OpenRaiser/Intern-Atlas huggingface dataset:
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
Venue: arXiv cs.CV 2022
Year: 2022

Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges through utilizing the framework of deep Multitask Learning (dMTL) and incorporating image depth estimation as an auxiliary task. On a customized and depth-augmented derivation of the MNIST dataset, we show a) multitask loss functions are the most effective approach of implementing dMTL, b) limited dataset size primarily contributes to classification inaccuracy, and c) depth estimation is mostly impacted by noise. In order to further validate the results, we manually labeled the NYU Depth V2 dataset for scene classification tasks. As a contribution to the field, we have made the data in python native format publicly available as an open-source dataset and provided the scene labels. Our experiments on MNIST and NYU-Depth-V2 show dMTL improves generalizability of the classifiers when the dataset is noisy and the number of examples is limited.
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
Venue: arXiv cs.LG 2022
Year: 2022

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both \emph{intra-group} and \emph{inter-group} AUCs. Importantly, considering only one category of AUCs is not sufficient to mitigate unfairness in AUC optimization. In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. Based on this Rawlsian framework, we design an efficient stochastic optimization algorithm and prove its convergence to the minimum group-level AUC. We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm.
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset. Instead of condensing the dataset directly in the original input space, we assume a generative process of the dataset with a set of learnable codes defined in a compact latent space followed by a set of tiny decoders which maps them differently to the original input space. By combining different codes and decoders interchangeably, we can dramatically increase the number of synthetic examples with essentially the same parameter count, because the latent space is much lower dimensional and since we can assume as many decoders as necessary to capture different styles represented in the dataset with negligible cost. Such knowledge factorization allows efficient sharing of information between synthetic examples in a systematic way, providing far better trade-off between compression ratio and quality of the generated examples. We experimentally show that our method achieves new state-of-the-art records by significant margins on various benchmark datasets such as SVHN, CIFAR10, CIFAR100, and TinyImageNet.
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be beneficial as it allows institutes with complementary features or images for a shared set of patients to jointly develop more robust and generalizable models. In this work, we propose "Split-U-Net" and successfully apply SL for collaborative biomedical image segmentation. Nonetheless, SL requires the exchanging of intermediate activation maps and gradients to allow training models across different feature spaces, which might leak data and raise privacy concerns. Therefore, we also quantify the amount of data leakage in common SL scenarios for biomedical image segmentation and provide ways to counteract such leakage by applying appropriate defense strategies.
Concurrent Validity of Automatic Speech and Pause Measures During Passage Reading in ALS
Venue: arXiv cs.SD 2022
Year: 2022

The analysis of speech measures in individuals with amyotrophic lateral sclerosis (ALS) can provide essential information for early diagnosis and tracking disease progression. However, current methods for extracting speech and pause features are manual or semi-automatic, which makes them time-consuming and labour-intensive. The advent of speech-text alignment algorithms provides an opportunity for inexpensive, automated, and accurate analysis of speech measures in individuals with ALS. There is a need to validate speech and pause features calculated by these algorithms against current gold standard methods. In this study, we extracted 8 speech/pause features from 646 audio files of individuals with ALS and healthy controls performing passage reading. Two pretrained forced alignment models - one using transformers and another using a Gaussian mixture / hidden Markov architecture - were used for automatic feature extraction. The results were then validated against semi-automatic speech/pause analysis software, with further subgroup analyses based on audio quality and disease severity. Features extracted using transformer-based forced alignment had the highest agreement with gold standards, including in terms of audio quality and disease severity. This study lays the groundwork for future intelligent diagnostic support systems for clinicians, and for novel methods of tracking disease progression remotely from home.
ABL: An original active blacklist based on a modification of the SMTP
Venue: arXiv cs.CR 2022
Year: 2022

