OpenRaiser/Intern-Atlas huggingface dataset:
Agricultural Plantation Classification using Transfer Learning Approach based on CNN
Venue: arXiv cs.CV 2022
Year: 2022

Hyper-spectral images are images captured from a satellite that gives spatial and spectral information of specific region.A Hyper-spectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image. It makes them well suited for the classification of objects in a snap. In the past years, the efficiency of hyper-spectral image recognition has increased significantly with deep learning. The Convolution Neural Network(CNN) and Multi-Layer Perceptron(MLP) has demonstrated to be an excellent process of classifying images. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data, to achieve the expected outcome. These issues become more complex while dealing with hyper-spectral images. To decrease the training time and reduce the dependence on large labeled data-set, we propose using the method of transfer learning.The features learned by CNN and MLP models are then used by the transfer learning model to solve a new classification problem on an unseen dataset. A detailed comparison of CNN and multiple MLP architectural models is performed, to determine an optimum architecture that suits best the objective. The results show that the scaling of layers not always leads to increase in accuracy but often leads to over-fitting, and also an increase in the training time.The training time is reduced to greater extent by applying the transfer learning approach rather than just approaching the problem by directly training a new model on large data-sets, without much affecting the accuracy.
Efficient End-to-End AutoML via Scalable Search Space Decomposition
Venue: arXiv cs.LG 2022
Year: 2022

End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VolcanoML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VolcanoML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VolcanoML further supports a Volcano-style execution model -- akin to the one supported by modern database systems -- to execute the plan constructed. Our evaluation demonstrates that, not only does VolcanoML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn. This paper is the extended version of the initial VolcanoML paper appeared in VLDB 2021.
Object Localization Assistive System Based on CV and Vibrotactile Encoding
Venue: arXiv cs.RO 2022
Year: 2022

Intelligent assistive systems can navigate blind people, but most of them could only give non-intuitive cues or inefficient guidance. Based on computer vision and vibrotactile encoding, this paper presents an interactive system that provides blind people with intuitive spatial cognition. Different from the traditional auditory feedback strategy based on speech cues, this paper firstly introduces a vibration-encoded feedback method that leverages the haptic neural pathway and enables the users to interact with objects other than manipulating an assistance device. Based on this strategy, a wearable visual module based on an RGB-D camera is adopted for 3D spatial object localization, which contributes to accurate perception and quick object localization in the real environment. The experimental results on target blind individuals indicate that vibrotactile feedback reduces the task completion time by over 25% compared with the mainstream voice prompt feedback scheme. The proposed object localization system provides a more intuitive spatial navigation and comfortable wearability for blindness assistance.
A Universal Adversarial Policy for Text Classifiers
Venue: arXiv cs.LG 2022
Year: 2022

Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts. However, unlike the vision domain, adding the same perturbation to different inputs results in noticeably unnatural inputs. Therefore, we introduce a new universal adversarial setup - a universal adversarial policy, which has many advantages of other universal attacks but also results in valid texts - thus making it relevant in practice. We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts. This formulation is universal in that the policy is successful in finding adversarial examples on new texts efficiently. Our approach uses text perturbations which were extensively shown to produce natural attacks in the non-universal setup (specific synonym replacements). We suggest a strong baseline approach for this formulation which uses reinforcement learning. It's ability to generalise (from as few as 500 training texts) shows that universal adversarial patterns exist in the text domain as well.
A Critical Review of Communications in Multi-Robot Systems
Venue: arXiv cs.RO 2022
Year: 2022

Purpose of Review. This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems. Recent Findings. Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machine learning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the "sim-to-real" gap in this context. Summary. We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.
Integrated Weak Learning
Venue: arXiv cs.LG 2022
Year: 2022

We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the end-model and a label model that aggregates multiple sources of weak supervision. We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training. We show that our approach outperforms existing weak learning techniques across a set of 6 benchmark classification datasets. When both a small amount of labeled data and weak supervision are present the increase in performance is both consistent and large, reliably getting a 2-5 point test F1 score gain over non-integrated methods.
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Link prediction is one important application of graph neural networks (GNNs). Most existing GNNs for link prediction are based on one-dimensional Weisfeiler-Lehman (1-WL) test. 1-WL-GNNs first compute node representations by iteratively passing neighboring node features to the center, and then obtain link representations by aggregating the pairwise node representations. As pointed out by previous works, this two-step procedure results in low discriminating power, as 1-WL-GNNs by nature learn node-level representations instead of link-level. In this paper, we study a completely different approach which can directly obtain node pair (link) representations based on \textit{two-dimensional Weisfeiler-Lehman (2-WL) tests}. 2-WL tests directly use links (2-tuples) as message passing units instead of nodes, and thus can directly obtain link representations. We theoretically analyze the expressive power of 2-WL tests to discriminate non-isomorphic links, and prove their superior link discriminating power than 1-WL. Based on different 2-WL variants, we propose a series of novel 2-WL-GNN models for link prediction. Experiments on a wide range of real-world datasets demonstrate their competitive performance to state-of-the-art baselines and superiority over plain 1-WL-GNNs.
Efficient and Flexible Sublabel-Accurate Energy Minimization
Venue: arXiv cs.CV 2022
Year: 2022

