OpenRaiser/Intern-Atlas huggingface dataset:
PyHGL: A Python-based Hardware Generation Language Framework
Venue: arXiv cs.AR 2023
Year: 2023

Hardware generation languages (HGLs) increase hardware design productivity by creating parameterized modules and test benches. Unfortunately, existing tools are not widely adopted due to several demerits, including limited support for asynchronous circuits and unknown states, lack of concise and efficient language features, and low integration of simulation and verification functions. This paper introduces PyHGL, an open-source Python framework that aims to provide a simple and unified environment for hardware generation, simulation, and verification. PyHGL language is a syntactical superset of Python, which greatly reduces the lines of code (LOC) and improves productivity by providing unique features such as dynamic typing, vectorized operations, and automatic port deduction. In addition, PyHGL integrates an event-driven simulator that simulates the asynchronous behaviors of digital circuits using three-state logic. We also propose an algorithm that eliminates the calculation and transmission overhead of unknown state propagation for binary stimuli. The results suggest that PyHGL code is up to 6.1x denser than traditional RTL and generates high-quality synthesizable RTL code. Moreover, the optimized simulator achieves 2.9x speed up and matches the performance of a commonly used open-source logic simulator.
Exploring Music Genre Classification: Algorithm Analysis and Deployment Architecture
Venue: arXiv cs.SD 2023
Year: 2023

Music genre classification has become increasingly critical with the advent of various streaming applications. Nowadays, we find it impossible to imagine using the artist's name and song title to search for music in a sophisticated music app. It is always difficult to classify music correctly because the information linked to music, such as region, artist, album, or non-album, is so variable. This paper presents a study on music genre classification using a combination of Digital Signal Processing (DSP) and Deep Learning (DL) techniques. A novel algorithm is proposed that utilizes both DSP and DL methods to extract relevant features from audio signals and classify them into various genres. The algorithm was tested on the GTZAN dataset and achieved high accuracy. An end-to-end deployment architecture is also proposed for integration into music-related applications. The performance of the algorithm is analyzed and future directions for improvement are discussed. The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification.
Design of a Low-Power High-Gain Bio-Medical Operational Amplifier in 65nm Technology using gm/ID Methodology
Venue: arXiv cs.AR 2023
Year: 2023

Operational Amplifiers (Op-Amps) play a crucial role in the field of biomedical engineering, as they enable signal amplification and processing in various medical devices. With the increasing demand for portable and low-power biomedical devices, designing Op-Amps specifically tailored for such applications is essential. In response to this need, a low-power high-gain Op-Amp designed for biomedical applications using TSMC 65nm technology has been proposed. This Op-Amp incorporates a two-stage miller compensated topology, which is well-known for its superior performance in gain, gain bandwidth product and power consumption. The proposed Op-Amp contributes to the field of biomedical engineering by offering a tailored solution that enhances signal processing capabilities, enables accurate data acquisition, and improves overall efficiency in healthcare systems. The design methodology and simulation results presented in this paper provide insights into the performance and potential impact of the Op-Amp in advancing biomedical devices and systems.
Characterizing Cyber Attacks against Space Systems with Missing Data: Framework and Case Study
Venue: arXiv cs.CR 2023
Year: 2023

Cybersecurity of space systems is an emerging topic, but there is no single dataset that documents cyber attacks against space systems that have occurred in the past. These incidents are often scattered in media reports while missing many details, which we dub the missing-data problem. Nevertheless, even "low-quality" datasets containing such reports would be extremely valuable because of the dearth of space cybersecurity data and the sensitivity of space systems which are often restricted from disclosure by governments. This prompts a research question: How can we characterize real-world cyber attacks against space systems? In this paper, we address the problem by proposing a framework, including metrics, while also addressing the missing-data problem, by "extrapolating" the missing data in a principled fashion. To show the usefulness of the framework, we extract data for 72 cyber attacks against space systems and show how to extrapolate this "low-quality" dataset to derive 4,076 attack technique kill chains. Our findings include: cyber attacks against space systems are getting increasingly sophisticated; and, successful protection against on-path and social engineering attacks could have prevented 80% of the attacks.
SortedAP: Rethinking evaluation metrics for instance segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code at https://www.github.com/looooongChen/sortedAP.
Transient Attacks against the VMG-KLJN Secure Key Exchanger
Venue: arXiv cs.CR 2023
Year: 2023

