Reviewerly/exHarmony huggingface dataset:
SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks

Generative adversarial networks (GANs) have proven useful for several medical imaging tasks, including image reconstruction and stochastic object model generation. Thus far, most of the work with GANs has been constrained to twodimensional images. Considering that medical imaging data are often inherently three-dimensional (3D), a 3D GAN would be a more principled way to synthesize realistic volumes. Training a 3D GAN is both computationally and memory intensive. However, prior work has not considered the anisotropic nature of many medical imaging systems. In this paper, the SlabGAN is proposed to reduce the inefficiencies associated with training a 3D GAN. The SlabGAN uses the progressive GAN architecture extended to 3D, but removes the requirement of the three dimensions being equal sizes. This permits the generation of anisotropic 3D volumes with large <i>x </i>and<i> y</i> dimensions. The SlabGAN is trained on MRI brain images from the fastMRI dataset to generate images of dimension 256&times;256&times;16. The <i>x</i> and <i>y</i> dimensions of these images are comparable to previously published results while requiring significantly fewer computational resources to generate. The trained SlabGAN is applicable to tasks such as 3D medical image reconstruction and thin-slice MR super resolution.
Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.
A novel unsupervised learning model for diffeomorphic image registration

Deformable medical image registration is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a novel unsupervised learning model (denoted as BSADM) for 3D diffeomorphic medical image registration. Inspired by spatial attention module, we propose a new network architecture BSAU-Net by introducing a novel Binary Spatial Attention Module (BSAM) into skip connection, which can take full advantages of the spatial information extracted from the encoding path and corresponding decoding path. In addition, from variational method in differential geometry, monitor function f is used to control the Jacobian determinant (JD) of registration field ɸ. So, we also propose a novel orientation-consistent regularization loss to penalize the local regions with negative Jacobian determinant, which further encourages the diffeomorphic property of the transformations. We verify our method on two datasets including ADNI and PPMI dataset, and obtain excellent improvement on magnetic resonance (MR) image registration with higher average Dice scores and better diffeomorphic registration.
Mobile Computational Photography: A Tour

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography -the science and engineering of making great images from small form factor, mobile cameras. Modern algorithmic and computing advances, including machine learning, have changed the rules of photography, bringing to it new modes of capture, post-processing, storage, and sharing. In this paper, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution. At each step, we may draw naive parallels to the human visual system.
Auto-Completion of Queries

Search engines are exceedingly dependent on the query auto-completion. Query auto-completion is an ongoing activity that puts forwards a group of words for every click dynamically. Query suggestions help in formulating the query and improving the quality of the search. Graphs are data structures that are universal and extensively used in computer science and related fields. The graph machine learning approach is growing rapidly with applications such as friendship recommendation, social network, and information retrieval. Node2vec algorithm is used to study the feature illustration of nodes in a graph. It is derived by word embedding algorithm Word2vec. A supervised Recurrent Neural Network using Long short-term memory (LSTM) is employed to compute the accuracy. This model confirms 89% accuracy for query auto-completion. Greater the accuracy better the model.
Reproducibility in deep learning algorithms for digital pathology applications: a case study using the CAMELYON16 datasets

Reproducibility as a cornerstone of science has in recent years attracted significant attention in a variety of applications that involve big data and complex algorithms. Artificial Intelligence (AI) applications, in particular, have been referred by some authors as having a “reproducibility crisis”. The application of deep learning algorithms in digital pathology, which has been demonstrated in some studies to achieve pathologist-level performance, involves both big data and complex algorithms. It is important to identify technical factors influencing AI performance for such studies to be reproducible. In this work, we conducted a reproducibility study using the public datasets shared by the CAMELYON16 challenge, which aimed to develop and assess algorithms for the detection of breast cancer metastasis using whole-slide images (WSIs) of lymph node sections. We used the two-stage classification framework of the CAMELYON16 challenge’s top-performing algorithm, i.e., a convolutional neural network (Inception V1) to generate heatmaps followed by feature extraction from the heatmaps and a random forest classifier for classification. We investigated the effects of variations in training/testing procedures on the performance of AI/ML algorithms including color augmentation methods, color normalization methods, and the resolution of heatmaps. Our results showed that, despite the differences in color augmentation and color normalization methods, the utilization of these techniques improved the classification performance by an AUC value of 0.07 compared to without using them, which is consistent with the CAMELYON16 findings. We concluded that sufficient details of technical description should be provided for a study to be fully reproducible.
Research on Robotic Arm Movement Grasping System Based on MYO

