Reviewerly/exHarmony huggingface dataset:
An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.
Power Output Reconstruction of Photovoltaic Curtailment

The use of renewable energy sources in the grid’s energy mix has recently gained popularity. Especially as solar photovoltaic (PV) generation production has almost zero emissions during its operations, they are preferred over fuel-based electricity production. However, expanding PV generation in grid capacity increases the chance of PV curtailment occurrence. Not to mention that microgrids supported with large-scale PV generation almost certainly create PV curtailment regularly. As the forecast of PV production is one of the electricity grid operation cornerstones, the prediction model should be as accurate as possible. The latest trend is utilizing machine learning (ML) models to predict PV output, thanks to their excellent learning and regression capabilities. However, its performance can be highly influenced by measurements used during the model design. Unfortunately, only some of the research on this topic deals with the PV curtailment problem resulting in underperforming ML models. This paper proposes a novel approach to identify and replace curtailed PV measurements. The methodology includes the physical model as a baseline of truly producable energy, which is then investigated and corrected as a piecewise linear system using Pearson correlation and weather measurements. Through real-life comparative scenarios, the suggested data reconstruction method provides increased accuracy of supervised ML-based solar power prediction.
Structured Kernel Estimation for Photon-Limited Deconvolution

Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong. In this paper, we propose a new blur estimation technique customized for photon-limited conditions. The proposed method employs a gradient-based backpropagation method to estimate the blur kernel. By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem. When plugged into an iterative framework, our novel low-dimensional representation provides improved kernel estimates and hence significantly better deconvolution performance when compared to end-to-end trained neural networks. The source code and pretrained mdoels are available at https://github.com/sanghviyashiitb/structured-kernel-cvpr23
Intelligent Question Answering Technology in Electric Power Field Based on Knowledge Graphs and Improved Editing Distance Fusion

In order to effectively optimize the power business environment, it is essential to build an intelligent question and answer system for business expansion and installation. The intelligent question answering system can efficiently search the relevant content from the massive knowledge base, and directly return the answer to the user, so as to accurately capture the user’s search intention and significantly improve the work efficiency. Therefore, this paper proposes an intelligent question answering system based on knowledge atlas and improved semantic similarity. First of all, the corresponding proprietary thesaurus is constructed according to the actual business requirements, and the triplet data used for training is constructed. Then, the named entity recognition model based on knowledge atlas and the sum of knowledge base are used to extract the entity relationship in customer questions, and the exact answers to the questions are output through triple group matching. Finally, a semantic similarity measurement method with improved editing distance is proposed, which outputs five answers with the greatest relevance to customer questions through fuzzy matching method to improve the reliability of question answering. The performance test of the experiment shows that the proposed intelligent question answering system achieves more than 90% of the question answering accuracy.
Deep Neural Network Based Object Detection Algorithm With optimized Detection Head for Small Targets

Target detection is a significant research direction in the realm of computer vision, aimed at analyzing and localizing targets in input images to obtain their categories and locations. However, this task is faced with several challenges in complex scenarios, such as large variations in target scales, similarity to the background, dense number, and overlapping with each other, making it difficult for the target detection model to extract different scales and discriminative target features adaptively, leading to low detection accuracy and weak generalization ability. To tackle the aforementioned problems, this study proposes a novel approach by optimizing the detection head of the model, invoking decoupled detection heads, dividing the classification and localization tasks, and optimizing for small targets. This approach effectively reduces the false detection rate and the missed detection rate, enabling the network to better discriminate and recognize distant objects as well as objects of relatively small size. Moreover, the proposed approach outperforms the yolov7 model by 0.8% in the COCO dataset, not only improving the accuracy of detecting distant and small targets but also exhibiting better performance in conventional target detection. Additionally, this approach is capable of handling small targets, overlapping targets, or relatively dense targets, significantly expanding its practical use range.
Research on Adaptive Density Normalized Crowd Counting Algorithm

