OpenRaiser/Intern-Atlas huggingface dataset:
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
Venue: arXiv cs.CV 2023
Year: 2023

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2,370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209,000 satellite images. Out of the 2,370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction
Online Influence Maximization: Concept and Algorithm
Venue: arXiv cs.SI 2023
Year: 2023

In this survey, we offer an extensive overview of the Online Influence Maximization (IM) problem by covering both theoretical aspects and practical applications. For the integrity of the article and because the online algorithm takes an offline oracle as a subroutine, we first make a clear definition of the Offline IM problem and summarize those commonly used Offline IM algorithms, which include traditional approximation or heuristic algorithms and ML-based algorithms. Then, we give a standard definition of the Online IM problem and a basic Combinatorial Multi-Armed Bandit (CMAB) framework, CMAB-T. Here, we summarize three types of feedback in the CMAB model and discuss in detail how to study the Online IM problem based on the CMAB-T model. This paves the way for solving the Online IM problem by using online learning methods. Furthermore, we have covered almost all Online IM algorithms up to now, focusing on characteristics and theoretical guarantees of online algorithms for different feedback types. Here, we elaborately explain their working principle and how to obtain regret bounds. Besides, we also collect plenty of innovative ideas about problem definition and algorithm designs and pioneering works for variants of the Online IM problem and their corresponding algorithms. Finally, we encapsulate current challenges and outline prospective research directions from four distinct perspectives.
DeepEn2023: Energy Datasets for Edge Artificial Intelligence
Venue: arXiv cs.LG 2023
Year: 2023

Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2 million deaths and losses exceeding $3.64 trillion USD. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications. For more information, including access to the dataset and code, please visit https://amai-gsu.github.io/DeepEn2023.
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Venue: arXiv cs.CV 2023
Year: 2023

Most real-world applications that employ deep neural networks (DNNs) quantize them to low precision to reduce the compute needs. We present a method to improve the robustness of quantized DNNs to white-box adversarial attacks. We first tackle the limitation of deterministic quantization to fixed ``bins'' by introducing a differentiable Stochastic Quantizer (SQ). We explore the hypothesis that different quantizations may collectively be more robust than each quantized DNN. We formulate a training objective to encourage different quantized DNNs to learn different representations of the input image. The training objective captures diversity and accuracy via mutual information between ensemble members. Through experimentation, we demonstrate substantial improvement in robustness against $L_\infty$ attacks even if the attacker is allowed to backpropagate through SQ (e.g., > 50\% accuracy to PGD(5/255) on CIFAR10 without adversarial training), compared to vanilla DNNs as well as existing ensembles of quantized DNNs. We extend the method to detect attacks and generate robustness profiles in the adversarial information plane (AIP), towards a unified analysis of different threat models by correlating the MI and accuracy.
Navigating News Narratives: A Media Bias Analysis Dataset
Venue: arXiv cs.CL 2023
Year: 2023

The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change. This paper introduces the "Navigating News Narratives: A Media Bias Analysis Dataset", a comprehensive dataset to address the urgent need for tools to detect and analyze media bias. This dataset encompasses a broad spectrum of biases, making it a unique and valuable asset in the field of media studies and artificial intelligence. The dataset is available at https://huggingface.co/datasets/newsmediabias/news-bias-full-data.
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Venue: arXiv cs.CV 2023
Year: 2023

Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against these attacks is of the utmost importance when designing computer vision applications, especially for safety-critical domains such as CCTV systems. In most practical situations, monitoring open spaces requires multi-view systems to overcome acquisition challenges such as occlusion handling. Multiview object systems are able to combine data from multiple views, and reach reliable detection results even in difficult environments. Despite its importance in real-world vision applications, the vulnerability of multiview systems to adversarial patches is not sufficiently investigated. In this paper, we raise the following question: Does the increased performance and information sharing across views offer as a by-product robustness to adversarial patches? We first conduct a preliminary analysis showing promising robustness against off-the-shelf adversarial patches, even in an extreme setting where we consider patches applied to all views by all persons in Wildtrack benchmark. However, we challenged this observation by proposing two new attacks: (i) In the first attack, targeting a multiview CNN, we maximize the global loss by proposing gradient projection to the different views and aggregating the obtained local gradients. (ii) In the second attack, we focus on a Transformer-based multiview framework. In addition to the focal loss, we also maximize the transformer-specific loss by dissipating its attention blocks. Our results show a large degradation in the detection performance of victim multiview systems with our first patch attack reaching an attack success rate of 73% , while our second proposed attack reduced the performance of its target detector by 62%
On the Interplay Between Stepsize Tuning and Progressive Sharpening
Venue: arXiv cs.LG 2023
Year: 2023

