pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
Authors: Jiahe Fan, Mohammud J. Bocus, Brett Hosking, Rigen Wu, Yanan Liu, Sergey Vityazev, Rui Fan
Date: 2021-12-24 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module then reweighs the channel features to enhance the consistency of different feature maps. Subsequently, we employ an atrous spatial pyramid pooling module (comprising of atrous convolutions in series, with progressive rates of dilation) to integrate the spatial context information. This helps better distinguish between potholes and undamaged road areas. Finally, the feature maps in the adjacent layers are fused using our proposed multi-scale feature fusion module. This further reduces the semantic gap between different feature channel layers. Extensive experiments were carried out on the Pothole-600 dataset to demonstrate the effectiveness of our proposed method. The quantitative comparisons suggest that our method achieves the state-of-the-art (SoTA) performance on both RGB images and transformed disparity images, outperforming three SoTA single-modal semantic segmentation networks.
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
Authors: Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou
Date: 2021-12-24 00:00:00
Tasks: Graph Learning

Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise knowledge graph. It is a critical and challenge problem on how to learn the numerical representation for each node to characterize subtle structures. However, most previous studies treat all node relations in BMHG as the same class of relation without distinguishing the different characteristics between the intra-class relations and inter-class relations of the bi-typed nodes, causing the loss of significant structure information. To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism. Specifically, the former encoder aggregates information from the same type of nodes, while the latter aggregates node representations from its different types of neighbors. Moreover, to sufficiently model node multi-relational information in BMHG, we adopt a newly proposed hierarchical mechanism. By doing so, the proposed dual hierarchical attention operations enable our model to fully capture the complex structures of the bi-typed multi-relational heterogeneous graphs. Experimental results on various tasks against the state-of-the-arts sufficiently confirm the capability of DHAN in learning node representations on the BMHGs.
Virtuoso: Video-based Intelligence for real-time tuning on SOCs
Authors: Jayoung Lee, Pengcheng Wang, ran Xu, Venkat Dasari, Noah Weston, Yin Li, Saurabh Bagchi, Somali Chaterji
Date: 2021-12-24 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Video Object Detection

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network, DNN) or the system by designing an adaptive system with approximation knobs. In spite of several recent efforts, we show that existing solutions suffer from two major drawbacks. First, the system does not consider energy consumption of the models while making a decision on which model to run. Second, the evaluation does not consider the practical scenario of contention on the device, due to other co-resident workloads. In this work, we propose an efficient and adaptive video object detection system, Virtuoso, which is jointly optimized for accuracy, energy efficiency, and latency. Underlying Virtuoso is a multi-branch execution kernel that is capable of running at different operating points in the accuracy-energy-latency axes, and a lightweight runtime scheduler to select the best fit execution branch to satisfy the user requirement. To fairly compare with Virtuoso, we benchmark 15 state-of-the-art or widely used protocols, including Faster R-CNN (FRCNN), YOLO v3, SSD, EfficientDet, SELSA, MEGA, REPP, FastAdapt, and our in-house adaptive variants of FRCNN+, YOLO+, SSD+, and EfficientDet+ (our variants have enhanced efficiency for mobiles). With this comprehensive benchmark, Virtuoso has shown superiority to all the above protocols, leading the accuracy frontier at every efficiency level on NVIDIA Jetson mobile GPUs. Specifically, Virtuoso has achieved an accuracy of 63.9%, which is more than 10% higher than some of the popular object detection models, FRCNN at 51.1%, and YOLO at 49.5%.
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation
Authors: Jiaxing Yan, Hong Zhao, Penghui Bu, YuSheng Jin
Date: 2021-12-24 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Scene Understanding, Self-Supervised Learning

