Reviewerly/exHarmony huggingface dataset:
A Novel Method for Maneuvering Extended Vehicle Tracking with Automotive Radar

In high-resolution automotive radar tracking systems, vehicle targets are often regarded as extended targets, which means multiple measurements originated from scattering centers of vehicle targets can be detected at each scan and thus the traditional point target tracking schemes are unsuitable. Meanwhile, vehicle maneuvers, e.g., braking and swerving, cause serious degradation of the classical extended target tracking methods. In this paper, a novel method is proposed for maneuvering extended vehicle tracking with automotive radar. The data-region association (DRA) strategy is adopted to handle the vehicle extension effect, which is superior in describing the complex spatial distribution of vehicle target measurements. The interacting multiple model (IMM) method is combined with this DRA strategy to describe the evolution of target motion models. Accordingly, the proposed DRA-IMM method achieves satisfying tracking performance of extended vehicles and also guarantees the robustness in case of maneuvers. Furthermore, in view of the correlation between vehicle extension and its kinematic state, a ray-based strategy is devised to improve the prior distribution of the data-region association of the basic DRA-IMM, and accordingly an enhanced DRA-IMM (EDRA-IMM) method is proposed. Simulation result validates the effectiveness of the proposed DRA-IMM method for maneuvering extended vehicle tracking and the further improvement of the proposed EDRA-IMM method.
Gradual Migration and Style Consistency for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation(UDA) aims to learn domain-invariant characteristics of source and target domains so that classifiers learned from the source domain can be applied to the unlabeled target domain. Now some methods trying to build an intermediate domain to alleviate the domains discrepancy. However, since the target samples are unlabeled, it will cause great confusion to the model if the proportion of source and target domain samples in the intermediate domain samples nearly equals. Moreover, the stylistic bias of the CNNs is often ignored. Therefore, we propose to gradually reduce the distribution discrepancy between domains by constructing continuous multiple intermediate domains. To address the style discrepancy between domains, we propose Self-Exchange and Cross-Exchange modules to reduce the style discrepancy between domains. In addition, we propose to focus on sample probability divergence to select reliable samples. Experiments show the proposed method has achieved significant performance improvement in several cross-domain benchmark tests.
Mobile AR Application for Navigation and Emergency Response

Emergency response, navigation, and evacuation are key essentials for effective rescue and safety management. Situational awareness is a key ingredient when fire responders or emergency response personnel responds to an emergency. They have to quickly assess the layout of a building or a campus upon entry. Moreover, the occupants of a building or campus also need situational awareness for navigation and emergency response. We have developed an integrated situational awareness mobile augmented reality (AR) application for smart campus planning, management, and emergency response. Through the visualization of integrated geographic information systems and real-time data analysis, our mobile application provides insights into operational implications and offers information to support effective decision-making. Using existing building features, the authors demonstrate how the mobile AR application provides contextualized 3D visualizations that promote and support spatial knowledge acquisition and cognitive mapping thereby enhancing situational awareness. A limited user study was conducted to test the effectiveness of the proposed mobile AR application using the mobile phone usability questionnaire (MPUQ) framework. The results show that the mobile AR application was relatively easy to use and that it can be considered a useful application for navigation and evacuation.
Scene Text Involved "Text"-to-Image Retrieval through Logically Hierarchical Matching

Text-to-image retrieval, one of the most important cross-modality tasks, aims to search the most relevant images through a given text query. Most recent approaches are based on large-scale models. The huge time costs make it impossible for real-time searching. They also ignore the fine-grained information, i.e., the scene text. To tackle these issues, we propose a novel matching method that considers the scene text in both modalities and adopts a fast matching way by aligning from objects to relations and finally to the global. This logically hierarchical process emulates the way humans understand information. To better implement our method, we relabel the TextCaps-OCR dataset, which contains 110K captions with word-level POS labeling and 22K corresponding scene text images with bounding boxes. Extensive experiments demonstrate the superiority and efficiency of our method, whose performance is significantly higher than the past SOTA on both OCR-contained and OCR-free datasets.
Investigating Gender and Racial Bias in ELECTRA

