Reviewerly/exHarmony huggingface dataset:
SAR ship detection in complex background based on multi-feature fusion and non-local channel attention mechanism

With the development of artificial intelligence (AI) and synthetic aperture radar (SAR) technology, SAR ship target automatic detection has made significant progress. However, due to the complex structure and various sizes of ship, detecting multi-scale ships under complex backgrounds is still challenging. The existing convolution neural networks (CNNs)-based SAR ships detection methods suffer from insufficient feature extraction capabilities and poor feature reusability. To address the issues, an SAR ship detection method based on multi-feature fusion and non-local channel attention mechanism (MFNL-Net) is proposed in this article. MFNL-Net is a one-stage detector and designed to improve the performance of detecting multi-scale ships by enhancing the feature extraction ability and nonlinear relationship between different features. Specially, the non-local channel attention block (NLCA-Block) is employed to enhance the nonlinear relationship between different channel features and the ability of local feature expression; the pooling feature fusion (PFF) module and the deconvolution feature fusion (DFF) module are reasonably combined to obtain abundant ships target feature information and suppress interference caused by surroundings; the multi-scale spatial pyramid pooling (MSPP) is used to boost feature information interaction and reduce the loss of significant feature; the focus classification loss is used to alleviate influences of the positive-negative samples imbalance. To evaluate the effectiveness of MFNL-Net, experiments are conducted on real SAR ship datasets SSDD and GS-Ship. Experimental results show that our method obtains 9.10%, 8.61%, 7.23%, and 4.44% gains in mean average precision (mAP) compared with the four state-of-the-art CNNs-based methods, respectively.
Making person search enjoy the merits of person re-identification

Person search is an extended task of person re-identification (Re-ID). However, most existing one-step person search works do not study how to employ existing Re-ID models to improve the one-step person search. To address this issue, we propose a Teacher-guided Disentangling Network (TDN) to make the one-step person search enjoy the merits of existing Re-ID research. The proposed TDN can significantly boost person search performance by transferring the advanced person Re-ID knowledge to the person search model. In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a new one-step person search base framework by partially disentangling the two subtasks. Besides, we propose a Knowledge Transfer Bridge module to bridge the scale gap caused by different input formats between the Re-ID model and the one-step person search model. Moreover, we also propose a Ranking with Context Persons strategy to exploit the context information in panoramic images for better ranking. Experiments on two public person search datasets demonstrate the favorable performance of the proposed method.
CogKR: Cognitive Graph for Multi-hop Knowledge Reasoning

Inferring new facts from an existing knowledge graph with explainable reasoning processes is an important problem, known as knowledge graph (KG) reasoning. The problem is often formulated as finding the specific path that represents the query relation and connects the query entity and the correct answer. However, due to the limited expressiveness of individual paths, the majority of previous works failed to capture the complex subgraph structure in the graph. We propose CogKR that traverses the knowledge graph to conduct multi-hop reasoning. More specifically, motivated by the dual process theory from cognitive science, our framework is composed of an extension module and a reasoning module. By setting up a cognitive graph through iteratively coordinating the two modules, CogKR can cope with more complex reasoning scenarios in the form of subgraphs instead of individual paths. Experiments on three knowledge graph reasoning benchmarks demonstrate that CogKR achieves significant improvements in accuracy compared with previous methods while providing the explainable capacity. Moreover, we evaluate CogKR on the challenging one-shot link prediction task, exhibiting the superiority of the framework on accuracy and scalability compared to the state-of-the-art approaches.
Sentiment analysis of Twitter data by making use of SVM, Random Forest and Decision Tree algorithm

Data production over the internet has been increased tremendously because of social media sites' growth. A significant number of users share their thoughts, images, videos etc., on their particular accounts. Among these, all varieties available for the thoughts expression twitter provide a concise way to share users' thoughts. This thought shared by users is called tweets. These tweets may lead to a big revolution for a good change or, at the same time, lead to a big issue over any particular topic. So sentiments involved in such tweets needed to be classified, and then only the tweet may be shared with other users. Here in our paper, we have taken a dataset from the Kaggle website, which was collected based on KFC's challenge and McDonald's of AI. We have used more than 14000 tweets for analysis. The cleaning of the dataset is done using Term Frequency- Inverse Document Frequency (TF-IDF). After cleaning the dataset, we have applied three classification algorithms for the testing purpose that are Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT). Among these three algorithms, the highest accuracy is obtained from the Decision tree algorithm is 88.51%. The experiment calculates accuracy, recall, precision and F1 measures.
Creating electronic oscillator-based Ising machines without external injection locking

