OpenRaiser/Intern-Atlas huggingface dataset:
Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture
Venue: arXiv cs.CV 2024
Year: 2024

Radiometric accuracy of data is crucial in quantitative precision agriculture, to produce reliable and repeatable data for modeling and decision making. The effect of exposure time and gain settings on the radiometric accuracy of multispectral images was not explored enough. The goal of this study was to determine if having a fixed exposure (FE) time during image acquisition improved radiometric accuracy of images, compared to the default auto-exposure (AE) settings. This involved quantifying the errors from auto-exposure and determining ideal exposure values within which radiometric mean absolute percentage error (MAPE) were minimal (< 5%). The results showed that FE orthomosaic was closer to ground-truth (higher R2 and lower MAPE) than AE orthomosaic. An ideal exposure range was determined for capturing canopy and soil objects, without loss of information from under-exposure or saturation from over-exposure. A simulation of errors from AE showed that MAPE < 5% for the blue, green, red, and NIR bands and < 7% for the red edge band for exposure settings within the determined ideal ranges and increased exponentially beyond the ideal exposure upper limit. Further, prediction of total plant nitrogen uptake (g/plant) using vegetation indices (VIs) from two different growing seasons were closer to the ground truth (mostly, R2 > 0.40, and MAPE = 12 to 14%, p < 0.05) when FE was used, compared to the prediction from AE images (mostly, R2 < 0.13, MAPE = 15 to 18%, p >= 0.05).
Motion Guided Token Compression for Efficient Masked Video Modeling
Venue: arXiv cs.CV 2024
Year: 2024

Recent developments in Transformers have achieved notable strides in enhancing video comprehension. Nonetheless, the O($N^2$) computation complexity associated with attention mechanisms presents substantial computational hurdles when dealing with the high dimensionality of videos. This challenge becomes particularly pronounced when striving to increase the frames per second (FPS) to enhance the motion capturing capabilities. Such a pursuit is likely to introduce redundancy and exacerbate the existing computational limitations. In this paper, we initiate by showcasing the enhanced performance achieved through an escalation in the FPS rate. Additionally, we present a novel approach, Motion Guided Token Compression (MGTC), to empower Transformer models to utilize a smaller yet more representative set of tokens for comprehensive video representation. Consequently, this yields substantial reductions in computational burden and remains seamlessly adaptable to increased FPS rates. Specifically, we draw inspiration from video compression algorithms and scrutinize the variance between patches in consecutive video frames across the temporal dimension. The tokens exhibiting a disparity below a predetermined threshold are then masked. Notably, this masking strategy effectively addresses video redundancy while conserving essential information. Our experiments, conducted on widely examined video recognition datasets, Kinetics-400, UCF101 and HMDB51, demonstrate that elevating the FPS rate results in a significant top-1 accuracy score improvement of over 1.6, 1.6 and 4.0. By implementing MGTC with the masking ratio of 25\%, we further augment accuracy by 0.1 and simultaneously reduce computational costs by over 31\% on Kinetics-400. Even within a fixed computational budget, higher FPS rates paired with MGTC sustain performance gains when compared to lower FPS settings.
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
Venue: arXiv cs.IR 2024
Year: 2024

The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. This marks a fundamental paradigm shift in the realm of recommendations. Amidst the dynamic research landscape, researchers actively harness the language comprehension and generation capabilities of LLMs to redefine the foundations of recommendation tasks. The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across diverse domains, adoption of unified approaches, holistic learning strategies leveraging shared data reservoirs, transparent decision-making, and iterative improvements. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations, necessitating continuous refinement and evolution in LLM-driven recommender systems.
A$^3$PIM: An Automated, Analytic and Accurate Processing-in-Memory Offloader
Venue: arXiv cs.AR 2024
Year: 2024

The performance gap between memory and processor has grown rapidly. Consequently, the energy and wall-clock time costs associated with moving data between the CPU and main memory predominate the overall computational cost. The Processing-in-Memory (PIM) paradigm emerges as a promising architecture that mitigates the need for extensive data movements by strategically positioning computing units proximate to the memory. Despite the abundant efforts devoted to building a robust and highly-available PIM system, identifying PIM-friendly segments of applications poses significant challenges due to the lack of a comprehensive tool to evaluate the intrinsic memory access pattern of the segment. To tackle this challenge, we propose A$^3$PIM: an Automated, Analytic and Accurate Processing-in-Memory offloader. We systematically consider the cross-segment data movement and the intrinsic memory access pattern of each code segment via static code analyzer. We evaluate A$^3$PIM across a wide range of real-world workloads including GAP and PrIM benchmarks and achieve an average speedup of 2.63x and 4.45x (up to 7.14x and 10.64x) when compared to CPU-only and PIM-only executions, respectively.
ICE-SEARCH: A Language Model-Driven Feature Selection Approach
Venue: arXiv cs.LG 2024
Year: 2024

