Reviewerly/exHarmony huggingface dataset:
Noise-robust interferometric ISAR imaging of 3-D maneuvering motion targets with fine image registration

Low signal-to-noise ratio (SNR) and three-dimensional (3-D) maneuvering motion of targets present enormous challenges to the interferometric inverse synthetic aperture radar (InISAR) imaging. In this paper, we propose a noise-robust InISAR imaging algorithm to acquire high-precision InISAR images of 3-D maneuvering motion targets at low SNR. With respect to time-variant wave path difference (TVWPD) and spatial-variant wave path difference (SVWPD), a novel algorithm, named non-coherent fusion image registration algorithm (NCFIR), is developed to realize fine image registration. By implementing the non-coherent fusion processing on the multi-look ISAR images, it improves the image SNR and therefore makes possible the joint compensation of TVWPD and SVWPD at low SNR. In addition, underpinned by the Bayesian compressive sensing (BCS) theory, a joint multi-channel BCS ISAR imaging algorithm (JMC-BCS) is also proposed. Its capability for suppressing the strong noise and high side lobes of ISAR imaging results effectively improves the SNRs of imaging results. Through iterative processing of JMC-BCS and NCFIR, high-quality 3-D InISAR images of 3-D maneuvering motion targets at low SNR can be obtained. The proposed algorithm improves the SNR of image twice through JMC-BCS and NCFIR, rendering it more robust against noise. Extensive experimental results from both simulated and real data corroborate the effectiveness and robustness of the proposed algorithm which outperforms other available InISAR imaging approaches at low SNR.
Vision-based Collision Avoidance for Mobile Robots through Sim-to-Real Transfer

In mobile robots, collision avoidance has been generally performed using range sensors, but due to its limits, additional RGB image-based control is required. In this paper, we propose an end-to-end visuomotor training method for avoiding collision based on deep reinforcement learning. We created a compact and intensive Gazebo simulation environment for training a robot and tested it both in a real environment and the simulated virtual environment. We demonstrated that the robot is able to successfully explore the test environment without collision, as well as obtain training results that can be generalized in a compact and randomized environment. The result shows the potential for learning-based collision avoidance in outdoor environments, since we do not utilize depth information.
Three-Dimensional Visualization Analysis of Distributed Virtual Reality Taking into Account Grid Scientific Computing Model

With the in-depth development of the application of virtual reality technology, people have higher and higher requirements for the complexity and realism of virtual scene, which far exceeds the real-time processing ability of computer graphics hardware. Back to this, there is an urgent need to solve the contradiction between the complexity of the scene and the real-time interaction. In this paper, the real-time visualization of real-time distributed virtual reality is studied in many aspects. Starting with the analysis of the characteristics of real-time visualization technology of distributed virtual reality, this paper studies the system structure of real-time distributed virtual reality, puts forward the idea of realizing real-time distributed virtual reality visualization, introduces grid scientific computing model, and constructs a three-dimensional vertical visualization platform of distributed virtual reality. The visual analysis of virtual reality is carried out for the corresponding slope and corresponding geological conditions; the comprehensive analysis of spatial distribution is carried out by using three-dimensional visualization, the relevant contents of real-time display, and the generation and real-time display of realistic graphics. In order to increase the realism of 3D image, visibility judgment, and blanking technology, the level of detail model and texture mapping is used. The simulation results show that the grid scientific computing model is effective and can support the three-dimensional visual analysis of distributed virtual reality.
A Web-Based Auxiliary Teaching System for College English

In order to optimize the application quality of the English auxiliary teaching system, shorten the system response time and CPU utilization, design a Web-based college English auxiliary teaching system, and test its application effect. Using the B/S mode in the Web, install the developed auxiliary teaching system on the server of the control center. The B/S function modules in the system software include the presentation layer, application service layer, and resource layer. Based on this, design the user management module and college English auxiliary teaching functions and allocate bandwidth to the platform resource transmission rate to maximize network utility, reduce platform data transmission failures, and optimize the quality of English auxiliary teaching. The experimental results demonstrate that the system based on the B/S multilayer structure passed all of its tests, showing that it is relatively robust and capable of meeting the needs of college English teaching aids for students and teachers while also achieving the desired outcomes. The teaching information communication delay and response time of the designed system are shorter, and the central processing unit (CPU) utilization rate is also lower.
Effective Data Augmentation Methods for Neural Text-to-Speech Systems

