OpenRaiser/Intern-Atlas huggingface dataset:
Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching
Venue: arXiv cs.HC 2022
Year: 2022

Distance teaching has become popular these years because of the COVID-19 epidemic. However, both students and teachers face several challenges in distance teaching, like being easy to distract. We proposed Focus+, a system designed to detect learners' status with the latest AI technology from their web camera to solve such challenges. By doing so, teachers can know students' status, and students can regulate their learning experience. In this research, we will discuss the expected model's design for training and evaluating the AI detection model of Focus+.
Self-explaining Hierarchical Model for Intraoperative Time Series
Venue: arXiv cs.LG 2022
Year: 2022

Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series.
Uncertainty-aware LiDAR Panoptic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties. In this work, we introduce a novel approach for solving the task of uncertainty-aware panoptic segmentation using LiDAR point clouds. Our proposed EvLPSNet network is the first to solve this task efficiently in a sampling-free manner. It aims to predict per-point semantic and instance segmentations, together with per-point uncertainty estimates. Moreover, it incorporates methods for improving the performance by employing the predicted uncertainties. We provide several strong baselines combining state-of-the-art panoptic segmentation networks with sampling-free uncertainty estimation techniques. Extensive evaluations show that we achieve the best performance on uncertainty-aware panoptic segmentation quality and calibration compared to these baselines. We make our code available at: https://github.com/kshitij3112/EvLPSNet
Comparing the carbon costs and benefits of low-resource solar nowcasting
Venue: arXiv cs.LG 2022
Year: 2022

Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting solar PV yield, using a dataset of UK satellite imagery and solar PV energy readings over a 1 to 4-hour time range. The paper also estimates the carbon emissions generated and averted by deploying models, and finds that even small models that could be deployable in low-resource settings may have a benefit several orders of magnitude greater than its carbon cost. The paper also examines prediction errors and the activations in a CNN.
Visually Similar Products Retrieval for Shopsy
Venue: arXiv cs.CV 2022
Year: 2022

Visual search is of great assistance in reseller commerce, especially for non-tech savvy users with affinity towards regional languages. It allows resellers to accurately locate the products that they seek, unlike textual search which recommends products from head brands. Product attributes available in e-commerce have a great potential for building better visual search systems as they capture fine grained relations between data points. In this work, we design a visual search system for reseller commerce using a multi-task learning approach. We also highlight and address the challenges like image compression, cropping, scribbling on the image, etc, faced in reseller commerce. Our model consists of three different tasks: attribute classification, triplet ranking and variational autoencoder (VAE). Masking technique is used for designing the attribute classification. Next, we introduce an offline triplet mining technique which utilizes information from multiple attributes to capture relative order within the data. This technique displays a better performance compared to the traditional triplet mining baseline, which uses single label/attribute information. We also compare and report incremental gain achieved by our unified multi-task model over each individual task separately. The effectiveness of our method is demonstrated using the in-house dataset of product images from the Lifestyle business-unit of Flipkart, India's largest e-commerce company. To efficiently retrieve the images in production, we use the Approximate Nearest Neighbor (ANN) index. Finally, we highlight our production environment constraints and present the design choices and experiments conducted to select a suitable ANN index.
Universal Adversarial Perturbations: Efficiency on a small image dataset
Venue: arXiv cs.CV 2022
Year: 2022

Although neural networks perform very well on the image classification task, they are still vulnerable to adversarial perturbations that can fool a neural network without visibly changing an input image. A paper has shown the existence of Universal Adversarial Perturbations which when added to any image will fool the neural network with a very high probability. In this paper we will try to reproduce the experience of the Universal Adversarial Perturbations paper, but on a smaller neural network architecture and training set, in order to be able to study the efficiency of the computed perturbation.
SampleHST: Efficient On-the-Fly Selection of Distributed Traces
Venue: arXiv cs.DC 2022
Year: 2022

Since only a small number of traces generated from distributed tracing helps in troubleshooting, its storage requirement can be significantly reduced by biasing the selection towards anomalous traces. To aid in this scenario, we propose SampleHST, a novel approach to sample on-the-fly from a stream of traces in an unsupervised manner. SampleHST adjusts the storage quota of normal and anomalous traces depending on the size of its budget. Initially, it utilizes a forest of Half Space Trees (HSTs) for trace scoring. This is based on the distribution of the mass scores across the trees, which characterizes the probability of observing different traces. The mass distribution from HSTs is subsequently used to cluster the traces online leveraging a variant of the mean-shift algorithm. This trace-cluster association eventually drives the sampling decision. We have compared the performance of SampleHST with a recently suggested method using data from a cloud data center and demonstrated that SampleHST improves sampling performance up to by 9.5x.
YFACC: A Yorùbá speech-image dataset for cross-lingual keyword localisation through visual grounding
Venue: arXiv cs.CL 2022
Year: 2022

