Reviewerly/exHarmony huggingface dataset:
Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.
A self-attention based neural architecture for Chinese medical named entity recognition

&lt;abstract&gt; &lt;p&gt;The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1-score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.&lt;/p&gt; &lt;/abstract&gt;
Optical flow-based structure-from-motion for the reconstruction of epithelial surfaces

This paper details a novel optical flow-based structure from motion (SfM) approach for the reconstruction of surfaces with few textures using video sequences acquired under strong illumination changes. An original image search and grouping strategy allows to reconstruct each 3D scene point using a large set of 2D homologous points extracted from a reference image and its superimposed images acquired from different viewpoints. A variational optical flow scheme with a descriptor-based data term leads to a robust, accurate and dense homologous point determination between the image pairs. Thus, contrary to classical SfM usable for textured scenes, the proposed dense point cloud reconstruction algorithm requires neither a feature point tracking method nor any multi-view stereo technique. The performance of the proposed SfM approach is assessed on phantoms with known ground truth and on very complex patient data of various medical examinations and image modalities.
The Why, What, and How of Immersive Experience

Assessing and modelling the quality of immersive experience has become a trending topic in QoE.To achieve this purpose, we would firstly want to know why an immersive experience is important to us, what exactly is defined as an immersive experience, and how we could actually measure an immersive experience.This paper aims to answer these questions.We start from the rationales of designing immersive experience, and then briefly go through the definitions of immersion from the system and user perspectives.We also provide our own definition of immersion that fits into the QoE assessment and measurement paradigm.We continue to discuss the pros and cons of four popular measurements of immersion, namely, the psychometric questionnaires, the continuous subjective measures, the primary or secondary task performance, and the neuro-psycho-physiological methods.We also provide a global view of comparing and evaluating these measurements by profiling them along five quality dimensions.Finally, we postulate, briefly, four novel methods of measuring immersion, linking the definitions and theories of immersion with the measurements we have discussed in this paper.
A survey of CAPTCHA technologies to distinguish between human and computer

CAPTCHA, Completely Automated Public Turing test to tell Computers and Humans Apart, is widely used as a security mechanism to classify human and computer. This security mechanism is based on the Turing Test, which has been conceived to ensure network security. Usability is another fundamental issue, which can avoid human users proceeding tedious and time-consuming operation. CAPTCHA design should consider security and usability simultaneously. This paper provides a review on the development of CAPTCHA technologies for human and computer classification, along with their applications and instantiations. Different from previous CAPTCHA survey, this review discusses the CAPTCHA mechanism from usability and security aspects, therefore attacking (anti-classification) and defending (classification) technologies towards current CAPTCHA are both reviewed. Besides, recent emerging CAPTCHA and the attacking techniques are also introduced in this paper, such as game CAPTCHA, deep learning-based attacking, and etc.
Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.
Liver segmentation from abdominal CT volumes based on level set and sparse shape composition

Liver segmentation from abdominal CT volumes is a primary step for computer-aided surgery and liver disease diagnosis. However, accurate liver segmentation remains a challenging task for intensity inhomogeneity and serious pathologies occurring in liver CT volume. This paper presents a novel framework for accurate liver segmentation from CT images. Firstly, a novel level set integrated with intensity bias and position constraint is applied, and for normal liver, the generated liver regions are regarded as the final results. Then, for pathological liver, a sparse shape composition (SSC)-based method is presented to refine liver shapes, followed by an improved graph cut to further optimize segmentation results. The level set-based method is capable of overcoming intensity inhomogeneity in object regions, and the SSC- and graph cut-based strategy has outstanding power to address under-segmentation appearing in pathological livers. The experiments conducted on public databases SLIVER07 and 3Dircadb show that the proposed method can segment both healthy and pathological liver effectively. The segmentation performance in terms of mean ASD, RMSD, MSD, VOE and RVD on SLIVER07 are 0.9mm, 1.8mm, 19.4mm, 5.1% and 0.1%, respectively, and on 3Dircadb are 1.6mm, 3.1mm, 27.2mm, 9.2% and 0.5%, respectively, which outperforms many existing methods. The proposed method does not require complex training procedure on numerous liver samples, and has satisfying and robust segmentation performance on both normal and pathological liver in various shapes.
The rise of confidential computing: Big tech companies are adopting a new security model to protect data while it's in use - [News]

A handful of major technology companies are going all in on a new security model they're calling confidential computing in an effort to better protect data in all its forms.
A video information driven football recommendation system

