OpenRaiser/Intern-Atlas huggingface dataset:
Disentanglement and Compositionality of Letter Identity and Letter Position in Variational Auto-Encoder Vision Models
Venue: arXiv cs.CV 2024
Year: 2024

Human readers can accurately count how many letters are in a word (e.g., 7 in ``buffalo''), remove a letter from a given position (e.g., ``bufflo'') or add a new one. The human brain of readers must have therefore learned to disentangle information related to the position of a letter and its identity. Such disentanglement is necessary for the compositional, unbounded, ability of humans to create and parse new strings, with any combination of letters appearing in any positions. Do modern deep neural models also possess this crucial compositional ability? Here, we tested whether neural models that achieve state-of-the-art on disentanglement of features in visual input can also disentangle letter position and letter identity when trained on images of written words. Specifically, we trained beta variational autoencoder ($β$-VAE) to reconstruct images of letter strings and evaluated their disentanglement performance using CompOrth - a new benchmark that we created for studying compositional learning and zero-shot generalization in visual models for orthography. The benchmark suggests a set of tests, of increasing complexity, to evaluate the degree of disentanglement between orthographic features of written words in deep neural models. Using CompOrth, we conducted a set of experiments to analyze the generalization ability of these models, in particular, to unseen word length and to unseen combinations of letter identities and letter positions. We found that while models effectively disentangle surface features, such as horizontal and vertical `retinal' locations of words within an image, they dramatically fail to disentangle letter position and letter identity and lack any notion of word length. Together, this study demonstrates the shortcomings of state-of-the-art $β$-VAE models compared to humans and proposes a new challenge and a corresponding benchmark to evaluate neural models.
FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Optimized Foveated Rendering System Performance in Virtual Reality
Venue: arXiv cs.CV 2024
Year: 2024

Leveraging real-time eye-tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to render high-resolution graphics only in the foveal region-the small area of the retina where visual acuity is highest, while the peripheral view is rendered at lower resolution. However, modern deep learning-based gaze-tracking solutions often exhibit a long-tail distribution of tracking errors, which can degrade user experience and reduce the benefits of foveated rendering by causing misalignment and decreased visual quality. This paper introduces \textit{FovealNet}, an advanced AI-driven gaze tracking framework designed to optimize system performance by strategically enhancing gaze tracking accuracy. To further reduce the implementation cost of the gaze tracking algorithm, FovealNet employs an event-based cropping method that eliminates over $64.8\%$ of irrelevant pixels from the input image. Additionally, it incorporates a simple yet effective token-pruning strategy that dynamically removes tokens on the fly without compromising tracking accuracy. Finally, to support different runtime rendering configurations, we propose a system performance-aware multi-resolution training strategy, allowing the gaze tracking DNN to adapt and optimize overall system performance more effectively. Evaluation results demonstrate that FovealNet achieves at least $1.42\times$ speed up compared to previous methods and 13\% increase in perceptual quality for foveated output.
Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold
Venue: arXiv cs.CV 2024
Year: 2024

Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspaces of effective motion. In this review, we present a comprehensive overview of manifold applications in human motion generation, one of the first in this domain. We explore methods for extracting manifolds from unstructured data, their application in motion generation, and discuss their advantages and future directions. This survey aims to provide a broad perspective on the field and stimulate new approaches to ongoing challenges.
Automatic Detection, Positioning and Counting of Grape Bunches Using Robots
Venue: arXiv cs.CV 2024
Year: 2024

In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added to eliminate relocation. Then it obtains the accurate 3D spatial position of the central points of grape bunches using the depth distance and the spatial restriction method. Finally, the counting of grape bunches is completed. It is verified using the agricultural robot in the simulated vineyard environment. The project code is released at: https://github.com/XuminGaoGithub/Grape_bunches_count_using_robots.
Comparative Analysis of Mel-Frequency Cepstral Coefficients and Wavelet Based Audio Signal Processing for Emotion Detection and Mental Health Assessment in Spoken Speech
Venue: arXiv cs.SD 2024
Year: 2024

