Reviewerly/exHarmony huggingface dataset:
A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records

Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.
Efficient forward secure searchable encryption supporting multi-keyword query

Searchable encryption (SE) allows client to outsource personal data to an untrusted server while protecting the data privacy, which is widely used by corporations and individuals. Recently, many works show that forward privacy is the fundamental property for secure SE cryptosystem, and several forward secure SE schemes have been proposed. However, most forward secure schemes are focus on the single-keyword query, which limit their wide application in cloud computing. In this paper, we propose an efficient forward secure searchable encryption scheme supporting multi-keyword query. Our scheme involves two new storage structures as temporary map and search tree. Specifically, when the client uploads multiple files with the same keyword, our scheme uses an encryption key to replace multiple encryption keys in the existing schemes, so that improves query efficiency. Moreover, our scheme overcomes the shortcoming that the query complexity increases linearly with the number of updated files in the existing schemes. We prove our scheme is secure with forward privacy under the random oracle model. The experimental results show that our scheme is more efficient than the existing schemes.
VR panoramic video technology and its application in rail transit personnel training in the 5G era

VR (Virtual Reality, virtual reality) panoramic video technology is supported by 5G high-speed, low-latency, and large-capacity. The application industry scenarios are more diversified, and it is more prominent in rail transit personnel training. This article first gives an overview of panoramic video technology and explains the classification of VR panoramic video. Secondly, it introduces the network requirements of two kinds of VR panoramic video, and combined with the particularity of the rail transit industry, it focuses on the analysis of online on- demand methods. Finally, it is concluded that the bandwidth and time delay of the current network cannot satisfy the virtual experience above the excellent experience, and only the 5G network is more suitable. Combined with relevant VR technology training cases, it is found that the driving, maintenance, debugging and design of vehicles are equally applicable, to adapt the better needs of contemporary industry development.
A Visualization System that Ensures the Localization of Private Geographic Information

Common geographic information visualization systems are based on online map tools to render geographic information. However, in an intranet environment where Internet access is not possible, online map tools cannot be used. Therefore, this paper designs a localized visualization system based on C/S architecture that can ensure private geographic information. The system can support Windows, Linux, MacOS. The system can ensure the confidentiality of private geographic information and realize the visualization of private geographic information. The test results show that the system has the localization and visualization of private geographic information, and the operation is stable and reliable.
On the convergence of reinforcement learning with Monte Carlo Exploring Starts

A basic simulation-based reinforcement learning algorithm is the Monte Carlo Exploring Starts (MCES) method, also known as optimistic policy iteration, in which the value function is approximated by simulated returns and a greedy policy is selected at each iteration. The convergence of this algorithm in the general setting has been an open question. In this paper, we investigate the convergence of this algorithm for the case with undiscounted costs, also known as the stochastic shortest path problem. The results complement existing partial results on this topic and thereby help further settle the open problem.
Task-Aware Neural Architecture Search

The design of handcrafted neural networks requires a lot of time and resources. Recent techniques in Neural Architecture Search (NAS) have proven to be competitive or better than traditional handcrafted design, although they require domain knowledge and have generally used limited search spaces. In this paper, we propose a novel framework for neural architecture search, utilizing a dictionary of models of base tasks and the similarity between the target task and the atoms of the dictionary; hence, generating an adaptive search space based on the base models of the dictionary. By introducing a gradient-based search algorithm, we can evaluate and discover the best architecture in the search space without fully training the networks. The experimental results show the efficacy of our proposed task-aware approach.
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data

We propose a novel network initialization method using Perlin noise for training image classification networks with a limited amount of data. Our main idea is to initialize the network parameters by solving an artificial noise classification problem, where the aim is to classify Perlin noise samples into their noise categories. Specifically, the proposed method consists of two steps. First, it generates Perlin noise samples with category labels defined based on noise complexity. Second, it solves a classification problem, in which network parameters are optimized to classify the generated noise samples. This method produces a reasonable set of initial weights (filters) for image classification. To the best of our knowledge, this is the first work to initialize networks by solving an artificial optimization problem without using any real-world images. Our experiments show that the proposed method outperforms conventional initialization methods on four image classification datasets.
Real Number Signal Processing can Detect Denial-of-Service Attacks

