Reviewerly/exHarmony huggingface dataset:
Consistent Intra-Video Contrastive Learning With Asynchronous Long-Term Memory Bank

Unsupervised representation learning for videos has recently achieved remarkable performance owing to the effectiveness of contrastive learning. Most works on video contrastive learning (VCL) pull all snippets from the same video into the same category, even if some of them are from different actions, leading to temporal collapse, i.e., the snippet representations of a video are invariable with the evolution of time. In this paper, we introduce a novel intra-video contrastive learning (intra-VCL) that further distinguishes intra-video actions to alleviate this issue, which includes an asynchronous long-term memory bank (that caches the representations of all snippets of each video) and mines an extra positive/negative snippet within a video based on the asynchronous long-term memory bank. In addition, since an asynchronous long-term memory bank is required for performing intra-VCL and asynchronous update of the long-term memory leads to inconsistencies when performing contrastive learning, we further propose a consistent contrastive module (CCM) to perform consistent intra-VCL. Specifically, in the CCM, we propose an intra-video self-attention refinement function to reduce the inconsistencies within the asynchronously updated representations (of all snippets of each video) in the long-term memory and an adaptive loss re-weighting to reduce unreliable self-supervision produced by inconsistent contrastive pairs. We call our method as consistent intra-VCL. Extensive experiments demonstrate the effectiveness of the proposed consistent intra-VCL, which achieves state-of-the-art performance on the standard benchmarks of self-supervised action recognition, with top-1 accuracies of 64.2% and 91.0% on HMDB-51 and UCF-101, respectively.
Syntactic Factors Associated With Performance of Dependency Parsers Using Stack-Pointer Network and Graph Attention Networks Between English and Korean

A Stack-Pointer Network (StackPtr) parser is a pointer network with an internal stack on the decoder. Several studies use the StackPtr as the backbone of a dependency parser because it can traverse a parse tree depth-first without backtracking and can handle high-order parsing information easily thanks to the internal stack. The parser can use information from previously derived subtrees stored in the internal stack upon selecting a child node. In this work, we introduce a new StackPtr parser with Graph Attention Networks (GATs) that can encode a previously derived subtree. We evaluated our proposed parser on the Sejong and Everyone’s corpora for Korean and on the Penn Treebank and Universal Dependency corpora for English. In addition, we analyzed and compared our proposed parser with other variants of the StackPtr parser, examining the syntactic information that each parser can reference at every decoding step. We found that Korean parse trees tend to have more consecutive immediate single-child nodes than English parse trees. The proposed StackPtr parser with GATs performed best on almost all metrics for Korean because it can utilize more context to analyze these parse trees by grasping Korean syntactic factors than any other variants. However, for English, no particular variant of the StackPtr parser outperforms the others.
FACToGRADE: Automated Essay Scoring System

The significance of technology has exponentially grown in this increasingly virtual world, making online learning and evaluation the new normal. In the evaluation of writing assignments, many existing automated methods either focus on semantics or machine-learned features alone. In our project, we incorporate content analysis with structural analysis to provide a complete grading system. Also, revision and feedback are essential aspects of the writing process, with the help of which, students may increase their writing quality. Here, Automated Essay Scoring (AES) systems can be very useful as they can provide the student with a score as well as a feedback within seconds. Below we present an automated scoring system, built using the concepts of Long Short Term Memory (LSTM) and Entity Detection, incorporating a User Interface to input an essay and obtain its score along with the breakdown analysis of the essay.
Prototype to estimate breast cancer suspicion in a hospital of the Mexican public health system

Breast cancer is a public health problem because it is the type of cancer with the highest incidence and mortality in women. X-ray studies of the breasts, mammograms, represent one of the main tools used by doctors for the early detection of this disease. In this work, it is proposed to mitigate this problem using a system prototype for estimating the suspicion of breast cancer from the risk factors. The system prototype includes a gradient boosting decision tree model for cancer risk prediction and a web system that can be used in hospitals to speed up the consultation of breast cancer patients. The prototype was validated with stress tests, black box tests, white box tests and Iadov techniques for the satisfaction of end users. The results obtained lead us to conclude that the prototype can be incorporated into the daily consultations of Mexican hospitals as a support tool in the early detection of breast cancer, contributing to the decrease in the death rate caused by this disease in Mexico.
Synthetic Occluded Masked Face Recognition using Convolutional Neural Networks

