Reviewerly/exHarmony huggingface dataset:
Guided Dual Networks for Single Image Super-Resolution

The PSNR-oriented super-resolution (SR) methods pursue high reconstruction accuracy, but tend to produce over-smoothed results and lose plenty of high-frequency details.The GAN-based SR methods aim to generate more photo-realistic images, but the hallucinatory details are often accompanied with unsatisfying artifacts and noise.To address these problems, we propose a guided dual super-resolution network (GDSR), which exploits the advantages of both the PSNR-oriented and the GAN-based methods to achieve a good trade-off between reconstruction accuracy and perceptual quality.Specifically, our network contains two branches, where one is trained to extract global information and the other to focus on detail information.In this way, our network simultaneously generates SR images with high accuracy and satisfactory visual quality.To obtain more high-frequency features, we use the global features extracted from the low-frequency branch to guide the training of the high-frequency branch.Besides, our method utilizes a mask network to adaptively recover the final super-resolved image.Extensive experiments on several standard benchmarks show that our proposed method achieves better performance compared with state-of-the-art methods.The source code and the results of our GDSR are available at https://github.com/wenchen4321/GDSR.
Trajectory-Tracking Control of Robotic System via Proximal Policy Optimization

This paper aims to solve the trajectory tracking problem of manipulator and mobile robot by using proximal policy optimization (PPO) with generalized advantage estimation (GAE). We adopt a distributed framework of PPO to improve the speed of sample collection and reduce the correlation of transitions when updating the networks. Random reference state initialization is used during the training process to enable the robot to learn effectively from the reference trajectory. Additionaly, we introduce Long Short-term Memory (LSTM) to represent the actor and critic, and buffers to store the cell state and hidden state of LSTM for the initialization of each episode, which helps solve the problem of inaccurate inital LSTM states. Distributed PPO with fully-connected neural networks and LSTM is utilized to train the manipulator and mobile robot with an exponential reward function to track the given trajectories. Numerical simulations are provided to demonstrate the effectiveness of the proposed method. It can be seen that distributed PPO with LSTM can improve the tracking performance.
An Active Learning with Two-step Query for Medical Image Segmentation

Active learning (AL) is a special machine learning method, in which a learning algorithm is designed to query the user to obtain the desired label interactively. It has been widely used in the area of image classification and not effectively introduced to the field of segmentation so far. However, the cost of segmentation’s annotation is dramatically greater than the classification. It’s more useful to apply active learning in the segmentation task. Thus we bring forward a novel two-step query method in this paper. First, we design a fusion of multiple estimate models to calculate sample complexity as the middle variable. Second, we define a function of sample complexity and potential value, which can also be interpreted as activation and help the interactive query collect more ponderable samples. Through the proposed two-step query method, we progressively optimize the model’s capacity for sample selection and object segmentation. Experimental results of the bladder segmentation task show that our method outperforms random sampling about 12% relatively using the same number of data.
More Intelligence and Less Clouds in Our Smart Homes

Cloud-based services are useful, sometimes necessary for the users. However, especially in smart homes, exploiting cloud services for enabling multimodal human–computer interfaces, e.g., for speech recognition and synthesis, face recognition, face expression interpretation, and so on, is nowadays not necessary and, in several cases, disadvantageous for the end user. We argue that in those cases "local computing", i.e., processing multimedia data locally, where they are acquired, is currently a better choice. We describe, as a proof of concept, the Red architecture, a totally local virtual assistant for home automation, and illustrate its abilities.
Coreference Resolution in Ambiguous Pronouns Using BERT and SVM

Coreference resolution is the method of matching the expressions in a text to the corresponding entity which it is referring. This is a major task in many Natural Language Processing problems and a prolonged challenge in the field of NLP. One of the major challenges in the existing coreference resolution systems is the absence of a better solution in the task of ambiguous pronoun resolution. So in this work, a coreference resolution system for ambiguous pronoun resolution using BERT based approach is proposed. First, the dataset is trained on a BERT model for obtaining the contextual embeddings in the text and then applying it to the SVM classifier for classification and thus obtain the coreference resolution for the target pronoun. The dataset used in this work is Gendered Ambiguous Pronouns (GAP) dataset, released by Google AI Language.
An Efficient Hybrid Model for Kidney Tumor Segmentation in CT Images