Visually grounded speech (VGS) models are trained on images paired with unlabelled spoken captions. Such models could be used to build speech systems in settings where it is impossible to get labelled data, e.g. for documenting unwritten languages. However, most VGS studies are in English or other high-resource languages. This paper attempts to address this shortcoming. We collect and release a new single-speaker dataset of audio captions for 6k Flickr images in Yorùbá -- a real low-resource language spoken in Nigeria. We train an attention-based VGS model where images are automatically tagged with English visual labels and paired with Yorùbá utterances. This enables cross-lingual keyword localisation: a written English query is detected and located in Yorùbá speech. To quantify the effect of the smaller dataset, we compare to English systems trained on similar and more data. We hope that this new dataset will stimulate research in the use of VGS models for real low-resource languages.
Towards Developing and Analysing Metric-Based Software Defect Severity Prediction Model
Venue: arXiv cs.SE 2022
Year: 2022

In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software. To reduce the time and effort of a tester, many machine learning models have been proposed in the literature, which use the documented defect reports to automatically predict the severity of the defective software modules. In contrast to the traditional approaches, in this work we propose a metric-based software defect severity prediction (SDSP) model that uses a self-training semi-supervised learning approach to classify the severity of the defective software modules. The approach is constructed on a mixture of unlabelled and labelled defect severity data. The self-training works on the basis of a decision tree classifier to assign the pseudo-class labels to the unlabelled instances. The predictions are promising since the self-training successfully assigns the suitable class labels to the unlabelled instances. On the other hand, numerous research studies have covered proposing prediction approaches as well as the methodological aspects of defect severity prediction models, the gap in estimating project attributes from the prediction model remains unresolved. To bridge the gap, we propose five project specific measures such as the Risk-Factor (RF), the Percent of Saved Budget (PSB), the Loss in the Saved Budget (LSB), the Remaining Service Time (RST) and Gratuitous Service Time (GST) to capture project outcomes from the predictions. Similar to the traditional measures, these measures are also calculated from the observed confusion matrix. These measures are used to analyse the impact that the prediction model has on the software project.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
Venue: arXiv cs.CV 2022
Year: 2022

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.
PyHopper -- Hyperparameter optimization
Venue: arXiv cs.LG 2022
Year: 2022

Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's goal is to integrate with existing code with minimal effort and run the optimization process with minimal necessary manual oversight. With simplicity as the primary theme, PyHopper is powered by a single robust Markov-chain Monte-Carlo optimization algorithm that scales to millions of dimensions. Compared to existing tuning packages, focusing on a single algorithm frees the user from having to decide between several algorithms and makes PyHopper easily customizable. PyHopper is publicly available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.
End-to-end verifiable voting for developing countries -- what's hard in Lausanne is harder still in Lahore
Venue: arXiv cs.CY 2022
Year: 2022

In recent years end-to-end verifiable voting (E2EVV) has emerged as a promising new paradigm to conduct evidence-based elections. However, E2EVV systems thus far have primarily been designed for the developed world and the fundamental assumptions underlying the design of these systems do not readily translate to the developing world, and may even act as potential barriers to adoption of these systems. This is unfortunate because developing countries account for 80\% of the global population, and given their economic and socio-political dilemmas and their track record of contentious elections, these countries arguably stand to benefit most from this exciting new paradigm. In this paper, we highlight various limitations and challenges in adapting E2EVV systems to these environments, broadly classed across social, political, technical, operational, and human dimensions. We articulate corresponding research questions and identify significant literature gaps in these categories. We also suggest relevant strategies to aid researchers, practitioners, and policymakers in visualizing and exploring solutions that align with the context and unique ground realities in these environments. Our goal is to outline a broader research agenda for the community to successfully adapt E2EVV voting systems to developing countries.
mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications
Venue: arXiv cs.CR 2022
Year: 2022

As nowadays most web application requests originate from mobile devices, authentication of mobile users is essential in terms of security considerations. To this end, recent approaches rely on machine learning techniques to analyze various aspects of user behavior as a basis for authentication decisions. These approaches face two challenges: first, examining behavioral data raises significant privacy concerns, and second, approaches must scale to support a large number of users. Existing approaches do not address these challenges sufficiently. We propose mPSAuth, an approach for continuously tracking various data sources reflecting user behavior (e.g., touchscreen interactions, sensor data) and estimating the likelihood of the current user being legitimate based on machine learning techniques. With mPSAuth, both the authentication protocol and the machine learning models operate on homomorphically encrypted data to ensure the users' privacy. Furthermore, the number of machine learning models used by mPSAuth is independent of the number of users, thus providing adequate scalability. In an extensive evaluation based on real-world data from a mobile application, we illustrate that mPSAuth can provide high accuracy with low encryption and communication overhead, while the effort for the inference is increased to a tolerable extent.
On the Importance of Calibration in Semi-supervised Learning
Venue: arXiv cs.LG 2022
Year: 2022

