OpenRaiser/Intern-Atlas huggingface dataset:
An Exploratory Deep Learning Approach for Predicting Subsequent Suicidal Acts in Chinese Psychological Support Hotlines
Venue: arXiv cs.LG 2024
Year: 2024

Psychological support hotlines are an effective suicide prevention measure that typically relies on professionals using suicide risk assessment scales to predict individual risk scores. However, the accuracy of scale-based predictive methods for suicide risk assessment can vary widely depending on the expertise of the operator. This limitation underscores the need for more reliable methods, prompting this research's innovative exploration of the use of artificial intelligence to improve the accuracy and efficiency of suicide risk prediction within the context of psychological support hotlines. The study included data from 1,549 subjects from 2015-2017 in China who contacted a psychological support hotline. Each participant was followed for 12 months to identify instances of suicidal behavior. We proposed a novel multi-task learning method that uses the large-scale pre-trained model Whisper for feature extraction and fits psychological scales while predicting the risk of suicide. The proposed method yields a 2.4\% points improvement in F1-score compared to the traditional manual approach based on the psychological scales. Our model demonstrated superior performance compared to the other eight popular models. To our knowledge, this study is the first to apply deep learning to long-term speech data to predict suicide risk in China, indicating grate potential for clinical applications. The source code is publicly available at: \url{https://github.com/songchangwei/Suicide-Risk-Prediction}.
Fostering Creative Visualisation Skills Through Data-Art Exhibitions
Venue: arXiv cs.HC 2024
Year: 2024

Data-art exhibitions offer a unique and real-world setting to foster creative visualisation skills among students. They serve as real-world platform for students to display their work, bridging the gap between classroom learning and professional practice. Students must develop a technical solution, grasp the context, and produce work that is appropriate for public presentation. This scenario helps to encourage innovative thinking, engagement with the topic, and helps to enhance technical proficiency. We present our implementation of a data-art exhibition within a computing curriculum, for third-year degree-level students. Students create art-based visualisations from selected datasets and present their work in a public exhibition. We have used this initiative over the course of two academic years with different cohorts, and reflect on its impact on student learning and creativity.
On-device AI: Quantization-aware Training of Transformers in Time-Series
Venue: arXiv cs.LG 2024
Year: 2024

Artificial Intelligence (AI) models for time-series in pervasive computing keep getting larger and more complicated. The Transformer model is by far the most compelling of these AI models. However, it is difficult to obtain the desired performance when deploying such a massive model on a sensor device with limited resources. My research focuses on optimizing the Transformer model for time-series forecasting tasks. The optimized model will be deployed as hardware accelerators on embedded Field Programmable Gate Arrays (FPGAs). I will investigate the impact of applying Quantization-aware Training to the Transformer model to reduce its size and runtime memory footprint while maximizing the advantages of FPGAs.
CogVLM2: Visual Language Models for Image and Video Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new generation of visual language models for image and video understanding including CogVLM2, CogVLM2-Video and GLM-4V. As an image understanding model, CogVLM2 inherits the visual expert architecture with improved training recipes in both pre-training and post-training stages, supporting input resolution up to $1344 \times 1344$ pixels. As a video understanding model, CogVLM2-Video integrates multi-frame input with timestamps and proposes automated temporal grounding data construction. Notably, CogVLM2 family has achieved state-of-the-art results on benchmarks like MMBench, MM-Vet, TextVQA, MVBench and VCGBench. All models are open-sourced in https://github.com/THUDM/CogVLM2 and https://github.com/THUDM/GLM-4, contributing to the advancement of the field.
CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments
Venue: arXiv cs.CR 2024
Year: 2024

Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous proposed detection and defense methods, existing approaches face two fundamental limitations in large-scale industrial applications: intolerable system overheads and notorious alert fatigue. To address these challenges, we propose CanCal, a real-time and lightweight ransomware detection system. Specifically, CanCal selectively filters suspicious processes by the monitoring layers and then performs in-depth behavioral analysis to isolate ransomware activities from benign operations, minimizing alert fatigue while ensuring lightweight computational and storage overhead. The experimental results on a large-scale industrial environment~(1,761 ransomware, ~3 million events, continuous test over 5 months) indicate that CanCal is as effective as state-of-the-art techniques while enabling rapid inference within 30ms and real-time response within a maximum of 3 seconds. CanCal dramatically reduces average CPU utilization by 91.04% (from 6.7% to 0.6%) and peak CPU utilization by 76.69% (from 26.6% to 6.2%), while avoiding 76.50% (from 3,192 to 750) of the inspection efforts from security analysts. By the time of this writing, CanCal has been integrated into a commercial product and successfully deployed on 3.32 million endpoints for over a year. From March 2023 to April 2024, CanCal successfully detected and thwarted 61 ransomware attacks, demonstrating the effectiveness of CanCal in combating sophisticated ransomware threats in real-world scenarios.
Adaptive Variational Continual Learning via Task-Heuristic Modelling
Venue: arXiv cs.LG 2024
Year: 2024

Variational continual learning (VCL) is a turn-key learning algorithm that has state-of-the-art performance among the best continual learning models. In our work, we explore an extension of the generalized variational continual learning (GVCL) model, named AutoVCL, which combines task heuristics for informed learning and model optimization. We demonstrate that our model outperforms the standard GVCL with fixed hyperparameters, benefiting from the automatic adjustment of the hyperparameter based on the difficulty and similarity of the incoming task compared to the previous tasks.
Spurfies: Sparse Surface Reconstruction using Local Geometry Priors
Venue: arXiv cs.CV 2024
Year: 2024

We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruction using dense multi-view setups, typically requiring hundreds of images. However, these methods often struggle with few-view scenarios. Existing sparse-view reconstruction techniques often rely on multi-view stereo networks that need to learn joint priors for geometry and appearance from a large amount of data. In contrast, we introduce a neural point representation that disentangles geometry and appearance to train a local geometry prior using a subset of the synthetic ShapeNet dataset only. During inference, we utilize this surface prior as additional constraint for surface and appearance reconstruction from sparse input views via differentiable volume rendering, restricting the space of possible solutions. We validate the effectiveness of our method on the DTU dataset and demonstrate that it outperforms previous state of the art by 35% in surface quality while achieving competitive novel view synthesis quality. Moreover, in contrast to previous works, our method can be applied to larger, unbounded scenes, such as Mip-NeRF 360.
TINA: Acceleration of Non-NN Signal Processing Algorithms Using NN Accelerators
Venue: arXiv cs.PF 2024
Year: 2024

This paper introduces TINA, a novel framework for implementing non Neural Network (NN) signal processing algorithms on NN accelerators such as GPUs, TPUs or FPGAs. The key to this approach is the concept of mapping mathematical and logic functions as a series of convolutional and fully connected layers. By mapping functions into such a small substack of NN layers, it becomes possible to execute non-NN algorithms on NN hardware (HW) accelerators efficiently, as well as to ensure the portability of TINA implementations to any platform that supports such NN accelerators. Results show that TINA is highly competitive compared to alternative frameworks, specifically for complex functions with iterations. For a Polyphase Filter Bank use case TINA shows GPU speedups of up to 80x vs a CPU baseline with NumPy compared to 8x speedup achieved by alternative frameworks. The framework is open source and publicly available at https://github.com/ChristiaanBoe/TINA.
Examination of Code generated by Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Large language models (LLMs), such as ChatGPT and Copilot, are transforming software development by automating code generation and, arguably, enable rapid prototyping, support education, and boost productivity. Therefore, correctness and quality of the generated code should be on par with manually written code. To assess the current state of LLMs in generating correct code of high quality, we conducted controlled experiments with ChatGPT and Copilot: we let the LLMs generate simple algorithms in Java and Python along with the corresponding unit tests and assessed the correctness and the quality (coverage) of the generated (test) codes. We observed significant differences between the LLMs, between the languages, between algorithm and test codes, and over time. The present paper reports these results together with the experimental methods allowing repeated and comparable assessments for more algorithms, languages, and LLMs over time.
Hyperdimensional Vector Tsetlin Machines with Applications to Sequence Learning and Generation
Venue: arXiv cs.LG 2024
Year: 2024

