Reviewerly/exHarmony huggingface dataset:
Hetero-Modal Learning and Expansive Consistency Constraints for Semi-supervised Detection from Multi-sequence Data

Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advances and increased data availability. However, the high costs of annotations hinder the collection of large and completely labeled datasets, motivating semi-supervised detection approaches. In this paper, we introduce mean teacher hetero-modal detection (MTHD), which addresses two important gaps in current semi-supervised detection. First, it is not obvious how to enforce unlabeled consistency constraints across the very different outputs of various detectors, which has resulted in various compromises being used in the state of the art. Using an anchor-free framework, MTHD formulates a mean teacher approach without such compromises, enforcing consistency on the soft-output of object centers and size. Second, multi-sequence data is often critical, e.g., for abdominal lesion detection, but unlabeled data is often missing sequences. To deal with this, MTHD incorporates hetero-modal learning in its framework. Unlike prior art, MTHD is able to incorporate an expansive set of consistency constraints that include geometric transforms and random sequence combinations. We train and evaluate MTHD on liver lesion detection using the largest MR lesion dataset to date (1099 patients with \(>5000\) volumes). MTHD surpasses the best fully-supervised and semi-supervised competitors by \(10.1\%\) and \(3.5\%\), respectively, in average sensitivity.
Watermarking in Computer Aided Design-Generated 3D Objects

Computer aided design (CAD) generates 2D and 3D design of the objects usually used for machine design. These data items are publically available over Internet. Wide accessibility of these data items security becomes an important issue. These data items can be protected use either by using cryptography, digital signature or watermarking from unauthorized access. In the proposed work, watermarking scheme for computer aided design (CAD)-generated 3D object is proposed that keeps geometrical shape and quality of watermarked objects intact. In the proposed method, geometrical shape of the 3D objects is analyzed to embed the watermark in least perceivable area of 3D objects. The experimental results report robustness against similarity transformation (distortion-less) and distortion attacks.
Hyper Spectral Image Noise Removal Using CNN Classifier

This project proposes Multilevel Cloud Detection in Remote Sensing Images (Hyperspectral Image) Based on Deep Learning and FLIC approach. The median filter is used for preprocessing. FLIC converts image into high-quality superpixels. DWT algorithm is used for the segmentation process. The proposed CNN and SRC classifier remove noises in the Hyperspectral cloud image. This project is implemented using MATLAB simulation.
Network training method based on Parallel Vision theory for object detection in battlefield environment

Visual perception and understanding in complex battlefield environments have few data samples, which cannot meet the need of deep network model training in variable and complex environments. In this paper, based on parallel vision theory, deep learning network training for object detection is conducted using artificial virtual battlefield environment. Virtual battlefield environment data are collected for experimental training and validation analysis, and further training tests are conducted in real scene data. The experiments verified that the deep network model training with virtual-real interaction can effectively solve the data deficient problem of object detection in the battlefield.
A Reinforcement Learning Framework for Multi-source Adaptive Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used in video streaming recently. With DASH, a video is stored in multiple equal-playing-time chunks with different quality levels. Video chunks are in-order delivered from a single source over a path in traditional DASH. The adaptation function in video player chooses a suitable quality level to request depending on current network status for each video chunk. In modern networks such as content delivery networks, edge caching, content-centric networks, etc., popular video contents are replicated at multiple cache nodes. Utilizing multiple sources for video streaming is investigated in this paper. We propose a reinforcement learning based algorithm, called RAMS, for rate adaptation in multi-source video streaming. The proposed algorithm outperforms the other notable adaptation methods.
GPS-ReID: A Benchmark for Cross-Modal Pedestrian Retrieval

Traditional person re-identification aims to retrieve the surveillance images containing the same pedestrian. As the quick development of modern cities, a large number of multimodal personal information, including mobile information and social network log, is available and useful for customer identification and crime tracking. Compared with traditional person reidentification (ReID) only based on image modality, how to make full use of multi-modal information for efficient person ReID is more challenging. In this paper, we propose a brand new cross-modal pedestrian retrieval task based on a novel multi-modal dataset containing GPS trajectories and surveillance images. Three sub-tasks are evolved in the benchmark: unsupervised GPS-to-Image, Image-to-GPS, and Image-to-Image retrieval. In order to further verify our ideas, we propose the Similarity Driven Model (SDM), which utilizes the attention mechanism to improve the performance of domain adaptation. Furthermore, we build a cross-modal heterogeneous graph based on SDM, and adopt Triplet-Walk to uniformly represent different modalities for retrieval. Experimental results demonstrate that our method achieves the state-of-the-art on the GPS-ReID dataset.
Study on Human-machine Interactive System based on Binocular Vision and Depth Camera