The intersection of technology and mental health has spurred innovative approaches to assessing emotional well-being, particularly through computational techniques applied to audio data analysis. This study explores the application of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models on wavelet extracted features and Mel-frequency Cepstral Coefficients (MFCCs) for emotion detection from spoken speech. Data augmentation techniques, feature extraction, normalization, and model training were conducted to evaluate the models' performance in classifying emotional states. Results indicate that the CNN model achieved a higher accuracy of 61% compared to the LSTM model's accuracy of 56%. Both models demonstrated better performance in predicting specific emotions such as surprise and anger, leveraging distinct audio features like pitch and speed variations. Recommendations include further exploration of advanced data augmentation techniques, combined feature extraction methods, and the integration of linguistic analysis with speech characteristics for improved accuracy in mental health diagnostics. Collaboration for standardized dataset collection and sharing is recommended to foster advancements in affective computing and mental health care interventions.
Crosstalk-induced Side Channel Threats in Multi-Tenant NISQ Computers
Venue: arXiv cs.ET 2024
Year: 2024

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.
Automated Image Captioning with CNNs and Transformers
Venue: arXiv cs.CV 2024
Year: 2024

This project aims to create an automated image captioning system that generates natural language descriptions for input images by integrating techniques from computer vision and natural language processing. We employ various different techniques, ranging from CNN-RNN to the more advanced transformer-based techniques. Training is carried out on image datasets paired with descriptive captions, and model performance will be evaluated using established metrics such as BLEU, METEOR, and CIDEr. The project will also involve experimentation with advanced attention mechanisms, comparisons of different architectural choices, and hyperparameter optimization to refine captioning accuracy and overall system effectiveness.
Too Big to Fool: Resisting Deception in Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
UbiTouch: Towards a Universal Touch Interface
Venue: arXiv cs.HC 2024
Year: 2024

Touch is one of the most intuitive ways for humans to interact with the world, and as we advance toward a ubiquitous computing environment where technology seamlessly integrates into daily life, natural interaction methods are essential. This paper introduces UbiTouch, a system leveraging thermal imaging to detect touch interactions on arbitrary surfaces. By employing a single thermal camera, UbiTouch differentiates between hovering and touch, detects multi-finger input, and completes trajectory tracking. Our approach emphasizes the use of lightweight, low-computation algorithms that maintain robust detection accuracy through innovative vision-based processing. UbiTouch aims to enable scalable, sustainable, and adaptable interaction systems for diverse applications, particularly with regards to on-human sensing.
Deviate or Not: Learning Coalition Structures with Multiple-bit Observations in Games
Venue: arXiv cs.GT 2024
Year: 2024

We consider the Coalition Structure Learning (CSL) problem in multi-agent systems, motivated by the existence of coalitions in many real-world systems, e.g., trading platforms and auction systems. In this problem, there is a hidden coalition structure within a set of $n$ agents, which affects the behavior of the agents in games. Our goal is to actively design a sequence of games for the agents to play, such that observations in these games can be used to learn the hidden coalition structure. In particular, we consider the setting where in each round, we design and present a game together with a strategy profile to the agents, and receive a multiple-bit observation -- for each agent, we observe whether or not they would like to deviate from the specified strategy. We show that we can learn the coalition structure in $O(\log n)$ rounds if we are allowed to design any normal-form game, matching the information-theoretical lower bound. For practicality, we extend the result to settings where we can only choose games of a specific format, and design algorithms to learn the coalition structure in these settings. For most settings, our complexity matches the theoretical lower bound up to a constant factor.
Study of Iterative Detection and Decoding for Multiuser Systems and MMSE Refinements with Active or Passive RIS
Venue: arXiv cs.IT 2024
Year: 2024

