pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Scenario Bimetric-Balanced IoT Resource Allocation: An Evolutionary Approach
Authors: Jiashu Wu, Hao Dai, Yang Wang, Zhiying Tu
Date: 2022-11-10 00:00:00

In this paper, we allocate IoT devices as resources for smart services with time-constrained resource requirements. The allocation method named as BRAD can work under multiple resource scenarios with diverse resource richnesses, availabilities and costs, such as the intelligent healthcare system deployed by Harbin Institute of Technology (HIT-IHC). The allocation aims for bimetric-balancing under the multi-scenario case, i.e., the profit and cost associated with service satisfaction are jointly optimised and balanced wisely. Besides, we abstract IoT devices as digital objects (DO) to make them easier to interact with during resource allocation. Considering that the problem is NP-Hard and the optimisation objective is not differentiable, we utilise Grey Wolf Optimisation (GWO) algorithm as the model optimiser. Specifically, we tackle the deficiencies of GWO and significantly improve its performance by introducing three new mechanisms to form the BRAD-GWA algorithm. Comprehensive experiments are conducted on realistic HIT-IHC IoT testbeds and several algorithms are compared, including the allocation method originally used by HIT-IHC system to verify the effectiveness of the BRAD-GWA. The BRAD-GWA achieves a 3.14 times and 29.6% objective reduction compared with the HIT-IHC and the original GWO algorithm, respectively.
Linear Discriminant Analysis with the Randomized Kaczmarz Method
Authors: Jocelyn T. Chi, Deanna Needell
Date: 2022-11-10 00:00:00

We present a randomized Kaczmarz method for linear discriminant analysis (rkLDA), an iterative randomized approach to binary-class Gaussian model linear discriminant analysis (LDA) for very large data. We harness a least squares formulation and mobilize the stochastic gradient descent framework to obtain a randomized classifier with performance that can achieve comparable accuracy to that of full data LDA. We present analysis for the expected change in the LDA discriminant function if one employs the randomized Kaczmarz solution in lieu of the full data least squares solution that accounts for both the Gaussian modeling assumptions on the data and algorithmic randomness. Our analysis shows how the expected change depends on quantities inherent in the data such as the scaled condition number and Frobenius norm of the input data, how well the linear model fits the data, and choices from the randomized algorithm. Our experiments demonstrate that rkLDA can offer a viable alternative to full data LDA on a range of step-sizes and numbers of iterations.
On power sum kernels on symmetric groups
Authors: Iskander Azangulov, Viacheslav Borovitskiy, Andrei Smolensky
Date: 2022-11-10 00:00:00
Tasks: Gaussian Processes

In this note, we introduce a family of "power sum" kernels and the corresponding Gaussian processes on symmetric groups $\mathrm{S}_n$. Such processes are bi-invariant: the action of $\mathrm{S}_n$ on itself from both sides does not change their finite-dimensional distributions. We show that the values of power sum kernels can be efficiently calculated, and we also propose a method enabling approximate sampling of the corresponding Gaussian processes with polynomial computational complexity. By doing this we provide the tools that are required to use the introduced family of kernels and the respective processes for statistical modeling and machine learning.
Bayesian score calibration for approximate models
Authors: Joshua J Bon, David J Warne, David J Nott, Christopher Drovandi
Date: 2022-11-10 00:00:00
Tasks: Bayesian Inference, scoring rule, Uncertainty Quantification

Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging since the corresponding likelihood function is often intractable and model simulation may be computationally burdensome. Fortunately, in many of these situations, it is possible to adopt a surrogate model or approximate likelihood function. It may be convenient to conduct Bayesian inference directly with the surrogate, but this can result in bias and poor uncertainty quantification. In this paper we propose a new method for adjusting approximate posterior samples to reduce bias and produce more accurate uncertainty quantification. We do this by optimizing a transform of the approximate posterior that maximizes a scoring rule. Our approach requires only a (fixed) small number of complex model simulations and is numerically stable. We demonstrate good performance of the new method on several examples of increasing complexity.
Equivariant Contrastive Learning for Sequential Recommendation
Authors: Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jae Boum Kim, Shoujin Wang, Sunghun Kim
Date: 2022-11-10 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
Authors: Anjie Zhu, Deqiang Ouyang, Shuang Liang, Jie Shao
Date: 2022-07-19 00:00:00
Proceeding: Knowledge-Based Systems 2022 7
Tasks: Hierarchical Reinforcement Learning, Knowledge Graphs, Question Answering, Relation