With the increased adaptation of natural language processing models in industrial applications such as hiring and recruitment, chatbots, social media monitoring, and targeted advertising, pretrained language models (PTM) need fair and equal behavior across all ranges of demographic groups. ELECTRA has substantially outperformed BERT by predicting the original identities of the corrupted tokens over all input tokens rather than just the small subset that was masked out. Considering such enhancement and the 1/4 less amount of computing required by ELECTRA, it can be one of the most suitable industrial applications. Therefore, it is crucial to understand its underlying architecture and tokenization protocol to identify any potential discrimination towards specific groups. This paper presents a fair operation from ELECTRAs' pretrained network that shows the accurate classification of token replacements. This result is achieved via using a dataset with racially and gender-associated personal names, finetuning ELECTRA with the general language understanding evaluation (GLUE) benchmark, which analyzes the interactions of encoders and decoders using the Contextualized Embedding Association Test (CEAT) and sentiment association test. In addition, this paper will demonstrate that ELECTRA can achieve Bias-aware Fair prediction with higher accuracy on downstream tasks after fully trained. This project is investigating the prediction of generator and discriminator on an initial word's token using the Named Entity Recognition (NER), and Part of Speech tagging (POS)
Discriminative and Contrastive Consistency for Semi-supervised Domain Adaptive Image Classification

With sufficient source and limited target supervised information, semi-supervised domain adaptation (SSDA) aims to perform well on unlabeled target domain. Although various strategies have been proposed in SSDA field, they fail to fully exploit limited target labels and adequately explore domain-invariant knowledge. In this study, we propose a framework that first introduces consistent processing of augmented training data based on contrastive learning. Specifically, supervised contrastive learning is introduced to assist the classical cross-entropy iteration to make full use of the limited target labels. Additionally, traditional unsupervised contrastive learning and pseudo-labeling are utilized to further minimize the intra-domain discrepancy. Besides, an adversarial loss is then combined with a sharpening function to acquire a more certain category center that is domain-invariant. Experimental results on DomainNet, Office-Home, and Office show the effectiveness of our method. Particularly, for 1-shot case of Office-Home with AlexNet as backbone, our method outperforms the previous state-of-the-art by 5.6% in terms of mean accuracy.
Object-Centric Inference for Language Conditioned Placement: A Foundation Model based Approach

We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions. Previous works based on rule-based language parsing or scene-centric visual representation have restrictions on the form of instructions and reference objects or require large amounts of training data. We propose an object-centric framework that leverages foundation models to ground the reference objects and spatial relations for placement, which is more sample efficient and generalizable. Experiments indicate that our model can achieve a 97.75% success rate of placement with only ~0.26M trainable parameters. Besides, our method generalizes better to both unseen objects and instructions. Moreover, with only 25% training data, we still outperform the top competing approach.
Weighted Fusion of Multiple Classifiers for Human Activity Recognition

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.
Towards Confidence-Aware Commonsense Knowledge Integration for Scene Graph Generation

Commonsense knowledge has been widely explored to improve Scene Graph Generation (SGG). Existing methods simply incorporate the described relations of knowledge bases into each part of the scene for a concrete understanding. However, they ignore the discussion about whether a visual scene needs to associate commonsense knowledge for making inferences. Specifically, the difficulty of relation recognition varies from its type. Some frequent spatial relations (e.g. on) usually produce less perception error even without any prior information, while others involved many rules and patterns (e.g. throwing) possess few samples and require to combine with some commonsense knowledge as supplementary. In this paper, we propose a novel confidence-aware commonsense knowledge integration for SGG. Firstly, we depend on mutual information maximization to design a hybrid-attention module, which decreases the uncertainty in representation learning given external knowledge. Second, we introduce an extra branch for SGG network to perform confidence estimation independent of any ground truth labels, in which the output scalar explicitly reflects the difficulty of visual recognition. This value is equipped with the ability to balance the demand for commonsense knowledge in a given scene. Experiments are conducted with the backbone of MOTIFS on Visual Genome (VG) and our method effectively promotes the metric of mRecall with little performance hit for metric Recall, especially for predicting unseen relations.
Fixing Domain Bias for Generalized Deepfake Detection