We construct a two-layered model for learning and generating sequential data that is both computationally fast and competitive with vanilla Tsetlin machines, adding numerous advantages. Through the use of hyperdimensional vector computing (HVC) algebras and Tsetlin machine clause structures, we demonstrate that the combination of both inherits the generality of data encoding and decoding of HVC with the fast interpretable nature of Tsetlin machines to yield a powerful machine learning model. We apply the approach in two areas, namely in forecasting, generating new sequences, and classification. For the latter, we derive results for the entire UCR Time Series Archive and compare with the standard benchmarks to see how well the method competes in time series classification.
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Venue: arXiv cs.CV 2024
Year: 2024

Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. This task is challenging due to the need for robust models that can generalize to a diverse set of driver behaviors without requiring extensive annotated datasets. In this paper, we propose KiD3, a novel method for distracted driver detection (DDD) by infusing auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver's pose. Specifically, we construct a unified framework that integrates the scene graphs, and driver pose information with the visual cues in video frames to create a holistic representation of the driver's actions.Our results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating such auxiliary knowledge with visual information.
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification
Venue: arXiv cs.CV 2024
Year: 2024

Extracting robust feature representation is critical for object re-identification to accurately identify objects across non-overlapping cameras. Although having a strong representation ability, the Vision Transformer (ViT) tends to overfit on most distinct regions of training data, limiting its generalizability and attention to holistic object features. Meanwhile, due to the structural difference between CNN and ViT, fine-grained strategies that effectively address this issue in CNN do not continue to be successful in ViT. To address this issue, by observing the latent diverse representation hidden behind the multi-head attention, we present PartFormer, an innovative adaptation of ViT designed to overcome the granularity limitations in object Re-ID tasks. The PartFormer integrates a Head Disentangling Block (HDB) that awakens the diverse representation of multi-head self-attention without the typical loss of feature richness induced by concatenation and FFN layers post-attention. To avoid the homogenization of attention heads and promote robust part-based feature learning, two head diversity constraints are imposed: attention diversity constraint and correlation diversity constraint. These constraints enable the model to exploit diverse and discriminative feature representations from different attention heads. Comprehensive experiments on various object Re-ID benchmarks demonstrate the superiority of the PartFormer. Specifically, our framework significantly outperforms state-of-the-art by 2.4\% mAP scores on the most challenging MSMT17 dataset.
H-SGANet: Hybrid Sparse Graph Attention Network for Deformable Medical Image Registration
Venue: arXiv cs.CV 2024
Year: 2024

The integration of Convolutional Neural Network (ConvNet) and Transformer has emerged as a strong candidate for image registration, leveraging the strengths of both models and a large parameter space. However, this hybrid model, treating brain MRI volumes as grid or sequence structures, faces challenges in accurately representing anatomical connectivity, diverse brain regions, and vital connections contributing to the brain's internal architecture. Concerns also arise regarding the computational expense and GPU memory usage associated with this model. To tackle these issues, a lightweight hybrid sparse graph attention network (H-SGANet) has been developed. This network incorporates a central mechanism, Sparse Graph Attention (SGA), based on a Vision Graph Neural Network (ViG) with predetermined anatomical connections. The SGA module expands the model's receptive field and seamlessly integrates into the network. To further amplify the advantages of the hybrid network, the Separable Self-Attention (SSA) is employed as an enhanced token mixer, integrated with depth-wise convolution to constitute SSAFormer. This strategic integration is designed to more effectively extract long-range dependencies. As a hybrid ConvNet-ViG-Transformer model, H-SGANet offers threefold benefits for volumetric medical image registration. It optimizes fixed and moving images concurrently through a hybrid feature fusion layer and an end-to-end learning framework. Compared to VoxelMorph, a model with a similar parameter count, H-SGANet demonstrates significant performance enhancements of 3.5% and 1.5% in Dice score on the OASIS dataset and LPBA40 dataset, respectively.
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
Venue: arXiv cs.AI 2024
Year: 2024

Recent advances in language models have achieved significant progress. GPT-4o, as a new milestone, has enabled real-time conversations with humans, demonstrating near-human natural fluency. Such human-computer interaction necessitates models with the capability to perform reasoning directly with the audio modality and generate output in streaming. However, this remains beyond the reach of current academic models, as they typically depend on extra TTS systems for speech synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni, an audio-based end-to-end conversational model, capable of real-time speech interaction. To achieve this capability, we propose a text-instructed speech generation method, along with batch-parallel strategies during inference to further boost the performance. Our method also helps to retain the original model's language capabilities with minimal degradation, enabling other works to establish real-time interaction capabilities. We call this training method "Any Model Can Talk". We also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for speech output. To our best knowledge, Mini-Omni is the first fully end-to-end, open-source model for real-time speech interaction, offering valuable potential for future research.
A Score-Based Density Formula, with Applications in Diffusion Generative Models
Venue: arXiv cs.LG 2024
Year: 2024

