Reviewerly/exHarmony huggingface dataset:
KnowRisk: An Interpretable Knowledge-Guided Model for Disease Risk Prediction

Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of data-driven clinical risk prediction approaches have been spawned in recent years. However, there remain three challenges, which if addressed would improve the performance and applicability of such models. (i) Due to the limited data sharing between different health care institutions, the EHR data collected by a single institution is often inadequate or missing some visits records. The limited number of data cannot meet the large sample required of recent approaches especially deep learning models. In addition, the missing records (due to visiting different institution) may contain important health condition of the patient, which if ignored would cause prediction bias. (ii) Few existing approaches take clinical knowledge into account. The auxiliary knowledge if included can greatly reduce the data dependency of many modern learning algorithms. (iii) Most existing deep learning based methods are unable to identify the contribution of each medical event to the final results, which prohibits such models from being widely accepted in practical clinical applications. In this paper, we propose an interpretable and knowledge-guided deep model to address these challenges. Specifically, we distill knowledge from a clinical knowledge graph both explicitly and implicitly, which can not only supplement inadequate patient records but also guide the predicting process of the model. Furthermore, skip-connections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several state-of-the-art methods. Finally, a series of case studies are presented to prove the interpretability of our model.
Two-View Fusion based Convolutional Neural Network for Urban Road Detection

In this paper, we propose a two-view fusion based convolutional neural network to estimate road areas in urban environments with LiDAR point clouds as input only. The proposed network takes two transformed LiDAR data representations, the LiDAR imageries and the camera-perspective maps, as inputs. It outputs pixel-wise road detection results in both the LiDAR's imagery view and the camera's perspective view simultaneously, in an end-to-end manner. To make better use of the data associations between two representations, we construct a novel mapping layer to transform features from the LiDAR's imagery view to the camera's perspective view in order to strengthen the road detection performance in the camera's perspective view. Experiments on the KITTI-Road dataset show that the proposed network can achieve the state-of-the-art performance among all LiDAR-only methods in real time.
Supporting Blockchain-Based Cryptocurrency Mobile Payment With Smart Devices

The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchain-based cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the two main obstacles for mobile payment using cryptocurrency. We propose two different schemes for cryptocurrency mobile payment, one involves a centralized bank and the other one does not require any centralized party. We also provide a solution for the bank to meet KYC (know your customer)/AML (antimoney laundering) compliance requirements when it is involved in cryptocurrency mobile payment processing.
Ultrasound Nerve Segmentation Using Deep Probabilistic Programming

Deep probabilistic programming concatenates the strengths of deep learning to the context of probabilistic modeling for efficient and flexible computation in practice. Being an evolving field, there exist only a few expressive programming languages for uncertainty management. This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images. Our method uses the probabilistic programming language Edward with the U-Net model and generative adversarial networks under different optimizers. The segmentation process showed the least Dice loss ("'0.54) and the highest accuracy (0.99) with the Adam optimizer in the U-Net model with the least time consumption compared to other optimizers. The smallest amount of generative network loss in the generative adversarial network model gained was 0.69 for the Adam optimizer. The Dice loss, accuracy, time consumption and output image quality in the results show the applicability of deep probabilistic programming in the long run. Thus, we further propose a neuroscience decision support system based on the proposed approach.
Automatic Modulation Classification Using a Deep Multi-Stream Neural Network

In wireless communication, modulation classification is an important part of the non-cooperative communication, and it is difficult to classify the various modulation schemes using conventional methods. The deep learning network has been used to handle the problem and acquire good results. In the deep convolutional neural network (CNN), the data length in the input is fixed. However, the signal length varies in communication, and it causes that the network cannot take advantage of the input signal data to improve the classification accuracy. In this paper, a novel deep network method using a multi-stream structure is proposed. The multi-stream network form increases the network width, and enriches the types of signal features extracted. The superposition convolutional unit in each stream can further improve the classification performance, while the shallower network form is easier to train for avoiding the over-fitting problem. Further, we show that the proposed method can learn more features of the signal data, and it is also shown to be superior to common deep networks.
Ebird: Elastic Batch for Improving Responsiveness and Throughput of Deep Learning Services

