Reviewerly/exHarmony huggingface dataset:
Disentangled Variational Information Bottleneck for Multiview Representation Learning

Multiview data contain information from multiple modalities and have potentials to provide more comprehensive features for diverse machine learning tasks. A fundamental question in multiview analysis is what additional information can be brought by additional views and can we quantitatively identify this additional information. In this work, we try to tackle this challenge by decomposing the entangled multiview features into shared latent representations that are common across all views and private representations that are specific to each single view. We formulate this feature disentanglement in the framework of information bottleneck and propose disentangled variational information bottleneck (DVIB). DVIB explicitly defines the properties of shared and private representations using constrains from mutual information. By deriving variational upper and lower bounds of mutual information terms, representations are efficiently optimized. We demonstrate the shared and private representations learned by DVIB well preserve the common labels shared between two views and unique labels corresponding to each single view, respectively. DVIB also shows comparable performance in classification task on images with corruptions. DVIB implementation is available at https://github.com/feng-bao-ucsf/DVIB.
ER-SAN: Enhanced-Adaptive Relation Self-Attention Network for Image Captioning

Image captioning (IC), bringing vision to language, has drawn extensive attention. Precisely describing visual relations between image objects is a key challenge in IC. We argue that the visual relations, that is geometric positions (i.e., distance and size) and semantic interactions (i.e., actions and possessives), indicate the mutual correlations between objects. Existing Transformer-based methods typically resort to geometric positions to enhance the representation of visual relations, yet only using the shallow geometric is unable to precisely cover the complex and actional correlations. In this paper, we propose to enhance the correlations between objects from a comprehensive view that jointly considers explicit semantic and geometric relations, generating plausible captions with accurate relationship predictions. Specifically, we propose a novel Enhanced-Adaptive Relation Self-Attention Network (ER-SAN). We design the direction-sensitive semantic-enhanced attention, which considers content objects to semantic relations and semantic relations to content objects attention to learn explicit semantic-aware relations. Further, we devise an adaptive re-weight relation module that determines how much semantic and geometric attention should be activated to each relation feature. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our ER-SAN, with improvements of CIDEr from 128.6% to 135.3%, achieving state-of-the-art performance. Codes will be released \url{https://github.com/CrossmodalGroup/ER-SAN}.
Improving Ego-Velocity Estimation of Low-Cost Doppler Radars for Vehicles

A low-cost automotive radar is often used in autonomous driving and advanced driver-assistance systems. However, the low cost radar often assumes all detected objects to be on the ground plane when estimating radar/vehicle ego-velocity, but when there are elevated background objects present, such as buildings and tall trees, the ego-velocity estimation tends to be biased. Here we analyze the source of estimation error and develop a new algorithm to recognize three types of object reflections using the discrepancy between the estimated ego-velocity and the measured Doppler velocity. We propose an elevation and background aware cost (EBAC) function to formulate an optimization framework which can distinguish the object types to improve ego-velocity estimation. We combine a robust estimation method with the optimization framework to handle outliers in radar readings. We have implemented the algorithm and tested it in both simulation and physical experiments using our autonomous vehicle. The results show that our estimation method significantly reduces ego-velocity estimation error while maintaining a smaller error variance without losing robustness. More specifically, it reduces the ego-velocity estimation error by 49% in the most common driving scenario.
Enhanced Decentralized Autonomous Aerial Robot Teams With Group Planning

Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works in this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In this paper, we propose an enhanced decentralized autonomous aerial robot team system with group planning. According to the spatial distribution of agents, the system dynamically divides the team into several groups and isolated agents. For conflicts within each group, we propose a novel coordination mechanism named group planning. The group planning consists of efficient multi-agent pathfinding (MAPF) and trajectory joint optimization, which can significantly improve planning quality and success rate. We demonstrate through simulations and real-world experiments that our method not only has applicability for a large-scale team but also has top-level planning quality.
How Blockchain Solutions Can Improve the Education Sector

Starting with identity management and the "laws of identity" [1] this article will discuss the current challenges and requirements imposed upon students in the education sector today.Building upon identity and record-keeping on a blockchain, the access granted to requesting entities is defined within access security.An exploration of the challenges and benefits by implementing blockchain and identity management with access security will highlight areas where complicated processes can be simplified with the use of blockchain technology.The summary below will detail how blockchain and identity management can be implemented to simplify and improve the current procedures that exist today.
Unsupervised framework for comment-based multi-document extractive summarization

