Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.
Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named “meteor-wrongs”. In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.
DART: Domain-Adversarial Residual-Transfer networks for unsupervised cross-domain image classification

The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but gains access to cheaply available unlabeled data, unsupervised domain adaptation is a promising technique to boost the performance without incurring extra labeling cost, by assuming images from different domains share some invariant characteristics. In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of deep neural networks to tackle cross-domain image classification tasks. In contrast to the existing unsupervised domain adaption approaches, the proposed DART not only learns domain-invariant features via adversarial training, but also achieves robust domain-adaptive classification via a residual-transfer strategy, all in an end-to-end training framework. We evaluate the performance of the proposed method for cross-domain image classification tasks on several well-known benchmark data sets, in which our method clearly outperforms the state-of-the-art approaches.
Naïve Bayes Classifier for Journal Quartile Classification

&lt;span&gt;Classification is a process for distinguishing data classes, with the aim of being able to estimate the class of an object with unknown label. One popular method that used for classifying data is Naïve Bayes Classifier. Naïve Bayes Classifier is an approach that adopts the Bayes theorem, by combining previous knowledge with new knowledge. The advantages of this method are the simple algorithm and high accuracy. In this study, it will show the ability of Naïve Bayes Classifier to classify the quality of a journal commonly called Quartile. This study use a dataset of 1491 instances. The results show an accuracy of 71.60% and an error rate of 28.40%&lt;/span&gt;
Efficient Redactable Signature and Application to Anonymous Credentials

Let us assume that Alice has received a constant-size signature on a set of messages $$\{m_i\}_{i=1}^n$$ from some organization. Depending on the situation, Alice might need to disclose, prove relations about or hide some of these messages. Ideally, the complexity of the corresponding protocols should not depend on the hidden messages. In particular, if Alice wants to disclose only k messages, then the authenticity of the latter should be verifiable in at most O(k) operations. Many solutions were proposed over the past decades, but they only provide a partial answer to this problem. In particular, we note that they suffer either from the need to prove knowledge of the hidden elements or from the inability to prove that the latter satisfy some relations. In this paper, we propose a very efficient constant-size redactable signature scheme that addresses all the problems above. Signatures can indeed be redacted to remain valid only on a subset of k messages included in $$\{m_i\}_{i=1}^n$$ . The resulting redacted signature consists of 4 elements and can be verified with essentially k exponentiations. Different shows of the same signature can moreover be made unlinkable leading to a very efficient anonymous credentials system.
Analyzing the Persuasive Effect of Style in News Editorial Argumentation

News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies.Previous research has investigated such persuasive effects for argumentative content.In contrast, this paper studies how important the style of news editorials is to achieve persuasion.To this end, we first compare content-and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations.We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content.Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation.
Simulation and field testing of multiple vehicles collision avoidance algorithms

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance (MVCA) algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently, without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore, MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay (<; 100ms) and low packet loss (<; 5%) can bring little influence to those trajectory planning algorithms that only depend on V2V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.
Meningkatkan Keaktifan Siswa Dalam Pembelajaran Daring Melalui Media Game Edukasi Quiziz pada Masa Pencegahan Penyebaran Covid-19

The study aims to determine the increased activity of learning in online learning through the media of educational Quiziz. This research is a class action study with subjects of grade VII students ' study. 6 SMP N 1 Gangga as much as 23 people. The study implemented 2 cycles, each cycle 2 times the meeting. Each meeting is based on 4 stages, namely planning, implementation, observation and reflection. The implementation of the research stages is slightly different from learning in the classroom because they are conducted separately or students are at home. Based on the results of the study, at a cycle I gained an average student who was active 52.25% with sufficient catagories. While in cycle II obtained average students active 73.9% with high catagori. So from cycle I and cycle II occurred an increase in the activity of 17.65% students. Thus, research can be concluded that educational game media Quiziz can increase the activity of students in online learning during the prevention of the spread of Covid-19 IPS subjects
Directional Temporal Modeling for Action Recognition

Many current activity recognition models use 3D convolutional neural networks (e.g. I3D, I3D-NL) to generate local spatial-temporal features. However, such features do not encode clip-level ordered temporal information. In this paper, we introduce a channel independent directional convolution (CIDC) operation, which learns to model the temporal evolution among local features. By applying multiple CIDC units we construct a light-weight network that models the clip-level temporal evolution across multiple spatial scales. Our CIDC network can be attached to any activity recognition backbone network. We evaluate our method on four popular activity recognition datasets and consistently improve upon state-of-the-art techniques. We further visualize the activation map of our CIDC network and show that it is able to focus on more meaningful, action related parts of the frame.
DART: Open-Domain Structured Data Record to Text Generation