This paper presents a novel Active Blacklist (ABL) based on a modification of the Simple Mail Transfer Protocol (SMTP). ABL was implemented in the Mail Transfer Agent (MTA) Postfix of the e-mail server Zimbra and assessed exhaustively in a series of experiments. The modified server Zimbra showed computational performance and costs similar to those of the original server Zimbra when receiving legitimate e-mails. When receiving spam, however, it showed better computing performance and costs than the original Zimbra. Moreover, there was a considerable computational cost on the spammer's server when it sent spam e-mails. ABL was assessed at the Federal University of Itajubá, Brazil, during a period of sixty-one days. It was responsible for rejecting a percentage of 20.94% of the spam e-mails received by the university during this period. After this period, it was deployed and remained in use, from July-2015 to July-2019, at the university. ABL is part of the new Open Machine-Learning-Based Anti-Spam (Open-MaLBAS). Both ABL and Open-MaLBAS are freely available on GitHub.
Evaluating Cardiovascular Surgical Planning in Mobile Augmented Reality
Venue: arXiv cs.HC 2022
Year: 2022

Advanced surgical procedures for congenital heart diseases (CHDs) require precise planning before the surgeries. The conventional approach utilizes 3D-printing and cutting physical heart models, which is a time and resource intensive process. While rapid advances in augmented reality (AR) technologies have the potential to streamline surgical planning, there is limited research that evaluates such AR approaches with medical experts. This paper presents an evaluation with 6 experts, 4 cardiothoracic surgeons, and 2 cardiologists, from Children's Healthcare of Atlanta (CHOA) Heart Center to validate the usability and technical innovations of CardiacAR, a prototype mobile AR surgical planning application. Potential future improvements based on user feedback are also proposed to further improve the design of CardiacAR and broaden its access.
Fake News Identification using Machine Learning Algorithms Based on Graph Features
Venue: arXiv cs.SI 2022
Year: 2022

The spread of fake news has long been a social issue and the necessity of identifying it has become evident since its dangers are well recognized. In addition to causing uneasiness among the public, it has even more devastating consequences. For instance, it might lead to death during pandemics due to unverified medical instructions. This study aims to build a model for identifying fake news using graphs and machine learning algorithms. Instead of scanning the news content or user information, the research explicitly focuses on the spreading network, which shows the interconnection among people, and graph features such as the Eigenvector centrality, Jaccard Coefficient, and the shortest path. Fourteen features are extracted from graphs and tested in thirteen machine learning models. After analyzing these features and comparing the test result of machine learning models, the results reflect that propensity and centrality contribute highly to the classification. The best performing models reach 0.9913 and 0.9987 separately from datasets Twitter15 and Twitter16 using a modified tree classifier and Support Vector Classifier. This model can effectively predict fake news, prevent potential negative social impact caused by fake news, and provide a new perspective on graph feature selection for machine learning models.
Machine Learning-Enabled Cyber Attack Prediction and Mitigation for EV Charging Stations
Venue: arXiv cs.CR 2022
Year: 2022

Safe and reliable electric vehicle charging stations (EVCSs) have become imperative in an intelligent transportation infrastructure. Over the years, there has been a rapid increase in the deployment of EVCSs to address the upsurging charging demands. However, advances in information and communication technologies (ICT) have rendered this cyber-physical system (CPS) vulnerable to suffering cyber threats, thereby destabilizing the charging ecosystem and even the entire electric grid infrastructure. This paper develops an advanced cybersecurity framework, where STRIDE threat modeling is used to identify potential vulnerabilities in an EVCS. Further, the weighted attack defense tree approach is employed to create multiple attack scenarios, followed by developing Hidden Markov Model (HMM) and Partially Observable Monte-Carlo Planning (POMCP) algorithms for modeling the security attacks. Also, potential mitigation strategies are suggested for the identified threats.
ECU Identification using Neural Network Classification and Hyperparameter Tuning
Venue: arXiv cs.CR 2022
Year: 2022