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LLMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.
Acoustic tactile sensing for mobile robot wheels
Venue: arXiv cs.RO 2024
Year: 2024

Tactile sensing in mobile robots remains under-explored, mainly due to challenges related to sensor integration and the complexities of distributed sensing. In this work, we present a tactile sensing architecture for mobile robots based on wheel-mounted acoustic waveguides. Our sensor architecture enables tactile sensing along the entire circumference of a wheel with a single active component: an off-the-shelf acoustic rangefinder. We present findings showing that our sensor, mounted on the wheel of a mobile robot, is capable of discriminating between different terrains, detecting and classifying obstacles with different geometries, and performing collision detection via contact localization. We also present a comparison between our sensor and sensors traditionally used in mobile robots, and point to the potential for sensor fusion approaches that leverage the unique capabilities of our tactile sensing architecture. Our findings demonstrate that autonomous mobile robots can further leverage our sensor architecture for diverse mapping tasks requiring knowledge of terrain material, surface topology, and underlying structure.
Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges
Venue: arXiv cs.IT 2024
Year: 2024

Both smart propagation engineering as well as integrated sensing and communication (ISAC) constitute promising candidates for next-generation (NG) mobile networks. We provide a synergistic view of these technologies, and explore their mutual benefits. First, moving beyond just intelligent surfaces, we provide a holistic view of the engineering aspects of smart propagation environments. By delving into the fundamental characteristics of intelligent surfaces, fluid antennas, and unmanned aerial vehicles, we reveal that more efficient control of the pathloss and fading can be achieved, thus facilitating intrinsic integration and mutual assistance between sensing and communication functionalities. In turn, with the exploitation of the sensing capabilities of ISAC to orchestrate the efficient configuration of radio environments, both the computational effort and signaling overheads can be reduced. We present indicative simulation results, which verify that cooperative smart propagation environment design significantly enhances the ISAC performance. Finally, some promising directions are outlined for combining ISAC with smart propagation engineering.
Embodied Supervision: Haptic Display of Automation Command to Improve Supervisory Performance
Venue: arXiv cs.HC 2024
Year: 2024

A human operator using a manual control interface has ready access to their own command signal, both by efference copy and proprioception. In contrast, a human supervisor typically relies on visual information alone. We propose supplying a supervisor with a copy of the operators command signal, hypothesizing improved performance, especially when that copy is provided through haptic display. We experimentally compared haptic with visual access to the command signal, quantifying the performance of N equals 10 participants attempting to determine which of three reference signals was being tracked by an operator. Results indicate an improved accuracy in identifying the tracked target when haptic display was available relative to visual display alone. We conjecture the benefit follows from the relationship of haptics to the supervisor's own experience, perhaps muscle memory, as an operator.
Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks
Venue: arXiv cs.CV 2024
Year: 2024

Backdoor attacks allow an attacker to embed a specific vulnerability in a machine learning algorithm, activated when an attacker-chosen pattern is presented, causing a specific misprediction. The need to identify backdoors in biometric scenarios has led us to propose a novel technique with different trade-offs. In this paper we propose to use model pairs on open-set classification tasks for detecting backdoors. Using a simple linear operation to project embeddings from a probe model's embedding space to a reference model's embedding space, we can compare both embeddings and compute a similarity score. We show that this score, can be an indicator for the presence of a backdoor despite models being of different architectures, having been trained independently and on different datasets. This technique allows for the detection of backdoors on models designed for open-set classification tasks, which is little studied in the literature. Additionally, we show that backdoors can be detected even when both models are backdoored. The source code is made available for reproducibility purposes.
Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games
Venue: arXiv cs.HC 2024
Year: 2024

Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with our judgement is unclear. Consequently, the result of their application can fail human expectations, leading to e.g. unappreciated content or unbelievable agent behaviour. We alleviate this gap through a multi-factorial study of two tile-based games in two representations, where participants (N=456) judged the similarity of level triplets. Based on this data, we construct domain-specific perceptual spaces, encoding similarity-relevant attributes. We compare 12 metrics to these spaces and evaluate their approximation quality through several quantitative lenses. Moreover, we conduct a qualitative labelling study to identify the features underlying the human similarity judgement in this popular genre. Our findings inform the selection of existing metrics and highlight requirements for the design of new similarity metrics benefiting game development and research.
Comparing Importance Sampling Based Methods for Mitigating the Effect of Class Imbalance
Venue: arXiv cs.CV 2024
Year: 2024