This paper proposes an effective self-augmentation method for improving the quality of neural text-to-speech (TTS) systems. As synthetic speech quality has been greatly improved, creating a neural TTS system using synthetic corpora is now possible. However, whether increasing the amount of synthetic data is always beneficial for improving training efficiency has not been verified. Our aim in this study is to selectively choose synthetic data whose characteristics are close to those of natural speech. Specifically, we adopt a ranking support vector machine (RankSVM) that is well known for effectively ranking relative attributes among binary classes. By setting the synthetic and recorded corpora as two opposite classes, RankSVM is used to determine how the synthesized speech is acoustically similar with the recorded data. As training data can be selectively chosen from large-scale synthetic corpora, the performance of the TTS model re-trained by those data is significantly improved. Subjective evaluation results verify that the proposed TTS model performs much better than the original model trained with recorded data alone and the similarly configured system re-trained with all the synthetic data without any selection method.
Classifying between computer generated and natural images: An empirical study from RAW to JPEG format

Computer generated (CG) images have been gradually overspread on the Internet, resulting in difficult discrimination from natural images (NIs) captured by an authentic imaging device. Although some discriminators can deal with NIs in JPEG format, the classification between uncompressed NIs (that are possibly generated in any imaging procedure before compression) and CG ones still remains unknown. Thus, this paper aims to establish multiple discriminators classifying between NIs and CG images. We first describe the main imaging procedure and its intrinsic property, which characterizes the discriminative features for classification. Then, the residual noise (representing intrinsic characteristic) is extracted. Its statistical distribution indeed helps us establish multiple discriminators, consisting of the generalized likelihood ratio test (GLRT) under the framework of hypothesis testing theory. Extensive experiments empirically verify our proposed multiple discriminators outperform many prior arts. Furthermore, the robustness of discriminators is validated with considering some post-processing attacks.
Investigating Language Relationships in Multilingual Sentence Encoders Through the Lens of Linguistic Typology

Abstract Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks. The success of this transfer is, however, dependent on the model’s ability to encode the patterns of cross-lingual similarity and variation. Yet, we know relatively little about the properties of individual languages or the general patterns of linguistic variation that the models encode. In this article, we investigate these questions by leveraging knowledge from the field of linguistic typology, which studies and documents structural and semantic variation across languages. We propose methods for separating language-specific subspaces within state-of-the-art multilingual sentence encoders (LASER, M-BERT, XLM, and XLM-R) with respect to a range of typological properties pertaining to lexical, morphological, and syntactic structure. Moreover, we investigate how typological information about languages is distributed across all layers of the models. Our results show interesting differences in encoding linguistic variation associated with different pretraining strategies. In addition, we propose a simple method to study how shared typological properties of languages are encoded in two state-of-the-art multilingual models—M-BERT and XLM-R. The results provide insight into their information-sharing mechanisms and suggest that these linguistic properties are encoded jointly across typologically similar languages in these models.
RadScore: An Automated Technique to Measure Radicalness Score of Online Social Media Users

Social media platforms provide effective mediums for expressing opinions and thoughts on several topics openly. This does protect our right to freedom of speech, however the enormous reach of social media makes it a potential tool for widespread radicalization among the youth, irrespective of the geographical and demographical boundaries. This necessitates the need to effectively identify the content which is a source of mass online radicalization. In order to curb the propagation, security agencies need an automatic radicalization detection mechanism for mining the huge volumes of social media content. In this article, we propose an approach for detecting online radicalized accounts and quantifying the degree to which these user accounts are propagating radical content. We propose to use three novel features, i.e., Similarity to domain, presence of radical content and sentiment to calculate the radicalness score for each online user. Our algorithm uses a CNN-LSTM-based technique to effectively differentiate between radical/non-radical content with an accuracy of 93%. Our empirical results show that radicalness scores for known radicalized websites are higher as compared to the non-radical users. We believe this is a first ever attempt at quantifying the level of radicalization of users using scientific methods which can be very helpful to national security agencies in tracking suspicious online users and stop the spread of anti-national content on social media.
Health evaluation methodology of remote maintenance control system of natural gas pipeline based on ACWGAN-GP algorithm