Intrusion detection for Controller Area Network (CAN) protocol requires modern methods in order to compete with other electrical architectures. Fingerprint Intrusion Detection Systems (IDS) provide a promising new approach to solve this problem. By characterizing network traffic from known ECUs, hazardous messages can be discriminated. In this article, a modified version of Fingerprint IDS is employed utilizing both step response and spectral characterization of network traffic via neural network training. With the addition of feature set reduction and hyperparameter tuning, this method accomplishes a 99.4% detection rate of trusted ECU traffic.
A First Look at Dataset Bias in License Plate Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have often proven robust in the dataset they were trained in but showed limited performance in unseen ones. Therefore, this work investigates the dataset bias problem in the LPR context. We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable "signature" since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95% accuracy. In our discussion, we draw attention to the fact that most LPR models are probably exploiting such signatures to improve the results achieved in each dataset at the cost of losing generalization capability. These results emphasize the importance of evaluating LPR models in cross-dataset setups, as they provide a better indication of generalization (hence real-world performance) than within-dataset ones.
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Venue: arXiv cs.NE 2022
Year: 2022

Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often needs high computational cost and human expertise. To mitigate the above issue, evolutionary computation (EC) as a powerful heuristic search approach has shown significant merits in the automated design of DL models, so-called evolutionary deep learning (EDL). This paper aims to analyze EDL from the perspective of automated machine learning (AutoML). Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem. According to the DL pipeline, we systematically introduce EDL methods ranging from feature engineering, model generation, to model deployment with a new taxonomy (i.e., what and how to evolve/optimize), and focus on the discussions of solution representation and search paradigm in handling the optimization problem by EC. Finally, key applications, open issues and potentially promising lines of future research are suggested. This survey has reviewed recent developments of EDL and offers insightful guidelines for the development of EDL.
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, several vital challenges remain to be addressed: (i) The majority of federated recommendation models only consider the model performance and the privacy-preserving ability, while ignoring the optimization of the communication process; (ii) Most of the federated recommenders are designed for heterogeneous systems, causing unfairness problems during the federation process; (iii) The personalization techniques have been less explored in many federated recommender systems. In this paper, we propose a Communication efficient and Fair personalized Federated personalized Sequential Recommendation algorithm (CF-FedSR) to tackle these challenges. CF-FedSR introduces a communication-efficient scheme that employs adaptive client selection and clustering-based sampling to accelerate the training process. A fairness-aware model aggregation algorithm that can adaptively capture the data and performance imbalance among different clients to address the unfairness problems is proposed. The personalization module assists clients in making personalized recommendations and boosts the recommendation performance via local fine-tuning and model adaption. Extensive experimental results show the effectiveness and efficiency of our proposed method.
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, several works have adopted the convolutional neural network (CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based on X-ray images or magnetic resonance imaging (MRI). However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis. MRI, on the other hand, has a long imaging time, is more expensive, making it impractical in mass screening. Computed tomography (CT) shows layer-wise tissues, is faster to image, and is less costly than MRI. However, to our knowledge, there is no work on CT-based automated diagnosis of AVNFH. In this work, we collected and labeled a large-scale dataset for AVNFH ranking. In addition, existing end-to-end CNNs only yields the classification result and are difficult to provide more information for doctors in diagnosis. To address this issue, we propose the structure regularized attentive network (SRANet), which is able to highlight the necrotic regions during classification based on patch attention. SRANet extracts features in chunks of images, obtains weight via the attention mechanism to aggregate the features, and constrains them by a structural regularizer with prior knowledge to improve the generalization. SRANet was evaluated on our AVNFH-CT dataset. Experimental results show that SRANet is superior to CNNs for AVNFH classification, moreover, it can localize lesions and provide more information to assist doctors in diagnosis. Our codes are made public at https://github.com/tomas-lilingfeng/SRANet.
Foraging in Particle Systems via Self-Induced Phase Changes
Venue: arXiv cs.DC 2022
Year: 2022