Score-based generative models (SGMs) have revolutionized the field of generative modeling, achieving unprecedented success in generating realistic and diverse content. Despite empirical advances, the theoretical basis for why optimizing the evidence lower bound (ELBO) on the log-likelihood is effective for training diffusion generative models, such as DDPMs, remains largely unexplored. In this paper, we address this question by establishing a density formula for a continuous-time diffusion process, which can be viewed as the continuous-time limit of the forward process in an SGM. This formula reveals the connection between the target density and the score function associated with each step of the forward process. Building on this, we demonstrate that the minimizer of the optimization objective for training DDPMs nearly coincides with that of the true objective, providing a theoretical foundation for optimizing DDPMs using the ELBO. Furthermore, we offer new insights into the role of score-matching regularization in training GANs, the use of ELBO in diffusion classifiers, and the recently proposed diffusion loss.
Secure Integration of 5G in Industrial Networks: State of the Art, Challenges and Opportunities
Venue: arXiv cs.CR 2024
Year: 2024

The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.
Measuring Transparency in Intelligent Robots
Venue: arXiv cs.RO 2024
Year: 2024

As robots become increasingly integrated into our daily lives, the need to make them transparent has never been more critical. Yet, despite its importance in human-robot interaction, a standardized measure of robot transparency has been missing until now. This paper addresses this gap by presenting the first comprehensive scale to measure perceived transparency in robotic systems, available in English, German, and Italian languages. Our approach conceptualizes transparency as a multidimensional construct, encompassing explainability, legibility, predictability, and meta-understanding. The proposed scale was a product of a rigorous three-stage process involving 1,223 participants. Firstly, we generated the items of our scale, secondly, we conducted an exploratory factor analysis, and thirdly, a confirmatory factor analysis served to validate the factor structure of the newly developed TOROS scale. The final scale encompasses 26 items and comprises three factors: Illegibility, Explainability, and Predictability. TOROS demonstrates high cross-linguistic reliability, inter-factor correlation, model fit, internal consistency, and convergent validity across the three cross-national samples. This empirically validated tool enables the assessment of robot transparency and contributes to the theoretical understanding of this complex construct. By offering a standardized measure, we facilitate consistent and comparable research in human-robot interaction in which TOROS can serve as a benchmark.
CalTag: Robust calibration of mmWave Radar and LiDAR using backscatter tags
Venue: arXiv cs.RO 2024
Year: 2024

The rise of automation in robotics necessitates the use of high-quality perception systems, often through the use of multiple sensors. A crucial aspect of a successfully deployed multi-sensor system is the calibration with a known object typically named fiducial. In this work, we propose a novel fiducial system for millimeter wave radars, termed as CalTag. CalTag addresses the limitations of traditional corner reflector-based calibration methods in extremely cluttered environments. CalTag leverages millimeter wave backscatter technology to achieve more reliable calibration than corner reflectors, enhancing the overall performance of multi-sensor perception systems. We compare the performance in several real-world environments and show the improvement achieved by using CalTag as the radar fiducial over a corner reflector.
GSTAM: Efficient Graph Distillation with Structural Attention-Matching
Venue: arXiv cs.LG 2024
Year: 2024

Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classification, involve computationally intensive processes, and fail to capture the true distribution of the full graph dataset. To address these issues, we introduce Graph Distillation with Structural Attention Matching (GSTAM), a novel method for condensing graph classification datasets. GSTAM leverages the attention maps of GNNs to distill structural information from the original dataset into synthetic graphs. The structural attention-matching mechanism exploits the areas of the input graph that GNNs prioritize for classification, effectively distilling such information into the synthetic graphs and improving overall distillation performance. Comprehensive experiments demonstrate GSTAM's superiority over existing methods, achieving 0.45% to 6.5% better performance in extreme condensation ratios, highlighting its potential use in advancing distillation for graph classification tasks (Code available at https://github.com/arashrasti96/GSTAM).