pwc-archive/papers-with-abstracts huggingface dataset:
Scientometric analysis of literature on distributed vehicular networks : VOSViewer visualization techniques
Authors: Sandeep Kumar Sood, Navin Kumar, Munish Saini
Date: 2021-03-08 00:00:00
Proceeding: Artificial Intelligence Review 2021 3

The idea of vehicular communication has evolved dramatically over the past two decades apparently with technological advancements that accord low power and memory consumption devices, and yet providing low latency and high performing abilities. In the 20th century, vehicular communications were primarily done through singling blinkers, tail lights, and hazard lamps, essentially serving rudimentary purposes. In the early 2000s, the term “VANET” was first introduced for demonstrating road safety and navigation. By the end of 2012, the term vehicle-to-everything(V2X) was coined with IEEE 802.11p as the supporting standard for underneath technology. The adequately increasing publications and development of VANET simulators like SUMO acknowledge the fact that there has been an intriguing interest in vehicular research. While publications are increasing massively in numbers, perhaps, we identified a lack of comprehensive scientometric analysis for this field. This paper aims at analyzing the research carried out between 2009 and 2020 using Scopus data with a scientometric approach thereby highlighting the citation patterns and indulging journals, authors, institutes, and countries in the field of vehicular communication. Furthermore, keywords analysis is also staged using VOSViewer to visualize the research patterns. This paper also tries to classify the publications into distinct clusters so formed in VOSViewer using classifying techniques, hence, to make the voluminous data acumen for the publication stride.
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data
Authors: Samundra Deep, Xi Zheng
Date: 2019-12-05 00:00:00
Proceeding: 19452854 2019 12
Tasks: Activity Recognition, Human Activity Recognition

The traditional methods of recognizing human activities involve typical machine learning (ML) algorithms which uses heuristic engineered features. Human activities are dynamic in nature and are encoded with a sequence of actions. ML methods are able to perform activity recognition tasks but may not exploit the temporal correlations of the input data. Therefore, in this paper, we proposed and showed the effectiveness of employing a new combination of deep learning (DL) methods for human activity recognition (HAR). DL methods are capable of extracting discriminative features automatically from the raw sensor data. Specifically, in this paper, we proposed a hybrid architecture which features a combination of Convolutional neural networks (CNN) and Long short-term Memory (LSTM) networks for HAR task. The model is tested on UCI HAR dataset which is a benchmark dataset and comprises of accelerometer and gyroscope data obtained from a smartphone. Our experimental results showed that our proposed method outperformed the recent results which used pure LSTM and bidirectional LSTM networks on the same dataset.
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Authors: Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, Lujia Wang
Date: 2022-07-01 00:00:00
Tasks: CARLA MAP Leaderboard

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods.
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
Authors: Shengshan Hu, Ziqi Zhou, Yechao Zhang, Leo Yu Zhang, Yifeng Zheng, Yuanyuan HE, Hai Jin
Date: 2022-07-01 00:00:00
Tasks: Adversarial Attack, Backdoor Attack, Contrastive Learning, Deep Hashing, Generative Adversarial Network, image-classification, Image Classification, Image Retrieval, Retrieval

Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonstrated that deep neural networks (DNNs) are susceptible to adversarial examples, and exploring adversarial attack against deep hashing has attracted many research efforts. Nevertheless, backdoor attack, another famous threat to DNNs, has not been studied for deep hashing yet. Although various backdoor attacks have been proposed in the field of image classification, existing approaches failed to realize a truly imperceptive backdoor attack that enjoys invisible triggers and clean label setting simultaneously, and they also cannot meet the intrinsic demand of image retrieval backdoor. In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label. Specifically, we first propose a new conditional generative adversarial network (cGAN) pipeline to effectively generate poisoned samples. For any given benign image, it seeks to generate a natural-looking poisoned counterpart with a unique invisible trigger. In order to improve the attack effectiveness, we introduce a label-based contrastive learning network LabCLN to exploit the semantic characteristics of different labels, which are subsequently used for confusing and misleading the target model to learn the embedded trigger. We finally explore the mechanism of backdoor attacks on image retrieval in the hash space. Extensive experiments on multiple benchmark datasets verify that BadHash can generate imperceptible poisoned samples with strong attack ability and transferability over state-of-the-art deep hashing schemes.
Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound
Authors: Han Huang, Yijie Dong, Xiaohong Jia, Jianqiao Zhou, Dong Ni, Jun Cheng, Ruobing Huang
Date: 2022-07-01 00:00:00
Tasks: Cancer Classification, Decision Making, Diagnostic

Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.
Behavioral Player Rating in Competitive Online Shooter Games
Authors: Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher
Date: 2022-07-01 00:00:00