Wearing a face mask is the norm during the COVID–19 pandemic and is advised for enclosed spaces such as workplaces. In face recognition, a face mask is considered a partial occlusion which degrades recognition accuracy. This study focuses on the occlusion factor by a variety of face mask designs. This study aims to mitigate the impact of face masks as an occlusion on a face recognition system. We superimposed a synthetic face mask and black occlusions on top of the face images (FI). FaceNet, a deep convolutional neural network, was used to extract facial embeddings. The faces were classified using a support vector machine. We experimented with different scenarios by using different training sets and testing sets, contains differing mask designs. It achieved a performance of recognizing occluded lower FI with an average accuracy rate of 98.93% in a controlled environment.
Emotion Recognition of Students’ Bilingual Tweets during COVID-19 Pandemic using Attention-based Bi-GRU

This paper studied the emotions manifested by students from March 2020 to April 2021, a year of the Coronavirus Disease-2019 (COVID-19) pandemic. Our tweet compromises Taglish (Tagalog—English) texts, a low-resource code-switching language. The texts were cleaned and translated from Taglish to English. WordNet Affect was used to annotate the text with Happy, Angry, Sad, Surprise, and Fear as the output. A neural network, Bidirectional Gated Recurrent unit (Bi-GRU) with Attention layer, was used, and it was compared to Bernoulli Naïve Bayes (BNB) and Support Vector Machine (SVM), which are commonly used algorithms for Taglish emotion recognition. A 100-dimensional GloVe word embedding was applied to the data before training. The augmentation method does not affect the model’s performance negatively; instead has helped the Bi-GRU with Attention boost its performance. Bi-GRU with attention has a higher F1-score on all emotions compared to the other three algorithms but, as expected, requires a large amount of data. The results show that the most dominant emotion manifested by students throughout the year is surprise immediately followed by Sad and Fear. The three are close in values.
Human Activity Recognition from Skeletal Data using Covariance Descriptor and Temporal Subspace Clustering

Human Activity Recognition (HAR) is one of the most active research areas in fields of computer vision and pattern analysis. Most of the existing HAR algorithms are devised in supervised manner by excluding the temporal aspects of skeletal data which is a key parameter in HAR. Motivated by this, we have designed and developed an efficient subspace clustering algorithm for HAR by explicitly considering the time series aspects of human activity data. Designing this algorithm in an unsupervised manner is another challenge that we are dealing with. The work involves design of an efficient covariance descriptor for encoding the skeletal data. Later a subspace clustering algorithm called temporal subspace clustering (TSC) algorithm is designed by exploiting the principles of Laplacian regularization and dictionary learning. Experimental analysis shows that the proposed method outperforms the state-of-the-art methods employed for HAR.
Residual Fully Convolutional Deformable Registration Network for Deformable Lung CT Images

Deformable registration of lung Computed Tomography (CT) is an important research topic in the field of medical image registration, which can help doctors better observe the changing pattern of lung respiratory motion of patients and is of great significance for tracking lung respiratory motion, disease diagnosis, and radiotherapy. In this paper, a fully convolutional deformable registration method with residual modules is proposed for the registration of 4D-CT images of lungs. The residual blocks are inserted into the ordinary Fully Convolutional Network (FCN) to increase the depth of the intermediate layer hence improving the feature representation ability of the network to register image pairs. At the same time, in order to improve the registration multi-scale convolution into the network. During training, unsupervised learning is used to deal with the problem of less labeled data. Experiments show that the proposed method can effectively improve registration accuracy, and the registration speed can meet the needs of practical use.
A Stochastic Asynchronous Gradient Descent Algorithm with Delay Compensation Mechanism

A large amount of idle computing power exists in mobile devices, which can be deployed with large-scale machine learning applications. One of the key problems is how to reduce the communication overhead between different nodes. In recent years, gradient sparsity is introduced to reduce the communication overhead. However, in the federated learning scenario, the traditional synchronous gradient optimization algorithm can not adapt to the complex network environment and high communication costs. In this paper, we propose a stochastic gradient descent algorithm with delay compensation mechanism (FedDgd) for asynchronous distributed training and further optimize it for federated asynchronous training. It is proved theoretically that FedDgd can converge at the same rate as ASGD for non-convex neural networks. Moreover, FedDgd way converge quickly and tolerates staleness in various app applications as well.
Prediction of Sea Surface Current Velocity and Direction using Gated Recurrent Unit (GRU)