Generalizing deepfake detection has posed a great challenge to digital media forensics, as inferior performance is obtained when training sets and testing sets are domain-mismatched. In this paper, we show that a CNN-based detection model can significantly improve performance by fixing domain bias. Specifically, we propose a novel Fixing Domain Bias network (FDBN). FDBN does not rely on manual features, but is based on three core designs. Firstly, a domain-invariant network based on randomly stylized normalization is devised to constrain the domain discrepancy in the feature space. Then, through adversarial learning, a generalizing representation in the stylized distribution is learned to enhance the shared feature bias among manipulation methods in the domain-specific network. Finally, to encourage equality of biases among different domains, we utilize the bias extrapolation penalty strategy by suppressing the expected bias on the extremely-performing domains. Extensive experiments demonstrate that our framework achieves effectiveness and generalization towards unseen face forgeries.
Large-Scale Space Object Tracking in a Proliferated LEO Scenario

The proliferation of large satellite constellations in low Earth orbit (LEO) is dramatically increasing demand on existing systems for space domain awareness. The rapidly growing number of objects in LEO will reduce the average rate of observations per object, necessitating the development of multi-target algorithms that can handle higher levels of data sparsity without sacrificing computational efficiency. In this paper, we demonstrate that a multi-target filter that combines a number of useful capabilities is able to track and maintain custody of a simulated population of over 16,000 LEO objects without requiring the use of high-performance computing facilities. The filter is based on the generalized labeled multi-Bernoulli filter, and includes three previously-presented features: label space partitioning based on sensor fields of view, the ensemble Gaussian mixture filter (EnGMF), and bi-fidelity orbit uncertainty propagation. We also introduce a new, algorithmically simple method for adjusting the number of EnGMF particles to balance accuracy and computational efficiency, which we refer to as progressive resampling.
GFNet: Gaze Focus Network using Attention for Gaze Estimation

Gaze estimation can be applied to human visual attention understanding. The current methods mainly obtain gaze mapping from facial or eye images, and most of them only focus on gaze point or gaze direction estimation. In this paper, we propose a multitask gaze focus network for gaze point and gaze direction estimation. Focus attention layer is used to guide the generation of facial features. By connecting eye and face features, feature similarity is used to get attention weights, and make it tend to eyes position. We propose four loss functions to constrain the network in 2D and 3D spaces. The combination of eye position constraint and focus attention layer ensures the accuracy of gaze point estimation. Gaze focus is used to obtain gaze depth. Through comprehensive experiments, the advantages of proposed method in gaze tracking are verified. In addition, the application prospect of proposed method in depth-overlapping is proved.
“You look like a Troll”: Using K-Means Algorithm with Trolling Score to Spot Trolling Behaviour

This work presents the results obtained after targeting down and following the Ecuadorian Presidential 2021 Election trolls. To achieve this, new tweets after the Elections were downloaded, and “trolling behavior” was spotted and measured, as an attempt to increase the accuracy of a K-Means Algorithm, to cluster Twitter trolls.
A Masked Attention Network with Query Sparsity Measurement for Time Series Anomaly Detection

Time series aomaly detection has been widely studied in recent years. Previous research focuses on point-wise features and pairwise associations for feature learning or designed anomaly scores based on prior knowledge. However, these methods cannot fully learn the intricate abnormal dynamic information and can only identify a limited class of anomalies. We propose a Masked Attention Network with Query Sparsity Measurement (MAN-QSM) to address the above challenges. This model uses two kinds of prior knowledge to fully exploit the differences between normal and abnormal points from two perspectives: pairwise association and sequence-level information. We designs the anomaly mask mechanism to collaborate with the training strategy to amplify the difference between normal and abnormal points. In experiments, we compare the model with classical methods, reconstruction-based models, autoregressive-based models, and state-of-the-art models, and the MAN-QSM achieves state-of-the-art results on SMD, PSM, and MSL datasets with an average of 16% reduction in error rate.
ADATS: Adaptive RoI-Align based Transformer for End-to-End Text Spotting