The security vulnerability of the Vadai, Mingesz, and Gingl (VMG) Kirchhoff-Law-Johnson-Noise (KLJN) key exchanger, as presented in the publication "Nature, Science Report 5 (2015) 13653," has been exposed to transient attacks. Recently an effective defense protocol was introduced (Appl. Phys. Lett. 122 (2023) 143503) to counteract mean-square voltage-based (or mean-square current-based) transient attacks targeted at the ideal KLJN framework. In the present study, this same mitigation methodology has been employed to fortify the security of the VMG-KLJN key exchanger. It is worth noting that the protective measures need to be separately implemented for the HL and LH scenarios. This conceptual framework is corroborated through computer simulations, demonstrating that the application of this defensive technique substantially mitigates information leakage to a point of insignificance.
Multi-document Summarization: A Comparative Evaluation
Venue: arXiv cs.CL 2023
Year: 2023

This paper is aimed at evaluating state-of-the-art models for Multi-document Summarization (MDS) on different types of datasets in various domains and investigating the limitations of existing models to determine future research directions. To address this gap, we conducted an extensive literature review to identify state-of-the-art models and datasets. We analyzed the performance of PRIMERA and PEGASUS models on BigSurvey-MDS and MS$^2$ datasets, which posed unique challenges due to their varied domains. Our findings show that the General-Purpose Pre-trained Model LED outperforms PRIMERA and PEGASUS on the MS$^2$ dataset. We used the ROUGE score as a performance metric to evaluate the identified models on different datasets. Our study provides valuable insights into the models' strengths and weaknesses, as well as their applicability in different domains. This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets.
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices
Venue: arXiv cs.CV 2023
Year: 2023

Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT models suffer from vast amounts of parameters and high computation cost, leading to difficult deployment on resource-constrained edge devices. Existing solutions mostly compress ViT models to a compact model but still cannot achieve real-time inference. To tackle this issue, we propose to explore the divisibility of transformer structure, and decompose the large ViT into multiple small models for collaborative inference at edge devices. Our objective is to achieve fast and energy-efficient collaborative inference while maintaining comparable accuracy compared with large ViTs. To this end, we first propose a collaborative inference framework termed DeViT to facilitate edge deployment by decomposing large ViTs. Subsequently, we design a decomposition-and-ensemble algorithm based on knowledge distillation, termed DEKD, to fuse multiple small decomposed models while dramatically reducing communication overheads, and handle heterogeneous models by developing a feature matching module to promote the imitations of decomposed models from the large ViT. Extensive experiments for three representative ViT backbones on four widely-used datasets demonstrate our method achieves efficient collaborative inference for ViTs and outperforms existing lightweight ViTs, striking a good trade-off between efficiency and accuracy. For example, our DeViTs improves end-to-end latency by 2.89$\times$ with only 1.65% accuracy sacrifice using CIFAR-100 compared to the large ViT, ViT-L/16, on the GPU server. DeDeiTs surpasses the recent efficient ViT, MobileViT-S, by 3.54% in accuracy on ImageNet-1K, while running 1.72$\times$ faster and requiring 55.28% lower energy consumption on the edge device.
An Overview of Formulae for the Higher-Order Kinematics of Lower-Pair Chains with Applications in Robotics and Mechanism Theory
Venue: arXiv cs.RO 2023
Year: 2023

