Reviewerly/exHarmony huggingface dataset:
Enhanced word embeddings using multi-semantic representation through lexical chains

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
Depth-aware blending of smoothed images for Bokeh effect generation

Bokeh effect is used in photography to capture images where the closer objects look sharp and every-thing else stays out-of-focus. Bokeh photos are generally captured using Single Lens Reflex cameras using shallow depth-of-field. Most of the modern smartphones can take bokeh images by leveraging dual rear cameras or a good auto-focus hardware. However, for smartphones with single-rear camera without a good auto-focus hardware, we have to rely on software to generate bokeh images. This kind of system is also useful to generate bokeh effect in already captured images. In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images. The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network. The proposed approach is compared against a saliency detection based baseline and a number of approaches proposed in AIM 2019 Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order to understand different parts of the proposed algorithm. The network is lightweight and can process an HD image in 0.03 seconds. This approach ranked second in AIM 2019 Bokeh effect challenge-Perceptual Track.
Online Detection of Action Start via Soft Computing for Smart City

Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set.
Developing Snake Ladder Game Learning Media to Increase Students’ Interest and Learning Outcomes on Social Studies in Elementary School

Background and Objective. One of the challenges that social studies presents to teachers in class is the lack of students’ interest in the material. This study aims to develop snakes and ladders learning media for use in the subject of social studies in elementary school. Method. The research design used is the Four-D Research and Development (R&amp;D) model by Thiagarajan (Santoso &amp; Albaniah). The criteria used as a reference for the effectiveness of the learning media are students’ participation, interest, and learning outcomes, whereas for the practicality, the criteria are the implementation and teachers’ ability to use the learning media. The research employed descriptive statistics analysis and inductive-deductive flow analysis. Results. The results show that the snakes and ladders learning media on social studies appear to be valid and practical, and can effectively improve elementary school students’ interest and learning outcomes. The learning media is considered valid based on the experts’ validation with average 4.3 (good). The effectiveness of this learning media meets the criteria: (1) student participation in the learning process is high and (2) student learning outcomes meet the minimum learning mastery standard average at 79.04 of the ideal score of 100 and the completeness test of learning outcomes with the probability value of one sample t-test of 0.00 &lt;0.05. The practicality of the learning media is shown through the observation result of the implementation of the media with an average of 4.4 (good), whereas the results of the student interest questionnaire show that of 25 students (92%) responded that they were happy with the subject matter taught, 80% of students responded that they were happy with the learning. Conclusion. The snakes and ladders learning media on social studies can enhance students’ interest and learning outcomes in elementary education.
The Winner-Take-All Mechanism for All-Optical Systems of Pattern Recognition and Max-Pooling Operation

The winner-take-all (WTA) mechanism based on the inhibitory dynamics of vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) neurons is proposed for the first time. The WTA mechanism is shown numerically in a photonic spiking neural network (SNN). The effect of bias current on WTA time window is analyzed based on the proposed SNN. Moreover, a pattern recognition approach is presented numerically based on the WTA mechanism in all-optical neural network consisting of VCSEL-SA neurons. For the pattern recognition, the robustness of noisy inputs is examined. The effects of bias current, strength of inhibition and weight matrix on the speed of pattern recognition are analyzed carefully. Furthermore, the max-pooling operation is implemented numerically in the all-optical VCSEL-SA neural network model based on the WTA mechanism for the first time. The results hold great promise for the development of energy-efficient and high-speed photonic spiking neural network.
Blockchain-Based Intelligent Transportation: A Sustainable GCU Application System

The purpose of this study is to explore how to apply blockchain technology to intelligent transportation, create a hierarchical theoretical framework of intelligent transportation, and explore a sustainable application system of intelligent transportation under the blockchain. However, not only this hierarchical theoretical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria, but also the sustainable application system must be in consideration in multiple stakeholders. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decision-making trial and evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and construct a hierarchical theoretical framework. Finally, the research develops a sustainable GCU application system for intelligent transportation under the blockchain. The results show that (1) solving social problems is the primary link, (2) economic tasks are mainly focused on smart contracts and affected by the social problems, (3) the continuous improvement of environmental issues requires a solution to social problems, and (4) the application system of blockchain in intelligent transportation needs to be built from three levels including the government layer, the company layer, and the user layer. This theoretical hierarchical framework aims to guide intelligent transportation toward the application of blockchain. This study also proposes the engagement of stakeholders for establishing a sustainable application system.
A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest regions. Then the integrity of ROI is verified using the hash algorithm, and the recovery data of the ROI region is calculated at the same time. Also, binary images with the basic information of patients are processed by logistic chaotic map encryption, and then the synthetic watermark is embedded in the medical carrier image using IWT transform. The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs. Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability. The maximum peak signal to noise ratio (PSNR) value of 51.24 has been achieved, which proves that the watermark is invisible and has strong robustness against noise, compression, and filtering attacks.
Quantum optimal control with quantum computers: A hybrid algorithm featuring machine learning optimization