Scene text spotting has attracted great attention in recent years. Compared with two-stage approaches that locate scene texts in the first stage and recognize them in the second stage, the advantages of joint location and recognition training are not fully explored. In this paper, we present an ADaptive RoI-Align based transformer for end-to-end Text Spotting (ADATS), which simultaneously locates and recognizes text with a single forward pass. By employing an Adaptive RoI-Align, the text features are extracted from the feature extraction network with the original aspect ratio, such that less information is lost during the alignment of arbitrarily-shaped scene text. Attention-based segmentation and recognition heads allow us to simultaneously optimize detection and recognition. Experiments on ICDAR 2015, MSRA-TD500, Total-Text, and CTW1500 demonstrate the effectiveness of our method.
Cross-domain Federated Object Detection

Detection models trained by one party (including server) may face severe performance degradation when distributed to other users (clients). Federated learning can enable multi-party collaborative learning without leaking client data. In this paper, we focus on a special cross-domain scenario in which the server has large-scale labeled data and multiple clients only have a small amount of labeled data; meanwhile, there exist differences in data distributions among the clients. In this case, traditional federated learning methods can’t help a client learn both the global knowledge of all participants and its own unique knowledge. To make up for this limitation, we propose a cross-domain federated object detection framework, named FedOD. The proposed framework first performs the federated training to obtain a public global aggregated model through multi-teacher distillation, and sends the aggregated model back to each client for fine-tuning its personalized local model. After a few rounds of communication, on each client we can perform weighted ensemble inference on the public global model and the personalized local model. We establish a federated object detection dataset which has significant background differences and instance differences based on multiple public autonomous driving datasets, and then conduct extensive experiments on the dataset. The experimental results validate the effectiveness of the proposed method.
Multiple Watermarking Based on DCT Coefficients Selection

This paper proposes a multiple watermarking scheme based on Discrete Cosine Transform (DCT) coefficients selection. In the proposed method, the multiple watermarks are embedded into the low and / or middle frequency band in DCT domain of the image blocks. Firstly, the texture complexity is calculated, then based on that, two block selection methods are proposed to select the corresponding adjacent blocks. After that, embedding coefficients and reference coefficients are calculated and the multiple watermarks are embedded by adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector. In this way, the embedding efficiency is thus improved. In the experiments, cover images of different texture complexity are used, and various attacks have been simulated for evaluation of the proposed watermarking scheme. Experimental results show that the proposed multiple watermarking scheme can achieve a satisfied performance.
A conceptual multi-object Kalman filter based on an approximate Gaussian point process

A conceptual Kalman filter for random fields is proposed for estimating multiple objects. The result exploits an approximation of point processes with Gaussian random fields. The motivation is to develop a solution for multi-object filtering problem in terms of the first two moments of a random field. Applications are discussed for a multi-target tracking model.
Image-text Retrieval via Preserving Main Semantics of Vision

Image-text retrieval is one of the major tasks of cross-modal retrieval. Several approaches for this task map images and texts into a common space to create correspondences between the two modalities. However, due to the content (semantics) richness of an image, redundant secondary information in an image may cause false matches. To address this issue, this paper presents a semantic optimization approach, implemented as a Visual Semantic Loss (VSL), to assist the model in focusing on an image’s main content. This approach is inspired by how people typically annotate the content of an image by describing its main content. Thus, we leverage the annotated texts corresponding to an image to assist the model in capturing the main content of the image, reducing the negative impact of secondary content. Extensive experiments on two benchmark datasets (MSCOCO and Flickr30K) demonstrate the superior performance of our method. The code is available at: https://github.com/ZhangXu0963/VSL.
Performance Assessment for Scheduling Algorithms In Cloud Computing

The concept of cloud computing describes a new category of network-based computing that has taken over the internet technology. The primary advantage of cloud computing technology is application scalability. Cloud computing offers great benefits for the application that share their resources on nodes that are different. Scheduling the tasks can be quite challenging in a cloud computing environment. However, there is need to propose new scheduling strategies to counter the challenges that are presented by network properties between the user and resources. The new strategies can make use of the available conventional scheduling concepts to join them with other network strategies to bring solutions for more effective and efficient job scheduling. Job scheduling is a core technology in cloud computing. This paper aims at providing a study on performance assessment of scheduling algorithms and systemizes the schedule challenges in both existing and proposed cloud computing scheduling algorithms. A brief analysis of numerous scheduling parameters considered in these algorithms is also presented. The results of the experiments show that the best performance level can be achieved by using HPC approach.