Abstract The remote maintenance system of natural gas pipeline is of great significance to ensure the safe and stable operation of the pipeline network. The multi-classification method based on machine learning is more effective for the health evaluation of remote maintenance control system than the traditional evaluation method based on expert experience. In view of the sev ere imb alance i n the nu mber of s amples of f ive health levels, a health evaluation methodology of remote maintenance control system based on Wasserstein distance sand auxiliary classification generative adversarial network (ACWGAN-GP) is proposed. Firstly, the model stability is improved by introducing Wasserstein distance and gradient penalty. The generator generates balanced data, while the discriminator trains with generated and actual data. In this way, several ACWGAN-GP sub-models are trained. Then, the health levels of the sub-model are directly obtained by using the discriminator to classify the samples. Finally, according to the hierarchical relationship of the system, a parallel-serial combined evaluation method is adopted. By this means, the health evaluation model of remote maintenance control system including ACWGAN-GP sub-models is constructed. The experimental results based on 13 sets of KEEL and UCI multi-class imbalanced datasets and actual sampling data show that the effectiveness and advancement of the proposed method improved significantly compared with the existing similar typical algorithms.
Shifting Priorities and Neoliberal Ideologies in Refugee Health Intervention Design in the US

We discuss the design of a technology-based vaccine education intervention for Somali refugees in the US. Originally conceived of as a culturally and linguistically appropriate project to be co-designed by refugees, funder demands for a "social enterprise" led to future iterations being developed for a "generic" audience. We explore epistemological negotiations and shifting priorities that shaped intervention design, highlighting how nonprofits engage neoliberal ideologies such as "social enterprise" and "design thinking" while attempting to meet community needs. We argue that social enterprises and design thinking can suffuse neoliberal ideologies into nonprofits to the detriment of community-engaged solutions.
Face-Crypt Messenger: Enhancing Security of Messaging Systems using AI based Facial Recognition and Encryption

Information Security has always been a major discipline in the field of Computer Science. Many software has been developed around the world for fulfilling various tasks. However, it is important to ensure the security of the data involved in using that particular software. In the recent times, the issue of security related to chat systems has been trending. There have been instances where an unauthorized user has viewed the messages of a particular user. That is where this project presents a pragmatic solution. This project implements a chat system which authorizes a user into the application using facial authentication. The user messages are encoded using cryptographic algorithms. In case of sending a secret message, only the user, whose face matches with that at the time of sign-up can view the message. This feature is implemented using Machine Learning algorithms.
Machine Reading Comprehension with Rich Knowledge

Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). With the development of deep learning, language models have achieved excellent results. However, these models still cannot answer complex questions. Currently, researchers often utilize structured knowledge, such as knowledge bases (KBs), as external knowledge by directly extracting triples to enhance the results of machine reading. Although they can support certain background knowledge, the triples are limited to the interrelationships among entities or words. Unlike structured knowledge, unstructured knowledge is rich and extensive. However, these methods ignore unstructured knowledge resources, such as Wikipedia. In addition, the effect of combining the two types of knowledge is still not known. In this study, we first attempt to explore the usefulness of combining them. We introduce a fusion mechanism into a rich knowledge fusion layer (RKF) to obtain more useful and relevant knowledge from different external knowledge resources. Further to promote interaction among different types of knowledge, a bi-matching layer is added. We propose the RKF-NET framework based on BERT, and our experimental results demonstrate the effectiveness of two classic datasets: SQuAD1.1 and the Easy-Challenge (ARC).
Systematic Thinking and Evaluation of Construction Quality Management Standardization of Power Engineering in China