Recently, knowledge graph reasoning has sparked great interest in research community, which aims at inferring missing information in triples and provides critical support to various tasks (e.g., question answering and recommendation). To date, multi-hop reasoning is a dominant approach which infers the target answer by walking along the path connecting entities and relations, ensuring both accuracy and interpretability. However, in most knowledge graphs, there are multiple relations related to an identical entity, and multiple tail entities for an identical pair of head entity and relation. Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning. In order to deal with such an issue, this work presents a novel paradigm for knowledge graph reasoning by decomposing it to a two-level hierarchical decision process. We apply the hierarchical reinforcement learning framework which dismantles the task into a high-level process for relation detector and a low-level process for entity reasoning, respectively. In this way, the action space is effectively controlled where the policies can be optimized. The interactions between entity and relation decision enhance the rationality of reasoning. Moreover, we introduce a dynamic prospect mechanism for lowlevel policy where the information can guide us to a refined and improved action space, assisted by embedding based method. Our proposed model is evaluated on four benchmark datasets and the results validate its superiority over state-of-the-art baselines, showing the interpretability of reasoning process simultaneously.
Temporal knowledge graph representation learning with local and global evolutions
Authors: Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao
Date: 2022-09-05 00:00:00
Proceeding: Knowledge-Based Systems 2022 9
Tasks: Graph Representation Learning, Representation Learning

Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKG to low-dimensional vector space while preserving the evolutionary nature of TKG. Most existing methods separately treat knowledge that happened at different times, which fails to explore how temporal knowledge graph evolves over time. Actually, TKG should evolve both on local and global structures. The local structure evolution describes the formation process of graph structure in a detailed manner, while the global structure evolution refers to the dynamic topology (e.g., community partition) of graph, which is derived from the continuous formation process. Both of them are key factors for understanding the evolutionary nature of TKG. Unfortunately, seldom attention has been paid to this aspect. In this paper, we propose a new TKG representation learning framework with local and global structure evolutions, named EvoExplore. Specifically, we define the local structure evolution as the establishment process of relations between entities, and propose a hierarchical-attention-based temporal point process to capture the formation process of graph structure in a fine-grained manner. For global structure evolution, we propose a novel soft modularity parameterized by entity representations to capture the dynamic community partition of TKG. Finally, we employ a multi-task loss function to jointly optimize the above two parts, which allows EvoExplore to learn the mutual influences of local and global structure evolutions. Experimental results on three realworld datasets demonstrate the superiority of EvoExplore compared with baseline methods.
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization
Authors: Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner
Date: 2022-11-02 00:00:00
Tasks: Autonomous Vehicles, Object, object-detection, Object Detection

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could compromise the safety of autonomous vehicles. The problem would be amplified even more when uncertainty errors could not propagate into the submodules, if these are not a part of the end-to-end system design. To address these concerns, a parallel module which verifies the predictions of the object proposals coming out of Deep Neural Networks are required. This work aims to verify 3D object proposals from MonoRUn model by proposing a plausibility framework that leverages cross sensor streams to reduce false positives. The verification metric being proposed uses prior knowledge in the form of four different energy functions, each utilizing a certain prior to output an energy value leading to a plausibility justification for the hypothesis under consideration. We also employ a novel two-step schema to improve the optimization of the composite energy function representing the energy model.
Deep Visual-Linguistic Fusion Network Considering Cross-Modal Inconsistency for Rumor Detection
Authors: Yang Yang 1, Ran Bao 2, Weili Guo 3, De-Chuan Zhan 4, YiLong Yin 5, Yang Jian 6
Date: 2022-07-08 00:00:00
Proceeding: Science China Information Sciences 2022 7