The human-machine interactive system to be introduced in this paper is based on a binocular-vision imaging system[1-3] and a Leapmotion depth camera[4]. The system is developed and studied to use non-contactive gesture-recognition technology to directly interact with the virtual stereoscopic object synthesized in the real space. And a method is also presented to accurately integrate the gesture and the virtual stereoscopic image synthesized in the real space. In this way, in terms of different-gesture recognition, the operator can conduct different interactions with the virtual 3D object in the real space and realize vivid multiple-gesture operation. The operational and interactive effects of the system are demonstrated to be close to real-object operation through trials on 5 testers and the results will be presented in the last part of the paper.
Development and Research of Quantum Models for Image Conversion

Quantum imaging is a new trend that is showing promising results as a powerful addition to the arsenal of imaging techniques. Per-pixel representation of an image using classical information requires a huge amount of computational resources. Hence, exploring techniques for representing images in a different information paradigm is important. This paper describes the variety of options for representing images in quantum information. Image processing is a well-established area of computer science with many applications in today’s world such as face recognition, image analysis, image segmentation and noise reduction using a wide range of techniques. A promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers versus the FFT in classical computers. In addition, images encoded in quantum information can obey unique quantum properties such as superposition or entanglement. The laws of quantum mechanics can reduce the required resources for some tasks by many orders of magnitude if the image data is encoded in the quantum state of a suitable physical system. The aim of this work is to develop and study quantum models of image transformation.
Semantic Segmentation using Generative Adversarial Networks with a Feature Reconstruction Loss

The image-to-semantic labels classification is a very challenging task in image processing. Convolutional neural networks (CNN) have managed to achieve the state-of-the-art quality of the segmented image in semantic segmentation tasks. Still, the classification capability of such algorithms is not satisfactory to segment images that contain complex object boundaries and minimal regions. Recently, the Generative Adversarial Networks (GAN) were introduced, which can solve the overfitting of the generator network using the adversarial loss. In this paper, a GAN-based segmentation model is proposed, in which the Conditional GAN (CGAN) model is used as base architecture. Feature reconstruction loss is introduced in this composite model to solve the identification and classification of visually small elements in images. A pre-trained deep convolution neural network is adopted to calculate feature reconstruction loss. The usage of Feature reconstruction loss has demonstrated the high quality of the output labels and improved segmentation masks. The evaluation of the proposed model on the cityscapes dataset has shown the effectiveness of GAN-based architecture in semantic segmentation of multiclass images. The proposed model achieved 83.3% accuracy on the cityscapes test dataset, which is superior to most semantic segmentation state-of-the-art methods.
Quantum Private Query of Blocks Based on D-dimensional Bell State

research-article Share on Quantum Private Query of Blocks Based on D-dimensional Bell State Authors: Siwen Hu Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Xiuli Song Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Aidong Xu GuangdongProvincial Key Laboratory of Power System Network Security, China GuangdongProvincial Key Laboratory of Power System Network Security, ChinaView Profile Authors Info & Claims ICBDC 2021: 2021 6th International Conference on Big Data and ComputingMay 2021 Pages 104–108https://doi.org/10.1145/3469968.3469984Online:06 October 2021Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A systematic exposition of Punjabi Named Entity Recognition using different Machine Learning models

Named Entity Recognition plays a vital role in diverse Natural Language Processing applications like Information Extraction, Information Retrieval, Text Mining, Machine Translation, and Question Answering etc. This paper presents the evaluation of Named Entity Recognition task for a resource poor language like Punjabi. Different challenges posed in the task have also been discussed. A annotated corpus of 2,00,000 words have been developed for recognition task. The system has been evaluated on different machine learning models like Hidden Markov Model, Maximum Entropy and Conditional Random Fields with f-score values of 77.61, 83.65 and 93.21 respectively.
Intelligent System for Assessing the Socio-economic Situation in the Region