Power engineering is complex, involving many participants, numerous management factors, and different stages. To improve the quality management level of power engineering and realize the standardization of power engineering quality management, power engineering construction quality management standardization (CQMS) is evaluated using the order relation analysis method (G1 method) and the cloud model method. Firstly, based on systematic thinking and practice in China, the CQMS framework is proposed, and the evaluation index system including organizational performance, management behaviour performance, and entity performance related to power engineering construction quality is established. Subsequently, the G1 method is used to determine the weight of each index, and the cloud model method is used to implement the evaluation process. The results are divided into five grades, representing the five grades of construction quality management. Finally, taking a practical multiterminal DC demonstration project in China as an example, the CQMS performance is described and evaluated. The evaluation results directly reflect the level and difference of CQMS and show that this evaluation method can provide a reference for CQMS in power engineering.
Hy-MOM: Hybrid Recommender System Framework Using Memory-Based and Model-Based Collaborative Filtering Framework

Abstract Lack of personalization, rating sparsity, and cold start are commonly seen in e-Learning based recommender systems. The proposed work here suggests a personalized fused recommendation framework for e-Learning. The framework consists of a two-fold approach to generate recommendations. Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach. Secondly, it generates a matrix of ratings given by the learners. The outcome of the first stage is merged with the second stage to generate recommendations for the learner. Learner characteristics, namely knowledge level, learning style, and learner preference, have been considered to bring in the personalization factor on the recommendations. As the stochastic gradient approach predicts the learner-course rating matrix, it helps overcome the rating sparsity and cold-start issues. The fused model is compared with traditional stand-alone methods and shows performance improvement.
Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning

Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.
‘This app can help you change your voice’: Authenticity and authority in mobile applications for transgender voice training

Mobile applications for transgender voice training seek to help trans people alter their speaking voices, often with the goal of alignment with one or another pole of binary gender expression (i.e., voice ‘feminization’ or ‘masculinization’). These apps offer instruction, audio tools and feedback mechanisms that allow users to record, evaluate and track their progress relative to a desired – and gendered – goal. As with digital technologies generally, however, these apps draw on and reproduce particular ideas of both the needs and capacities of imagined users and broader social and political phenomena – in this case, voice, gender expression and gender transition. In the following paper, we undertake a critical discourse analysis of five mobile voice training apps for transgender people. We find that rather than offering expansive or open-ended conceptions of gender, these apps reproduce ideals of gender as not only binary but also white, affluent and able-bodied. We also offer a critique of the apps’ characterization of transition as reifying the normative authority of clinical and technoscientific knowledge and conditioning ‘authenticity’ on an ability to conform to (racialized, classed and ableist) binary gender norms.
PHDMF: A Flexible and Scalable Personal Health Data Management Framework Based on Blockchain Technology

Currently, most of the personal health data (PHD) are managed and stored separately by individual medical institutions. When these data need to be shared, they must be transferred to a trusted management center and approved by data owners through the third-party endorsement technology. Therefore, it is difficult for personal health data to be shared and circulated over multiple medical institutions. On the other hand, the use of directly exchanging and sharing the original data has become inconsistent with the data rapid growth of medical institutions because of the need of massive data transferring across agencies. In order to secure sharing and managing the mass personal health data generated by various medical institutions, a federal personal health data management framework (PHDMF, https://hvic.biosino.org/PHDMF) has been developed, which had the following advantages: 1) the blockchain technology was used to establish a data consortium over multiple medical institutions, which could provide a flexible and scalable technical solution for member extension and solve the problem of third-party endorsement during data sharing; 2) using data distributed storage technology, personal health data could be majorly stored in their original medical institutions, and the massive data transferring process was of no further use, which could match up with the data rapid growth of these institutions; 3) the distributed ledger technology was utilized to record the hash value of data, given the anti-tampering feature of the technology, malicious modification of data could be identified by comparing the hash value; 4) the smart contract technology was introduced to manage users' access and operation of data, which made the data transaction process traceable and solved the problem of data provenance; and 5) a trusted computing environment was provided for meta-analysis with statistic information instead of original data, the trusted computing environment could be further applied to more health data, such as genome sequencing data, protein expression data, and metabolic profile data through combining the federated learning and blockchain technology. In summary, the framework provides a convenient, secure, and trusted environment for health data supervision and circulation, which facilitate the consortium establish over medical institutions and help achieve the value of data sharing and mining.