pwc-archive/papers-with-abstracts huggingface dataset:
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Authors: Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Nicha C. Dvornek, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan
Date: 2022-09-27 00:00:00
Tasks: Anatomy, Contrastive Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings.
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Authors: Yulin Wang, Yang Yue, Xinhong Xu, Ali Hassani, Victor Kulikov, Nikita Orlov, Shiji Song, Humphrey Shi, Gao Huang
Date: 2022-09-27 00:00:00
Tasks: Video Recognition

Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e.g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame. However, in most existing works, either type of redundancy is typically modeled with another absent. This paper explores the unified formulation of spatial-temporal dynamic computation on top of the recently proposed AdaFocusV2 algorithm, contributing to an improved AdaFocusV3 framework. Our method reduces the computational cost by activating the expensive high-capacity network only on some small but informative 3D video cubes. These cubes are cropped from the space formed by frame height, width, and video duration, while their locations are adaptively determined with a light-weighted policy network on a per-sample basis. At test time, the number of the cubes corresponding to each video is dynamically configured, i.e., video cubes are processed sequentially until a sufficiently reliable prediction is produced. Notably, AdaFocusV3 can be effectively trained by approximating the non-differentiable cropping operation with the interpolation of deep features. Extensive empirical results on six benchmark datasets (i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2 and Diving48) demonstrate that our model is considerably more efficient than competitive baselines.
Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset
Authors: Hong Liu, Hao Peng, Zhijian Ou, Juanzi Li, Yi Huang, Junlan Feng
Date: 2022-09-27 00:00:00

Recently, there have merged a class of task-oriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual. Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customer-service staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset. We introduce how the two baselines are constructed, the problems encountered, and the results. We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.
BanglaSarc: A Dataset for Sarcasm Detection
Authors: Tasnim Sakib Apon, Ramisa Anan, Elizabeth Antora Modhu, Arjun Suter, Ifrit Jamal Sneha, Md. Golam Rabiul Alam
Date: 2022-09-27 00:00:00
Tasks: Sarcasm Detection

Being one of the most widely spoken language in the world, the use of Bangla has been increasing in the world of social media as well. Sarcasm is a positive statement or remark with an underlying negative motivation that is extensively employed in today's social media platforms. There has been a significant improvement in sarcasm detection in English over the previous many years, however the situation regarding Bangla sarcasm detection remains unchanged. As a result, it is still difficult to identify sarcasm in bangla, and a lack of high-quality data is a major contributing factor. This article proposes BanglaSarc, a dataset constructed specifically for bangla textual data sarcasm detection. This dataset contains of 5112 comments/status and contents collected from various online social platforms such as Facebook, YouTube, along with a few online blogs. Due to the limited amount of data collection of categorized comments in Bengali, this dataset will aid in the of study identifying sarcasm, recognizing people's emotion, detecting various types of Bengali expressions, and other domains. The dataset is publicly available at https://www.kaggle.com/datasets/sakibapon/banglasarc.
Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation
Authors: Abhilash Chakraborty, Anupam Biswas, Ajoy Kumar Khan
Date: 2022-09-27 00:00:00

With the advent of the digital era, every day-to-day task is automated due to technological advances. However, technology has yet to provide people with enough tools and safeguards. As the internet connects more-and-more devices around the globe, the question of securing the connected devices grows at an even spiral rate. Data thefts, identity thefts, fraudulent transactions, password compromises, and system breaches are becoming regular everyday news. The surging menace of cyber-attacks got a jolt from the recent advancements in Artificial Intelligence. AI is being applied in almost every field of different sciences and engineering. The intervention of AI not only automates a particular task but also improves efficiency by many folds. So it is evident that such a scrumptious spread would be very appetizing to cybercriminals. Thus the conventional cyber threats and attacks are now ``intelligent" threats. This article discusses cybersecurity and cyber threats along with both conventional and intelligent ways of defense against cyber-attacks. Furthermore finally, end the discussion with the potential prospects of the future of AI in cybersecurity.
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Authors: Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin Bonilla, Gholamreza Haffari, Dinh Phung
Date: 2022-09-27 00:00:00
Tasks: counterfactual, Diversity, feature selection, Interpretable Machine Learning, Privacy Preserving, Stochastic Optimization

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks.
Design Perspectives of Multitask Deep Learning Models and Applications
Authors: Yeshwant Singh, Anupam Biswas, Angshuman Bora, Debashish Malakar, Subham Chakraborty, Suman Bera
Date: 2022-09-27 00:00:00
Tasks: Deep Learning, Multi-Task Learning, Transfer Learning

In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under learning-related tasks, multi-task learning has been able to generalize the models even better. We try to enhance the feature mapping of the multi-tasking models by sharing features among related tasks and inductive transfer learning. Also, our interest is in learning the task relationships among various tasks for acquiring better benefits from multi-task learning. In this chapter, our objective is to visualize the existing multi-tasking models, compare their performances, the methods used to evaluate the performance of the multi-tasking models, discuss the problems faced during the design and implementation of these models in various domains, and the advantages and milestones achieved by them
Scaling Laws For Deep Learning Based Image Reconstruction
Authors: Tobit Klug, Reinhard Heckel
Date: 2022-09-27 00:00:00
Tasks: Deep Learning, Denoising, Image Denoising, Image Reconstruction, Super-Resolution

Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are only trained on a few hundreds or thousands of images as opposed to the millions of examples deep networks are trained on in other domains. In this work, we study whether major performance gains are expected from scaling up the training set size. We consider image denoising, accelerated magnetic resonance imaging, and super-resolution and empirically determine the reconstruction quality as a function of training set size, while simultaneously scaling the network size. For all three tasks we find that an initially steep power-law scaling slows significantly already at moderate training set sizes. Interpolating those scaling laws suggests that even training on millions of images would not significantly improve performance. To understand the expected behavior, we analytically characterize the performance of a linear estimator learned with early stopped gradient descent. The result formalizes the intuition that once the error induced by learning the signal model is small relative to the error floor, more training examples do not improve performance.
Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
Authors: Yeganeh Madadi, Vahid Seydi, Jian Sun, Edward Chaum, Siamak Yousefi
Date: 2022-09-27 00:00:00
Tasks: Domain Adaptation, Ensemble Learning, image-classification, Image Classification

Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
Authors: Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp, Evren Catak, Umit Cali
Date: 2022-09-27 00:00:00
Tasks: Management, Semantic Segmentation

Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies. The main goal of these networks is to connect billions of devices, systems, and users with high-speed data transmission, high cell capacity, and low latency, as well as to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, advanced manufacturing, and many more. To achieve these goals, spectrum sensing has been paid more attention, along with new approaches using artificial intelligence (AI) methods for spectrum management in cellular networks. This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models for identifying cellular network signals under adversarial attacks with and without defensive distillation methods. The results showed that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks.
Deep learning and machine learning for Malaria detection: overview, challenges and future directions
Authors: Imen Jdey, Ghazala Hcini, Hela Ltifi
Date: 2022-09-27 00:00:00
Tasks: Decision Making, Deep Learning

To have the greatest impact, public health initiatives must be made using evidence-based decision-making. Machine learning Algorithms are created to gather, store, process, and analyse data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning has ended up curious about it as of late. This study uses a variety of machine learning and image processing approaches to detect and forecast the malarial illness. In our research, we discovered the potential of deep learning techniques as smart tools with broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We examine the common confinements of deep learning for computer frameworks and organising, counting need of preparing data, preparing overhead, realtime execution, and explain ability, and uncover future inquire about bearings focusing on these restrictions.
SmartFPS: Neural Network based Wireless-inertial fusion positioning system
Authors: Luchi Hua, Jun Yang
Date: 2022-09-27 00:00:00
Tasks: Transfer Learning

The current fusion positioning systems are mainly based on filtering algorithms, such as Kalman filtering or particle filtering. However, the system complexity of practical application scenarios is often very high, such as noise modeling in pedestrian inertial navigation systems, or environmental noise modeling in fingerprint matching and localization algorithms. To solve this problem, this paper proposes a fusion positioning system based on deep learning and proposes a transfer learning strategy for improving the performance of neural network models for samples with different distributions. The results show that in the whole floor scenario, the average positioning accuracy of the fusion network is 0.506m. The experiment results of transfer learning show that the estimation accuracy of the inertial navigation positioning step size and rotation angle of different pedestrians can be improved by 53.3% on average, the Bluetooth positioning accuracy of different devices can be improved by 33.4%, and the fusion can be improved by 31.6%.
Identifying and Extracting Football Features from Real-World Media Sources using Only Synthetic Training Data
Authors: Jose Cerqueira Fernandes, Benjamin Kenwright
Date: 2022-09-27 00:00:00

Real-world images used for training machine learning algorithms are often unstructured and inconsistent. The process of analysing and tagging these images can be costly and error prone (also availability, gaps and legal conundrums). However, as we demonstrate in this article, the potential to generate accurate graphical images that are indistinguishable from real-world sources has a multitude of benefits in machine learning paradigms. One such example of this is football data from broadcast services (television and other streaming media sources). The football games are usually recorded from multiple sources (cameras and phones) and resolutions, not to mention, occlusion of visual details and other artefacts (like blurring, weathering and lighting conditions) which make it difficult to accurately identify features. We demonstrate an approach which is able to overcome these limitations using generated tagged and structured images. The generated images are able to simulate a variety views and conditions (including noise and blurring) which may only occur sporadically in real-world data and make it difficult for machine learning algorithm to 'cope' with these unforeseen problems in real-data. This approach enables us to rapidly train and prepare a robust solution that accurately extracts features (e.g., spacial locations, markers on the pitch, player positions, ball location and camera FOV) from real-world football match sources for analytical purposes.
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation
Authors: Xue Sun, Changhao Li, Lei Yan, Suzhi Cao
Date: 2022-09-27 00:00:00
Tasks: Management, Reinforcement Learning (RL)

In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the case of excessive network dynamics. DTN node buffer management is still an active area of research, as the current implementation of the DTN core protocol still relies on the assumption that there is always enough memory available in different network nodes to store and forward bundles. In addition, the classical queuing theory does not apply to the dynamic management of DTN node buffers. Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C). The method aims to explore training a geosynchronous earth orbit intelligent agent to manage all DTN nodes in an LEO satellite constellation scenario. The goal of the A2C agent is to maximize delivery success rate and minimize network resource consumption cost while considering node memory utilization. The intelligent agent can dynamically adjust the radio data rate and perform drop operations based on bundle priority. In order to measure the effectiveness of applying A2C technology to DTN node management issues in LEO satellite constellation scenarios, this paper compares the trained intelligent agent strategy with the other two non-RL policies, including random and standard policies. Experiments show that the A2C strategy balances delivery success rate and cost, and provides the highest reward and the lowest node memory utilization.
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
Authors: Ying Bi, Bing Xue, Mengjie Zhang
Date: 2022-09-27 00:00:00
Tasks: Classification, Deep Learning, image-classification, Image Classification

Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffer from limitations such as poor interpretability. To address this, this paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or gray-scale images, and construct effective and diverse ensembles for image classification. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability.