Reviewerly/exHarmony huggingface dataset:
A Comparison of Feature Selection Techniques for First-day Mortality Prediction in the ICU

First-day mortality prediction is a critical task in the Intensive Care Unit (ICU), as it can help clinicians identify which patients are at the highest risk for death and thus may need more intensive care. Many heuristic-based metrics for first-day mortality exist. However many signals exist in the Electronic Health Record (EHR) that are not used in these metrics. For the implementation of these metrics, it is important to keep the number of required signals as small as possible so that the user can quickly receive an assessment of their patient. In this paper, we leverage techniques from classical machine learning to compare sets of signals from a patients record (like demographic information, lab values, and vital sign measurements) to find the minimum feature set that best informs first day mortality. We compare several feature selection techniques to identify various feature sets with differing number of features. We found that Elastic Net was the overall best performing method and was able to reach the same performance as the current state of the art with less than half the features. This suggests that an optimal feature set is clinically meaningful.
A Spatial Calibrated and Colour Corrected Light Field Outdoor Video Dataset from a $5 \times 5$ Dense Camera Array

In this paper, a new and calibrated light field (LF) video dataset is introduced, which focuses on outdoor scenes and objects. Each video stream is 10 seconds long and it is captured with a dense camera array that consists of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times 5$</tex> camera modules in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1640\times 1232$</tex> resolution at 40 frames per second. As multiple cameras in an array setup may suffer from various conditions of camera settings, lens structure, and lighting variations, the resulting images can be negatively affected by geometric distortion and colour difference. To address that, a unified calibration method involving both spatial calibration and colour correction is employed to correct inconsistences and achieve a better image quality with reduced image distortion. This video dataset would be suitable for further research and investigation of a variety LF applications, such as autonomous driving and immersive media.
Live Demonstration: Layer-wise Configurable CNN Accelerator with High PE Utilization

We demonstrate two end-to-end frameworks, ShortcutFusion [1] and ShortcutFusion++ [2], that effectively map many well-known deep neural networks, such as YOLO-v3, MobileNet-v2, EfficientNet-B0, and Resnet-50, to a generic CNN accelerator on FPGA. The experimental results show that ShortcutFusion++ achieves a processing element utilization of 80.95% for the well-known object detector YOLO-v3.
EACNN: Efficient CNN Accelerator Utilizing Linear Approximation and Computation Reuse

This paper proposes an efficient hardware accelerator named EACNN for use in Convolution Neural Networks. EACNN is an efficient CNN architecture that is based on co-optimization of algorithms and hardware. The proposed approach is based on linear approximation of the weights for pre-trained networks with low loss of accuracy. Furthermore, a weight substitution and remapping technique adopts linear approximation coefficients to replace CNN weights. That leads to a repetition of the weight values across different kernels and enables the reuse of CNN computations for various output feature maps. The input activations corresponding to the same linear co-efficient can be multiplied and accumulated first and then reused to generate multiple output feature maps. This computational reuse method reduces the number of multiplication and addition operations and memory accesses, which is efficiently supported by a dedicated element in the proposed EACNN. Experimental results on CIFAR 10 and CIFAR 100 datasets show that the proposed method eliminates around 61% of the multiplications in the network without significant loss of accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(&lt; 3\%)$</tex> . As a demonstration, a hardware accelerator based on EACNN was implemented on Xilinx FPGA Artix 7 and achieved a 50% reduction in the FPGA hardware resources.
Apples and Oranges? Assessing Image Quality over Content Recognition

Image recognition and quality assessment are two important viewing tasks, while potentially following different vis-ual mechanisms. This paper investigates if the two tasks can be performed in a multitask learning manner. A sequential spatial-channel attention module is proposed to simulate the visual attention and contrast sensitivity mechanisms that are crucial for con-tent recognition and quality assessment. Spatial attention is shared between content recognition and quality assessment, while channel attention is solely for quality assessment. Such attention module is integrated into Transformer to build a uniform model for the two viewing tasks. The experimental results have demonstrated that the proposed uniform model can achieve promising performance for both quality assessment and content recognition tasks.
GlobalDepth: Global-Aware Attention Model for Unsupervised Monocular Depth Estimation

Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.
Bayesian Contrastive Learning with Manifold Regularization for Self-Supervised Skeleton Based Action Recognition

In this paper, we address skeleton-based action recognition under the self-supervised setting. We propose a novel framework Bayesian Contrastive Learning with Manifold Regularization (BCLR). In Bayesian contrastive learning, we employ Monte Carlo Dropout sampling on the adjacency matrix of the skeleton data to obtain positive/negative samples for model robustness. A novel entropy-based memory bank updating strategy is further proposed to take full advantage of hard negative samples for better separability. The feature manifold regularization, including projection-based data reconstruction and similarity-based feature decoupling, on the other hand, is designed to extract comprehensive information to avoid overfitting and increase feature diversity to prevent a collapse of the model. With Bayesian contrastive learning and feature manifold regularization, our model learns stronger and more discriminative features. Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method achieves remarkable action recognition performance.
Acceleration of Decision-Tree Ensemble Models on the IBM Telum Processor

This paper presents a tensor-based algorithm that leverages a hardware accelerator for inferencing decision-tree-based machine learning models. The algorithm has been integrated in a public software library and is demonstrated on an IBM z16 server, using the Telum processor with the Integrated Accelerator for AI. We describe the architecture and implementation of the algorithm and present experimental results that demonstrate its superior runtime performance compared with popular CPU-based machine learning inference implementations.
GERALT: Real-time Detection of Evasion Attacks in Deep Learning Systems

Evasion attacks constitute an important class of security vulnerabilities in deep learning systems. In this attack, the adversary can coerce the victim DNN into targeted misclassification with slightly modified input data. While detection and defense methods have been proposed for evasion attacks, they incur high overhead and cannot be employed for real time detection on resource-constrained devices. In this paper, we propose an infrastructure, Geralt, to optimize evasion attack detection for real-time execution. Geralt includes a software component to optimize detection methods enabling the use of a smaller detection network, and hardware architecture to accelerate inter-network inference with intermediate data reuse techniques. Our evaluation demonstrates that Geralt achieves more than 3x improvement in performance over standard accelerators like Eyeriss without affecting detection and classification accuracy.
An Efficient Frequency Domain Vision Pipeline From RAW Images to Backend Tasks

Though high resolution benefits computer vision performance, they are not commonly used in convolutional neural network (CNN)-based vision algorithms due to the limitation of memory and computation resource. Learning in the frequency domain makes high resolution images directly acceptable by CNNs, but the computation, time and energy overhead for pre-processing, including image signal processing (ISP) and domain transformation, can be large. This paper explores different image processing and domain transformation operations and proposes an efficient end-to-end frequency domain learning pipeline from RAW images to vision tasks. In particular, we simplify the pre-processing part by skipping the entire ISP pipeline and replacing the Discrete Cosine Transform (DCT) with a multiplication-free approximated one. Experimental results show that the final vision performance of the proposed pipeline is very close to that of the conventional pipeline, while significant amount of redundant operations can be saved.
Live Demonstration: An Aliasing-Free Hybrid Digital-Analog Music Synthesizer Prototype

Analog subtractive sound synthesis is widely used in music production since the 1960s, with popular synthesizers from Moog Music, Sequential, and ARP Instruments. While analog synthesizers are considered superior in terms of sound quality compared to their digital counterparts, analog circuitry typically suffers from temperature instabilities, component variations, and lack of flexibility. Digital music synthesizers can avoid all of these drawbacks, but it is challenging to design digital oscillators and filters that do not cause aliasing artifacts and sound as impressive as their analog counterparts. In this live demonstration, we show a polyphonic hybrid digital-analog music synthesizer prototype in which the oscillator signals are aliasing-free and generated by an FPGA, whereas the filters and amplifiers are implemented with analog circuits, thus combining the best of both worlds.
Feature-Embedding Triplet Networks with a Separately Constrained Loss Function