We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recognition. While discrete optimization methods are able to give theoretical optimality guarantees, they can only handle a finite number of labels and therefore suffer from label discretization bias. Existing continuous optimization methods can find sublabel-accurate solutions, but they are not efficient for large label spaces. In this work, we propose an efficient sublabel-accurate method that utilizes the best properties of both continuous and discrete models. We separate the problem into two sequential steps: (i) global discrete optimization for selecting the label range, and (ii) efficient continuous sublabel-accurate local refinement of a convex approximation of the energy function in the chosen range. Doing so allows us to achieve a boost in time and memory efficiency while practically keeping the accuracy at the same level as continuous convex relaxation methods, and in addition, providing theoretical optimality guarantees at the level of discrete methods. Finally, we show the flexibility of the proposed approach to general pairwise smoothness terms, so that it is applicable to a wide range of regularizations. Experiments on the illustrating example of the image denoising problem demonstrate the properties of the proposed method. The code reproducing experiments is available at \url{https://github.com/nurlanov-zh/sublabel-accurate-alpha-expansion}.
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
Venue: arXiv cs.NE 2022
Year: 2022

Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary information processing. To improve the energy-efficiency and throughput, SNNs can be implemented on memristive crossbars where Multiply-and-Accumulate (MAC) operations are realized in the analog domain using emerging Non-Volatile-Memory (NVM) devices. Despite the compatibility of SNNs with memristive crossbars, there is little attention to study on the effect of intrinsic crossbar non-idealities and stochasticity on the performance of SNNs. In this paper, we conduct a comprehensive analysis of the robustness of SNNs on non-ideal crossbars. We examine SNNs trained via learning algorithms such as, surrogate gradient and ANN-SNN conversion. Our results show that repetitive crossbar computations across multiple time-steps induce error accumulation, resulting in a huge performance drop during SNN inference. We further show that SNNs trained with a smaller number of time-steps achieve better accuracy when deployed on memristive crossbars.
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Real-time video segmentation is a crucial task for many real-world applications such as autonomous driving and robot control. Since state-of-the-art semantic segmentation models are often too heavy for real-time applications despite their impressive performance, researchers have proposed lightweight architectures with speed-accuracy trade-offs, achieving real-time speed at the expense of reduced accuracy. In this paper, we propose a novel framework to speed up any architecture with skip-connections for real-time vision tasks by exploiting the temporal locality in videos. Specifically, at the arrival of each frame, we transform the features from the previous frame to reuse them at specific spatial bins. We then perform partial computation of the backbone network on the regions of the current frame that captures temporal differences between the current and previous frame. This is done by dynamically dropping out residual blocks using a gating mechanism which decides which blocks to drop based on inter-frame distortion. We validate our Spatial-Temporal Mask Generator (STMG) on video semantic segmentation benchmarks with multiple backbone networks, and show that our method largely speeds up inference with minimal loss of accuracy.
Keep on Running! An Analysis of Running Tracking Application Features and their Potential Impact on Recreational Runner's Intrinsic Motivation
Venue: arXiv cs.HC 2022
Year: 2022

Physical activity is known to help improve and maintain one's health. In particular, recreational running has become increasingly popular in recent years. Yet, lack of motivation often interferes with people's routines and thus may prohibit regular uptake. This is where running tracking applications are frequently used to overcome one's weaker self and offer support. While technology artifacts, such as sport watches or running applications, usually count as extrinsic drivers, they can also impact one's intrinsic motivation levels. The aim of this study was thus to investigate upon the motivational impact of distinct features found within applications specifically used for running. Focusing on the 22 most famous running applications, a semi-structured, problem-centered interview study with $n=15$ recreational runners showed that intrinsic motivation is stimulated from diverting runners, aiding them in their goal setting, decreasing their efforts, improving and sharing their run performance, allowing them to receive acknowledgements, as well as providing them with guidance, information, and an overall variety in their training routines.
Revisiting lp-constrained Softmax Loss: A Comprehensive Study
Venue: arXiv cs.LG 2022
Year: 2022

Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large. The impact of particular forms of normalization, however, has so far been investigated in limited domain-specific classification tasks and not in a general fashion. Motivated by the lack of such a comprehensive study, in this paper we investigate the performance of lp-constrained softmax loss classifiers across different norm orders, magnitudes, and data dimensions in both proof-of-concept classification problems and real-world popular image classification tasks. Experimental results suggest collectively that lp-constrained softmax loss classifiers not only can achieve more accurate classification results but, at the same time, appear to be less prone to overfitting. The core findings hold across the three popular deep learning architectures tested and eight datasets examined, and suggest that lp normalization is a recommended data representation practice for image classification in terms of performance and convergence, and against overfitting.
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Venue: arXiv cs.LG 2022
Year: 2022

Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach. However, most existing stochastic exploration approaches either explore new actions heuristically regardless of Q-values or inevitably introduce bias into the learning process to couple the sampling with Q-values. In this paper, we propose a novel preference-guided $ε$-greedy exploration algorithm that can efficiently learn the action distribution in line with the landscape of Q-values for DQN without introducing additional bias. Specifically, we design a dual architecture consisting of two branches, one of which is a copy of DQN, namely the Q-branch. The other branch, which we call the preference branch, learns the action preference that the DQN implicit follows. We theoretically prove that the policy improvement theorem holds for the preference-guided $ε$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values. Consequently, preference-guided $ε$-greedy exploration motivates the DQN agent to take diverse actions, i.e., actions with larger Q-values can be sampled more frequently whereas actions with smaller Q-values still have a chance to be explored, thus encouraging the exploration. We assess the proposed method with four well-known DQN variants in nine different environments. Extensive results confirm the superiority of our proposed method in terms of performance and convergence speed. Index Terms- Preference-guided exploration, stochastic policy, data efficiency, deep reinforcement learning, deep Q-learning.
Great Expectations: Unsupervised Inference of Suspense, Surprise and Salience in Storytelling
Venue: arXiv cs.CL 2022
Year: 2022

Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. The thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating original stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow-up work, the insights could help improve computer models for tasks such as automatic story writing and assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, and listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach for applying machine learning in many areas of interest.
The Cost of the GDPR for Apps? Nearly Impossible to Study without Platform Data
Venue: arXiv cs.CY 2022
Year: 2022

A recently published pre-print titled 'GDPR and the Lost Generation of Innovative Apps' by Janßen et al. observes that a third of apps on the Google Play Store disappeared from this app store around the introduction of the GDPR in May 2018. The authors deduce 'that GDPR is the cause'. The effects of the GDPR on the app economy are an important field to study. Unfortunately, the paper currently lacks a control condition and a key variable. As a result, the effects on app exits reported in the paper are likely overestimated, as we will discuss. We believe there are other factors which may better explain these changes in the Play Store aside from the GDPR.
Parameterisation of lane-change scenarios from real-world data
Venue: arXiv cs.RO 2022
Year: 2022

Recent Autonomous Vehicles (AV) technology includes machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant crucial road situations, it can reduce the effort required in testing. Encoding real-world traffic participants' behaviour is essential to efficiently assess the System Under Test (SUT) in scenario-based testing. So, it is necessary to capture the scenario parameters from the real-world data that can model scenarios realistically in simulation. The primary emphasis of the paper is to identify the list of meaningful parameters that adequately model real-world lane-change scenarios. With these parameters, it is possible to build a parameter space capable of generating a range of challenging scenarios for AV testing efficiently. We validate our approach using Root Mean Square Error(RMSE) to compare the scenarios generated using the proposed parameters against the real-world trajectory data. In addition to that, we demonstrate that adding a slight disturbance to a few scenario parameters can generate different scenarios and utilise Responsibility-Sensitive Safety (RSS) metric to measure the scenarios' risk.
A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization
Venue: arXiv cs.LG 2022
Year: 2022

In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classification. Since the data are imbalanced, we used various class-imbalance learning methods for training and improved the RUSBoost algorithm. Experimental results show that the improved RUSBoost has the highest Area Under the ROC Curve on the target among these algorithms. Additionally, we compared these class-imbalance learning methods with some common machine learning algorithms. We combined the improved RUSBoost with dynamic web resource development techniques to build an evaluation system with information entry and vaccination response prediction capabilities for relevant medical practitioners.
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold
Venue: arXiv cs.CL 2022
Year: 2022

The prototypical NLP experiment trains a standard architecture on labeled English data and optimizes for accuracy, without accounting for other dimensions such as fairness, interpretability, or computational efficiency. We show through a manual classification of recent NLP research papers that this is indeed the case and refer to it as the square one experimental setup. We observe that NLP research often goes beyond the square one setup, e.g, focusing not only on accuracy, but also on fairness or interpretability, but typically only along a single dimension. Most work targeting multilinguality, for example, considers only accuracy; most work on fairness or interpretability considers only English; and so on. We show this through manual classification of recent NLP research papers and ACL Test-of-Time award recipients. Such one-dimensionality of most research means we are only exploring a fraction of the NLP research search space. We provide historical and recent examples of how the square one bias has led researchers to draw false conclusions or make unwise choices, point to promising yet unexplored directions on the research manifold, and make practical recommendations to enable more multi-dimensional research. We open-source the results of our annotations to enable further analysis at https://github.com/google-research/url-nlp