Kidney tumor segmentation from CT-volumes is essential for lesion diagnosis. Considering excessive GPU memory requirements for 3D medical images, slices and patches are exploited for training and inference in conventional U-Net variant architectures' which inevitably hampers contextual learning. In this paper, we propose a novel effective hybrid model for kidney tumor segmentation in CT images with two parts: 1) Foreground Segmentation Network; 2) Sparse PointCloud Segmentation Network. Specifically, Foreground Segmentation Network firstly segments the foreground, i.e., kidneys with tumors, from background in voxel grid using classical V-Net. Secondly, we represent the obtained foreground regions as point clouds and feed them into the Sparse PointCloud Segmentation Networks to conduct fine-grained segmentation for kidney and tumor. The critical module embedded in the second part is an efficient Submanifold Sparse Convolutional Networks (SSCNs). By exploiting SSCNs, our proposed model can take all foreground as input for better context learning in a memory-efficient manner, and consider the anisotropy of CT images as well. Experiments show that our model can achieve state-of-the-art tumor segmentation while reducing GPU resource demand significantly.
A Multi-Person Pose Estimation with LSTM for Video Stream

Application of human pose estimation bring great help to people's life. Most of the applications in real life scenes are based on single-frame images. The work based on a single-frame image has better accuracy, but it often abandons some temporal information of real life. In order to preserve the information, we choose to combine the Long Short-Term Memory Network with a single-frame estimation network to carry out the multi-person pose estimation for video stream. In the design of single-frame network, this paper adds the deconvolution layers to the residual network to obtain high-resolution image information and adds a loss function with an area of bounding-box to train the single-frame model. In the design of multi-person, pose estimation network for video Stream, this paper uses the Long Short-Term Memory Network to process the temporal information extracted from the single-frame network to carry out multi-person pose estimation. In the experiment, COCO dataset and PoseTrack2018 dataset verify the effectiveness of the method.
Orchard: Exploring Multivariate Heterogeneous Networks on Mobile Phones

Abstract People are becoming increasingly sophisticated in their ability to navigate information spaces using search, hyperlinks, and visualization. But, mobile phones preclude the use of multiple coordinated views that have proven effective in the desktop environment (e.g., for business intelligence or visual analytics). In this work, we propose to model information as multivariate heterogeneous networks to enable greater analytic expression for a range of sensemaking tasks while suggesting a new, list‐based paradigm with gestural navigation of structured information spaces on mobile phones. We also present a mobile application, called Orchard, which combines ideas from both faceted search and interactive network exploration in a visual query language to allow users to collect facets of interest during exploratory navigation. Our study showed that users could collect and combine these facets with Orchard, specifying network queries and projections that would only have been possible previously using complex data tools or custom data science.
Monocular Camera Calibration for Autonomous Driving — a comparative study

Autonomous driving is currently a widely researched topic worldwide. With a large research effort being taken by industrial research units in the automotive sector, it is no longer exclusive to academic research labs. Essential to this ongoing effort towards level-5 vehicle autonomy, are the sensors used for tracking and detection, mainly lasers, radars and cameras. Most of the cameras for automotive application systems use wide-angle or fish-eye lens, which present high distortion levels. Cameras need to be calibrated for correct perception, particularly for capturing geometry features, or for distance-based calculations. This paper describes a case-study concerning monocular camera calibration for a small scale autonomous driving vehicle vision system. It describes the fundamentals on camera calibration and implementation, with results given for different lenses and distortion models. The aim of the paper is not only to provide a detailed and comprehensive review on the application of these calibration methods, but to serve also as a reference document for other researchers and developers starting to use monocular vision in their robotic applications.
Airway segmentation in speech MRI using the U-net architecture