Self-supervised learning has shown very promising results for monocular depth estimation. Scene structure and local details both are significant clues for high-quality depth estimation. Recent works suffer from the lack of explicit modeling of scene structure and proper handling of details information, which leads to a performance bottleneck and blurry artefacts in predicted results. In this paper, we propose the Channel-wise Attention-based Depth Estimation Network (CADepth-Net) with two effective contributions: 1) The structure perception module employs the self-attention mechanism to capture long-range dependencies and aggregates discriminative features in channel dimensions, explicitly enhances the perception of scene structure, obtains the better scene understanding and rich feature representation. 2) The detail emphasis module re-calibrates channel-wise feature maps and selectively emphasizes the informative features, aiming to highlight crucial local details information and fuse different level features more efficiently, resulting in more precise and sharper depth prediction. Furthermore, the extensive experiments validate the effectiveness of our method and show that our model achieves the state-of-the-art results on the KITTI benchmark and Make3D datasets.
Generating and Exploring S-Box Multivariate Quadratic Equation Systems with SageMath
Authors: A. -M. Leventi-Peetz, J. -V. Peetz
Date: 2015-06-13 00:00:00

A new method to derive Multivariate Quadratic equation systems (MQ) for the input and output bit variables of a cryptographic S-box from its algebraic expressions with the aid of the computer mathematics software system SageMath is presented. We consolidate the deficiency of previously presented MQ metrics, supposed to quantify the resistance of S-boxes against algebraic attacks.
DARTS without a Validation Set: Optimizing the Marginal Likelihood
Authors: Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal
Date: 2021-12-24 00:00:00
Tasks: GPU, Neural Architecture Search

The success of neural architecture search (NAS) has historically been limited by excessive compute requirements. While modern weight-sharing NAS methods such as DARTS are able to finish the search in single-digit GPU days, extracting the final best architecture from the shared weights is notoriously unreliable. Training-Speed-Estimate (TSE), a recently developed generalization estimator with a Bayesian marginal likelihood interpretation, has previously been used in place of the validation loss for gradient-based optimization in DARTS. This prevents the DARTS skip connection collapse, which significantly improves performance on NASBench-201 and the original DARTS search space. We extend those results by applying various DARTS diagnostics and show several unusual behaviors arising from not using a validation set. Furthermore, our experiments yield concrete examples of the depth gap and topology selection in DARTS having a strongly negative impact on the search performance despite generally receiving limited attention in the literature compared to the operations selection.
There is an elephant in the room: Towards a critique on the use of fairness in biometrics
Authors: Ana Valdivia, Júlia Corbera-Serrajòrdia, Aneta Swianiewicz
Date: 2021-12-16 00:00:00
Tasks: Fairness

In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal dismissed an asylum appeal basing the decision on the output of a biometric system, alongside other discrepancies. The fingerprints of the asylum seeker were found in a biometric database which contradicted the appellant's account. The Tribunal found this evidence unequivocal and denied the asylum claim. Nowadays, the proliferation of biometric systems is shaping public debates around its political, social and ethical implications. Yet whilst concerns towards the racialised use of this technology for migration control have been on the rise, investment in the biometrics industry and innovation is increasing considerably. Moreover, fairness has also been recently adopted by biometrics to mitigate bias and discrimination on biometrics. However, algorithmic fairness cannot distribute justice in scenarios which are broken or intended purpose is to discriminate, such as biometrics deployed at the border. In this paper, we offer a critical reading of recent debates about biometric fairness and show its limitations drawing on research in fairness in machine learning and critical border studies. Building on previous fairness demonstrations, we prove that biometric fairness criteria are mathematically mutually exclusive. Then, the paper moves on illustrating empirically that a fair biometric system is not possible by reproducing experiments from previous works. Finally, we discuss the politics of fairness in biometrics by situating the debate at the border. We claim that bias and error rates have different impact on citizens and asylum seekers. Fairness has overshadowed the elephant in the room of biometrics, focusing on the demographic biases and ethical discourses of algorithms rather than examine how these systems reproduce historical and political injustices.
Stochastic Learning Equation using Monotone Increasing Resolution of Quantization
Authors: Jinwuk Seok, Jeong-Si Kim
Date: 2021-12-24 00:00:00
Tasks: global-optimization, Quantization