Recent empirical work has revealed an intriguing property of deep learning models by which the sharpness (largest eigenvalue of the Hessian) increases throughout optimization until it stabilizes around a critical value at which the optimizer operates at the edge of stability, given a fixed stepsize (Cohen et al, 2022). We investigate empirically how the sharpness evolves when using stepsize-tuners, the Armijo linesearch and Polyak stepsizes, that adapt the stepsize along the iterations to local quantities such as, implicitly, the sharpness itself. We find that the surprisingly poor performance of a classical Armijo linesearch in the deterministic setting may be well explained by its tendency to ever-increase the sharpness of the objective. On the other hand, we observe that Polyak stepsizes operate generally at the edge of stability or even slightly beyond, outperforming its Armijo and constant stepsizes counterparts in the deterministic setting. We conclude with an analysis that suggests unlocking stepsize tuners requires an understanding of the joint dynamics of the step size and the sharpness.
Cascaded Channel Decoupling Based Solution for RIS Regulation Matrix
Venue: arXiv cs.IT 2023
Year: 2023

This article presents a novel solution for reconfigurable intelligent surfaces (RISs) based on cascaded channel decoupling. The proposed mechanism simplifies the RIS regulation matrix, by decomposing the electromagnetic wave regulation process into two sub-processes: virtual receiving response and virtual regular transmission, which leads to the decoupling of the RIS cascaded channel. This article further discusses the concrete implementation of the proposed channel decoupling mechanism in two scenarios of single-user access and multi-user access, and gives the corresponding detailed scheme. The numerical simulation results demonstrate that the proposed channel decoupling scheme is a low-complexity and effective solution for resolving the RIS regulation matrix.
Negotiated Representations to Prevent Forgetting in Machine Learning Applications
Venue: arXiv cs.LG 2023
Year: 2023

Catastrophic forgetting is a significant challenge in the field of machine learning, particularly in neural networks. When a neural network learns to perform well on a new task, it often forgets its previously acquired knowledge or experiences. This phenomenon occurs because the network adjusts its weights and connections to minimize the loss on the new task, which can inadvertently overwrite or disrupt the representations that were crucial for the previous tasks. As a result, the the performance of the network on earlier tasks deteriorates, limiting its ability to learn and adapt to a sequence of tasks. In this paper, we propose a novel method for preventing catastrophic forgetting in machine learning applications, specifically focusing on neural networks. Our approach aims to preserve the knowledge of the network across multiple tasks while still allowing it to learn new information effectively. We demonstrate the effectiveness of our method by conducting experiments on various benchmark datasets, including Split MNIST, Split CIFAR10, Split Fashion MNIST, and Split CIFAR100. These datasets are created by dividing the original datasets into separate, non overlapping tasks, simulating a continual learning scenario where the model needs to learn multiple tasks sequentially without forgetting the previous ones. Our proposed method tackles the catastrophic forgetting problem by incorporating negotiated representations into the learning process, which allows the model to maintain a balance between retaining past experiences and adapting to new tasks. By evaluating our method on these challenging datasets, we aim to showcase its potential for addressing catastrophic forgetting and improving the performance of neural networks in continual learning settings.
Self-similarity of Communities of the ABCD Model
Venue: arXiv cs.SI 2023
Year: 2023

The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster and can be investigated analytically. In this paper, we show that the ABCD model exhibits some interesting self-similar behaviour, namely, the degree distribution of ground-truth communities is asymptotically the same as the degree distribution of the whole graph (appropriately normalized based on their sizes). As a result, we can not only estimate the number of edges induced by each community but also the number of self-loops and multi-edges generated during the process. Understanding these quantities is important as (a) rewiring self-loops and multi-edges to keep the graph simple is an expensive part of the algorithm, and (b) every rewiring causes the underlying configuration models to deviate slightly from uniform simple graphs on their corresponding degree sequences.
Academic competitions
Venue: arXiv cs.LG 2023
Year: 2023

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed.
Adaptability of Computer Vision at the Tactical Edge: Addressing Environmental Uncertainty
Venue: arXiv cs.CV 2023
Year: 2023