We develop a fully automated airway segmentation method to segment the vocal tract airway from surrounding soft tissue in speech MRI. We train a U-net architecture to learn the end to end mapping between a mid-sagittal image (at the input), and the manually segmented airway (at the output). We base our training on the open source University of Southern California's (USC) speech morphology MRI database consisting of speakers producing a variety of sustained vowel and consonant sounds. Once trained, our model performs fast airway segmentations on unseen images at the order of 210 ms/slice on a modern CPU with 12 cores. Using manual segmentation as a reference, we evaluate the performances of the proposed U-net airway segmentation, against existing seed-growing segmentation, and manual segmentation from a different user. We demonstrate improved DICE similarity with U-net compared to seed-growing, and minor differences in DICE similarity of U-net compared to manual segmentation from the second user.
Rounding-augmented reality book and smartphone for deaf students in achieving basic competence

Abstract Deaf students’ hearing problem results in their mastery of language and mathematics lower than hearing students. Learning the topic of rounding is difficult for deaf students because it requires a good understanding of language and a true perception in math concepts, in which are abstract in nature. Learning concepts needs a special strategy for this type of students, as generally mathematics is difficult to be translated and communicated by sign language. This specificity forces them to possess unique behavior in understanding everything through visual power. This paper discusses a study of the creation of a book and smartphone applications as devices based on augmented-reality (AR) software on the topic of rounding accompanied by rich visualization to facilitate deaf students in achieving mathematical basic competencies. This study aims to describe and reveal the validity and the effectiveness of the designed devices through their implementation in a classroom. The research and development method refer to ADDIE which consisting of five stages, namely: analysis, design, development, implementation, and evaluation. The results of this study show that the AR-Book and the AR-Smartphone application are valid used by deaf students and both devices are effective in achieving their mathematical basic competencies.
Happiness Intensity Estimation for a Group of People in Images using Convolutional Neural Networks

In this paper, we present a group-level happiness intensity estimation approach based on convolutional neural networks. First, Multi-task Cascaded Convolutional Networks (MTCNN) are applied to detect all faces in the image. Then, the face-level happiness intensity of each detected individual face is respectively predicted by using a deep convolutional neural network with transfer learning from pre-trained VGGFace model. Finally, the weighted average of face-level happiness intensities for all detected faces in the group is used to estimate the happiness intensity of a group of people in the image. Experimental results on validation dataset of HAPPEI database demonstrate that the proposed method has promising performance, which is 0.58 in terms of Root Mean Square Error (RMSE), and outperforms the baseline approach that uses the CENsus TRansform hISTogram (CENTRIST) descriptor and Support Vector Regression with a non-linear Chi-square kernel, which gives an RMSE of 0.78.
Research on interest reading recommendation method of intelligent library based on big data technology

In order to overcome the problems of inaccurate recommendation results and high response delay existing in traditional recommendation methods, this paper proposes an interest reading recommendation method based on big data technology for intelligent library. Build the recommendation platform of int elligent library, design the cataloging subsystem, book category setting subsystem, new book storage subsystem and new journal storage subsystem. Through big data processing technology, users’ reading interest in each subsystem is clustered and fully mined. Based on the results of reading interest mining, the user interest model is established according to the semantic topic and the updating scheme of user interest model is designed. Combined with the user’s score, we recommend the reading target which accords with the user’s reading interest to the user. The experimental results show that compared with the traditional recommendation method, the proposed method can achieve high-precision reading recommendation with low response delay, and the minimum delay is only 0.019.
A Fast Approach for Up-Scaling Frequent Itemsets

With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem. However, the relationships among datasets of different scales are not fully utilized. A fast approach proposed in this paper is as follows: the frequent itemsets on the large-scale data are directly inferred based on the frequent itemsets that are belonged small-scale datasets, instead of mined from the large-scale dataset again on condition that the frequent itemsets on the small-scale datasets have been mined. We conduct extensive experiments on one synthetic data and four UCI data sets. The experimental results show that our algorithm is significantly faster and consumes less memory than these leading algorithms.
When Texture Matters: Texture-Focused Cnns Outperform General Data Augmentation and Pretraining in Oral Cancer Detection