Aiming at the problems of uneven distribution of crowds in static crowd images, changes in head size, and diverse crowd densities, the accuracy of crowd counting decreases. This paper proposes an adaptive density-normalized crowd counting network YOLO-FPDM-DNM., the network improves the Darknet-53 structure, improves the feature extraction ability, introduces adaptive geometric propagation parameters in the Gaussian density module to solve the problem of head size change, and designs an adaptive density normalization module to solve the problem of excessive crowd density in some areas. The resulting network training is difficult and the accuracy rate is reduced. The experimental results show that compared with the DM-Count network structure with the best effect in the UCFQNRF dataset in recent years, the MAE is reduced by 2.9. MSE decreased by 4.7.
A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process.
Characterizing Acceptance in Post-Selection One-Shot Quantum Hypothesis Testing

In post-selection hypothesis testing, a third outcome is added, corresponding to not selecting any of the hypotheses. In this paradigm, minimum error for various cases is characterized in literature conditioned on the fact that one of the selected outcomes occurs. However, not selecting any of the hypotheses decimates the quality of testing. There is no discussion on the quality of post-selection hypothesis testing. In this work, we characterize the quality of post-selection hypothesis testing by defining a new metric acceptance. We find out measurements which lead to optimal post-selection error. For these measurements, we have derived the expression for acceptance in post-selection symmetric and asymmetric hypothesis testing problems. We have also given closed-form expressions in some specific cases.
Comparing Poisson and Gaussian channels

Consider a pair of input distributions which after passing through a Poisson channel become ϵ-close in total variation. We show that they must necessarily then be ϵ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5+o(1)</sup> -close after passing through a Gaussian channel as well. In the opposite direction, we show that distributions inducing ϵ-close outputs over the Gaussian channel must induce ϵ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1+o(1)</sup> -close outputs over the Poisson. This quantifies a well-known intuition that "smoothing" induced by Poissonization and Gaussian convolution are similar. As an application, we improve a recent upper bound of Han-Miao-Shen’2021 for estimating mixing distribution of a Poisson mixture in Gaussian optimal transport distance from n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−0.1+o(1)</sup> to n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−0.25+o(1)</sup> .
Dynamic Generative Targeted Attacks with Pattern Injection

Adversarial attacks can evaluate model robustness and have been of great concern in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instance-specific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instance-agnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However, they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of the target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversa rial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method shows superior performance than 10 existing adversarial attacks against 13 models.
Towards World Wide Context Management: Architecting Distributed Contextual Intelligence Systems for Real-Time IoT Applications

Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while maintaining adequate Quality of Context and delivering in time has been non-trivial for state-of-the-art centralized Context Management Systems. Further, handling complex situations and entity relations based on local awareness are areas that still need investigation. In this paper, we propose a novel edge-computing based architecture for distributed contextual intelligence systems that could address these research problems. First, we critically evaluate the current state-of-the-art in context-awareness and establish the necessity of a distributed architecture. Then, our proposed architecture is introduced along with the protocols and algorithms accompanied by real-world examples. The paper also highlights the future direction for research work in the area.
On the Communication Complexity of Private Function Sharing and Computation

This paper studies the communication complexity of sharing and computing a private function f among n parties holding private inputs x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> with 1 ≤ i ≤ n in the information theoretical setting. Each party locally computes a message from a share of f and send it to a referee in parallel. The referee obtains the output f (x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,…,x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ) while keeping f and x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> private other than what can be inferred from the output. The communication complexity is evaluated by the maximum length of shares and messages. First, a lower bound on the communication complexity is presented. In particular, the length of the message is lower bounded by a restricted f. Second, the lower bound increases as the robustness level becomes larger, indicating a tradeoff between efficiency and robustness. For well-known families of functions, the lower bounds are instantiated, some of which matches the latest upper bounds up to a constant factor.
δ-CHUCPM: A δ-closed high utility co-location pattern miner