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several features from in-game statistics to model players and create ratings that accurately represent their behavior and true performance level. We then compare the estimating power of our behavioral ratings against ratings created with three mainstream rating systems by predicting rank of players in four popular game modes from the competitive shooter genre. Our results show that the behavioral ratings present more accurate performance estimations while maintaining the interpretability of the created representations. Considering different aspects of the playing behavior of players and using behavioral ratings for matchmaking can lead to match-ups that are more aligned with players' goals and interests, consequently resulting in a more enjoyable gaming experience.
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer
Authors: Philipp Spitzer, Niklas Kühl, Marc Goutier
Date: 2022-07-01 00:00:00
Tasks: Transfer Learning

Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success. These humans possess task-specific expert knowledge (TSEK) and hence, represent subject matter experts (SMEs). However, not only demographic changes but also personnel downsizing strategies lead and will continue to lead to departures of SMEs within organizations, which constitutes the challenge of how to retain that expert knowledge and train novices to keep the competitive advantage elicited by that expert knowledge. SMEs training novices is time- and cost-intensive, which intensifies the need for alternatives. Human-AI collaboration (HAIC) poses a way out of this dilemma, facilitating alternatives to preserve expert knowledge and teach it to novices for tasks conducted by SMEs beforehand. In this workshop paper, we (1) propose a framework on how HAIC can be utilized to train novices on particular tasks, (2) illustrate the role of explicit and tacit knowledge in this training process via HAIC, and (3) outline a preliminary experiment design to assess the ability of AI systems in HAIC to act as a trainer to transfer TSEK to novices who do not possess prior TSEK.
Learning Job Titles Similarity from Noisy Skill Labels
Authors: Rabih Zbib, Lucas Alvarez Lacasa, Federico Retyk, Rus Poves, Juan Aizpuru, Hermenegildo Fabregat, Vaidotas Simkus, Emilia García-Casademont
Date: 2022-07-01 00:00:00
Tasks: Representation Learning, Semantic Similarity, Semantic Textual Similarity

Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.
Online Reflective Learning for Robust Medical Image Segmentation
Authors: Yuhao Huang, Xin Yang, Xiaoqiong Huang, Jiamin Liang, Xinrui Zhou, Cheng Chen, Haoran Dou, Xindi Hu, Yan Cao, Dong Ni
Date: 2022-07-01 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation robustness. Based on the reflection-on-action conception, our RefSeg firstly drives the deep model to take action to obtain semantic segmentation. Then, RefSeg triggers the model to reflect itself. Because making deep models realize their segmentation failures during testing is challenging, RefSeg synthesizes a realistic proxy image from the semantic mask to help deep models build intuitive and effective reflections. This proxy translates and emphasizes the segmentation flaws. By maximizing the structural similarity between the raw input and the proxy, the reflection-on-action loop is closed with segmentation robustness improved. RefSeg runs in the testing phase and is general for segmentation models. Extensive validation on three medical image segmentation tasks with a public cardiac MR dataset and two in-house large ultrasound datasets show that our RefSeg remarkably improves model robustness and reports state-of-the-art performance over strong competitors.
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound
Authors: Yuxin Zou, Haoran Dou, Yuhao Huang, Xin Yang, Jikuan Qian, Chaojiong Zhen, Xiaodan Ji, Nishant Ravikumar, Guoqiang Chen, Weijun Huang, Alejandro F. Frangi, Dong Ni
Date: 2022-07-01 00:00:00
Tasks: Anatomy, Reinforcement Learning (RL)

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search. Finally, we propose a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously. We explore the efficacy of our approach on localizing four SPs on uterus and fetal brain datasets. The experiments indicate that our approach achieves a high localization accuracy as well as robust performance.
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling
Authors: Jiamin Liang, Xin Yang, Yuhao Huang, Kai Liu, Xinrui Zhou, Xindi Hu, Zehui Lin, Huanjia Luo, Yuanji Zhang, Yi Xiong, Dong Ni
Date: 2022-07-01 00:00:00
Tasks: Keypoint Detection, Vocal Bursts Intensity Prediction

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-fidelity US videos. Specifically, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are three-fold. First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection. These keypoints then provide vital information for handling complex high dynamic motions in US videos. Second, we decouple content and texture learning using the dual decoders to effectively reduce the model learning difficulty. Last, we adopt the adversarial training strategy with GAN losses for further improving the sharpness of the generated videos, narrowing the gap between real and synthesis videos. We validate our method on a large in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics and user study prove the effectiveness of our proposed method.
Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality
Authors: Oliver Lloyd, Yi Liu, Tom Gaunt
Date: 2022-07-01 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Node Classification, Sensitivity