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so- called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the communication. Accordingly, it is of crucial interest for the legitimate users to detect such DoS attacks. Turing machines provide the fundamental limits of today’s digital computers and therewith of the traditional signal processing. It has been shown that these are incapable of detecting DoS attacks. This stimulates the question of how powerful the signal processing must be to enable the detection of DoS attacks. This paper investigates the general computation framework of Blum-Shub-Smale machines which allows the processing and storage of arbitrary reals. It is shown that such real number signal processing then enables the detection of DoS attacks.
A Hybrid Multi-Modal System for Conducting Virtual Workshops Using Interactive Voice Response and the WhatsApp Business API

Abstract: Interactive voice response (IVR) forums such as CGNet Swara and Avaaj Otalo have played a pivotal role in empowering marginalised communities by providing an avenue to make their voice heard through simple phone calls. At the same time, growing internet penetration and affordable data plans are altering the ways in which rural Indian communities access and consume information. Within the context of a shift from voice to richer content environments, we present the design of a multi-modal awareness generation and data collection platform built around IVR and the WhatsApp Business API. This model was deployed for delivering virtual training modules to cotton farmers in rural Maharashtra. During the 27 day deployment, 176 people participated in the intervention, out of which 122 and 54 attempted the modules on IVR and WhatsApp, respectively. In this paper, we highlight some of the interesting findings and lessons learnt during the intervention.
Evaluating Presence in VR with Self-Representing Auditory-Vibrotactile Input

In this paper, we present the results of an experiment testing various pairings of auditory feedback devices on immersion and emotion in Virtual Reality (VR). We investigate the effects of bone conduction headphones, a chest-mounted vibration speaker, headphones, and combinations thereof, in combination with internal (self-representing) sounds and vibrations in two simulated scenarios. Results suggest that certain auditory-vibrotactile inputs can influence immersion in an intense virtual scene and evoke emotions in a relaxing virtual scene. In addition, self-representing sounds were observed to significantly weaken immersion in the relaxing scene.
Perturbative readout-error mitigation for near-term quantum computers

Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors can be mitigated by classical postprocessing given the access of an experimental \emph{response matrix} that describes the error associated with measurement of each computational basis state. However, the resources required to characterize a complete response matrix and to compute the corrected probability distribution scale exponentially in the number of qubits $n$. In this work, we modify standard matrix inversion techniques using two perturbative approximations with significantly reduced complexity and bounded error when the likelihood of high order bitflip events is strongly suppressed. Given a characteristic error rate $q$, our first method recovers the probability of the all-zeros bitstring $p_0$ by sampling only a small subspace of the response matrix before inverting readout error resulting in a relative speedup of $\text{poly}\left(2^{n} / \big(\begin{smallmatrix} n \\ w \end{smallmatrix}\big)\right)$, which we motivate using a simplified error model for which the approximation incurs only $O(q^w)$ error for some integer $w$. We then provide a generalized technique to efficiently recover full output distributions with $O(q^w)$ error in the perturbative limit. These approximate techniques for readout error correction may greatly accelerate near term quantum computing applications.
VPU Specific CNNs through Neural Architecture Search

The success of deep learning at computer vision tasks has led to an ever-increasing number of applications on edge devices. Often with the use of edge AI hardware accelerators like the Intel Movidius Vision Processing Unit (VPU). Performing computer vision tasks on edge devices is challenging. Many Convolutional Neural Networks (CNNs) are too complex to run on edge devices with limited computing power. This has created large interest in designing efficient CNNs and one promising way of doing this is through Neural Architecture Search (NAS). NAS aims to automate the design of neural networks. NAS can also optimize multiple different objectives together, like accuracy and efficiency, which is difficult for humans. In this paper, we use a differentiable NAS method to find efficient CNNs for VPU that achieves state-of-the-art classification accuracy on ImageNet. Our NAS designed model outperforms MobileNetV2, having almost 1% higher top-1 accuracy while being 13% faster on MyriadX VPU. To the best of our knowledge, this is the first time a VPU specific CNN has been designed using a NAS algorithm. Our results also reiterate the fact that efficient networks must be designed for each specific hardware. We show that efficient networks targeted at different devices do not perform as well on the VPU.
Accessibility of environmental data for sharing: The role of UX in large cyberinfrastructure projects