Abstract This paper studies the continuous motion capture system of a robotic arm based on MYO. The MYO sensor can collect the electromyographic signal (sEMG) and the motion signal (IMU) of the arm during exercise, and filter the electromyographic signal, extract the features, and return the features. After the first transformation, the machine learning algorithm is used for model training. In order to verify the effect of the recognition of the EMG signal on the deep learning method, the accuracy comparison experiment with the BP neural network is also added. In addition, a two-link arm model was built, and the pose information of the end of the arm was obtained through Euler angle transformation, and then the end pose information of the robotic arm was obtained through coordinate mapping, and the end pose simulation was performed using Matlab software. The simulation results prove that the performance of the system meets expectations, and points out the direction for subsequent research.
ANALISIS FRASA TEKS CERITA PENDEK KARYA SISWA KELAS XI SMK NEGERI 1 PADANG PANJANG

ABSTRACT The purpose of this research is to describe the wrong type of phrases in the short story text, describe the causes of phrase errors in the short story text, describe the pattern of revision of the short story text written by student of 11th grade SMKN 1 Padang Panjang. This type of research is a qualitative research with descriptive methods. The instrument of this research is the research itself. This research data is in the wrong phrase. The data source of this research is a short story text written by a student of 11th grade SMKN 1 Padang Panjang. The research data was collected using documentation. Based on the results of the study obtained data as follows. First, the wrong type of phrase in the students' short story text. The wrong type of phrases in the short story texts of students in this study are endocentric phrases and exocentric phrases. Second, the cause of phrase errors in the students' short story text in the form of front-word phrases, mismanagement, wrong due to excessive, the addition of certain words to phrases whose elements are inseparable, and the omission of certain words that connect parts of phrases. third, the pattern of revising the short story text by selecting the language corpus, recognizing errors in the corpus, classifying errors, explaining errors, and evaluating errors. Kata kunci: Frasa, Kualitatif, Teks Cerita Pendek
PyraD-DCNN: A Fully Convolutional Neural Network to Replace BLSTM in Offline Text Recognition Systems

We present in this paper a fast and efficient multi-task fully convolutional neural network (FCNN). The proposed architecture uses a multi-resolution Pyramid of Densely connected Dilated Convolution (PyraD-DCNN). Our design also implements optimized convolutional building blocks that enable large dimensional representation with a low computational cost. Besides its ability to perform semantic image segmentation by itself as an auto-encoder, it may also be coupled with a signal encoder to build an end-to-end signal-to-sequence system without the help of recurrent layers (RNN). In the current work, we present the PyraD-DCNN through an application on Optical Character Recognition task and how it holds the comparison with Bidirectional Long Short-Term Memory (BLSTM) RNN. The pyramid-like structure using dilated kernels provides short and long term context management without recurrence. Thus we managed to improve inference time on CPU up to three times faster on our own datasets compared to a classical CNN-LSTM, with slight accuracy improvements in addition to faster training cycles (up to 24 times faster). Furthermore, the lightness of this structure makes it naturally adapted to mobile applications without any accuracy loss.
Effects of Class Imbalance Using Machine Learning Algorithms

Class imbalance is the major hurdle for machine learning-based systems. Data set is the backbone of machine learning and must be studied to handle the class imbalance. The purpose of this paper is to investigate the effect of class imbalance on the data sets. The proposed methodology determines the model accuracy for class distribution. To find possible solutions, the behaviour of an imbalanced data set was investigated. The study considers two case studies with data set divided balanced to unbalanced class distribution. Testing of the data set with trained and test data was carried out for standard machine learning algorithms. Model accuracy for class distribution was measured with the training data set. Further, the built model was tested with individual binary class. Results show that, for the improvement of the system performance, it is essential to work on class imbalance problems. The study concludes that the system produces biased results due to the majority class. In the future, the multiclass imbalance problem can be studied using advanced algorithms.
Image Recognition of Small UAVs Based on Faster RCNN