Computer Vision (CV) systems are increasingly being adopted into Command and Control (C2) systems to improve intelligence analysis on the battlefield, the tactical edge. CV systems leverage Artificial Intelligence (AI) algorithms to help visualize and interpret the environment, enhancing situational awareness. However, the adaptability of CV systems at the tactical edge remains challenging due to rapidly changing environments and objects which can confuse the deployed models. A CV model leveraged in this environment can become uncertain in its predictions, as the environment and the objects existing in the environment begin to change. Additionally, mission objectives can rapidly change leading to adjustments in technology, camera angles, and image resolutions. All of which can negatively affect the performance of and potentially introduce uncertainty into the system. When the training environment and/or technology differs from the deployment environment, CV models can perform unexpectedly. Unfortunately, most scenarios at the tactical edge do not incorporate Uncertainty Quantification (UQ) into their deployed C2 and CV systems. This concept paper explores the idea of synchronizing robust data operations and model fine-tuning driven by UQ all at the tactical edge. Specifically, curating datasets and training child models based on the residuals of predictions, using these child models to calculate prediction intervals (PI), and then using these PI to calibrate the deployed models. By incorporating UQ into the core operations surrounding C2 and CV systems at the tactical edge, we can help drive purposeful adaptability on the battlefield.
TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning
Venue: arXiv cs.RO 2023
Year: 2023

As safety is of paramount importance in robotics, reinforcement learning that reflects safety, called safe RL, has been studied extensively. In safe RL, we aim to find a policy which maximizes the desired return while satisfying the defined safety constraints. There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC. We first derive the upper bound on CVaR and then approximate the upper bound in a differentiable form in a trust region. Using this approximation, a subproblem to get policy gradients is formulated, and policies are trained by iteratively solving the subproblem. TRC is evaluated through safe navigation tasks in simulations with various robots and a sim-to-real environment with a Jackal robot from Clearpath. Compared to other safe RL methods, the performance is improved by 1.93 times while the constraints are satisfied in all experiments.
LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices
Venue: arXiv cs.LG 2023
Year: 2023

Deploying Large Language Models (LLMs) locally on mobile devices presents a significant challenge due to their extensive memory requirements. In this paper, we introduce LinguaLinked, a system for decentralized, distributed LLM inference on mobile devices. LinguaLinked enables collaborative execution of the inference task across multiple trusted devices. LinguaLinked ensures data privacy by processing information locally. LinguaLinked uses three key strategies. First, an optimized model assignment technique segments LLMs and uses linear optimization to align segments with each device's capabilities. Second, an optimized data transmission mechanism ensures efficient and structured data flow between model segments while also maintaining the integrity of the original model structure. Finally, LinguaLinked incorporates a runtime load balancer that actively monitors and redistributes tasks among mobile devices to prevent bottlenecks, enhancing the system's overall efficiency and responsiveness. We demonstrate that LinguaLinked facilitates efficient LLM inference while maintaining consistent throughput and minimal latency through extensive testing across various mobile devices, from high-end to low-end Android devices. In our evaluations, compared to the baseline, LinguaLinked achieves an inference performance acceleration of $1.11\times$ to $1.61\times$ in single-threaded settings, $1.73\times$ to $2.65\times$ with multi-threading. Additionally, runtime load balancing yields an overall inference acceleration of $1.29\times$ to $1.32\times$.
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space
Venue: arXiv cs.LG 2023
Year: 2023

Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for HAR in a smart space without privacy and accessibility issues, data streams generated by deployed pervasive sensors are leveraged. In this paper, we focus on a group activity by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme which extracts causality patterns from pervasive sensor event sequences generated by a group of users to support as good recognition accuracy as the state-of-the-art graphical model. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern matching algorithm computes the likelihoods of stored group activities to the given test event sequence by means of matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from our testbed and CASAS datasets where users perform their tasks on a daily basis and validate its effectiveness in a real environment. Experiment results show that the proposed scheme performs higher recognition accuracy and with a small amount of runtime overhead than the existing schemes.
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.
Beyond the Screen: Reshaping the Workplace with Virtual and Augmented Reality
Venue: arXiv cs.HC 2023
Year: 2023

Although extended reality technologies have enjoyed an explosion in popularity in recent years, few applications are effectively used outside the entertainment or academic contexts. This work consists of a literature review regarding the effective integration of such technologies in the workplace. It aims to provide an updated view of how they are being used in that context. First, we examine existing research concerning virtual, augmented, and mixed-reality applications. We also analyze which have made their way to the workflows of companies and institutions. Furthermore, we circumscribe the aspects of extended reality technologies that determined this applicability.