The foraging problem asks how a collective of particles with limited computational, communication and movement capabilities can autonomously compress around a food source and disperse when the food is depleted or shifted, which may occur at arbitrary times. We would like the particles to iteratively self-organize, using only local interactions, to correctly gather whenever a food particle remains in a position long enough and search if no food particle has existed recently. Unlike previous approaches, these search and gather phases should be self-induced so as to be indefinitely repeatable as the food evolves, with microscopic changes to the food triggering macroscopic, system-wide phase transitions. We present a stochastic foraging algorithm based on a phase change in the fixed magnetization Ising model from statistical physics: Our algorithm is the first to leverage self-induced phase changes as an algorithmic tool. A key component of our algorithm is a careful token passing mechanism ensuring a dispersion broadcast wave will always outpace a compression wave. We also present a highly structured alternative algorithm that gathers by incrementally building a spiral tightly wrapped around the food particle.
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques
Venue: arXiv cs.IR 2022
Year: 2022

We present an effective way to predict search query-item relationship. We combine pre-trained transformer and LSTM models, and increase model robustness using adversarial training, exponential moving average, multi-sampled dropout, and diversity based ensemble, to tackle an extremely difficult problem of predicting against queries not seen before. All of our strategies focus on increasing robustness of deep learning models and are applicable in any task where deep learning models are used. Applying our strategies, we achieved 10th place in KDD Cup 2022 Product Substitution Classification task.
SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs
Venue: arXiv cs.AR 2022
Year: 2022

Stencil computation is one of the fundamental computing patterns in many application domains such as scientific computing and image processing. While there are promising studies that accelerate stencils on FPGAs, there lacks an automated acceleration framework to systematically explore both spatial and temporal parallelisms for iterative stencils that could be either computation-bound or memory-bound. In this paper, we present SASA, a scalable and automatic stencil acceleration framework on modern HBM-based FPGAs. SASA takes the high-level stencil DSL and FPGA platform as inputs, automatically exploits the best spatial and temporal parallelism configuration based on our accurate analytical model, and generates the optimized FPGA design with the best parallelism configuration in TAPA high-level synthesis C++ as well as its corresponding host code. Compared to state-of-the-art automatic stencil acceleration framework SODA that only exploits temporal parallelism, SASA achieves an average speedup of 3.74x and up to 15.73x speedup on the HBM-based Xilinx Alveo U280 FPGA board for a wide range of stencil kernels.
Computational valency lexica and Homeric formularity
Venue: arXiv cs.CL 2022
Year: 2022

Distributional semantics, the quantitative study of meaning variation and change through corpus collocations, is currently one of the most productive research areas in computational linguistics. The wider availability of big data and of reproducible algorithms for analysis has boosted its application to living languages in recent years. But can we use distributional semantics to study a language with such a limited corpus as ancient Greek? And can this approach tell us something about such vexed questions in classical studies as the language and composition of the Homeric poems? Our paper will compare the semantic flexibility of formulae involving transitive verbs in archaic Greek epic to similar verb phrases in a non-formulaic corpus, in order to detect unique patterns of variation in formulae. To address this, we present AGVaLex, a computational valency lexicon for ancient Greek automatically extracted from the Ancient Greek Dependency Treebank. The lexicon contains quantitative corpus-driven morphological, syntactic and lexical information about verbs and their arguments, such as objects, subjects, and prepositional phrases, and has a wide range of applications for the study of the language of ancient Greek authors.
DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Model
Venue: arXiv cs.GR 2022
Year: 2022

The dynamic effects of smoke are impressive in illustration design, but it is a troublesome and challenging issue for common users to design the smoke effect without domain knowledge of fluid simulations. In this work, we propose DualSmoke, two stage global-to-local generation framework for the interactive smoke illustration design. For the global stage, the proposed approach utilizes fluid patterns to generate Lagrangian coherent structure from the user's hand-drawn sketches. For the local stage, the detailed flow patterns are obtained from the generated coherent structure. Finally, we apply the guiding force field to the smoke simulator to design the desired smoke illustration. To construct the training dataset, DualSmoke generates flow patterns using the finite-time Lyapunov exponents of the velocity fields. The synthetic sketch data is generated from the flow patterns by skeleton extraction. From our user study, it is verified that the proposed design interface can provide various smoke illustration designs with good user usability. Our code is available at: https://github.com/shasph/DualSmoke