Image recognition of small UAVs is the basis of anti-UAV technology based on photoelectric detection. In this paper, an automatic image recognition method for small UAVs based on Faster RCNN is proposed. The deep residual network is adopted to extract image features of small UAVs. Then, the extracted features are input into the region proposal network, which can generate a region box that contains UAVs. The final recognition result is obtained through classification and regression. After training and testing the recognition model based on the dataset which contains thousands of images of common UAV on the market, the result shows that the recall of UAV recognition is 98%, the average precision is close to 97%, and the misdetection rate is low. The results of samples show that UAVs can be accurately recognized with less time, so the model owns good recognition performance.
The multi-start mayfly optimization algorithm

Although there would be multiple ways for individuals in the mayfly optimization (MO) algorithm to update their velocities, the individuals would still be trapped easily in local optima. To reduce the probability being trapped, the mayflies might be reinitialized during iterations. In this paper, the multi-start methods was introduced to the MO algorithm and consequently, the male mayflies would be reinitialized same as the way at the beginning. Simulation experiments were carried out and results verifed that the multi-start MO algorithm would perform better in optimizing the benchmark functions than the original version.
Digital twin-trained deep convolutional neural networks for fringe analysis

High-speed three-dimensional (3D) fringe projection profilometry (FPP) is widely used in many fields. Recently, researchers have successfully tested the feasibility of performing fringe analysis using deep convolutional networks (CNN). However, the existing methods require tremendous real-world scanning trials for model training, which is not trivial. In this work, we propose a framework to establish the digital twin of a real-world system in a virtual environment and a process to automatically generate 3D training data. Experiments are conducted to demonstrated that a physical system can adopt the CNN trained in the virtual environment to perform accurate real-world 3D shape measurements.
Caps-YOLO: PEDESTRIAN DETECTION METHOD OF COMPLEX POSTURE COMBINED WITH CAPSULES NETWORK

Pedestrian detection algorithm has a great significance to the fields of modern intelligent transportation, monitoring, and autonomous driving. However, in the case of pedestrians with complex postures, the accuracy of traditional convolution model will obviously decrease. In order to improve the accuracy and robustness of pedestrian detection in complex environment and deal with the problem that the traditional convolutional network model is insensitive to object space structure and detail information, this paper has proposed a Caps-YOLO model based on the YOLOv3 (You Only Look Once) real-time object detection model. This model adopts dense connection to replace the shortcut connection in the original network, and constructs the dense block component, improving the utilization rate of the feature map. Inspired by capsules network, the model adopts vector neurons combined with dynamic routing mechanism to realize the detection function of this model. Compared with the traditional neural network model, Caps-YOLO can learn the intrinsic attribute of objects from multiple aspects to capture various states of object. As for pedestrians with complex postures, it can effectively decrease the probability of being missed and false detections. By verifying the datasets of COCO (Microsoft Common Objects in Context) 2017, COCO2014, VOC (The Pascal Visual Object Classes) 2012 and VOC2007, and comparing the original YOLOv3, the accuracy of the Caps-YOLO model for pedestrian detection has been increased by 3.36%, 6.63%, 1.81%, and 3.86% respectively, which can effectively solve the problem of pedestrian detection with complex postures.
A total variation and group sparsity-based algorithm for nuclear radiation-contaminated video restoration