The development of tourism activities in Labuan Bajo, especially in marine tourism, which is increasing, shows the need for knowledge regarding the conditions of the water area. One of the components in these waters is the condition of the ocean currents in Labuan Bajo. Therefore, this study aims to predict the sea surface's current velocity and direction. This study uses the Gated Recurrent Unit (GRU) algorithm. The data used in this study is the u component, namely the current velocity from east to west, and the component v is the current velocity from north to south. The data is processed through two gates, namely the update gate and the reset gate. The results of the output are used as input in the next stage. Based on trials with several parameters, 50 hidden layers, 32 batch size, and 150 learning rate drop with 70:30 data division, the smallest MAPE value for u component is 9.32% and the v component is 27.94%. he calculation of the sea surface currents direction at u and v component points is towards the southwest with a range between 207° and 213°.
A Three-layer Security Assurance Model for a Decentralized Federated Learning System

We focus on the security issues of decentralized federated learning systems for the core requirements of secure environments and trusted computing for applications such as medical big data, distributed cognitive learning, and autonomous driving. We propose a three-layer security assurance model that aims to ensure secure trusted computing in decentralized networks. Therein, the first-layer security model realizes the authentication of trusted users through blockchain technology to ensure the safe access of legitimate users to the greatest extent; the second-layer security model utilizes the trusted execution environment (TEE) technology to realize the privacy protection and trusted computing of each local user; the last layer of security model adopts a set of detection and location algorithms for defending the internal attackers, in case the first two layers of models fail. Our experimental results show that the proposed three-layer security model can effectively defend against external and internal attacks in the network, thereby promoting secure and trusted computing in distributed federated learning networks.
Design of Home Security System Using Face Recognition with Convolutional Neural Network Method

Home security system with good accuracy and efficiency in controlling access to the door system is needed in order to identify people who enter the house accurately. Home security conventionally uses a key to open the door, making security low due to several factors. Various face recognition methods has been studied to determine the most accurate method in identifying people who has access to the house. In this study, Haar Cascade and CNN (Convolutional Neural Network) method were applied to face detection and classify 5 class of family member that can access the house. Based on the results of the analysis, the CNN model in this study uses an 64x64 sizes of input, 0.001 learning rate value, 3x3 filter size, 10 number of epochs, 1200 training data with 240 data for each class, and 150 testing data with 30 data for each class. The classification process yields the accuracy of 99% in identifying the family member of the house, hence giving access to open the door.
Hypersonic Flight Control With Vector Field Orbital Path

This paper presents a method for constructing accurate paths for hypersonic vehicles using Vector Field (VF) approach. Hypersonic Flight Vehicles (HVF’s) is a vehicle that can fly up to five times the speed of sound (5 Mach), with this speed will cause a strong non-linear state. Hypersonic Flight Control (HFC) will be a method in regulating the flight path of an aircraft, Hypersonic Flight Control (HFC) will be a method in regulating the flight path of an airplane, by using a Vector Field Orbital Path we can build a hypersonic airplane flight path in its flight path. The calculation process for this system will be carried out using the MATLAB application and visualization of the results using FlightGear.
Using Generative Adversarial Networks for Conditional Creation of Anime Posters

Japanese animation, known as anime, has become one of the most accessible forms of entertainment across globe. Recent advances in generative adversarial networks (GAN) and deep learning have contributed greatly to multiple interesting applications in the domain of anime, particularly in face generation, style transfer, and colorization. However, there are no existing implementations for generating composite anime posters with a genre accompaniment prompt. This work proposes a novel application of genre to anime poster generation conditioned on BERT-tokenized binary genre-tags of light-hearted or heavy-hearted categorized based on the thematic subject content of the medium. A dataset of 9,840 image with genre tags and synopses was constructed by scraping MyAnimeList. The conditional Deep Convolution GAN with Spectral Normalization produced the best posters, achieving the quantitative scores of FID: 90.17, average IS: 3.505, 1KNN with PSNR: 0.445 across inter-label discernability, and FID: 166.4, across genuine versus generated poster distinguishability. The primary contribution of this work is to present results outlining the feasibility of various GAN architectures in synthesizing controllable and complex composite anime posters. The larger implication of this project is to provide an introductory approach showing the promise of a creativity assistant for authors, artists, and animators, where they can simply enter a key phrase representing a concept they have in mind, to generate a baseline idea as an initial phase.
Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.
PTF-SimCM: A Simple Contrastive Model with Polysemous Text Fusion for Visual Similarity Metric