In this paper, we propose a quantized learning equation with a monotone increasing resolution of quantization and stochastic analysis for the proposed algorithm. According to the white noise hypothesis for the quantization error with dense and uniform distribution, we can regard the quantization error as i.i.d.\ white noise. Based on this, we show that the learning equation with monotonically increasing quantization resolution converges weakly as the distribution viewpoint. The analysis of this paper shows that global optimization is possible for a domain that satisfies the Lipschitz condition instead of local convergence properties such as the Hessian constraint of the objective function.
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification
Authors: Joseph N Stember, Hrithwik Shalu
Date: 2021-12-24 00:00:00
Tasks: Deep Reinforcement Learning, Meta-Learning

Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets. We seek to realize this potential via a proof-of-principle application to MRI brain sequence classification. Methods: We analyzed a training set of 20 patients, each with four sequences/weightings: T1, T1 post-contrast, T2, and T2-FLAIR. We trained the parameters of a relatively small convolutional neural network (CNN) as follows: First, we randomly mutated the CNN weights. We then measured the CNN training set accuracy, using the latter as the fitness evaluation metric. The fittest child CNNs were identified. We incorporated their mutations into the parent CNN. This selectively mutated parent became the next generation's parent CNN. We repeated this process for approximately 50,000 generations. Results: DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. Conclusions: DNE can achieve perfect accuracy with small training sets and small CNNs. Particularly when combined with Deep Reinforcement Learning, DNE may provide a path forward in the quest to make radiology AI more human-like in its ability to learn. DNE may very well turn out to be a key component of the much-anticipated meta-learning regime of radiology AI algorithms that can adapt to new tasks and new image types, similar to human radiologists.
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
Authors: Daniel F. Perez-Ramirez, Carlos Pérez-Penichet, Nicolas Tsiftes, Thiemo Voigt, Dejan Kostic, Magnus Boman
Date: 2021-12-24 00:00:00
Tasks: Combinatorial Optimization, Deep Learning, Scheduling

Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network's capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with relatively small optimal schedules obtained from a constraint optimization solver, achieving a performance within 3% of the optimal scheduler. Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic. Our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.
Deep ensembles in bioimage segmentation
Authors: Loris Nanni, Daniela Cuza, Alessandra Lumini, Andrea Loreggia, Sheryl Brahnam
Date: 2021-12-24 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones. During the last few years, a lot of attention shifted to this kind of task. Many computer vision researchers tried to apply autoencoder structures to develop models that can learn the semantics of the image as well as a low-level representation of it. In an autoencoder architecture, given an input, an encoder computes a low dimensional representation of the input that is then used by a decoder to reconstruct the original data. In this work, we propose an ensemble of convolutional neural networks (CNNs). In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers. The approach leverages on differences of various classifiers to improve the performance of the whole system. Diversity among the single classifiers is enforced by using different loss functions. In particular, we present a new loss function that results from the combination of Dice and Structural Similarity Index. The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment. The proposal is evaluated through an extensive empirical evaluation on two real-world scenarios: polyp and skin segmentation. All the code is available online at https://github.com/LorisNanni.
Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling
Authors: Trisha Singhal, Junhua Liu, Lucienne T. M. Blessing, Kwan Hui Lim
Date: 2021-12-24 00:00:00