An iterative detection and decoding (IDD) scheme is proposed for multiuser multiple-antenna systems assisted by an active or a passive Reconfigurable Intelligent Surface (RIS). The proposed approach features an IDD strategy that incorporates Low-Density Parity-Check (LDPC) codes, RIS processing with refinements of soft information in the form of log likelihood ratios (LLRs) and truncation. Specifically, a minimum mean square error (MMSE) receive filter is used for refinement of LLRs and truncation at the RIS, and for soft interference cancellation at the receiver. An analysis of the proposed MMSE refinement is also devised along with a study of the computational complexity of the proposed and existing schemes. Simulation results demonstrate significant improvements in system capacity and bit error rate in the presence of block-fading channels
Inference Scaling for Bridging Retrieval and Augmented Generation
Venue: arXiv cs.CL 2024
Year: 2024

Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However, existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. In this work, we show such bias can be mitigated, from inference scaling, aggregating inference calls from the permuted order of retrieved contexts. The proposed Mixture-of-Intervention (MOI) explicitly models the debiased utility of each passage with multiple forward passes to construct a new ranking. We also show that MOI can leverage the retriever's prior knowledge to reduce the computational cost by minimizing the number of permutations considered and lowering the cost per LLM call. We showcase the effectiveness of MOI on diverse RAG tasks, improving ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by ~7 points.
Beyond Quantile Methods: Improved Top-K Threshold Estimation for Traditional and Learned Sparse Indexes
Venue: arXiv cs.IR 2024
Year: 2024

Top-k threshold estimation is the problem of estimating the score of the k-th highest ranking result of a search query. A good estimate can be used to speed up many common top-k query processing algorithms, and thus a number of researchers have recently studied the problem. Among the various approaches that have been proposed, quantile methods appear to give the best estimates overall at modest computational costs, followed by sampling-based methods in certain cases. In this paper, we make two main contributions. First, we study how to get even better estimates than the state of the art. Starting from quantile-based methods, we propose a series of enhancements that give improved estimates in terms of the commonly used mean under-prediction fraction (MUF). Second, we study the threshold estimation problem on recently proposed learned sparse index structures, showing that our methods also work well for these cases. Our best methods substantially narrow the gap between the state of the art and the ideal MUF of 1.0, at some additional cost in time and space.
A Post a Day Keeps the Doctor Away: Sharing Personal Information on Self-Diagnosis Platforms
Venue: arXiv cs.HC 2024
Year: 2024

For many, it can be intimidating or even impossible to seek professional medical help if they have symptoms of an illness. As such, some people approach platforms like Reddit or Quora for a community-based conversation in an attempt to diagnose themselves. In this paper, we unearth what motivates people to share personal health information on these platforms. From an online survey and in-depth interviews, we present who this population of users are, and what, where, and why they are posting. Our evaluation finds that tech-savvy young adults are more likely to post on online platforms about potentially sensitive or highly specific topics for convenience, fast response, and a sense of community. Most importantly, we found that anonymity, distrust of physicians, and prior experience with platforms were key factors that affected behavior.
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
Venue: arXiv cs.LG 2024
Year: 2024

With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploration (DSE) algorithms a significant tool for $μ$-arch design. In recent years, efforts have been made in the development of DSE algorithms, and promising results have been achieved. However, the existing DSE algorithms, e.g., Bayesian Optimization and ensemble learning, suffer from poor interpretability, hindering designers' understanding of the decision-making process. To address this limitation, we propose utilizing Fuzzy Neural Networks to induce and summarize knowledge and insights from the DSE process, enhancing interpretability and controllability. Furthermore, to improve efficiency, we introduce a multi-fidelity reinforcement learning approach, which primarily conducts exploration using cheap but less precise data, thereby substantially diminishing the reliance on costly data. Experimental results show that our method achieves excellent results with a very limited sample budget and successfully surpasses the current state-of-the-art. Our DSE framework is open-sourced and available at https://github.com/fanhanwei/FNN\_MFRL\_ArchDSE/\ .
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