High utility co-location patterns (HUCPs) are more meaningful than traditional prevalent co-location patterns (PCPs) since HUCPs consider the utility of instances that reflects the significance of the instances in reality. However, the mining result normally contains too many HUCPs, this makes HUCPs difficult for users to understand, absorb and apply. This demonstration presents a δ-closed high utility co-location pattern miner (δ-CHUCPM), a mining system that removes redundant HUCPs and presents a concise representation of the mining result to users. We achieve the removing redundant HUCP objective by allowing a tolerance between the utility of a pattern and the utility of its superset. If the tolerance is not larger than a user-specified small value δ, the pattern is removed. Users can freely set different δ tolerance values according to their own application scenarios and requirements to control the number of HUCPs. δ-CHUCPM can provide users with a more understandable result and help them make informed decisions.
Secure Embedding Aggregation for Federated Representation Learning

We consider a federated representation learning framework, where with the assistance of a central server, a group of N distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e.g., users in a social network). Under this framework, for the key step of aggregating local embeddings trained privately at the clients, we develop a secure embedding aggregation protocol named SecEA, which leverages all potential aggregation opportunities among all the clients, while providing privacy guarantees for the set of local entities and corresponding embeddings simultaneously at each client, against a curious server and up to T < N/2 colluding clients.
Distributed Fact Checking

We formulate the problem of fake news detection using distributed inexpert agents. We consider the source for news/statements as a binary source (to model true vs. false statements). Upon observing news, each agent labels the news as true or false, which equals the validity of the statement with some probability depending on the reliability of the agent. In other words, each agent is viewed as a Binary Symmetric Channel (BSC) that misclassifies each statement with some error probability. For an algorithm that estimates the validity by thresholding a linear combination of the individual agents’ labels, we characterize the optimal weights and threshold to minimize the probability of error. We establish an upper bound on this probability of error as well as the naive majority rule.
A Generalized Covering Algorithm for Chained Codes

The covering radius is a fundamental property of linear codes that characterizes the trade-off between storage and access in linear data-query protocols. The generalized covering radius was recently defined by Elimelech and Schwartz for applications in joint-recovery of linear data-queries. In this work we extend a known bound on the ordinary covering radius to the generalized one for all codes satisfying the chain condition—a known condition which is satisfied by most known families of codes. Given a generator matrix of a special form, we also provide an algorithm which finds codewords which cover the input vector(s) within the distance specified by the bound. For the case of Reed-Muller codes we provide efficient construction of such generator matrices, therefore providing a faster alternative to a previous generalized covering algorithm for Reed-Muller codes.
Towards Flexible Multi-modal Document Models

Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multitask learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Please find the code and models at: https://cyberagentailab.github.io/flex-dm
Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation

In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manner. Pixels are grouped into several clusters where the part-level embeddings serve as cluster centers. Afterwards, object masks are obtained by compositing the part proposals. This bottom-up interaction is shown to be effective in integrating information from lower semantic levels to higher semantic levels. Based on that, our novel approach Compositor produces part and object segmentation masks simultaneously while improving the mask quality. Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4.4% and 7.1% on part and object respectively.
Strong Data Processing Inequalities for Locally Differentially Private Mechanisms

We investigate the strong data processing inequalities of locally differentially private mechanisms under a specific f -divergence, namely the E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</inf> -divergence. More specifically, we characterize an upper bound on the E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</inf> -divergence between PK and QK, the output distributions of an ε-LDP mechanism K, in terms of the E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</inf> -divergence between the corresponding input distributions P and Q. Interestingly, the tightest such upper bound in the binary case turns out to have a non-multiplicative form. We then extend our results to derive a tight upper bound for general f-divergences. As an application of our main findings, we derive a lower bound on the locally private Bayesian estimation risk that is tighter than the available divergence-based bound in the literature.
Minimal Derivative Descendants of Cyclic Codes

This paper defines minimal derivative descendants (DDs) of an extended cyclic code from the derivative of Mattson-Solomon polynomials. It proves that the minimal DDs in different directions are equivalent codes. This allows us to perform derivative decoding based on decodings for minimal DDs with cyclic shifting. In particular, the small dimension and the large minimum Hamming distance of minimal DDs make it attractive to perform derivative decoding based on the ordered statistics decoding (OSD). Simulation results show that the derivative decoding based on the OSD can provide considerable gain.