We present DART, an open domain structured DAta-Record-to-Text generation dataset with over 82k instances (DARTs).Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title.Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion and predicate unification, all with minimum post-editing.We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization.Our data and code can be found at https://github.com/Yale-LILY/dart.
Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare

Recently, the techniques of Internet of Things (IoT) and mobile communications have been developed to gather human and environment information data for a variety of intelligent services and applications. Remote monitoring of elderly and disabled people living in smart homes is highly challenging due to probable accidents which might occur due to daily activities such as falls. For elderly people, fall is considered as a major reason for death of post-traumatic complication. So, early identification of elderly people falls in smart homes is needed to increase the survival rate of the person or offer required support. Recently, the advent of artificial intelligence (AI), IoT, wearables, smartphones, etc. makes it feasible to design fall detection systems for smart homecare. In this view, this paper presents an IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCNN) for smart homecare. The goal of the IMEFD-ODCNN model is to enable smartphones and intelligent deep learning (DL) algorithms to detect the occurrence of falls in the smart home. Primarily, the input video captured by the IoT devices is pre-processed in different ways like resizing, augmentation, and min-max based normalization. Besides, SqueezeNet model is employed as a feature extraction technique to derive appropriate feature vectors for fall detection. In addition, the hyperparameter tuning of the SqueezeNet model takes place using the salp swarm optimization (SSO) algorithm. Finally, sparrow search optimization algorithm (SSOA) with variational autoencoder (VAE), called SSOA-VAE based classifier is employed for the classification of fall and non-fall events. Finally, in case of fall event detected, the smartphone sends an alert to the caretakers and hospital management. The performance validation of the IMEFD-ODCNN model takes place on UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the promising performance of the IMEFD-ODCNN model over the recent methods with the maximum accuracy of 99.76% and 99.57% on the multiple cameras fall and UR fall detection dataset.
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples. However, existing adversarial examples against face recognition systems either lack transferability to black-box models, or fail to be implemented in practice. In this paper, we propose a unified adversarial face generation method - Adv-Makeup, which can realize imperceptible and transferable attack under the black-box setting. Adv-Makeup develops a task-driven makeup generation method with the blending module to synthesize imperceptible eye shadow over the orbital region on faces. And to achieve transferability, Adv-Makeup implements a fine-grained meta-learning based adversarial attack strategy to learn more vulnerable or sensitive features from various models. Compared to existing techniques, sufficient visualization results demonstrate that Adv-Makeup is capable to generate much more imperceptible attacks under both digital and physical scenarios. Meanwhile, extensive quantitative experiments show that Adv-Makeup can significantly improve the attack success rate under black-box setting, even attacking commercial systems.
An investigation of XGBoost-based algorithm for breast cancer classification

Breast cancer is one of the leading cancers affecting women around the world. The Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the process of diagnosing cancer, which effectively identifies the presence of cancerous cells. A standard CAD system includes processes of pre-processing, feature extraction, feature selection and classification. In this paper, we propose an enhanced breast cancer classification technique called Deep Learning and eXtreme Gradient Boosting (DLXGB) on histopathology breast cancer images using the BreaKHis dataset. This method first applies data augmentation and stain normalization for pre-processing, then pre-trained DenseNet201 will automatically learn features within an image and combine with a powerful gradient boosting classifier. The proposed classification technique is designed to classify breast cancer histology images into binary benign and malignant, and additionally one of eight non-overlapping/overlapping categories: i.e., Adenosis (A), Fibroadenoma (F), Phyllodes Tumour (PT), And Tubular Adenoma (TA) Ductal Carcinoma (DC), Lobular Carcinoma (LC), Mucinous Carcinoma (MC), And Papillary Carcinoma (PC). With DLXGB, we have obtained an accuracy of 97% for both binary and multi-classification improving the exiting work done by researchers using the BreaKHis dataset. The results indicated that this method could produce a powerful prediction for breast cancer image classification.
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency. In this work, we propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis. Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps. In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS. Furthermore, to boost up the inference speed, we leverage the accelerated sampling method that allows Diff-TTS to generate raw waveforms much faster without significantly degrading perceptual quality. Through experiments, we verified that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are still two challenges that are not addressed: (1) there is no prior SS-OD work on anchor-free detectors, and (2) prior works are ineffective when pseudo-labeling bounding box regression. In this paper, we present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors and also introduces Listen2Student mechanism for the unsupervised regression loss. Specifically, we first present a study examining the effectiveness of existing SS-OD methods on anchor-free detectors and find that they achieve much lower performance improvements under the semi-supervised setting. We also observe that box selection with centerness and the localization-based labeling used in anchor-free detectors cannot work well under the semi-supervised setting. On the other hand, our Listen2Student mechanism explicitly prevents misleading pseudo-labels in the training of bounding box regression; we specifically develop a novel pseudo-labeling selection mechanism based on the Teacher and Student's relative uncertainties. This idea contributes to favorable improvement in the regression branch in the semi-supervised setting. Our method, which works for both anchor-free and anchor-based methods, consistently performs favorably against the state-of-the-art methods in VOC, COCO-standard, and COCO-additional.
Analysis and forecasting of weather conditions in Oman for renewable energy applications