Unmanned aerial vehicle-assisted disaster recovery missions have been promoted recently due to their reliability and flexibility. Machine learning algorithms running onboard significantly enhance the utility of UAVs by enabling real-time data processing and efficient decision-making, despite being in a resource-constrained environment. However, the limited bandwidth and intermittent connectivity make transmitting the outputs to ground stations challenging. This paper proposes a novel semantic extractor that can be adopted into any machine learning downstream task for identifying the critical data required for decision-making. The semantic extractor can be executed onboard which results in a reduction of data that needs to be transmitted to ground stations. We test the proposed architecture together with the semantic extractor on two publicly available datasets, FloodNet and RescueNet, for two downstream tasks: visual question answering and disaster damage level classification. Our experimental results demonstrate the proposed method maintains high accuracy across different downstream tasks while significantly reducing the volume of transmitted data, highlighting the effectiveness of our semantic extractor in capturing task-specific salient information.
Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Deep neural networks (DNNs) are a contemporary solution for semantic segmentation and are usually trained to operate on a predefined closed set of classes. In open-set environments, it is possible to encounter semantically unknown objects or anomalies. Road driving is an example of such an environment in which, from a safety standpoint, it is important to ensure that a DNN indicates it is operating outside of its learned semantic domain. One possible approach to anomaly segmentation is entropy maximization, which is paired with a logistic regression based post-processing step called meta classification, which is in turn used to improve the reliability of detection of anomalous pixels. We propose to substitute the logistic regression meta classifier with a more expressive lightweight fully connected neural network. We analyze advantages and drawbacks of the proposed neural network meta classifier and demonstrate its better performance over logistic regression. We also introduce the concept of informative out-of-distribution examples which we show to improve training results when using entropy maximization in practice. Finally, we discuss the loss of interpretability and show that the behavior of logistic regression and neural network is strongly correlated.
Omni Differential Drive for Simultaneous Reconfiguration and Omnidirectional Mobility of Wheeled Robots
Venue: arXiv cs.RO 2024
Year: 2024

Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, limited by degrees of freedom, struggle to meet varying footprint needs and achieve omnidirectional mobility. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differential Drive (ODD). The ODD model innovatively utilizes a lateral differential drive to adjust wheel spacing without adding additional actuators to the existing omnidirectional drive. This approach enables wheeled robots to achieve both simultaneous reconfiguration and omnidirectional mobility. Additionally, a prototype was developed to validate the ODD, followed by kinematic analysis. Control systems for self-balancing and motion were designed and implemented. Experimental validations confirmed the feasibility of the ODD mechanism and the effectiveness of the control strategies. The results underline the potential of this innovative drive system to enhance the mobility and adaptability of robotic platforms.
Affine EKF: Exploring and Utilizing Sufficient and Necessary Conditions for Observability Maintenance to Improve EKF Consistency
Venue: arXiv cs.RO 2024
Year: 2024

Inconsistency issue is one crucial challenge for the performance of extended Kalman filter (EKF) based methods for state estimation problems, which is mainly affected by the discrepancy of observability between the EKF model and the underlying dynamic system. In this work, some sufficient and necessary conditions for observability maintenance are first proved. We find that under certain conditions, an EKF can naturally maintain correct observability if the corresponding linearization makes unobservable subspace independent of the state values. Based on this theoretical finding, a novel affine EKF (Aff-EKF) framework is proposed to overcome the inconsistency of standard EKF (Std-EKF) by affine transformations, which not only naturally satisfies the observability constraint but also has a clear design procedure. The advantages of our Aff-EKF framework over some commonly used methods are demonstrated through mathematical analyses. The effectiveness of our proposed method is demonstrated on three simultaneous localization and mapping (SLAM) applications with different types of features, typical point features, point features on a horizontal plane and plane features. Specifically, following the proposed procedure, the naturally consistent Aff-EKFs can be explicitly derived for these problems. The consistency improvement of these Aff-EKFs are validated by Monte Carlo simulations.