Coupled electronic oscillators have recently been explored as a compact, integrated circuit- and room temperature operation-compatible hardware platform to design Ising machines. However, such implementations presently require the injection of an externally generated second-harmonic signal to impose the phase bipartition among the oscillators. In this work, we experimentally demonstrate a new electronic autaptic oscillator (EAO) that uses engineered feedback to eliminate the need for the generation and injection of the external second harmonic signal to minimize the Ising Hamiltonian. Unlike conventional relaxation oscillators that typically decay with a single time constant, the feedback in the EAO is engineered to generate two decay time constants which effectively helps generate the second harmonic signal internally. Using this oscillator design, we show experimentally, that a system of capacitively coupled EAOs exhibits the desired bipartition in the oscillator phases without the need for any external second harmonic injection, and subsequently, demonstrate its application in solving the computationally hard Maximum Cut (MaxCut) problem. Our work not only establishes a new oscillator design aligned to the needs of the oscillator Ising machine but also advances the efforts to creating application specific analog computing platforms.
Futuristic person re-identification over internet of biometrics things (IoBT): Technical potential versus practical reality

This article presents an overview of how person re-identification can be achieved over the Internet of Biometric Things (IoBT) architecture by enabling technologies and protocols for multimodal biometric authentication leveraging futuristic cues. The Internet of Things (IoT), as a new era of technology, extends the power of the internet to a whole range of devices, thus reshaping our daily lives in the best possible way. IoT-enabled intelligent surveillance devices are the most indispensable part of public safety and security in smart cities. These IoT devices generate a vast amount of surveillance traffic that is practically impossible for humans to continuously monitor and/or analyze. Person re-identification (PRId), which aims to track and recognize a person in a multi-camera scene is an important feature of visual surveillance systems in IoT infrastructures and can utilize the aforementioned traffic. This is where the concept of IoBT, which is a cloud-centric biometric authentication architecture composed of these IoT-enabled devices for the PRId system, comes into play. This article conceptualizes an overview of interpreting various futuristic cues on the IoT platform for achieving PRId. We highlight some opportunities and key challenges of implementing this futuristic PRId system on IoBT. The article is a proof of concept of the technical potential of such implementation in the near future.
Guiding Clean Features for Object Detection in Remote Sensing Images

Recently, object detection has gained significant progress in remote sensing images. Nevertheless, we conclude two defects in remote sensing image object detection. At first, most methods rely on feature pyramid, but the features of different levels would influence each other when we use top-down operation. Second, the traditional label assignment strategy cannot assign suitable labels, as it adopts the fixed intersection over union (IoU) threshold to divide positive samples and negative samples during training. According to the problems we pointed out, a simple yet effective framework is employed to eliminate these two limitations. It integrates two novel components: aware feature pyramid network (AFPN) and group assignment strategy (GAS). AFPN is to mitigate the adverse effects caused by the first problem. Specifically, it learns a vector for the higher level features in the feature pyramid to obtain clean features. As for the second limitation, we recommend a new label assignment strategy named GAS to address this problem. Samples will be grouped according to their overlaps with ground truth, and then, they are assigned to positive or negative labels in each group. Extensive experiments are conducted on the large-scale object detection dataset DIOR and DOTA. With the newly introduced two key components, our model significantly improves the detection accuracy. Without bells and whistles, our proposed method achieves 2.0% and 1.9% higher mean average precision (mAP) than Faster R-CNN with FPN when using ResNet50 and ResNet101 as the backbones, respectively. Finally, we obtain 73.3% mAP on the DIOR dataset without any tricks. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hm-better/dior_detect</uri> .
Let’s (re)tweet about racism and sexism: responses to cyber aggression toward Black and Asian women