State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency regularization and pseudo-labeling. During pseudo-labeling, the model's predictions on unlabeled data are used for training and thus, model calibration is important in mitigating confirmation bias. Yet, many SOTA methods are optimized for model performance, with little focus directed to improve model calibration. In this work, we empirically demonstrate that model calibration is strongly correlated with model performance and propose to improve calibration via approximate Bayesian techniques. We introduce a family of new SSL models that optimizes for calibration and demonstrate their effectiveness across standard vision benchmarks of CIFAR-10, CIFAR-100 and ImageNet, giving up to 15.9% improvement in test accuracy. Furthermore, we also demonstrate their effectiveness in additional realistic and challenging problems, such as class-imbalanced datasets and in photonics science.
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression
Venue: arXiv cs.CV 2022
Year: 2022

Although melanoma occurs more rarely than several other skin cancers, patients' long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that provides potential concordance information to healthcare providers could help inform diagnostic, prognostic, and therapeutic decision-making for challenging melanoma cases. We present a melanoma concordance regression deep learning model capable of predicting the concordance rate of invasive melanoma or melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features corresponding to melanoma concordance were learned in a self-supervised manner with the contrastive learning method, SimCLR. We trained a SimCLR feature extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens originating from four distinct pathology labs. We trained a separate melanoma concordance regression model on 990 specimens with available concordance ground truth annotations from three pathology labs and tested the model on 211 specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the test set. We also investigated the performance of using the predicted concordance rate as a malignancy classifier, and achieved a precision and recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These results are an important first step for building an artificial intelligence (AI) system capable of predicting the results of consulting a panel of experts and delivering a score based on the degree to which the experts would agree on a particular diagnosis. Such a system could be used to suggest additional testing or other action such as ordering additional stains or genetic tests.
PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples
Venue: arXiv cs.CV 2022
Year: 2022

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples. We show its improved performance over different baselines, with special emphasis on MSE, effect of outliers, image dependence, and bias. We additionally derive the log-likelihood function for further insights and discuss real-world applicability.
In-Hand Object Rotation via Rapid Motor Adaptation
Venue: arXiv cs.RO 2022
Year: 2022

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive controller to achieve in-hand object rotation using only fingertips. The controller is trained entirely in simulation on only cylindrical objects, which then - without any fine-tuning - can be directly deployed to a real robot hand to rotate dozens of objects with diverse sizes, shapes, and weights over the z-axis. This is achieved via rapid online adaptation of the controller to the object properties using only proprioception history. Furthermore, natural and stable finger gaits automatically emerge from training the control policy via reinforcement learning. Code and more videos are available at https://haozhi.io/hora
Meta-Principled Family of Hyperparameter Scaling Strategies
Venue: arXiv cs.LG 2022
Year: 2022

In this note, we first derive a one-parameter family of hyperparameter scaling strategies that interpolates between the neural-tangent scaling and mean-field/maximal-update scaling. We then calculate the scalings of dynamical observables -- network outputs, neural tangent kernels, and differentials of neural tangent kernels -- for wide and deep neural networks. These calculations in turn reveal a proper way to scale depth with width such that resultant large-scale models maintain their representation-learning ability. Finally, we observe that various infinite-width limits examined in the literature correspond to the distinct corners of the interconnected web spanned by effective theories for finite-width neural networks, with their training dynamics ranging from being weakly-coupled to being strongly-coupled.
Ecovisor: A Virtual Energy System for Carbon-Efficient Applications
Venue: arXiv cs.OS 2022
Year: 2022

Cloud platforms' rapid growth is raising significant concerns about their carbon emissions. To reduce emissions, future cloud platforms will need to increase their reliance on renewable energy sources, such as solar and wind, which have zero emissions but are highly unreliable. Unfortunately, today's energy systems effectively mask this unreliability in hardware, which prevents applications from optimizing their carbon-efficiency, or work done per kilogram of carbon emitted. To address this problem, we design an "ecovisor", which virtualizes the energy system and exposes software-defined control of it to applications. An ecovisor enables each application to handle clean energy's unreliability in software based on its own specific requirements. We implement a small-scale ecovisor prototype that virtualizes a physical energy system to enable software-based application-level i) visibility into variable grid carbon-intensity and renewable generation and ii) control of server power usage and battery charging/discharging. We evaluate the ecovisor approach by showing how multiple applications can concurrently exercise their virtual energy system in different ways to better optimize carbon-efficiency based on their specific requirements compared to a general system-wide policy.