We develop an hybrid quantum-classical algorithm to solve an optimal population transfer problem for a molecule subject to a laser pulse. The evolution of the molecular wavefunction under the laser pulse is simulated on a quantum computer, while the optimal pulse is iteratively shaped via a machine learning (evolutionary) algorithm. A method to encode on the quantum computer the n-electrons wavefunction is discussed, the circuits accomplishing its quantum simulation are derived and the scalability in terms of number of operations is discussed. Performance on Noisy Intermediate-Scale Quantum devices (IBM Q X2) is provided to assess the current technological gap. Furthermore the hybrid algorithm is tested on a quantum emulator to compare performance of the evolutionary algorithm with standard ones. Our results show that such algorithms are able to outperform the optimization with a downhill simplex method and provide performance comparable to more advanced (but quantum-computer unfriendly) algorithms such as Rabitz's.
Meningkatkan Motivasi Belajar Bahasa Indonesia Melalui Penerapan Model Discovery Learning dan Media Video Dalam Kondisi Pandemi Covid-19 bagi Siswa SMPN 2 Gangga

This study aims to increase motivation to learn Indonesian with the discovery learning model and video recording media during the covid-19 pandemic. This research method uses classroom action research methods. The research subjects were students of class VII A Gangga 2 Junior High School, totaling 26 students. This research instrument uses a questionnaire, observation, and assignment. Research data analysis techniques using quantitative and qualitative descriptive analysis. The results of this study indicate that an increase in student responses to distance learning (PJJ) is proven in the first cycle the value is 0.47 with a positive category, while in the second cycle the value is 0.53 with a very positive category. From the appearance value in the video recording retelling the legend is also evidenced by the acquisition of the lowest value of 70 in cycle 1 and 80 in cycle 2, the highest in two cycles of 100 scores from 26 students, so it can be said that the discovery learning model and video recording media can improve student learning motivation during the co-19 pandemic. The positive thing that is obtained from the application of this method is the increase in motivation and activeness of student learning even from a distance which directly impacts on improving student learning outcomes.
Graph based feature extraction and hybrid classification approach for facial expression recognition

In the current trends, face recognition has a remarkable attraction towards favorable and inquiry of an image. Several algorithms are utilized for recognizing the facial expressions, but they lack in the issues like inaccurate recognition of facial expression. To overcome these issues, a Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) is proposed for recognizing the facial expressions. The main motive of this work is to recognize human emotions in an effective manner. Initially, the face image is identified using the Viola-Jones algorithm. Subsequently, the facial parts such as right eye, left eye, nose and mouth are extracted from the detected facial image. The edge-based invariant transform feature is utilized to extract the features from the extracted facial parts. From this edge-based invariant features, the dimensions are optimized using Weighted Visibility Graph which produces the graph-based features. Also, the shape appearance-based features from the facial parts are extracted. From these extracted features, facial expressions are recognized and classified using a Self-Organizing Map based Neural Network Classifier. The performance of this GFE-HCA approach is evaluated and compared with the existing techniques, and the superiority of the proposed approach is proved with its increased recognition rate.
An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a Visitor

This paper presents anomaly detection in activities of daily living based on entropy measures. It is shown that the proposed approach will identify anomalies when there are visitors representing a multi-occupant environment. Residents often receive visits from family members or health care workers. Therefore, the residents’ activity is expected to be different when there is a visitor, which could be considered as an abnormal activity pattern. Identifying anomalies is essential for healthcare management, as this will enable action to avoid prospective problems early and to improve and support residents’ ability to live safely and independently in their own homes. Entropy measure analysis is an established method to detect disorder or irregularities in many applications: however, this has rarely been applied in the context of activities of daily living. An experimental evaluation is conducted to detect anomalies obtained from a real home environment. Experimental results are presented to demonstrate the effectiveness of the entropy measures employed in detecting anomalies in the resident’s activity and identifying visiting times in the same environment.
Compressing 3DCNNs based on tensor train decomposition