This article analyzes the problems of monitoring and managing the socio-economic situation. The analysis of the socio-economic situation involves the determination of the quantitative characteristics of the dynamic series, the trend of growth, decline or stabilization, the identification of causal factors, in specific territories and for different groups. The criterion of fuzzy controllability was obtained to solve the problem of forecasting and controlling the socio-economic situation. A mathematical model and algorithm for solving the task of monitoring and managing the socio-economic situation based on interval mathematics and their software implementation are described. The social effect will be expressed in improving the safety of people’s lives. As a result, it will be possible to carry out preventive measures in the necessary territories.
A New Bottom-up Human Pose Estimation Method by Body Center and Anchor Points

There are two stages in bottom-up human pose estimation method, joint detection and joint candidate grouping. Optimizing grouping algorithms can significantly improve the performance of pose estimation. In this paper, we introduce body center, a center point of person instance, and anchor point, a corresponding assistant position point, for the task of grouping. The anchor point is the center of joint and body center, which can help joint grouping to the corresponding person instance like an anchor. The body center and anchor point can be predicted simultaneously with the joint candidate by the same backbone. So, this new grouping method can fully exploit the features extracted by the step of joint detection. On the COCO keypoints dataset, the proposed method performs on par with the existing state-of-the-art bottom-up method in accuracy.
Research on the Statistical Method of Massive Data by Analyzing the Mathematical Model of Feature Combination of Point Data

With the rapid development of science and technology in China, data mining technology has also been greatly improved. The most basic method of data mining technology is statistical method, and with the improvement of statistical technology, many new data mining technologies have emerged. Therefore, in order to provide valuable suggestions and practical research experience for data mining researchers and promote the progress and development of data mining technology, it is necessary to conduct in-depth research on the application of statistical technology in data mining. This paper introduces the significance and current situation of data research, and focuses on several typical statistical methods and techniques involved in data mining and their practical applications.
Multi-task and Multi-scale Face Recognition Based on CNN

Face recognition has been widely used in social security, privacy protection and biometrics due to its advantages. However, under wild world condition face recognition task can be very complex due to illumination variation, occlusion, facial expression, etc. Considering these, this paper proposes a multi-scale feature fusion convolutional neural network combined with multi-task learning. The face recognition main task is decomposed into pose estimation, illumination classification and occlusion classification subtasks, which are jointly used to promote the optimization of the main task. Then, the weight distribution strategy of the loss function among subtasks and its influence are discussed. Experiment results show that the proposed method achieves good performance on the public face verification dataset and our own face verification dataset.
A Simple Strategy for Choosing Network Structures in a Object Detection Project with Transfer Learning

Compare different network configurations in the early stages of an object detection project can be an interesting approach to identify the one that can provide the best performance and, thus, optimize the investment of time and research efforts for the next steps. In this work we will explore the issue through the study of object recognition applied to a category of items, specifically fruits, where the proposed strategy will be to select a public image dataset of these items and to train some different structures of deep learning networks. We built different combinations of structures composed of pre-trained base networks, in which the upper layers were replaced by new structures, with an increasing degree of complexity. Then will evaluate the results of these pre-trained networks with 25 images of individual fruits obtained on the internet. After we compare the performance between the different structures of networks, it is intended to demonstrate if there is a relationship between the training performance of specific models with the complexity of its upper layers when we apply them to a practical evaluation.
An Empirical Study on Knowledge Aggregation in Academic Virtual Community Based on Deep Learning

Academic virtual community provides an environment for users to exchange knowledge, so it gathers a large amount of knowledge resources and presents a trend of rapid and disorderly growth. We learn how to organize the scattered and disordered knowledge of network community effectively and provide personalized service for users. We focus on analyzing the knowledge association among titles in an all-round way based on deep learning, so as to realize effective knowledge aggregation in academic virtual community. We take ResearchGate (RG) “online community” resources as an example and use Word2Vec model to realize deep knowledge aggregation. Then, principal component analysis (PCA) is used to verify its scientificity, and Wide & Deep learning model is used to verify its running effect. The empirical results show that the knowledge aggregation system of “online community” works well and has scientific rationality.
SOZIL: Self-Optimal Zero-Shot Imitation Learning