Online, anyone’s words can easily be amplified – and on Twitter, the platform’s algorithm highlights tweets that gain attention from other users, which can exponentially reinforce a tweet’s popularity. Moreover, retweets can help spread a message well beyond the reach of its original poster. Thus, users’ interactions with posts containing or making reference to racism or sexism both illuminate the ways individuals accept, challenge, or engage with racism and sexism online, and shape how those messages spread. Using an original dataset of 59.5 million tweets, I test how particular features of messages referencing Black and Asian women predict user engagement (retweets, likes, and replies). This analysis further focuses on messages including terms that express racist or sexist content. Generally, messages including covert racist or sexist insults have a modest positive effect on all measures of user engagement (retweets, likes, and replies), which may suggest that social media environments allow individuals the time and opportunity to contend with topics that can be more difficult in-person. Additionally, variations in engagement with tweets that include references to women, Black or Asian individuals implies that users respond differently to messages involving references to and normative images of different racial, ethnic, and gendered identities. This research illuminates how specific manifestations of racialized and gendered language referencing women, Black and Asian people can not only encourage more engagement, but also share, accept, or challenge messages about marginalized identities.
User recommendation in online health communities using adapted matrix factorization

Purpose Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap. Design/methodology/approach This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community. Findings The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system. Practical implications This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs. Originality/value This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.
RUSSELLIAN DEFINITE DESCRIPTION THEORY—A PROOF THEORETIC APPROACH

Abstract The paper provides a proof theoretic characterization of the Russellian theory of definite descriptions (RDD) as characterized by Kalish, Montague and Mar (KMM). To this effect three sequent calculi are introduced: LKID0, LKID1 and LKID2. LKID0 is an auxiliary system which is easily shown to be equivalent to KMM. The main research is devoted to LKID1 and LKID2. The former is simpler in the sense of having smaller number of rules and, after small change, satisfies cut elimination but fails to satisfy the subformula property. In LKID2 an additional analysis of different kinds of identities leads to proliferation of rules but yields the subformula property. This refined proof theoretic analysis leading to fully analytic calculus with constructive proof of cut elimination is the main contribution of the paper.
Error Thresholds for Arbitrary Pauli Noise

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 20 May 2020Accepted: 30 March 2021Published online: 26 August 2021Keywordsquantum information theory, quantum channel capacities, computational group theory, quantum error correction, error thresholds, group actionsAMS Subject Headings81P45, 81P70, 94A40, 05E18Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Research on Evaluation System of Classroom Teaching Quality in Colleges and Universities Based on 5G Environment

With the rapid development of wireless technology, mobile communication business has experienced the development from voice service to mobile broadband data service, mobile communication network has gradually evolved from 3G, 4G to 5G, that is, the fifth generation of mobile communication network. With the advent of 5G technology, a new generation of information technology has accelerated the reform of higher education system. Building an information platform for the quality of classroom teaching in colleges and universities under the 5G environment has become an important research topic for the information construction of colleges and universities and financial work of colleges and universities. This system is based on B/S architecture pattern, using SSM framework and MYSQL database development. Constructing perfect classroom teaching quality evaluation system, objectively, justly and scientifically evaluate teachers' teaching work to the curriculum, and improve the teaching quality system of the school as a whole. The main core function of the system is to realize that students can evaluate the teaching quality of the course teachers online, teachers can evaluate peers through listening to the class and view the summary information of students' evaluation of their own and peer evaluation of their own summary information, teachers can intuitively get the feedback information of the level of teaching quality. More and more students begin to receive higher education. The quality of classroom teaching and the education level of students have been paid more and more attention by education and government departments. The high quality education level of teachers and the reception degree of students have been the focus of attention all the time. Therefore, an online classroom teaching quality evaluation system, strengthen the construction of teaching quality evaluation system is the top priority.
Improving neural machine translation with latent features feedback