Designing a football recommendation system requires collecting physical, technical and tactical information from football games. However, traditional technical and tactical statistics of football still depend on manual numbering, which is a huge labor consumption. Though GPS (Global Positioning System) devices could be applied to collect football data, they are very expensive and are forbidden in many football games. To solve these problems, we utilize video tracking to capture physical and tactical information of football players and propose a football recommendation system through combining players’ tracking techniques with recommendation algorithms. In our proposed system, the YOLOv2 (You Only Look Once version 2) algorithm and improved KCF (Kernelized Correlation Filter) method are applied to obtain and analyze the location information of football players. The proposed system could automatically recognize and track players according to match videos instead of using wearable GPS devices. Compared with GPS, the experimental results have shown that the data obtained from our system are much closer to reality and have lower standard deviation.
Effective Handwritten Digit Recognition using Deep Convolution Neural Network

This paper proposed a simple neural network approach towards handwritten digit recognition using convolution.With machine learning algorithms like KNN,SVM/SOM, recognizing digits is considered as one of the unsolvable tasks due to its distinctiveness in the style of writing.In this paper, Convolution Neural Networks are implemented with an MNIST dataset of 70000 digits with 250 distinct forms of writings.The proposed method achieved 98.51% accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.
Face Recognition based Attendance Management System

In this digital era, face recognition system plays a vital role in almost every sector.Face recognition is one of the mostly used biometrics.It can used for security, authentication, identification, and has got many more advantages.Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process.Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc.This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain.And there may be chances of proxy attendance.Thus, the need for this system increases.This system consists of four phases-database creation, face detection, face recognition, attendance updation.Database is created by the images of the students in class.Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively.Faces are detected and recognized from live streaming video of the classroom.Attendance will be mailed to the respective faculty at the end of the session.
Design and Development of a Drone for Spraying Pesticides, Fertilizers and Disinfectants

There are too many technologies involved in today's Agriculture, out of which spraying pesticides using drones is one of the emerging technologies.Manual pesticide spraying causes many harmful side effects to the personnel involved in the spraying process.The Exposure effects can range from mild skin irritation to birth defects, tumors, genetic changes, blood and nerve disorders, endocrine disruption, coma or death.The WHO (World Health Organization) estimated as one million cases of ill affected, when spraying the pesticides in the crop field manually.This paved the way to design a drone mounted with spraying mechanism having 12 V pump, 6 Litre storage capacity tank,4 nozzles to atomize in fine spray , an octocopter configuration frame ,suitable landing frame, 8 Brushless Direct Current (BLDC) motors with suitable propellers to produce required thrust about 38.2 KG(at 100% RPM) and suitable Lithium-Polymer (LI-PO) battery of current capacity 22000 mAh and 22.2 V to meet necessary current and voltage requirements.A First-Person View (FPV) camera and transmitter can also be fixed in the drone for monitoring the spraying process and also for checking pest attacks on plants.This pesticide spraying drone reduces the time, number of labor and cost of pesticide application.This type of drone can also be used to spray disinfectant liquids over buildings, water bodies and in highly populated areas by changing the flow discharge of the pump.
Exploiting Mutual Information for Substructure-aware Graph Representation Learning

In this paper, we design and evaluate a new substructure-aware Graph Representation Learning (GRL) approach. GRL aims to map graph structure information into low-dimensional representations. While extensive efforts have been made for modeling global and/or local structure information, GRL can be improved by substructure information. Some recent studies exploit adversarial learning to incorporate substructure awareness, but hindered by unstable convergence. This study will address the major research question: is there a better way to integrate substructure awareness into GRL? As subsets of the graph structure, interested substructures (i.e., subgraph) are unique and representative for differentiating graphs, leading to the high correlation between the representation of the graph-level structure and substructures. Since mutual information (MI) is to evaluate the mutual dependence between two variables, we develop a MI inducted substructure-aware GRL method. We decompose the GRL pipeline into two stages: (1) node-level, where we introduce to maximize MI between the original and learned representation by the intuition that the original and learned representation should be highly correlated; (2) graph-level, where we preserve substructures by maximizing MI between the graph-level structure and substructure representation. Finally, we present extensive experimental results to demonstrate the improved performances of our method with real-world data.
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces

We present INSTAMAP, an instance-based method for learning projection-based crosslingual word embeddings.Unlike prior work, it deviates from learning a single global linear projection.INSTAMAP is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point's nearest neighbours in the training dictionary.We report performance gains with INSTAMAP over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs.We note prominent improvements, especially for more distant language pairs (i.e., languages with nonisomorphic monolingual spaces).
Rigid Formats Controlled Text Generation