Zero-shot imitation learning has demonstrated its superiority to learn complex robotic tasks with less human participation. Recent studies show convincing performance under the condition that the robot follows the demonstration strictly by the learned inverse model. However, these methods are difficult to achieve satisfactory performance in imitation when the demonstration is suboptimal, and the learning of the learned inverse models is vulnerable to label ambiguity issues. In this article, we propose self-optimal zero-shot imitation learning (SOZIL) to tackle these problems. The contribution of SOZIL is twofold. First, goal consistency loss (GCL) is designed to learn the multistep goal-conditioned policy from exploration data. By directly using the goal state as supervision, GCL solves the label ambiguity problem caused by trajectory and action diversity. Second, estimation-based keyframe extraction (EKE) is developed to optimize demonstrations. We formulate the keyframe extraction process as a path optimization problem under suboptimal control. By predicting the performance of the learned policy in executing transitions of any two states, EKE creates a directed graph containing all candidate paths and extracts keyframes by solving the graph’s shortest path problem. Furthermore, the proposed method is evaluated with various simulated and real-world robotic manipulating experiments, such as cable harness assembly, rope manipulation, and block moving. Experimental results show that SOZIL achieves a superior success rate and manipulation efficiency than baselines.
Les anthroponymes italiens délexicaux formés sur des issues de protoroman régional */karne-lakˈsare/ ‘mardi gras’ ou : comment PatRom complète le LEI

Abstract The comparison of the lexical data gathered by the LEI and the medieval and contemporary anthroponymic data gathered by PatRom makes it possible to measure, on a concrete example (the delexical personal names formed on continuators of regional Proto-Romance */karne-lakˈsare/ ‘Shrove Tuesday’), the contributions, modest but not negligible, of anthroponymy to historical lexicology.
Quantum Private Size Comparison Protocol Based On Grover Algorithm

research-article Share on Quantum Private Size Comparison Protocol Based On Grover Algorithm Authors: Yuzheng Wu Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Xiuli Song Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Aidong Xu GuangdongProvincial Key Laboratory of Power System Network Security, China GuangdongProvincial Key Laboratory of Power System Network Security, ChinaView Profile Authors Info & Claims ICBDC 2021: 2021 6th International Conference on Big Data and ComputingMay 2021 Pages 82–85https://doi.org/10.1145/3469968.3469983Online:06 October 2021Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Adaptor: A Real-time Module for a Conversational Agent to Adapt Policy Based on Negative User Behavior

Online spoken language understanding is a challenging task for large-scale conversational agents. For a given user query, there would be several candidate skills capable to process it. It is critical to choose the desired one from them. Moreover, if the user expresses dissatisfaction for the chosen skill, the agent should be capable to adapt its policy accordingly. However, traditional conversational agents rarely pay attention to real-time user behaviors, which makes them relatively static, tending to distribute same queries to same skills even when there were negative behaviors captured. In this paper, we propose Adaptor, a real-time module for the conversational agents to adapt their policy based on real-time negative user behaviors. We collect both explicit and implicit negative user behaviors, and add punishing bias to corresponding skills accordingly. Online results demonstrate that Adaptor can help the conversational agents adapt their policy at conversational-turn level, bringing flexibility and robustness for the agents, and continued positive impacts on user experience.
Incomplete Maximum Matching Segmentation Based on Semantics

This paper combines the advantages of regular segmentation and statistical segmentation, proposing an incomplete maximum matching segmentation method based on semantics. On the basis of ensuring time consumption, the new method solves the defect of word adhesion in the maximum matching algorithm. The innovative work includes: in the preprocessing stage, the forward semantic similarity dictionary is constructed to realize the follow-up word recognition. In the stage of word segmentation, the formula of three feature weight is proposed to redefine the segmentation principle. The experimental results show that the new method has a certain improvement in the accuracy and recall rate, which is suitable for the field of text processing.
Application of Artificial Intelligence Aided Cognitive Computing Method in Building System

Teaching quality evaluation is a process in which a specific evaluation institution analyzes the evaluation information and makes a conclusion according to the activity information of the evaluation object, and finally feeds back the evaluation results to the school management department. Data analysis is a process of identifying effective, potential and understandable patterns from sample data sets by data mining technology. The information system based on Web technology has the characteristics of low cost, good scalability, easy maintenance and management, which is the best way to realize information management. This paper attempts to use rich Internet application (RIA), the most advanced rich client technology, and Java2 Platform Enterprise Edition (J2EE), which is the most widely used enterprise server-side development technology, to realize a teaching quality evaluation system based on the integration of Flex technology and J2EE technology.