Embedding knowledge graphs into low-dimensional spaces is a popular method for applying approaches, such as link prediction or node classification, to these databases. This embedding process is very costly in terms of both computational time and space. Part of the reason for this is the optimisation of hyperparameters, which involves repeatedly sampling, by random, guided, or brute-force selection, from a large hyperparameter space and testing the resulting embeddings for their quality. However, not all hyperparameters in this search space will be equally important. In fact, with prior knowledge of the relative importance of the hyperparameters, some could be eliminated from the search altogether without significantly impacting the overall quality of the outputted embeddings. To this end, we ran a Sobol sensitivity analysis to evaluate the effects of tuning different hyperparameters on the variance of embedding quality. This was achieved by performing thousands of embedding trials, each time measuring the quality of embeddings produced by different hyperparameter configurations. We regressed the embedding quality on those hyperparameter configurations, using this model to generate Sobol sensitivity indices for each of the hyperparameters. By evaluating the correlation between Sobol indices, we find substantial variability in the hyperparameter sensitivities between knowledge graphs, with differing dataset characteristics being the probable cause of these inconsistencies. As an additional contribution of this work we identify several relations in the UMLS knowledge graph that may cause data leakage via inverse relations, and derive and present UMLS-43, a leakage-robust variant of that graph.
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
Authors: Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo, Xiangkun He, Chen Lv
Date: 2022-07-01 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Reinforcement Learning (RL)

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision-making problem. However, poor runtime safety hinders RL-based decision-making strategies from complex driving tasks in practice. To address this problem, human demonstrations are incorporated into the RL-based decision-making strategy in this paper. Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL. A complex lane change task in an off-ramp scenario is established to examine the performance of the developed strategy. Simulation results suggest that human demonstrations can effectively improve the safety of decisions of RL. And the proposed strategy surpasses other existing learning-based decision-making strategies with respect to multiple driving performances.
Modular Lifelong Reinforcement Learning via Neural Composition
Authors: Jorge A. Mendez, Harm van Seijen, Eric Eaton
Date: 2022-07-01 00:00:00
Proceeding: ICLR 2022 4
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse of the subproblem solutions when tackling future tasks that share part of the underlying compositional structure. In a continual or lifelong reinforcement learning (RL) setting, this ability to decompose knowledge into reusable components would enable agents to quickly learn new RL tasks by leveraging accumulated compositional structures. We explore a particular form of composition based on neural modules and present a set of RL problems that intuitively admit compositional solutions. Empirically, we demonstrate that neural composition indeed captures the underlying structure of this space of problems. We further propose a compositional lifelong RL method that leverages accumulated neural components to accelerate the learning of future tasks while retaining performance on previous tasks via off-line RL over replayed experiences.
Swiss German Speech to Text system evaluation
Authors: Yanick Schraner, Christian Scheller, Michel Plüss, Manfred Vogel
Date: 2022-07-01 00:00:00
Tasks: Speech-to-Text

We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and referred to as system a-d in this report. We compare the four systems to our STT model, referred to as FHNW from hereon after, and provide details on how we trained our model. To evaluate the models, we use two STT datasets from different domains. The Swiss Parliament Corpus (SPC) test set and a private dataset in the news domain with an even distribution across seven dialect regions. We provide a detailed error analysis to detect the three systems' strengths and weaknesses. This analysis is limited by the characteristics of the two test sets. Our model scored the highest bilingual evaluation understudy (BLEU) on both datasets. On the SPC test set, we obtain a BLEU score of 0.607, whereas the best commercial system reaches a BLEU score of 0.509. On our private test set, we obtain a BLEU score of 0.722 and the best commercial system a BLEU score of 0.568.
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Authors: Jorge F. Lazo, Chun-Feng Lai, Sara Moccia, Benoit Rosa, Michele Catellani, Michel de Mathelin, Giancarlo Ferrigno, Paul Breedveld, Jenny Dankelman, Elena De Momi
Date: 2022-07-01 00:00:00
Tasks: Autonomous Navigation, Decision Making, Navigate

Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The development of tools to automate certain tasks could alleviate the physical and mental load of doctors during interventions, allowing them to focus on diagnosis and decision-making tasks. In this paper, we present a synergic solution for intraluminal navigation consisting of a 3D printed endoscopic soft robot that can move safely inside luminal structures. Visual servoing, based on Convolutional Neural Networks (CNNs) is used to achieve the autonomous navigation task. The CNN is trained with phantoms and in-vivo data to segment the lumen, and a model-less approach is presented to control the movement in constrained environments. The proposed robot is validated in anatomical phantoms in different path configurations. We analyze the movement of the robot using different metrics such as task completion time, smoothness, error in the steady-state, and mean and maximum error. We show that our method is suitable to navigate safely in hollow environments and conditions which are different than the ones the network was originally trained on.