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs). To miniaturize 3DCNNs for the deployment in confining environments such as embedded devices, neural network compression is a promising approach. In this work, we adopt the tensor train (TT) decomposition, a straightforward and simple in situ training compression method, to shrink the 3DCNN models. Through proposing tensorizing 3D convolutional kernels in TT format, we investigate how to select appropriate TT ranks for achieving higher compression ratio. We have also discussed the redundancy of 3D convolutional kernels for compression, core significance and future directions of this work, as well as the theoretical computation complexity versus practical executing time of convolution in TT. In the light of multiple contrast experiments based on VIVA challenge, UCF11, and UCF101 datasets, we conclude that TT decomposition can compress 3DCNNs by around one hundred times without significant accuracy loss, which will enable its applications in extensive real world scenarios.
Position-aware self-attention based neural sequence labeling

Sequence labeling is a fundamental task in natural language processing and has been widely studied. Recently, RNN-based sequence labeling models have increasingly gained attentions. Despite superior performance achieved by learning the long short-term (i.e., successive) dependencies, the way of sequentially processing inputs might limit the ability to capture the non-continuous relations over tokens within a sentence. To tackle the problem, we focus on how to effectively model successive and discrete dependencies of each token for enhancing the sequence labeling performance. Specifically, we propose an innovative attention-based model (called position-aware self-attention, i.e., PSA) as well as a well-designed self-attentional context fusion layer within a neural network architecture, to explore the positional information of an input sequence for capturing the latent relations among tokens. Extensive experiments on three classical tasks in sequence labeling domain, i.e., part-of-speech (POS) tagging, named entity recognition (NER) and phrase chunking, demonstrate our proposed model outperforms the state-of-the-arts without any external knowledge, in terms of various metrics.
TermInformer: unsupervised term mining and analysis in biomedical literature

Terminology is the most basic information that researchers and literature analysis systems need to understand. Mining terms and revealing the semantic relationships between terms can help biomedical researchers find solutions to some major health problems and motivate researchers to explore innovative biomedical research issues. However, how to mine terms from biomedical literature remains a challenge. At present, the research on text segmentation in natural language processing (NLP) technology has not been well applied in the biomedical field. Named entity recognition models usually require a large amount of training corpus, and the types of entities that the model can recognize are limited. Besides, dictionary-based methods mainly use pre-established vocabularies to match the text. However, this method can only match terms in a specific field, and the process of collecting terms is time-consuming and labour-intensive. Many scenarios faced in the field of biomedical research are unsupervised, i.e. unlabelled corpora, and the system may not have much prior knowledge. This paper proposes the TermInformer project, which aims to mine the meaning of terms in an open fashion by calculating terms and find solutions to some of the significant problems in our society. We propose an unsupervised method that can automatically mine terms in the text without relying on external resources. Our method can generally be applied to any document data. Combined with the word vector training algorithm, we can obtain reusable term embeddings, which can be used in any NLP downstream application. This paper compares term embeddings with existing word embeddings. The results show that our method can better reflect the semantic relationship between terms. Finally, we use the proposed method to find potential factors and treatments for lung cancer, breast cancer, and coronavirus.
Understanding causation via correlations and linear response theory

In spite of the (correct) common-wisdom statement correlation does not imply causation, a proper employ of time correlations and of fluctuation-response theory allows to understand the causal relations between the variables of a multi-dimensional linear Markov process. It is shown that the fluctuation-response formalism can be used both to find the direct causal links between the variables of a system and to introduce a degree of causation, cumulative in time, whose physical interpretation is straightforward. Although for generic non-linear dynamics there is no simple exact relationship between correlations and response functions, the described protocol can still give a useful proxy also in presence of weak nonlinear terms.
Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too cumbersome for humans to comprehend. Therefore, the application of MLs in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to 1) briefly summarize ML and compare the different types of MLs, 2) review recent ML applications to improve neuro ICU management and 3) describe the future implications of ML to neuro ICU management.
No-Go Theorems for Quantum Resource Purification: New Approach and Channel Theory

A universal and powerful method for analyzing the fundamental limitations on quantum resource purification is presented, enabling a robust no-purification theory for dynamical quantum resources and wide-ranging applications for practical quantum information processing
Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN’s accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.