Neural text generation has made tremendous progress in various tasks.One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating.However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classical Chinese poetry of the Song dynasty), etc.The typical characteristics of these texts are in three folds: (1) They must comply fully with the rigid predefined formats.(2) They must obey some rhyming schemes.(3) Although they are restricted to some formats, the sentence integrity must be guaranteed.To the best of our knowledge, text generation based on the predefined rigid formats has not been well investigated.Therefore, we propose a simple and elegant framework named SongNet to tackle this problem.The backbone of the framework is a Transformer-based auto-regressive language model.Sets of symbols are tailor-designed to improve the modeling performance especially on format, rhyme, and sentence integrity.We improve the attention mechanism to impel the model to capture some future information on the format.A pre-training and fine-tuning framework is designed to further improve the generation quality.Extensive experiments conducted on two collected corpora demonstrate that our proposed framework generates significantly better results in terms of both automatic metrics and the human evaluation. 1
BFBox: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector

Popular backbones designed on image classification have demonstrated their considerable compatibility on the task of general object detection. However, the same phenomenon does not appear on the face detection. This is largely due to the average scale of ground-truth in the WiderFace dataset is far smaller than that of generic objects in theCOCO one. To resolve this, the success of Neural Archi-tecture Search (NAS) inspires us to search face-appropriate backbone and featrue pyramid network (FPN) architecture.Firstly, we design the search space for backbone and FPN by comparing performance of feature maps with different backbones and excellent FPN architectures on the face detection. Second, we propose a FPN-attention module to joint search the architecture of backbone and FPN. Finally,we conduct comprehensive experiments on popular bench-marks, including Wider Face, FDDB, AFW and PASCALFace, display the superiority of our proposed method.
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling

Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate.Current models for jointly learning sentence and token labeling are limited to binary classification.We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors.Our model produces 3.78%, 4.20%, 2.08% improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively.
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning

Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages. Their success heavily depends on the availability of a large amount of labeled data or parallel corpus. In this paper, we investigate an extreme scenario of cross-lingual sentiment classification, in which the low-resource language does not have any labels or parallel corpus. We propose an unsupervised cross-lingual sentiment classification model named multi-view encoder-classifier (MVEC) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM) based fine-tuning approaches that adjust parameters solely based on the classification error on training data, we employ the encoder-decoder framework of a UMT as a regularization component on the shared network parameters. In particular, the cross-lingual encoder of our model learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Extensive experiments on five language pairs verify that our model significantly outperforms other models for 8/11 sentiment classification tasks.
Automated Evaluation of Writing – 50 Years and Counting

In this theme paper, we reflect on the progress of Automated Writing Evaluation (AWE), using Ellis Page's seminal 1966 paper to frame the presentation.We discuss some of the current frontiers in the field, and offer some thoughts on the emergent uses of this technology.
MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product

Graph Convolutional Networks (GCNs) have been extensively studied in recent years. Most of existing GCN approaches are designed for the homogenous graphs with a single type of relation. However, heterogeneous graphs of multiple types of relations are also ubiquitous and there is a lack of methodologies to tackle such graphs. Some previous studies address the issue by performing conventional GCN on each single relation and then blending their results. However, as the convolutional kernels neglect the correlations across relations, the strategy is sub-optimal. In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs. In particular, our multi-dimension convolution operator extends the graph spectral analysis into the eigen-decomposition of a Laplacian tensor. And the eigen-decomposition is formulated with a generalized tensor product, which can correspond to any unitary transform instead of limited merely to Fourier transform. We conduct comprehensive experiments on four real-world multi-relational graphs to solve the semi-supervised node classification task, and the results show the superiority of MR-GCN against the state-of-the-art competitors.
Soft Threshold Weight Reparameterization for Learnable Sparsity

Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform or heuristic non-uniform sparsity budgets which have sub-optimal layer-wise parameter allocation resulting in a) lower prediction accuracy or b) higher inference cost (FLOPs). This work proposes Soft Threshold Reparameterization (STR), a novel use of the soft-threshold operator on DNN weights. STR smoothly induces sparsity while learning pruning thresholds thereby obtaining a non-uniform sparsity budget. Our method achieves state-of-the-art accuracy for unstructured sparsity in CNNs (ResNet50 and MobileNetV1 on ImageNet-1K), and, additionally, learns non-uniform budgets that empirically reduce the FLOPs by up to 50%. Notably, STR boosts the accuracy over existing results by up to 10% in the ultra sparse (99%) regime and can also be used to induce low-rank (structured sparsity) in RNNs. In short, STR is a simple mechanism which learns effective sparsity budgets that contrast with popular heuristics. Code, pretrained models and sparsity budgets are on GitHub.