With the development of internet, users can freely publish posts on various social media, which brings great convenience to keep abreast of the world. However, those posts usually carry a lot of rumors, which require plenty of manpower to monitor. Fortunately, thanks to the success of modern machine learning techniques, especially deep learning models, we tried to automatically detect the rumors as a classification problem. Early attempts always focus on building classifiers relying on image or text information, i.e., single modality in the posts. Thereafter, several multi-modal detection approaches employ early or late fusion operator for aggregating multiple source information. Nevertheless, they only take the advantage of multiple modal embeddings for fusion, ignore another important detection factor, i.e., the inter-modal inconsistency between modalities. To solve this problem, we develop the DVLFN, \textcolor{blue}{a novel Deep Visual-Linguistic Fusion Network considering cross-modal inconsistency, which detects rumors by comprehensively considering the modal aggregation and contrast information. Specifically, DVLFN firstly utilizes visual and textual deep encoders, i.e., BERT and Faster R-CNN, to extract global and regional embeddings for image and text modalities, then predicts the posts' authenticity from two aspects: 1) inter-modal inconsistency, which employs the wasserstein distance to better measure the similarity between regional embeddings of different modalities. 2) modal aggregation, which experimentally employs early fusion to aggregate two modal embeddings for prediction. Consequently, DVLFN can compose the final prediction based on the modal fusion and inconsistency measure. Experiments are conducted on three real-world multi-media rumor detection datasets collected from Reddit, GoodNews, and Weibo, the results validate the superior performances of the proposed DVLFN.
SMDT: Cross-View Geo-Localization with Image Alignment and Transformer
Authors: Xiaoyang Tian, Jie Shao, Deqiang Ouyang, Anjie Zhu, Feiyu Chen.
Date: 2022-04-06 00:00:00
Proceeding: IEEE International Conference on Multimedia and Expo 2022 2022 4
Tasks: geo-localization, Segmentation, Semantic Segmentation

The goal of cross-view geo-localization is to determine the location of a given ground image by matching with aerial images. However, existing methods ignore the variability of scenes, additional information and spatial correspondence of
covisibility and non-convisibility areas in ground-aerial image pairs. In this context, we propose a cross-view matching method called SMDT with image alignment and Transformer. First, we utilize semantic segmentation technique to segment different areas. Then, we convert the vertical view of aerial images to front view by mixing polar mapping and perspective mapping. Next, we simultaneously train dual conditional generative adversarial nets by taking the semantic segmentation images and converted images as input to synthesize the aerial image with ground view style. These steps are collectively referred to as image alignment. Last, we use Transformer to explicitly utilize the properties of selfattention. Experiments show that our SMDT method is superior to the existing ground-to-aerial cross-view methods.
Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis
Authors: Sudhandar Balakrishnan, Yihao Fang, Xioadan Zhu
Date: 2022-10-26 00:00:00
Tasks: Sentiment Analysis

The invention of transformer-based models such as BERT, GPT, and RoBERTa has enabled researchers and financial companies to finetune these powerful models and use them in different downstream tasks to achieve state-of-the-art performance. Recently, a lightweight alternative (approximately 0.1% - 3% of the original model parameters) to fine-tuning, known as prefix tuning has been introduced. This method freezes the model parameters and only updates the prefix to achieve performance comparable to full fine-tuning. Prefix tuning enables researchers and financial practitioners to achieve similar results with much fewer parameters. In this paper, we explore the robustness of prefix tuning when facing noisy data. Our experiments demonstrate that fine-tuning is more robust to noise than prefix tuning -- the latter method faces a significant decrease in performance on most corrupted data sets with increasing noise levels. Furthermore, prefix tuning has high variances in the F1 scores compared to fine-tuning in many corruption methods. We strongly advocate that caution should be carefully taken when applying the state-of-the-art prefix tuning method to noisy data.
Untargeted Backdoor Attack against Object Detection
Authors: Chengxiao Luo, Yiming Li, Yong Jiang, Shu-Tao Xia
Date: 2022-11-02 00:00:00
Tasks: Backdoor Attack, image-classification, Image Classification, Object, object-detection, Object Detection, Pedestrian Detection

Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The backdoored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns. Currently, most of the existing backdoor attacks were conducted on the image classification under the targeted manner. In this paper, we reveal that these threats could also happen in object detection, posing threatening risks to many mission-critical applications ($e.g.$, pedestrian detection and intelligent surveillance systems). Specifically, we design a simple yet effective poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns. We conduct extensive experiments on the benchmark dataset, showing its effectiveness in both digital and physical-world settings and its resistance to potential defenses.
Backdoor Defense via Suppressing Model Shortcuts
Authors: Sheng Yang, Yiming Li, Yong Jiang, Shu-Tao Xia
Date: 2022-11-02 00:00:00
Tasks: backdoor defense, model

Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process. Specifically, the adversaries intend to embed hidden backdoors in DNNs so that malicious model predictions can be activated through pre-defined trigger patterns. In this paper, we explore the backdoor mechanism from the angle of the model structure. We select the skip connection for discussions, inspired by the understanding that it helps the learning of model `shortcuts' where backdoor triggers are usually easier to be learned. Specifically, we demonstrate that the attack success rate (ASR) decreases significantly when reducing the outputs of some key skip connections. Based on this observation, we design a simple yet effective backdoor removal method by suppressing the skip connections in critical layers selected by our method. We also implement fine-tuning on these layers to recover high benign accuracy and to further reduce ASR. Extensive experiments on benchmark datasets verify the effectiveness of our method.
Automatic Creativity Measurement in Scratch Programs Across Modalities
Authors: Anastasia Kovalkov, Benjamin Paaßen, Avi Segal, Niels Pinkwart, Kobi Gal
Date: 2022-11-07 00:00:00

Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure.In this paper, we make the journey fromdefining a formal measure of creativity that is efficientlycomputable to applying the measure in a practical domain. The measure is general and relies on coretheoretical concepts in creativity theory, namely fluency, flexibility, and originality, integratingwith prior cognitive science literature. We adapted the general measure for projects in the popular visual programming language Scratch.We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on human expert creativity assessments in an extensive user study. Our results show that opinions about creativity in Scratch varied widely across experts. The automatic creativity assessment aligned with the assessment of the human experts more than the experts agreed with each other. This is a first step in providing computational models for measuring creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools.
Massive MIMO Channel Measurement Data Set for Localization and Communication
Authors: Achiel Colpaert, Sibren De Bast, Andrea P. Guevara, Zhuangzhuang Cui, Sofie Pollin
Date: 2022-11-10 00:00:00

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labelled CSI data. In this article, we focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We also show this data can be used for learning-based localization and enhanced communication research. The data set presented in this work is made fully available to the research community. We show a CSI-based joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users for improving the spectral efficiency (SE) of the communication system. Finally, we pose promising directions on further exploiting this data set and creating future data sets.
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
Authors: Tom He, Jasmina Gajcin, Ivana Dusparic
Date: 2022-11-02 00:00:00
Tasks: Causal Inference, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robotic Grasping

Reinforcement learning (RL) is used in various robotic applications. RL enables agents to learn tasks autonomously by interacting with the environment. The more critical the tasks are, the higher the demand for the robustness of the RL systems. Causal RL combines RL and causal inference to make RL more robust. Causal RL agents use a causal representation to capture the invariant causal mechanisms that can be transferred from one task to another. Currently, there is limited research in Causal RL, and existing solutions are usually not complete or feasible for real-world applications. In this work, we propose CausalCF, the first complete Causal RL solution incorporating ideas from Causal Curiosity and CoPhy. Causal Curiosity provides an approach for using interventions, and CoPhy is modified to enable the RL agent to perform counterfactuals. Causal Curiosity has been applied to robotic grasping and manipulation tasks in CausalWorld. CausalWorld provides a realistic simulation environment based on the TriFinger robot. We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.