The scientific world is changing at a rapid pace, with new technology being developed and new trends being set at an increasing frequency. This paper presents a framework for conducting scientific analyses of academic publications, which is crucial to monitor research trends and identify potential innovations. This framework adopts and combines various techniques of Natural Language Processing, such as word embedding and topic modelling. Word embedding is used to capture semantic meanings of domain-specific words. We propose two novel scientific publication embedding, i.e., PUB-G and PUB-W, which are capable of learning semantic meanings of general as well as domain-specific words in various research fields. Thereafter, topic modelling is used to identify clusters of research topics within these larger research fields. We curated a publication dataset consisting of two conferences and two journals from 1995 to 2020 from two research domains. Experimental results show that our PUB-G and PUB-W embeddings are superior in comparison to other baseline embeddings by a margin of ~0.18-1.03 based on topic coherence.
Counterfactual Memorization in Neural Language Models
Date: 2021-12-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: counterfactual, Language Modeling, Language Modelling, Memorization

Modern neural language models that are widely used in various NLP tasks risk memorizing sensitive information from their training data. Understanding this memorization is important in real world applications and also from a learning-theoretical perspective. An open question in previous studies of language model memorization is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing memorized familiar phrases, public knowledge, templated texts, or other repeated data. We formulate a notion of counterfactual memorization which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We estimate the influence of each memorized training example on the validation set and on generated texts, showing how this can provide direct evidence of the source of memorization at test time.
Profile Guided Optimization without Profiles: A Machine Learning Approach
Authors: Nadav Rotem, Chris Cummins
Date: 2021-12-24 00:00:00
Tasks: BIG-bench Machine Learning

Profile guided optimization is an effective technique for improving the optimization ability of compilers based on dynamic behavior, but collecting profile data is expensive, cumbersome, and requires regular updating to remain fresh. We present a novel statistical approach to inferring branch probabilities that improves the performance of programs that are compiled without profile guided optimizations. We perform offline training using information that is collected from a large corpus of binaries that have branch probabilities information. The learned model is used by the compiler to predict the branch probabilities of regular uninstrumented programs, which the compiler can then use to inform optimization decisions. We integrate our technique directly in LLVM, supplementing the existing human-engineered compiler heuristics. We evaluate our technique on a suite of benchmarks, demonstrating some gains over compiling without profile information. In deployment, our technique requires no profiling runs and has negligible effect on compilation time.
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning
Authors: Junyu Shi, Wei Wan, Shengshan Hu, Jianrong Lu, Leo Yu Zhang
Date: 2021-12-29 00:00:00
Tasks: Federated Learning

Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge communication burden and concerns about data privacy, this approach can not only save the network bandwidth but also protect the data privacy. Despite the promising prospect, byzantine attack, an intractable threat in conventional distributed network, is discovered to be rather efficacious against FL as well. In this paper, we conduct a comprehensive investigation of the state-of-the-art strategies for defending against byzantine attacks in FL. We first provide a taxonomy for the existing defense solutions according to the techniques they used, followed by an across-the-board comparison and discussion. Then we propose a new byzantine attack method called weight attack to defeat those defense schemes, and conduct experiments to demonstrate its threat. The results show that existing defense solutions, although abundant, are still far from fully protecting FL. Finally, we indicate possible countermeasures for weight attack, and highlight several challenges and future research directions for mitigating byzantine attacks in FL.
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis
Authors: Yuhao Chen, E. Zhixuan Zeng, Maximilian Gilles, Alexander Wong
Date: 2021-12-29 00:00:00
Tasks: Object, object-detection, Object Detection, Robotic Grasping, Segmentation

There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations. To tackle this big, diverse data problem, we are inspired by the recent rise in the concept of metaverse, which has greatly closed the gap between virtual worlds and the physical world. Metaverses allow us to create digital twins of real-world manufacturing scenarios and to virtually create different scenarios from which large volumes of data can be generated for training models. In this paper, we present MetaGraspNet: a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types and is split into 5 difficulties to evaluate object detection and segmentation model performance in different grasping scenarios. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance in a manner that is more appropriate for robotic grasp applications compared to existing general-purpose performance metrics. Our benchmark dataset is available open-source on Kaggle, with the first phase consisting of detailed object detection, segmentation, layout annotations, and a layout-weighted performance metric script.