Feature-embedding triplet networks (TNs) with three symmetric subchannels are very promising for similarity-measuring applications. This paper proposes a novel separately constrained triple loss (SCTL) function that applies to TNs for classification. Through minimizing the intra-class distance and maximizing the inter-class distance, SCTL eliminates possible false solutions and provides insight into the dependency of training based on these two terms. Based on this dependency, the strategy of selecting hyperparameters in SCTL is also analyzed to further improve performance. The effectiveness of the proposed SCTL is evaluated based on TNs with multi-layer perceptrons; the results show that compared to all existing loss functions, the use of SCTL offers the best classification accuracy for the TNs, while incurring in negligible hardware overhead (e.g., only a 0.0002% area overhead of the subnetworks).
An Novel Interpretable Fine-grained Image Classification Model Based on Improved Neural Prototype Tree

The fine-grained image classification task is a major task in computer vision. Although many deep learning inter-pretable models have been proposed for this task, the accuracy and interpretability of these models need to be improved. We propose an interpretable fine-grained image classification model based on an improved neural prototype tree. In our model, we design the new multi-grained feature extraction network with three new backbone networks to extract features of fine-grained and multi-grained images more effectively. Furthermore, we design a new background prototype removing mechanism in the soft neural binary decision tree layer to optimize prototype path decision. Afterwards, we design a new loss function with both a leaf node loss function and a fully connected layer loss function to improve the generalization ability. Finally, we evaluate our model on three public datasets CUB-200-2011, FGVC-Aircraft, and Chest X-ray to compare with other baseline models.
Efficient Learned Video Compression via Bidirectional Temporal Information Exploration

In recent years, learned video compression methods have improved substantially. However, most existing algorithms focus on exploring short-term temporal information, thus constraining the compression capability. In this paper, to further boost video compression performance, we exploit both long-and short-range temporal information and consider bidirectional temporal information. For long- and short-range temporal information exploration, we adopt temporal prior and progressive guided motion compensation. Specifically, with the continuously updating strategy, the temporal prior can provide rich mutual information between the overall prior and the current frame, facilitating Gaussian parameter prediction in the entropy model. Besides, the progressive guided motion compensation utilizes flow-to-kernel and scale-by-scale stable guiding strategy, thus achieving robust and effective inter coding. Furthermore, existing low-latency-oriented methods often suffer from strong error propagation, so we extend the framework with a bidirectional prediction scheme and propose the bidirectional temporal prior. Extensive experimental results demonstrate that our method can obtain competitive performance compared to the state-of-the-art learned video compression approaches and the standard reference software HM-16.22.
Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content

This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.
Content Adaptive Checkerboard Context Model for Learned Image Compression

Learned image compression methods are becoming popular and have achieved excellent performance, of which joint context and hyperprior architectures are the mainstream. In order to avoid the time-consuming serial decoding pipeline introduced by the autoregressive context model, the checkerboard context model (CCM) is proposed to implement fast two-pass coding. However, CCM sets half of the latents as anchors to extract spatial context for the other non-anchors, which is rough and redundant. We propose a more precise and flexible content adaptive checkerboard context model to decrease the numbers and bit consumption of anchors. By introducing pseudo-anchors for simple regions in latents, our method can preserve the capability of fast two-pass coding and outperform CCM in Rate-Distortion performance on several baseline models with negligible computational overhead.
DP-VoicePub: Differential Privacy-based Voice Publication

The widespread use of voice assistants has generated a vast amount of user voice data, promoting technologies such as speech recognition but also bringing privacy and security risks. Voice data contains the identity information of the speaker, and a little voice data is enough for a linking attack or other malicious attacks. We use differential privacy to formally define user privacy in speech publishing and propose a differential privacy-compliant algorithm to change the user's x-vector. The user can customize our scheme to balance privacy and voice authenticity, which is significant for exploring user data privacy protection. We also evaluate our scheme on real-world datasets using a variety of existing speaker anonymization evaluation metrics. The results show that our scheme is universal and can effectively balance the privacy and authenticity of anonymized speech under different evaluation metrics.
METFormer: A Motion Enhanced Transformer for Multiple Object Tracking

Multiple object tracking (MOT) is an important task in computer vision, especially video analytics. Transformer-based methods are emerging approaches using both tracking and detection queries. However, motion modeling in existing transformer-based methods lacks effective association capability. Thus, this paper introduces a new METFormer model, a Motion Enhanced TransFormer-based tracker with a novel global-local motion context learning technique to mitigate the lack of motion information in existing transformer-based methods. The global-local motion context learning technique first centers on difference-guided global motion learning to obtain temporal information from adjacent frames. Based on global motion, we leverage context-aware local object motion modelling to study motion patterns and enhance the feature representation for individual objects. Experimental results on the benchmark MOT17 dataset show that our proposed method can surpass the state-of-the-art Trackformer [21] by 1.8% on IDF1 and 21.7% on ID Switches under public detection settings.
LOCS: LOw-Latency and ConStant-Timing Implementation of Fixed-Weight Sampler for HQC

Post-quantum cryptography (PQC) has drawn significant attention from various communities recently and one of the recent advances is the hardware acceleration of PQC algorithms. While Hamming Quasi-Cyclic (HQC) is one of the recently announced National Institute of Standards and Technology (NIST) fourth-round PQC standardization candidates, very few related hardware implementation works have been reported, particularly lacking solid works on important components such as the sampler. As a fixed-weight sparse vector sampler with constant-time operation is critical to the hardware HQC accelerator, in this paper, we present a novel hardware-implemented LOw-latency and ConStant-timing fixed-weight sampler (LOCS). In total, we have proposed three stages of efforts. First of all, a new algorithm for efficient realization of the fixed-weight sparse vector generation based on Fisher-Yates shuffle algorithm is proposed. Then, we have innovatively designed the algorithm into a new hardware sampler: LOCS. Finally, we have conducted a thorough comparison to showcase the efficiency of the proposed sampler, e.g., the proposed LOCS involves 66.7% less latency time than the state-of-the-art design <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(n=17,669)$</tex> while remaining constant-time operation. To the authors' best knowledge, this is the first hardware-implemented pure constant-time (no failure probability) fixed-weight sampler for HQC.
CompoundEye: A 0.24-4.17 TOPS Scalable Multi-Node DNN Processor for Image Recognition

This paper proposes a scalable DNN processor that can be flexibly reconfigured to maximize inference efficiency on a wide range of DNN models. The processor consists of 18 computing nodes with various precision modes support. To improve the computation throughput, we propose a sub-image parallelization strategy, where the original input image is divided into multiple sub-images and computed on multiple nodes in parallel. In addition, the cross-layer pipeline is implemented to improve resource utilization. The proposed processor is implemented in 28nm CMOS technology and achieves a peak performance of 4.17 TOPS and an energy efficiency of 2.08 TOPS/W.
AEM: Facilitating Cross-Version Exploitability Assessment of Linux Kernel Vulnerabilities

This paper studies the problem of cross-version exploitability assessment for Linux kernels. Specifically, given an exploit demonstrating the exploitability of a vulnerability on a specific kernel version, we aim to understand the exploitability of the same vulnerability on other kernel versions. To tackle cross-version exploitability assessment, automated exploit generation (AEG), a recently popular topic, is the only existing, applicable solution. However, AEG is not well-suited due to its template-driven nature and ignorance of the capabilities offered by the available exploit.In this work, we introduce a new method, automated exploit migration (AEM), to facilitate cross-version exploitability assessment for Linux kernels. The key insight of AEM is the observation that the strategy adopted by the exploit is often applicable to other exploitable kernel versions. Technically, we consider the kernel version where the exploit works as a reference and adjust the exploit to force the other kernel versions to align with the reference. This way, we can reproduce the exploiting behaviors on the other versions. To reduce the cost and increase the feasibility, we strategically identify execution points that truly affect the exploitation and only enforce alignment at those points. We have designed and implemented a prototype of AEM. In our evaluation of 67 cases where exploit migration is needed, our prototype successfully migrates the exploit for 56 cases, producing a success rate of 83.5%.