GPUs have been widely adopted to serve online deep learning-based services that have stringent QoS requirements. However, emerging deep learning serving systems often result in long latency and low throughput of the inference requests that damage user experience and increase the number of GPUs required to host an online service. Our investigation shows that the poor batching operation and the lacking of data transfer-computation overlap are the root causes of the long latency and low throughput. To this end, we propose Ebird, a deep learning serving system that is comprised of a GPU-resident memory pool, a multi-granularity inference engine, and an elastic batch scheduler. The memory pool eliminates the unnecessary waiting of the batching operation and enables data transfer-computation overlap. The inference engine enables concurrent execution of different batches, improving the GPUs resource utilization. The batch scheduler organizes inference requests elastically. Our experimental results on an Nvidia Titan RTX GPU show that Ebird reduces the response latency of inferences by up to 70.9% and improves the throughput by up to 49.3% while guaranteeing the QoS target compared with TensorFlow Serving.
Object Detection Based on Improved YOLOv3-tiny

Deep learning has gradually become the mainstream object detection algorithm because of its powerful feature extraction ability and adaptive ability. However, how to guarantee the accuracy and speed is still a huge challenge in the field of object detection. This paper proposes an improved YOLOv3-tiny for object detection based on the idea of feature fusion. YOLOv3-tiny is chosen as the basic network framework to ensure the identification speed. In order to improve the poor detection accuracy of YOLOv3-tiny network, feature fusion is carried out based on Feature Pyramid Network. The last DBL (Darknetconv2d_ Batch Normalization_Leaky) output of the second scale output layer is fused with the fourth DBL output of the network. And a 52$\times$ 52 scale output is added on the basis of the original network. Experiments were carried out on our dataset. The experimental results show that compared with YOLOv3-tiny, the accuracy of the improved network structure is increased by 6.3%, and the detection speed is 31.8fps, ensuring the requirements of real-time detection.
Deep Multimodel Cascade Method Based on CNN and Random Forest for Pharmaceutical Particle Detection

The quality detection of pharmaceutical liquid products is inevitable and crucial in drug manufacture because drugs contaminated with foreign particles are definitely not to be used. However, with the current detection methods, it is still a challenge to detect and identify the small moving particles using an imaging system. In this article, a deep multimodel cascade method combining single-frame image and multiframe images processing method to detect and identify foreign particles is proposed. The proposed method consists of three stages. First, a Faster R-CNN convolutional neural network is adopted to detect and localize the multiple suspected foreign particles of each single-frame image. Then, the k-means clustering algorithm is used to cluster the trail of that detected multiple suspected foreign particles in the eight sequential images to obtain the moving object trajectory. Finally, trajectory features are extracted and the random forest (RF) classifier is used to distinguish noises and foreign particles according to the motion feature of the moving object trajectory. Experimental results demonstrate that the proposed multitask stepwise method improves the accuracy of foreign particles detection and reduces the rate of omission in the case of strong noise, which proves the effectiveness of this method.
MDFI: Multi-CNN Decision Feature Integration for Diagnosis of Cervical Precancerous Lesions

Colposcopy is an essential medical examination that mainly through visual inspection for cervical epithelial tissue to preventing cervical cancer. However, screening by artificial vision has the problems of missed diagnosis, low efficiency, and diversity. This paper proposes a deep learning-based method using multi-CNN decision feature integration for classification and diagnosis of cervical lesions. The proposed method first uses the k-means algorithm to aggregate training data into specific classes in data preprocessing and then trained in cross-validation to improve the generalization ability of the model. Subsequently, two kinds of CNN are separately fine-tuned based on the transfer learning, and then XGBoost algorithm is used to integrate the different CNN decision results for optimizing the final prediction. Two integration ways for decision features (named inner-model integration and outer-model integration) are proposed to embody multi-feature integration from the data level and the model structural level. Simultaneously, two specific integration strategies (inner-to-outer and outer-to-inner) are designed for as the final output based on the combination of the two ways. Experimental results show that the K-means data preprocessing method can improve the training of neural networks, and the proposed multi-decision feature fusion strategy can better obtain the results of computer-aided diagnosis that meet the needs of clinical diagnosis.
Webly-supervised learning for salient object detection

End-to-end training of a deep CNN-Based model for salient object detection usually requires a huge number of training samples with pixel-level annotations, which are costly and time-consuming to obtain. In this paper, we propose an approach that can utilize large amounts of web data for learning a deep salient object detection model. With thousands of images collected from the Web, we first employ several bottom-up saliency detection techniques to generate salient object masks for all images, and then use a novel quality evaluation method to pick out a subset of images with reliable masks for training. After that, we develop a self-training approach to boost the performance of our initial network, which iterates between the network training process and the training set updating process. Importantly, different from existing webly-supervised or weakly-supervised methods, our approach is able to automatically select reliable images for network training without requiring any human intervention (e.g., dividing images into different difficulty levels). Results of extensive experiments on several widely-used benchmarks demonstrate that our method has achieved state-of-the-art performance. It significantly outperforms existing unsupervised and weakly-supervised salient object detection methods, and achieves competitive or even better performance than fully supervised approaches.
A Unified Framework for Structured Graph Learning via Spectral Constraints