Existing extractive summarization systems suffer from the problem of identifying correct aspects while generating summary from multiple documents on a particular topic. Online access to documents allows readers to post comments on available documents/blogs which may be helpful in correctly identifying the important aspects from a document in a crowd-sourcing fashion. However, comments can be unstructured and boisterous, making them difficult to manage. The current paper builds an unsupervised summarization system utilizing documents and the corresponding comments by carefully selecting a subset of sentences from the document based on document-comment similarities. A binary multiobjective optimization technique exploring the search capability of differential evolution is utilized for simultaneously optimizing different aspects of summarization like diversity between sentences of the summary, user-attention and density-based score providing importance to the comments with respect to the documents, user-attention with a syntactic score between the summary phrases. Extensive analysis is performed by varying the combinations of different objective functions, on a dataset containing 45 topics belonging to different news themes as well as in a new French dataset having documents, summaries, and comments. The experimental result validates the importance of all the objective functions and superiority over other methods.
Meta-Learning-Based Proactive Online Planning for UAVs Under Degraded Conditions

Changes in model dynamics due to factors like actuator faults, platform aging, and unexpected disturbances can challenge an autonomous robot during real-world operations affecting its intended behavior and safety. Under such circumstances, it becomes critical to improve tracking performance, predict future states of the system, and replan to maintain safety and liveness conditions. In this letter, we propose a meta-learning-based framework to learn a model to predict the future system's states and their uncertainties under unforeseen and untrained conditions. Meta-learning is considered for this problem thanks to its ability to easily adapt to new tasks with a few data points gathered at runtime. We use the predictions from the meta-learned model to detect unsafe situations and proactively replan the system's trajectory when an unsafe situation is detected (e.g., a collision with an object). The proposed framework is applied and validated with both simulations and experiments on a faulty UAV performing an infrastructure inspection mission, demonstrating safety improvements.
Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.
Opposite learning and multi-migrating strategy-based self-organizing migrating algorithm with the convergence monitoring mechanism

This paper introduces a novel competition entry on Real Parameter Single Objective Bound Constrained Optimization at GECCO 2022 conference, namely Opposite learning and Multi-migrating strategy-based Self-Organizing Migrating Algorithm with Convergence monitoring mechanism (OMC_SOMA). Specifically, OMC_SOMA induces three techniques to improve the basic SOMA in the aspects of population initialization, convergence monitoring, and migrating strategies. Preliminary experiments on the 12 test functions reveal a good performance of our proposed method.
Localized distance and time-based differential evolution for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to find global multiple optimal solutions with high accuracy. Although many state-of-the-art algorithms are proposed to deal with MMOPs, there are still some challenges as how to avoid local optima and how to refine the solutions accuracy. Aim to these, this paper proposes a Localized Distance and Time-based Differential Evolution (LDTDE) for MMOPs, which includes three contributions. Firstly, a Random and Neighborhood-based Mutation (RNM) strategy is proposed to avoid local optima and refine the accuracy of found solutions. That is each individual not only performs random-based mutation operation but also performs neighborhood-based mutation operation by Euclidean distance. Secondly, a Locality-based Crowding Selection (LCS) strategy is introduced to accelerate the convergence and further approach the global optima. Thirdly, an Adaptive Parameter Control (APC) strategy is proposed to reduce the sensitivity of parameters, which can dynamically calculate the appropriate parameters for each individual based on the state of itself. The performance of LDTDE is tested on CEC'2013 and compared with state-of-the-art multimodal optimization algorithms.
COVID-19 contact tracking by group activity trajectory recovery over camera networks

Contact tracking plays an important role in the epidemiological investigation of COVID-19, which can effectively reduce the spread of the epidemic. As an excellent alternative method for contact tracking, mobile phone location-based methods are widely used for locating and tracking contacts. However, current inaccurate positioning algorithms that are widely used in contact tracking lead to the inaccurate follow-up of contacts. Aiming to achieve accurate contact tracking for the COVID-19 contact group, we extend the analysis of the GPS data to combine GPS data with video surveillance data and address a novel task named group activity trajectory recovery. Meanwhile, a new dataset called GATR-GPS is constructed to simulate a realistic scenario of COVID-19 contact tracking, and a coordinated optimization algorithm with a spatio-temporal constraint table is further proposed to realize efficient trajectory recovery of pedestrian trajectories. Extensive experiments on the novel collected dataset and commonly used two existing person re-identification datasets are performed, and the results evidently demonstrate that our method achieves competitive results compared to the state-of-the-art methods.
Answering Complex Questions on Knowledge Graphs

