OpenRaiser/Intern-Atlas huggingface dataset:
An Analysis of Personalized Speech Recognition System Development for the Deaf and Hard-of-Hearing
Venue: arXiv cs.SD 2023
Year: 2023

Deaf or hard-of-hearing (DHH) speakers typically have atypical speech caused by deafness. With the growing support of speech-based devices and software applications, more work needs to be done to make these devices inclusive to everyone. To do so, we analyze the use of openly-available automatic speech recognition (ASR) tools with a DHH Japanese speaker dataset. As these out-of-the-box ASR models typically do not perform well on DHH speech, we provide a thorough analysis of creating personalized ASR systems. We collected a large DHH speaker dataset of four speakers totaling around 28.05 hours and thoroughly analyzed the performance of different training frameworks by varying the training data sizes. Our findings show that 1000 utterances (or 1-2 hours) from a target speaker can already significantly improve the model performance with minimal amount of work needed, thus we recommend researchers to collect at least 1000 utterances to make an efficient personalized ASR system. In cases where 1000 utterances is difficult to collect, we also discover significant improvements in using previously proposed data augmentation techniques such as intermediate fine-tuning when only 200 utterances are available.
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
Venue: arXiv cs.CL 2023
Year: 2023

While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness. This is especially manifested as significant degradation in performance when faced with out-of-distribution data. Recent solutions that rely on counterfactually augmented datasets show promising results, but they are inherently limited because of the lack of access to explicit causal structure. In this paper, we present an alternative approach that relies on non-counterfactual data augmentation. Our proposal instead relies on using noisy, cost-efficient data augmentations that preserve semantics associated with the target aspect. Our approach then relies on modelling invariances between different versions of the data to improve robustness. A comprehensive suite of experiments shows that our proposal significantly improves upon strong pre-trained baselines on both standard and robustness-specific datasets. Our approach further establishes a new state-of-the-art on the ABSA robustness benchmark and transfers well across domains.
Thinking Like an Annotator: Generation of Dataset Labeling Instructions
Venue: arXiv cs.CV 2023
Year: 2023

Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation, composition, collection process, etc..."). However, almost no one has suggested the release of the detailed definitions and visual category examples provided to annotators - information critical to understanding the structure of the annotations present in each dataset. These labels are at the heart of public datasets, yet few datasets include the instructions that were used to generate them. We introduce a new task, Labeling Instruction Generation, to address missing publicly available labeling instructions. In Labeling Instruction Generation, we take a reasonably annotated dataset and: 1) generate a set of examples that are visually representative of each category in the dataset; 2) provide a text label that corresponds to each of the examples. We introduce a framework that requires no model training to solve this task and includes a newly created rapid retrieval system that leverages a large, pre-trained vision and language model. This framework acts as a proxy to human annotators that can help to both generate a final labeling instruction set and evaluate its quality. Our framework generates multiple diverse visual and text representations of dataset categories. The optimized instruction set outperforms our strongest baseline across 5 folds by 7.06 mAP for NuImages and 12.9 mAP for COCO.
Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs
Venue: arXiv cs.CV 2023
Year: 2023

Yaw estimation of boats from the viewpoint of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) or boats is a crucial task in various applications such as 3D scene rendering, trajectory prediction, and navigation. However, the lack of literature on yaw estimation of objects from the viewpoint of UAVs has motivated us to address this domain. In this paper, we propose a method based on HyperPosePDF for predicting the orientation of boats in the 6D space. For that, we use existing datasets, such as PASCAL3D+ and our own datasets, SeaDronesSee-3D and BOArienT, which we annotated manually. We extend HyperPosePDF to work in video-based scenarios, such that it yields robust orientation predictions across time. Naively applying HyperPosePDF on video data yields single-point predictions, resulting in far-off predictions and often incorrect symmetric orientations due to unseen or visually different data. To alleviate this issue, we propose aggregating the probability distributions of pose predictions, resulting in significantly improved performance, as shown in our experimental evaluation. Our proposed method could significantly benefit downstream tasks in marine robotics.
On the Uses of Large Language Models to Interpret Ambiguous Cyberattack Descriptions
Venue: arXiv cs.AI 2023
Year: 2023

The volume, variety, and velocity of change in vulnerabilities and exploits have made incident threat analysis challenging with human expertise and experience along. Tactics, Techniques, and Procedures (TTPs) are to describe how and why attackers exploit vulnerabilities. However, a TTP description written by one security professional can be interpreted very differently by another, leading to confusion in cybersecurity operations or even business, policy, and legal decisions. Meanwhile, advancements in AI have led to the increasing use of Natural Language Processing (NLP) algorithms to assist the various tasks in cyber operations. With the rise of Large Language Models (LLMs), NLP tasks have significantly improved because of the LLM's semantic understanding and scalability. This leads us to question how well LLMs can interpret TTPs or general cyberattack descriptions to inform analysts of the intended purposes of cyberattacks. We propose to analyze and compare the direct use of LLMs (e.g., GPT-3.5) versus supervised fine-tuning (SFT) of small-scale-LLMs (e.g., BERT) to study their capabilities in predicting ATT&CK tactics. Our results reveal that the small-scale-LLMs with SFT provide a more focused and clearer differentiation between the ATT&CK tactics (if such differentiation exists). On the other hand, direct use of LLMs offer a broader interpretation of cyberattack techniques. When treating more general cases, despite the power of LLMs, inherent ambiguity exists and limits their predictive power. We then summarize the challenges and recommend research directions on LLMs to treat the inherent ambiguity of TTP descriptions used in various cyber operations.
Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation using Reinforcement Learning
Venue: arXiv cs.RO 2023
Year: 2023