Nuclear radiation-contaminated video deblurring is an important issue of the robot vision system and has been widely studied. In this paper, a hybrid radiation-contaminated video frame enhancement algorithm is proposed that utilizes both intra-frame and inter-frame correlation by a two-stage strategy. In the first stage, total variation (TV) transformation are used to locate the spot areas, and then local TV is employed to restore spot areas. The preliminary deblurring result not only enhances the video frame and similar patch matching accuracy but also provides reliable estimates of filtering parameters. In the second stage, visual group technology and improved k-nearest neighbours (k-NN) method is used to select similar frames and reference patches respectively. The final enhanced video frame is obtained by a novel patch-based group sparse method. Experimental results clearly show that the proposed method outperforms other state-of-the-art methods in both quantitative evaluation indices and visual quality measurements.
Image Spam Detection Using Machine Learning and Natural Language Processing

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.
Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong RL in recent years, the lack of a realistic testbed makes robust evaluation of LLL algorithms difficult. Multi-agent RL (MARL), on the other hand, can be seen as a natural scenario for lifelong RL due to its inherent non-stationarity, since the agents' policies change over time. In this work, we introduce a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. Our setup is based on Hanabi -- a partially-observable, fully cooperative multi-agent game that has been shown to be challenging for zero-shot coordination. Its large strategy space makes it a desirable environment for lifelong RL tasks. We evaluate several recent MARL methods, and benchmark state-of-the-art LLL algorithms in limited memory and computation regimes to shed light on their strengths and weaknesses. This continual learning paradigm also provides us with a pragmatic way of going beyond centralized training which is the most commonly used training protocol in MARL. We empirically show that the agents trained in our setup are able to coordinate well with unseen agents, without any additional assumptions made by previous works. The code and all pre-trained models are available at this https URL.
Towards Determining the Effect of Age and Educational Level on Cyber-Hygiene

With the increasing rate of internet related challenges such as cyber-attacks, it has become imperative for users to adopt safe practices while using the internet. This safety measures are known as cyber-hygiene. As universities are making tremendous efforts towards minimising the rate of cyber-attacks in higher education, keeping good cyber-hygiene culture among faculty employees and students will play a critical role. Cyber-attacks sophistication has become a global issue, especially now that virtually all business transactions, learning, and communication are gradually becoming online as part of the impacts of COVID-19 pandemic. To be able to conduct this study, a qualitative research methodology was employed for an exploratory online study of cyber-hygiene among students and employees of University of Nigeria, Nsukka. In the study, 316 responses were used, and the findings revealed that age and level of education do not have statistical significance on the cyber hygiene culture and behaviour of the respondents. Also, it was discovered that mobile phone is the most widely used device with 50.26%, followed by laptop with 35.65%. Findings also showed that 159 (50.32%) respondents were found to have low cyber-hygiene culture while 157 (49.68%) respondents have high cyber hygiene culture. Hence, participants portray poor cyber-hygiene.
Comparative Study of Emotion Annotation Approaches in Korean Dialogue

Many researchers have recently attempted to predict the emotions in conversations, which is essential to developing a human-like chatbot system. However, it is challenging to build a desirable emotion recognition model due to the emotion-labeled data scarcity, especially in Korean. A previous study presented a distant supervision-based annotation procedure with the use of emotion lexicons. However, this procedure has two potential problems: (1) it is too dependent on the emotion lexicons; (2) it is hard to capture long-range contextual information during the conversation. This paper addresses two problems by utilizing a pre-trained deep learning model, which has achieved good performance on several dialogue emotion datasets, as an annotator. Experiments demonstrate that the pre-trained model is more desirable to create emotion labels on each utterance during the conversation.
Multiple Interpretations Improve Deep Learning Transparency for Prostate Lesion Detection

Detecting suspicious lesions in MRI imaging is a critical task in preventing deaths from cancer. Deep learning systems have produced remarkable accuracy for the task of detecting lesions in MRI images. Although these systems show remarkable performance, they often ignore an indispensable component which is interpretability. Interpretability is essential for many deep learning applications in medicine because of ethical, monetary, and legal factors. Interpretation also builds a necessary degree of trust and transparency between the doctor, patient, and system. This work proposes a framework for the interpretation of medical deep learning systems. The proposed approach is based on the idea that it is advantageous to use different interpretation techniques to show multiple views of reasoning behind the classification. This work demonstrates deep learning interpretations for various patient data modalities using the proposed Multiple Views of Interpretation for Deep Learning framework.