Early detection is essential to reduce cancer mortality. Oral cancer could be subject to screening programs (similar as for cervical cancer) by collecting Pap smear samples at any dentist visit. However, manual analysis of the resulting massive amount of data is prohibitively costly. Convolutional neural networks (CNNs) have shown promising results in discriminating between cancerous and non-cancerous cells, which enables efficient automated processing of cancer screening data. We investigate different CNN architectures which explicitly aim to utilize texture information, for cytological cancer classification, motivated by studies showing that chromatin texture is among the most important discriminative features for that purpose. Results show that CNN classifiers inspired by Local Binary Patterns (LBPs) achieve better performance than general purpose CNNs. This holds also when different levels of general data augmentation, as well as pretraining, are considered.
Detection of SLA Violation for Big Data Analytics Applications in Cloud

SLA violations do happen in real world.An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions.Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations.While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking.In this paper, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud.We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba).Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.
An Efficient Privacy-preserving IoT System for Face Recognition

Face recognition (FR) has become increasingly important in many real-world IoT applications like public safety surveillance camera system or CCTV, person re-identification system and face-based authentication system. The privacy of face images has been a growing concern, not only in the collected face dataset stored in the cloud platforms but also in its everyday use. However, most existing schemes (e.g., deep learning with differential privacy [1]) build privacy-preserving analytics models from the stored face data while ignoring the privacy concern in end devices. In this paper, we propose a novel efficient privacy-preserving face representation scheme in the Bloom filter space, which can satisfy the resource limits from IoT devices. Our solution allows analytics tasks on privacy-preserving face data representation but retains the high data utility on analytics (e.g., classification).
A power-aware scheduling algorithm for real-time workflow applications in clouds

Cloud computing has become increasingly prevalent, providing end-users with temporary accesses to scalable computational resources. For the sake of scalability and efficiency, an increasing number of DAG-type workflow applications are deployed in cloud platforms. The energy problem has become one of the major concerns in clouds. We proposed a power-aware and real-time scheduling (PRTS) algorithm for cloud schedulers to minimize the execution cost of workflow and reduce the energy consumption. The PRTS algorithm consists of two parts: scheduling the tasks to the most cost-effective virtual machines based on critical path without missing the deadline; monitoring the dynamic slack and reclaiming them to apply the DVFS technique for energy-saving. Finally, the simulation experiments reveal that slowdown factor technique can achieve the percents of energy-saving up to 12.3% (with an average of 8.3%) compared with the baseline algorithm ESS.
Prior-Aware CNN with Multi-Task Learning for Colon Images Analysis

Adenocarcinoma is the most common cancer, the pathological diagnosis for it is of great significance. Specifically, the degree of gland differentiation is vital for defining the grade of adenocarcinoma. Following this domain knowledge, we encode glandular regions as prior information in convolutional neural network (CNN), guiding the network's preference for glands when inferring. In this work, we propose a prior-aware CNN framework with multi-task learning for pathological colon images analysis, which contains gland segmentation and grading classification branches simultaneously. The segmentation's probability map also acts as the spatial attention for grading, emphasizing the glandular tissue and removing noise of irrelevant parts. Experiments reveal that the proposed framework achieves accuracy of 97.04% and AUC of 0.9971 on grading. Meanwhile, our model can predict gland regions with mIoU of 0.8134. Importantly, it is based on the clinical-pathological diagnostic criteria of adenocarcinoma, which makes our model more interpretable.
Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.
Improving Brain Tumor Segmentation with Multi-direction Fusion and Fine Class Prediction

Convolutional neural networks have been broadly used for medical image analysis. Due to its characteristics, segmentation of glioma is considered to be one of the most challenging tasks. In this paper, we propose a novel Multi-direction Fusion Network (MFNet) for brain tumor segmentation with 3D multimodal MRI data. Unlike conventional 3D networks, the feature-extracting process is decomposed and fused in the proposed network. Furthermore, we design an additional task called Fine Class Prediction to reinforce the encoder and prevent over-segmentation. The proposed methods finally obtain dice scores of 0.81796, 0.8227, 0.88459 for enhancing tumor, tumor core and whole tumor respectively on BraTS 2019 test set.