The motions of mechanisms can be described in terms of screw coordinates by means of an exponential mapping. The product of exponentials (POE) describes the configuration of a chain of bodies connected by lower pair joints. The kinematics is thus given in terms of joint screws. The POE serves to express loop constraints for mechanisms as well as the forward kinematics of serial manipulators. Besides the compact formulations, the POE gives rise to purely algebraic relations for derivatives wrt. joint variables. It is known that the partial derivatives of the instantaneous joint screws (columns of the geometric Jacobian) are determined by Lie brackets the joint screws. Lesser-known is that derivative of arbitrary order can be compactly expressed by Lie brackets. This has significance for higher-order forward/inverse kinematics and dynamics of robots and multibody systems. Various relations were reported but are scattered in the literature and insufficiently recognized. This paper aims to provide a comprehensive overview of the relevant relations. Its original contributions are closed form and recursive relations for higher-order derivatives and Taylor expansions of various kinematic relations. Their application to kinematic control and dynamics of robotic manipulators and multibody systems is discussed.
An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents
Venue: arXiv cs.AI 2023
Year: 2023

The development of believable, natural, and interactive digital artificial agents is a field of growing interest. Theoretical uncertainties and technical barriers present considerable challenges to the field, particularly with regards to developing agents that effectively simulate human emotions. Large language models (LLMs) might address these issues by tapping common patterns in situational appraisal. In three empirical experiments, this study tests the capabilities of LLMs to solve emotional intelligence tasks and to simulate emotions. It presents and evaluates a new chain-of-emotion architecture for emotion simulation within video games, based on psychological appraisal research. Results show that it outperforms standard LLM architectures on a range of user experience and content analysis metrics. This study therefore provides early evidence of how to construct and test affective agents based on cognitive processes represented in language models.
SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness
Venue: arXiv cs.HC 2023
Year: 2023

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.
GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants
Venue: arXiv cs.CR 2023
Year: 2023

Privacy policies of websites are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users questions about privacy policies. However, genAIs reliability is a concern due to its potential for producing inaccurate information. This study introduces GenAIPABench, a benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench includes: 1) A set of questions about privacy policies and data protection regulations, with annotated answers for various organizations and regulations; 2) Metrics to assess the accuracy, relevance, and consistency of responses; and 3) A tool for generating prompts to introduce privacy documents and varied privacy questions to test system robustness. We evaluated three leading genAI systems ChatGPT-4, Bard, and Bing AI using GenAIPABench to gauge their effectiveness as GenAIPAs. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.
A Skeleton-based Approach For Rock Crack Detection Towards A Climbing Robot Application
Venue: arXiv cs.CV 2023
Year: 2023

Conventional wheeled robots are unable to traverse scientifically interesting, but dangerous, cave environments. Multi-limbed climbing robot designs, such as ReachBot, are able to grasp irregular surface features and execute climbing motions to overcome obstacles, given suitable grasp locations. To support grasp site identification, we present a method for detecting rock cracks and edges, the SKeleton Intersection Loss (SKIL). SKIL is a loss designed for thin object segmentation that leverages the skeleton of the label. A dataset of rock face images was collected, manually annotated, and augmented with generated data. A new group of metrics, LineAcc, has been proposed for thin object segmentation such that the impact of the object width on the score is minimized. In addition, the metric is less sensitive to translation which can often lead to a score of zero when computing classical metrics such as Dice on thin objects. Our fine-tuned models outperform previous methods on similar thin object segmentation tasks such as blood vessel segmentation and show promise for integration onto a robotic system.
Certified Vision-based State Estimation for Autonomous Landing Systems using Reachability Analysis
Venue: arXiv cs.RO 2023
Year: 2023

This paper studies the problem of designing a certified vision-based state estimator for autonomous landing systems. In such a system, a neural network (NN) processes images from a camera to estimate the aircraft relative position with respect to the runway. We propose an algorithm to design such NNs with certified properties in terms of their ability to detect runways and provide accurate state estimation. At the heart of our approach is the use of geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs. We show that such geometric models enjoy mixed monotonicity properties that can be used to design state estimators with certifiable error bounds. We show the effectiveness of the proposed approach using an experimental testbed on data collected from event-based cameras.
SYSPART: Automated Temporal System Call Filtering for Binaries
Venue: arXiv cs.CR 2023
Year: 2023