This paper examines and analyses weather data in Oman to re-encode the appropriate climate dimension to generate solar energy. It also suggested prediction models that could accurately predict future weather information. The present study aims to help decision-makers take the necessary measures to address the demand for renewable energy generation and solutions to environmental problems by taking advantage of long daylight hours in Oman to increase the production of alternative and clean electricity. There is no doubt that different environmental factors such as temperature, humidity, wind intensity and rain have a significant impact on the amount of solar cells produced. However, accurate forecasting of temperature and humidity helps to select the best weather conditions that can help raise the generation of solar energy and reduce the cost of production, leading to an increase in the economic income of countries. This paper presents various mathematical prediction models based on a multi-boundary score (2, 3, 4), which has the value of the R2 determination factor equal to (0.9335, 0.9603, 0.9977), respectively. The column test results (Prob> F) proved that the null hypothesis was accepted and rejected the alternative hypothesis. Thus, all the results are less than the significant value (0.5), and each variable has an average value or less than the mean value of the test (26). Therefore, there are no significant differences or unusual cases in historical temperature data in Oman from 1991 to 2015. Also, the prediction values corresponding to the actual temperature in the future, which helps to predict and analyze the temperature data at any time.
Are morphological awareness and literacy skills reciprocally related? Evidence from a cross-linguistic study.

We examined the direction of the relation between morphological awareness and reading/spelling skills in 2 languages varying in orthographic consistency (English and Greek) and whether word reading fluency and vocabulary mediate the relation between morphological awareness and reading comprehension. One-hundred and 59 English-speaking Canadian and 224 Greek children were assessed 4 times between Grades 1 and 3 on measures of morphological awareness, phonological awareness, word reading fluency, and spelling to dictation. Vocabulary was assessed at the end of Grade 2 and reading comprehension at the end of Grade 2 and at the beginning of Grade 3. Cross-lagged analyses showed that earlier morphological awareness predicted later reading comprehension and spelling in both languages and reading fluency in English. The effect of morphological awareness on reading comprehension was not mediated by word reading fluency in either language, but an indirect effect through vocabulary emerged in English. Earlier reading fluency and spelling predicted later morphological awareness before Grade 3 only in English, but morphological awareness began to predict spelling as early as Grade 1 in Greek. Multigroup analyses further showed that the effects of morphological awareness on reading fluency and the effects of spelling on morphological awareness were stronger in English than in Greek. Theoretical implications of these findings are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
21st Century Skill “Problem Solving”: Defining the Concept

Only knowledge is not sufficient to make students succeed in the world. Students need to attain 21st century skills like problem-solving, creativity, innovation, metacognition, communication etc. to endure in the modern world. Problem-solving skill is one of the fundamental human cognitive processes. Whenever students face a situation where they do not know the way to complete a task, the problem occurs. Problem-solving is a process, which involves systematic observation and critical thinking to find an appropriate solution or way to reach the desired goal. The framework of problem-solving consisted of two major skills: observation and critical thinking skill. Observation skill refers to collecting data, understanding and interpreting the meaning of the information using all the senses. Critical thinking involves the individual’s ability to do the following: conceptualizing, logical reasoning, applying strategy, analytical thinking, decision making and synthesizing to solve any problem.
Repurposing Entailment for Multi-Hop Question Answering Tasks

Harsh Trivedi, Heeyoung Kwon, Tushar Khot, Ashish Sabharwal, Niranjan Balasubramanian. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.