Graph learning from data represents a canonical problem that has received substantial attention in the literature. However, insufficient work has been done in incorporating prior structural knowledge onto the learning of underlying graphical models from data. Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. Useful structured graphs include the multi-component graph, bipartite graph, connected graph, sparse graph, and regular graph. In general, structured graph learning is an NP-hard combinatorial problem, therefore, designing a general tractable optimization method is extremely challenging. In this paper, we introduce a unified graph learning framework lying at the integration of Gaussian graphical models and spectral graph theory. To impose a particular structure on a graph, we first show how to formulate the combinatorial constraints as an analytical property of the graph matrix. Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices. The proposed algorithms are provably convergent, computationally efficient, and practically amenable for numerous graph-based tasks. Extensive numerical experiments with both synthetic and real data sets illustrate the effectiveness of the proposed algorithms. The code for all the simulations is made available as an open source repository.
Development of a Kinect-Based English Learning System Based on Integrating the ARCS Model with Situated Learning

This study developed a Kinect-based somatosensory English learning system. The main design concept was to integrate Kinect as an interaction technique with theories of situated learning and the attention, relevance, confidence, and satisfaction (ARCS) model, to design relevant learning activities and materials, thereby enhancing students’ learning outcomes. The proposed system allows for planning and designing learning activities and content according to situated learning components and the ARCS model. The somatosensory interaction system Kinect was used to provide users with a virtual learning environment to achieve actual spatial and physical experiences, assisting learners’ engagement in stories and scenarios as well as enhancing their motivation to learn. English vocabulary related to supermarkets was set as the learning objective and 70 students ranging from third to sixth grade at a learning center in Tainan, Taiwan were selected as participants. During the experiment, participants were divided into two groups: the experimental group, which employed the proposed learning system, and the control group, in which students learned using printed materials coupled with mobile devices. Pre- and posttest scores of the two groups were used to assess learning outcomes and analyze the ARCS model-based questionnaire. The results revealed that the proposed system effectively improved learners’ motivation to learn and learning outcomes.
Predicting student performance in interactive online question pools using mouse interaction features

Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models. The result shows that our approach can achieve a much higher accuracy for student performance prediction in interactive online question pools than the traditional way of only using the statistical features (e.g., students' historical question scores) in various models. We further discuss the performance consistency of our approach across different prediction models and question classes, as well as the importance of the proposed interaction features in detail.
Shaping Visual Representations with Language for Few-Shot Classification

By describing the features and abstractions of our world, language is a crucial tool for human learning and a promising source of supervision for machine learning models.We use language to improve few-shot visual classification in the underexplored scenario where natural language task descriptions are available during training, but unavailable for novel tasks at test time.Existing models for this setting sample new descriptions at test time and use those to classify images.Instead, we propose language-shaped learning (LSL), an end-toend model that regularizes visual representations to predict language.LSL is conceptually simpler, more data efficient, and outperforms baselines in two challenging few-shot domains.
DeepCast: Towards Personalized QoE for Edge-Assisted Crowdcast With Deep Reinforcement Learning

Today’s anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crowdcast</i> ). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as Twitch.tv, Youtube Gaming, and Inke.tv, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepCast</i> , an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems.
Human Action Recognition in Video Sequences Using Deep Belief Networks

For the last several decades, Human Activity Recognition (HAR) has been an intriguing topic in the domain of artificial intelligence research, since it has applications in many areas, such as image and signal processing.Generally, every recognition system can be either an end-to-end system or including two phases: feature extraction and classification.In order to create an optimal HAR system that offers a better quality of classification prediction, in this paper we propose a new approach within two-phase recognition system paradigm.Probabilistic generative models, known as Deep Belief Networks (DBNs), are introduced.These DBNs comprise a series of Restricted Boltzmann Machines (RBMs) and are responsible for data reconstruction, feature construction and classification.We tested our approach on the KTH and UIUC human action datasets.The results obtained are very promising, with the recognition accuracy outperforming the recent state-of-the-art.