Most state-of-the-art neural machine translation (NMT) models progressively encode feature representation in a bottom-up feed-forward fashion. This traditional encoding mechanism has no guidance from external signals. In computer vision tasks, the feedback connection plays a crucial role, particularly for understanding tasks. In this paper, we propose a simple but effective approach to learn latent feature representations explicitly from input sentences via a latent feature encoder (LFE), which are fed back to an NMT encoder via a top-down feedback mechanism. Through the feedback mechanism, the representations in one layer are influenced by representations of both lower and higher layers, resulting in a more effective encoding mechanism. Besides, to enhance the capability of the LFE in better capturing latent features from the source sentences, we pre-train the LFE via a Denoising Auto-Encoder (DAE) strategy. Experimentation on the large-scale WMT 2014 English-to-German and WMT 2017 Chinese-to-English translation tasks demonstrates that our proposed LFE, either pre-trained with the DAE or not, significantly outperforms the strong baseline.
Flexible Interactive Retrieval SysTem 2.0 for Visual Lifelog Exploration at LSC 2021

With a huge collection of photos and video clips, it is essential to provide an efficient and easy-to-use system for users to retrieve moments of interest with a wide variation of query types. This motivates us to develop and upgrade our flexible interactive retrieval system for visual lifelog exploration. In this paper, we briefly introduce version 2 of our system with the following main features. Our system supports multiple modalities for interaction and query processing, including visual query by meta-data, text query and visual information matching based on a joint embedding model, scene clustering based on visual and location information, flexible temporal event navigation, and query expansion with visual examples. With the flexibility in system architecture, we expect our system can easily integrate new modules to enhance its functionalities.
Enhancing Neural Sign Language Translation by highlighting the facial expression information

Neural Sign Language Translation (SLT), which is an important cross-modal task to bridge the communication gap between the deaf and the hearing people, has attracted great attention in the field of artificial intelligence, computer vision, and multimedia, etc. Although some great progress has been achieved recently, current neural SLT models still suffer from translation errors caused by under-consideration of non-manual features such as facial expressions, which can carry critical information during communication among the deaf. This paper aims to enhance the traditional neural SLT models by highlighting the facial expression information in the CNN-based sign video representing part. Two novel schemes have been proposed. The first scheme is based on a Multi-stream Architecture, which extracts and represents the facial expression information in an additional stream and aggregates it with the information from the main stream. The second scheme is a pre-trained scheme based on Regions of Interest (RoIs), which first trains a multi-region detection module for recognizing the features of faces and bodies and then transfers the pre-trained parameters to the module in SLT model. To validate the proposed models, we conducted the experiments upon the publicly available SLT benchmark dataset: RWTH-PHOENIX-Weather-2014T. Experimental results showed that both the above-mentioned schemes can improve the performance of SLT models. Especially, the RoIs-based scheme can achieve an improvement up to 1.6+ BLEU-4 score gains, while the multi-stream scheme quantitatively analyzed the importance of the face mainly through flexible components, providing a sufficient theoretical basis for the RoIs-based scheme.
3D Measurement of Structured Specular Surfaces Using Stereo Direct Phase Measurement Deflectometry

With the rapid development of modern manufacturing processes, ultra-precision structured freeform surfaces are being widely explored for components with special surface functioning. Measurement of the 3D surface form of structured specular objects remains a challenge because of the complexity of the surface form. Benefiting from a high dynamic range and large measuring area, phase measurement deflectometry (PMD) exhibits great potential in the inspection of the specular surfaces. However, the PMD is insensitive to object height, which leads to the PMD only being used for smooth specular surface measurement. Direct phase measurement deflectometry (DPMD) has been introduced to measure structured specular surfaces, but the surface form measurement resolution and accuracy are limited. This paper presents a method named stereo-DPMD for measuring structured specular objects by introducing a stereo deflectometor into DPMD, so that it combines the advantages of slope integration of the stereo deflectometry and discontinuous height measurement from DPMD. The measured object is separated into individual continuous regions, so the surface form of each region can be recovered precisely by slope integration. Then, the relative positions between different regions are evaluated by DPMD system to reconstruct the final 3D shape of the object. Experimental results show that the structured specular surfaces can be measured accurately by the proposed stereo-DPMD method.