Restricting the system calls available to applications reduces the attack surface of the kernel and limits the functionality available to compromised applications. Recent approaches automatically identify the system calls required by programs to block unneeded ones. For servers, they even consider different phases of execution to tighten restrictions after initialization completes. However, they require access to the source code for applications and libraries, depend on users identifying when the server transitions from initialization to serving clients, or do not account for dynamically-loaded libraries. This paper introduces SYSPART, an automatic system-call filtering system designed for binary-only server programs that addresses the above limitations. Using a novel algorithm that combines static and dynamic analysis, SYSPART identifies the serving phases of all working threads of a server. Static analysis is used to compute the system calls required during the various serving phases in a sound manner, and dynamic observations are only used to complement static resolution of dynamically-loaded libraries when necessary. We evaluated SYSPART using six popular servers on x86-64 Linux to demonstrate its effectiveness in automatically identifying serving phases, generating accurate system-call filters, and mitigating attacks. Our results show that SYSPART outperforms prior binary-only approaches and performs comparably to source-code approaches.
SIM-Sync: From Certifiably Optimal Synchronization over the 3D Similarity Group to Scene Reconstruction with Learned Depth
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents SIM-Sync, a certifiably optimal algorithm that estimates camera trajectory and 3D scene structure directly from multiview image keypoints. SIM-Sync fills the gap between pose graph optimization and bundle adjustment; the former admits efficient global optimization but requires relative pose measurements and the latter directly consumes image keypoints but is difficult to optimize globally (due to camera projective geometry). The bridge to this gap is a pretrained depth prediction network. Given a graph with nodes representing monocular images taken at unknown camera poses and edges containing pairwise image keypoint correspondences, SIM-Sync first uses a pretrained depth prediction network to lift the 2D keypoints into 3D scaled point clouds, where the scaling of the per-image point cloud is unknown due to the scale ambiguity in monocular depth prediction. SIM-Sync then seeks to synchronize jointly the unknown camera poses and scaling factors (i.e., over the 3D similarity group). The SIM-Sync formulation, despite nonconvex, allows designing an efficient certifiably optimal solver that is almost identical to the SE-Sync algorithm. We demonstrate the tightness, robustness, and practical usefulness of SIM-Sync in both simulated and real experiments. In simulation, we show (i) SIM-Sync compares favorably with SE-Sync in scale-free synchronization, and (ii) SIM-Sync can be used together with robust estimators to tolerate a high amount of outliers. In real experiments, we show (a) SIM-Sync achieves similar performance as Ceres on bundle adjustment datasets, and (b) SIM-Sync performs on par with ORB-SLAM3 on the TUM dataset with zero-shot depth prediction.
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Venue: arXiv cs.CV 2023
Year: 2023

Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination and low signal-to-noise ratio in dark environments, a multitude of problems such as lack of detail and color distortion may be magnified in the super-resolution process compared to normal-lighting environments. Consequently, conventional low-light enhancement or super-resolution methods, whether applied individually or in a cascaded manner for such problem, often encounter limitations in recovering luminance, color fidelity, and intricate details. To conquer these issues, this paper proposes a specialized dual-modulated learning framework that, for the first time, attempts to deeply dissect the nature of the low-light super-resolution task. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint as a prior for addressing uneven lighting. Expanding on this, we develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Besides, instead of deploying naive up-sampling strategies, we design the Resolution-Sensitive Merging Up-sampler (RSMU) module that brings together different sampling modalities as substrates, effectively mitigating the presence of artifacts and halos. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions, outperforming state-of-the-art methods with a notable improvement (i.e., $\uparrow$5\% in PSNR, and $\uparrow$43\% in LPIPS). Especially noteworthy is the 19-fold increase in the RMSE score, underscoring our method's exceptional generalization across different darkness levels. The code will be available online upon publication of the paper.