the internet, have led to an increase in the scores of the pupils and the search for the vocabulary can be done faster than the printed dictionary.In conclusion, mobile phone can be considered as a good pedagogical tool in learning English as the second language (ESL).
Histogram of Oriented Gradients Based Off-Line Handwritten Devanagari Characters Recognition Using SVM, K-NN and NN Classifiers

This paper presents influence of Histogram of Oriented Gradients (HOG) features on three different popular classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Neural Network (NN).Devanagari script is one among the foremost standard scripts in India used in many languages.The main objective of our study is to recognize Handwritten Devanagari Characters using HOG features and also examine the efficiency of HOG features on it using multiple cells of HOG descriptor.Here, presented Handwritten Devanagari Character Recognition System (HDCRS) is used to recognize isolated characters by making use of SVM, K-NN and NN well known classifiers.HOG is very effective and dense technique used to extract features from every positions of an image by dividing images into cells.HOG tries to capture shape of an image by using gradients information.The features extracted are classified using above mentioned classifiers.Through this study, we observed that SVM+HOG combination achieved highest accuracy: a recognition rate of 87.38%.The findings of this research may help in digitization of Handwritten Devanagari Documents.
A Novel Hybrid Whale Optimization Algorithm to Solve a Production-Distribution Network Problem Considering Carbon Emissions

Nowadays, there is a great deal of attention for regulations of carbon emissions to enforce the decision-makers of production and distribution networks to redesign their systems satisfactorily. The literature has seen a rapid interest in developing novel metaheuristics to solve this problem as a complicated optimization problem. Such difficulties motivate us to address a production-distribution network design problem considering carbon emissions policies among the first studies in this area by a novel hybrid whale optimization algorithm. Accordingly, a mixed integer non-linear programming model has been developed. To tackle the proposed problem, a new hybrid metaheuristic based on whale optimization algorithm and simulated annealing as a successful optimizer is employed to solve the proposed problem. The calibration of the algorithms has been designed by Taguchi method, comprehensively. Finally, an extensive analysis has been evaluated through a comparative study along with some assessment metrics of Pareto solutions.
Reshaping Diverse Planning

The need for multiple plans has been established by various planning applications. In some, solution quality has the predominant role, while in others diversity is the key factor. Most recent work takes both plan quality and solution diversity into account under the generic umbrella of diverse planning. There is no common agreement, however, on a collection of computational problems that fall under that generic umbrella. This in particular might lead to a comparison between planners that have different solution guarantees or optimization criteria in mind. In this work we revisit diverse planning literature in search of such a collection of computational problems, classifying the existing planners to these problems. We formally define a taxonomy of computational problems with respect to both plan quality and solution diversity, extending the existing work. We propose a novel approach to diverse planning, exploiting existing classical planners via planning task reformulation and choosing a subset of plans of required size in post-processing. Based on that, we present planners for two computational problems, that most existing planners solve. Our experiments show that the proposed approach significantly improves over the best performing existing planners in terms of coverage, the overall solution quality, and the overall diversity according to various diversity metrics.
Review on techniques and file formats of image compression

This paper presents a review of the compression technique in digital image processing. As well as a brief description of the main technologies and traditional format that commonly used in image compression. It can be defined as image compression a set of techniques that are applied to the images to store or transfer them in an effective way. In addition, this paper presents formats that use to reduce redundant information in an image, unnecessary pixels and non-visual redundancy. The conclusion of this paper The results for this paper concludes that image compression is a critical issue in digital image processing because it allows us to store or transmit image data efficiently.