Image similarity metric, also known as metric learning (ML) in computer vision, is a significant step in various advanced image tasks. Nevertheless, existing well-performing approaches for image similarity measurement only focus on the image itself without utilizing the information of other modalities, while pictures always appear with the described text. Furthermore, those methods need human supervision, yet most images are unlabeled in the real world. Considering the above problems comprehensively, we present a novel visual similarity metric model named PTF-SimCM. It adopts a self-supervised contrastive structure like SimSiam and incorporates a multimodal fusion module to utilize textual modality correlated to the image. We apply a cross-modal model for text modality rather than a standard unimodal text encoder to improve late fusion productivity. In addition, the proposed model employs Sentence PIE-Net to solve the issue caused by polysemous sentences. For simplicity and efficiency, our model learns a specific embedding space where distances directly correspond to the similarity. Experimental results on MSCOCO, Flickr 30k, and Pascal Sentence datasets show that our model overall outperforms all the compared methods in this work, which illustrates that the model can effectively address the issues faced and enhance the performances on unsupervised visual similarity measuring relatively.
Learning High-Order Semantic Representation for Intent Classification and Slot Filling on Low-Resource Language via Hypergraph

Representation of language is the first and critical task for Natural Language Understanding (NLU) in a dialogue system. Pretraining, embedding model, and fine-tuning for intent classification and slot-filling are popular and well-performing approaches but are time consuming and inefficient for low-resource languages. Concretely, the out-of-vocabulary and transferring to different languages are two tough challenges for multilingual pretrained and cross-lingual transferring models. Furthermore, quality-proved parallel data are necessary for the current frameworks. Stepping over these challenges, different from the existing solutions, we propose a novel approach, the Hypergraph Transfer Encoding Network “HGTransEnNet. The proposed model leverages off-the-shelf high-quality pretrained word embedding models of resource-rich languages to learn the high-order semantic representation of low-resource languages in a transductive clustering manner of hypergraph modeling, which does not need parallel data. The experiments show that the representations learned by “HGTransEnNet” for low-resource language are more effective than the state-of-the-art language models, which are pretrained on a large-scale multilingual or monolingual corpus, in intent classification and slot-filling tasks on Indonesian and English datasets.
Effects of Design Play Program Using d.School Design Thinking Model on the Creativity of Early Childhood

Objectives This study aims to provide basic data for the field application of design play by verifying the effect on the creativity of infants and developing design play programs using the d.School design thinking model.&#x0D; Methods To this end, a design play program was developed through related literature analysis, expert consultation, play draft development, and field suitability verification process, and pre-creativity and post-creativity tests were conducted to verify the effectiveness. Covariance analysis (ANCOVA) was performed for the collected data using the SPSS 19.0 Program.&#x0D; Results First, the developed design play program is as follows. The goals of the program were to develop infants' empathy, develop infants' collaboration, develop infants' problem-solving skills, and develop infants' usefulness skills, and seven activities were selected in connection with the revised Nuri course's life theme. The teaching and learning method was reorganized into six stages, and the evaluation consisted of a formal evaluation of problem-solving ability, expression ability, and attitude as a process-based evaluation and an informal evaluation of infants' language expression, design play results, usefulness. Second, as a result of pre- and post-creativity tests, design play programs were found to have a positive effect on improving children's creativity, and among the sub-elements of creativity, there was a positive effect on the abstraction and sophistication of the title.&#x0D; Conclusions This study is meaningful in that it suggested the possibility of field application of design play programs using design thinking models through development of design play programs and verification of creativity effects at a time when learning and play support through children's voluntary play is emphasized.
Machine Learning Based Software Defect Categorization Using Crowd Labeling

Defect categorization is an important task which helps in software maintenance. It also helps in prioritizing the defects, resource allocation, etc. Standard machine learning techniques can be used to automate the categorization of defects. Labeled data is needed for learning models. The expert is required for obtaining the labeled data. Sometimes, it is costly or expert is not available. So, to overcome this dependency, crowd labeled data is used to train a model. Crowd (a set of novices) is asked to assign a category as defined by IBM’s Orthogonal Defect Classification (ODC) to the defect reports. Obtaining categories through crowd can be inaccurate or noisy. Inferencing ground truth is a challenge in crowd labeling. Support Vector Machine, k Nearest Neighbor and Gaussian Naive Bayes classifier, are learnt effectively using new methodology from data labeled by a set of novices. In this chapter, we have proposed a learning model which learns effectively to predict the impact category of software defects using the expectation maximization algorithm and shows the better performance according to the various types of metrics by improving the existing technique by 8% and 11% accuracy for Compendium and Mozilla datasets respectively.