Incorporating user experience (UX) testing when creating research cyberinfrastructure is often overlooked, but if left too late, the cost of retrofitting is considerable, and the very clients the cyberinfrastructure was built to serve may be lost. Successfully integrating UX testing into the product development cycle can be difficult but rewarding. This paper describes how UX evaluations were incorporated over ten years of operation of DataONE (www.dataone.org), a multi-sector science research cyberinfrastructure project created to support the discovery, access, and sustainability of data about life on Earth and the environment that sustains it. The diverse stakeholders in DataONE include data creators and users such as researchers and government workers across the broad scope of the earth and environmental sciences as well as those who hold and manage data such as libraries and data repositories. Between 2009 and 2019 DataONE members designed and constructed data management tools and services to fulfill the DataONE objectives. To assist in achieving its goals, a participatory design approach was used by establishing several largely volunteer and stakeholder-representative working groups, including the Usability and Assessment Working Group. This Working Group conducted over forty UX evaluations to assess the usability of DataONE products and websites at various stages of the development process. In addition to improving the usability of DataONE products, the UX evaluations fostered community involvement by building trust and engagement with the products being developed. The DataONE UX experience yields several important lessons which will improve the success of other projects. It is our conclusion that UX testing should be a mandatory part of the design of any cyberinfrastructure project.
Computer Vision System for Reading Analog Gauges at Power Substation

This paper proposes a computer vision system for reading analog gauges at a power substation. The gauges mentioned in this paper include oil level gauges, winding temperature gauges and SF6 gas density gauges. We present two different algorithms since an oil level gauge has different characteristics from a winding temperature gauge or an SF6 gas density gauge. For the oil level gauge, a color segmentation method is used to detect the pointer. Then we use Canny Edge Detector in combination with the Hough Circle Transform method to detect scale marks. Based on the position of the pointer and the marks, we detect the indicating value of the gauge. For winding temperature gauges and SF6 gas density gauges, we find the direction of the pointer based on the geometry properties of a double-edged pointer. With this information, we can calculate the value indicated on the gauge face using interpolation between the direction of the pointer and the value of the gauge. The system error is almost zero in case of an oil level gauge and remains low in the case of a winding temperature gauge and an SF6 gas density gauge. Mobile robots integrated with this computer vision system will work faster and more accurately than humans and will also help avoid dangerous accidents.
Improved Spatio-Temporal Convolutional Neural Networks for Traffic Police Gestures Recognition

In the era of artificial intelligence, human action recognition is a hot spot in the field of vision research, which makes the interaction between human and machine possible. Many intelligent applications benefit from human action recognition. Traditional traffic police gesture recognition methods often ignore the spatial and temporal information, so its timeliness in human computer interaction is limited. We propose a method that is Spatio-Temporal Convolutional Neural Networks (ST-CNN) which can detect and identify traffic police gestures. The method can identify traffic police gestures by using the correlation between spatial and temporal. Specifically, we use the convolutional neural network for feature extraction by taking into account both the spatial and temporal characteristics of the human actions. After the extraction of spatial and temporal features, the improved LSTM network can be used to effectively fuse, classify and recognize various features, so as to achieve the goal of human action recognition. We can make full use of the spatial and temporal information of the video and select effective features to reduce the computational load of the network. A large number of experiments on the Chinese traffic police gesture dataset show that our method is superior.
CC-Loss: Channel Correlation Loss for Image Classification