Most state-of-the-art computer vision models heavily depend on data. However, many datasets exhibit extreme class imbalance which has been shown to negatively impact model performance. Among the training-time and data-generation solutions that have been explored, one subset that leverages existing data is importance sampling. A good deal of this work focuses primarily on the CIFAR-10 and CIFAR-100 datasets which fail to be representative of the scale, composition, and complexity of current state-of-the-art datasets. In this work, we explore and compare three techniques that derive from importance sampling: loss reweighting, undersampling, and oversampling. Specifically, we compare the effect of these techniques on the performance of two encoders on an impactful satellite imagery dataset, Planet's Amazon Rainforest dataset, in preparation for another work. Furthermore, we perform supplemental experimentation on a scene classification dataset, ADE20K, to test on a contrasting domain and clarify our results. Across both types of encoders, we find that up-weighting the loss for and undersampling has a negigible effect on the performance on underrepresented classes. Additionally, our results suggest oversampling generally improves performance for the same underrepresented classes. Interestingly, our findings also indicate that there may exist some redundancy in data in the Planet dataset. Our work aims to provide a foundation for further work on the Planet dataset and similar domain-specific datasets. We open-source our code at https://github.com/RichardZhu123/514-class-imbalance for future work on other satellite imagery datasets as well.
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning
Venue: arXiv cs.NE 2024
Year: 2024

Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.
On Defeating Graph Analysis of Anonymous Transactions
Venue: arXiv cs.CR 2024
Year: 2024

In a ring-signature-based anonymous cryptocurrency, signers of a transaction are hidden among a set of potential signers, called a ring, whose size is much smaller than the number of all users. The ring-membership relations specified by the sets of transactions thus induce bipartite transaction graphs, whose distribution is in turn induced by the ring sampler underlying the cryptocurrency. Since efficient graph analysis could be performed on transaction graphs to potentially deanonymise signers, it is crucial to understand the resistance of (the transaction graphs induced by) a ring sampler against graph analysis. Of particular interest is the class of partitioning ring samplers. Although previous works showed that they provide almost optimal local anonymity, their resistance against global, e.g. graph-based, attacks were unclear. In this work, we analyse transaction graphs induced by partitioning ring samplers. Specifically, we show (partly analytically and partly empirically) that, somewhat surprisingly, by setting the ring size to be at least logarithmic in the number of users, a graph-analysing adversary is no better than the one that performs random guessing in deanonymisation up to constant factor of 2.
X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism
Venue: arXiv cs.NI 2024
Year: 2024

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring fine-grained quantum task parallelism that is uniquely enabled by the Reverse Annealing (RA) protocol. Unlike prior designs, X-ResQ has many desirable system properties for a parallel QA detector and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput (over 10 bits/s/Hz) for $4\times6$ MIMO with 16-QAM using six levels of parallelism with 240 qubits and $220~μ$s QA compute time, achieving 2.5--5$\times$ gains compared against other tested detectors. For more comprehensive evaluations, we implement and evaluate X-ResQ in the non-quantum digital setting. This non-quantum X-ResQ demonstration showcases the potential to realize ultra-large $1024\times1024$ MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
Batch size invariant Adam
Venue: arXiv cs.LG 2024
Year: 2024

We propose a batch size invariant version of Adam, for use in large-scale, distributed settings, in which the mini-batch is divided into micro-batches which are distributed among worker nodes. For the v term, standard Adam first computes the average over micro-batch gradients, then squares, while in the batch size invariant Adam proposed here, we first square the micro-batch gradients, then average. Previous work (e.g. Malladi et al. 2022) used an alternative approach that involved a square-root scaling of the learning rate, but this approach requires strong assumptions to work; in particular that the gradient variance dominates the square of the expected gradient. In contrast, the approach proposed here gives batch size invariance without this assumption. We confirm that in practice our scheme gives batch size invariance in a much larger range of scenarios than the previous approach.
Enhancing Steganographic Text Extraction: Evaluating the Impact of NLP Models on Accuracy and Semantic Coherence
Venue: arXiv cs.CV 2024
Year: 2024

This study discusses a new method combining image steganography technology with Natural Language Processing (NLP) large models, aimed at improving the accuracy and robustness of extracting steganographic text. Traditional Least Significant Bit (LSB) steganography techniques face challenges in accuracy and robustness of information extraction when dealing with complex character encoding, such as Chinese characters. To address this issue, this study proposes an innovative LSB-NLP hybrid framework. This framework integrates the advanced capabilities of NLP large models, such as error detection, correction, and semantic consistency analysis, as well as information reconstruction techniques, thereby significantly enhancing the robustness of steganographic text extraction. Experimental results show that the LSB-NLP hybrid framework excels in improving the extraction accuracy of steganographic text, especially in handling Chinese characters. The findings of this study not only confirm the effectiveness of combining image steganography technology and NLP large models but also propose new ideas for research and application in the field of information hiding. The successful implementation of this interdisciplinary approach demonstrates the great potential of integrating image steganography technology with natural language processing technology in solving complex information processing problems.
Energy-Efficient UAV Swarm Assisted MEC with Dynamic Clustering and Scheduling
Venue: arXiv cs.NI 2024
Year: 2024

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.