The topic of knowledge-based question answering ( $$\mathsf {KBQA}$$ ) has attracted wide attention for a long period. A series of techniques have been developed, especially for simple questions. To answer complex questions, most existing approaches apply a semantic parsing-based strategy that parses a question into a query graph for result identification. However, due to poor quality, query graphs often lead to incorrect answers. To tackle the issue, we propose a comprehensive approach for query graph generation, based on two novel models. One leverages attention mechanism with richer information from knowledge base, for core path generation and the other one employs a memory-based network for constraints selection. The experimental results show that our approach outperforms existing methods on typical benchmark datasets.
VCMatch: A Ranking-based Approach for Automatic Security Patches Localization for OSS Vulnerabilities

Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.
Lightweight Channel Estimation Networks for OFDM Systems

Channel estimation using neural networks has proven to be a breakthrough technology in the communications field. However, to achieve good performance, the existing studies mostly focus on building a complex neural network with a large number of layers, which consumes a lot of storage space and computing resources. In this letter, a lightweight channel estimation Transformer (LCET) is proposed for an orthogonal frequency division multiplexing (OFDM) system with pilots, based on the application of a convolutional neural network (CNN) and Transformer. The network uses a lightweight feature extraction CNN (LFEC) to extract channel features and then feeds the processed features into a lightweight-adjusted Transformer (LAT) for channel estimation. Specifically, LFEC extracts deep channel features at a low computational cost and retains the high frequency information. The LAT solves the problem of long-range feature information loss with low resource consumption. The experimental results revealed that LCET performed better than the traditional least squares estimation algorithm and state-of-the-art learning network.
WeTS: A Benchmark for Translation Suggestion

Translation suggestion (TS), which provides alternatives for specific words or phrases given the entire documents generated by machine translation (MT), has been proven to play a significant role in post-editing (PE).There are two main pitfalls for existing researches in this line.First, most conventional works only focus on the overall performance of PE but ignore the exact performance of TS, which makes the progress of PE sluggish and less explainable; Second, as no publicly available golden dataset exists to support in-depth research for TS, almost all of the previous works conduct experiments on their in-house datasets or the noisy datasets built automatically, which makes their experiments hard to be reproduced and compared.To break these limitations mentioned above and spur the research in TS, we create a benchmark dataset, called WeTS, which is a golden corpus annotated by expert translators on four translation directions.Apart from the golden corpus, we also propose several methods to generate synthetic corpora which can be used to improve the performance substantially through pre-training.As for the model, we propose the segment-aware self-attention based Transformer for TS.Experimental results show that our approach achieves the best results on all four directions, including Englishto-German, German-to-English, Chinese-to-English, and English-to-Chinese.Codes and corpus can be found at https://github.com/ZhenYangIACAS/WeTS.git.
Complementary Calibration: Boosting General Continual Learning With Collaborative Distillation and Self-Supervision

General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on six popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods. Code is available at https://github.com/lijincm/CoCa.
Searching Efficient Model-Guided Deep Network for Image Denoising

Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS). Specifically, we propose to construct a new search space under a model-guided framework and develop more stable and efficient differential search strategies. MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer as well as network width and depth via gradient descent. During the search process, the proposed MoD-NAS remains stable because of the smoother search space designed under the model-guided framework. Experimental results on several popular datasets show that our MoD-NAS method has achieved at least comparable even better PSNR performance than current state-of-the-art methods with fewer parameters, fewer flops, and less testing time. “The code associate with this paper is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://see.xidian.edu.cn/faculty/wsdong/Projects/Mod-NAS.htm</uri> ”.
Optimal Estimation of Conjugate Shifts in Position and Momentum by Classically Correlated Probes and Measurements

Multiparameter estimation is necessary for force sensing due to simultaneous and nontrivial small changes of position and momentum. The design of quantum probes that allow simultaneous estimation of all parameters is therefore an important task. The optimal methods for estimation of the conjugate changes of position and momentum of quantum harmonic oscillator employ probes in entangled or quantum non-Gaussian states. We show that the same results can be obtained in a significantly more feasible fashion by employing independent sets of differently squeezed Gaussian states classically correlated with position or momentum measurements. This result demonstrates an unexplored power of a classical correlation between the probe states and measurements directly applicable to force sensing.