The loss function is a key component in deep learning models.A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective application of information theory for classification problems.Based on this loss, many other loss functions have been proposed, e.g., by adding intra-class and inter-class constraints to enhance the discriminative ability of the learned features.However, these loss functions fail to consider the connections between the feature distribution and the model structure.Aiming at addressing this problem, we propose a channel correlation loss (CC-Loss) that is able to constrain the specific relations between classes and channels as well as maintain the intra-class and the inter-class separability.CC-Loss uses a channel attention module to generate channel attention of features for each sample in the training stage.Next, an Euclidean distance matrix is calculated to make the channel attention vectors associated with the same class become identical and to increase the difference between different classes.Finally, we obtain a feature embedding with good intra-class compactness and inter-class separability.Experimental results show that two different backbone models trained with the proposed CC-Loss outperform the state-of-the-art loss functions on three image classification datasets.
KAN: Knowledge-Augmented Networks for Few-Shot Learning

Few-shot learning task aims to explore a model that is able to quickly learn new concepts by learning a few examples. The current approaches learning new categories with few images or even a single image are only based on the the visual modality. However, it is difficult to learn the representative features of new categories by a few images. This is because some categories are similar in vision. Moreover, due to the viewpoint, luminosity and that sometimes individuals of the same species appear markedly different from one another, the models are not able to learn the exact representation of classes. Therefore, considering that semantic information can enhance understanding when visual information is limited, we propose Knowledge-Augmented Networks (KAN), which combines the visual features with the semantic information extracted from knowledge graph to represent the features of each class. We demonstrate the effectiveness of our method on standard few-shot learning tasks, and further observe that with the augmented semantic information from knowledge graph, KAN is able to learn more disentangled representations. Experiments show that our model outperforms the state-of-the-art methods.
Sample-aware Data Augmentor for Scene Text Recognition

Deep neural networks (DNNs) have been widely used in scene text recognition, and achieved remarkable performance. Such DNN-based scene text recognizers usually require plenty of training data for training, but data collection and annotation is usually cost-expensive in practice. To alleviate this issue, data augmentation is often applied to train the scene text recognizers. However, existing data augmentation methods including affine transformation and elastic transformation methods suffer from the problems of under- and over-diversity, due to the complexity of text contents and shapes. In this paper, we propose a sample-aware data augmentor to transform samples adaptively based on the contents of samples. Specifically, our data augmentor consists of three parts: gated module, affine transformation module, and elastic transformation module. In our data augmentor, affine transformation module focuses on keeping the affinity of samples, while elastic transformation module aims to improve the diversity of samples. With the gated module, our data augmentor determines transformation type adaptively based on the properties of training samples and the recognizer capability during the training process. Besides, our framework introduces an adversarial learning strategy to optimize the augmentor and the recognizer jointly. Extensive experiments on scene text recognition benchmarks show that our sample-aware data augmentor significantly improves the performance of state-of-the-art scene text recognizer.
Correlation and congruence modulo based clustering technique and its application in energy classification

Clustering is an unsupervised classification technique used to form groups of unlabeled data sets called clusters. K-means algorithm is a popular clustering algorithm in which random cluster centers are initially taken. The cluster centers are randomly picked in most of the clustering techniques for which the results obtained by these techniques might be compromised. In clustering techniques, the cluster centers are recalculated iteratively unless convergence is achieved, which once again may compromise the accuracy of the results. In all these iterations, the data elements continue to switch to the neighboring clusters, which may add a bias to the clustering results. Thus, a new clustering technique known as “Clustering through correlation and congruence modulo (CCCM),”is developed based on the correlation reward (reinforcement factor) and the congruence modulo operator. In the CCCM technique, cluster centroids are fixed and selected in the first iteration by arranging all the involved variables in order of importance that is calculated by using spearman ranked correlation analysis. After arranging these variables, the congruence modulo is used to convert these variables into equally sized buckets. The correlation values for the elements placed in these buckets are again calculated and the difference is reinforced by bucket rearrangement. When the initial cluster centers are selected, the points are placed in clusters (data instances) like the conventional K-means clustering algorithm. This newly developed algorithm is tested on energy data from 40 countries and each country has 16 energy parameters collected from the online sources over a period of ten years. The proposed technique produced more accurate clusters in less time (achieved accuracy and efficiency) as compared to the K-mean algorithm.