Indirect simultaneous positioning (ISP), where internal tissue points are placed at desired locations indirectly through the manipulation of boundary points, is a type of subtask frequently performed in robotic surgeries. Although challenging due to complex tissue dynamics, automating the task can potentially reduce the workload of surgeons. This paper presents a sim-to-real framework for learning to automate the task without interacting with a real environment, and for planning preoperatively to find the grasping points that minimize local tissue deformation. A control policy is learned using deep reinforcement learning (DRL) in the FEM-based simulation environment and transferred to real-world situation. Grasping points are planned in the simulator by utilizing the trained policy using Bayesian optimization (BO). Inconsistent simulation performance is overcome by formulating the problem as a state augmented Markov decision process (MDP). Experimental results show that the learned policy places the internal tissue points accurately, and that the planned grasping points yield small tissue deformation among the trials. The proposed learning and planning scheme is able to automate internal tissue point manipulation in surgeries and has the potential to be generalized to complex surgical scenarios.
A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification
Venue: arXiv cs.CV 2023
Year: 2023

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method's effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.
Masked conditional variational autoencoders for chromosome straightening
Venue: arXiv cs.CV 2023
Year: 2023

Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists from analyzing chromosome types. To address this issue, we propose a framework for chromosome straightening, which comprises a preliminary processing algorithm and a generative model called masked conditional variational autoencoders (MC-VAE). The processing method utilizes patch rearrangement to address the difficulty in erasing low degrees of curvature, providing reasonable preliminary results for the MC-VAE. The MC-VAE further straightens the results by leveraging chromosome patches conditioned on their curvatures to learn the mapping between banding patterns and conditions. During model training, we apply a masking strategy with a high masking ratio to train the MC-VAE with eliminated redundancy. This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results. Extensive experiments on three public datasets with two stain styles show that our framework surpasses the performance of state-of-the-art methods in retaining banding patterns and structure details. Compared to using real-world bent chromosomes, the use of high-quality straightened chromosomes generated by our proposed method can improve the performance of various deep learning models for chromosome classification by a large margin. Such a straightening approach has the potential to be combined with other karyotyping systems to assist cytogeneticists in chromosome analysis.
Fine-grained Modulation for Zigbee Codeword Translation
Venue: arXiv cs.NI 2023
Year: 2023

In Zigbee backscatter systems, tags piggyback information by adding phase shifts to the RF carriers. The instantaneous-phase shift (IPS) modulation adds phase shifts by toggling between discrete phases, which is easy to realize and is widely used in previous systems. However, as the spectrum efficiency of IPS is poor, it is not suitable for large networks. Thus, frequency-phase shift (FPS) modulation was proposed to make up this drawback. It adds continuous phase shifts to the non-productive carriers by toggling between square waves of different frequencies and has higher spectrum efficiency. In this paper, we realized IPS modulation and FPS modulation on Zigbee single-tone signals, respectively. In addition, we creatively proposed to apply FPS modulation to codeword translation. We prototype the system using a microchip transmitter, an off-the-shelf FPGA, and a commodity Zigbee receiver. Through extensive experiments, we proved that FPS modulated Zigbee transmissions have a bandwidth of 1.8 MHz, Which is 2x lower than that of IPS. In addtion, we conducted simulation experiments in MATLAB and demonstrated that FPS modulation can be used in Zigbee codeword translation.
YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction
Venue: arXiv cs.NI 2023
Year: 2023

Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this paper, we propose a semantic communication framework based on You Only Look Once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the artificial intelligence-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new Efficient Layer Aggregation Network-HorNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services.
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity
Venue: arXiv cs.CV 2023
Year: 2023

The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the "Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously released openly accessible dataset, one of the first datasets containing mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. In order to reduce the impact of racial bias, we utilize a color space data augmentation method to increase skin color variability during training. Additionally, by leveraging transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images, we achieved the best overall accuracy of $83.59\pm2.11\%$. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient.
Switch-BERT: Learning to Model Multimodal Interactions by Switching Attention and Input
Venue: arXiv cs.CV 2023
Year: 2023

The ability to model intra-modal and inter-modal interactions is fundamental in multimodal machine learning. The current state-of-the-art models usually adopt deep learning models with fixed structures. They can achieve exceptional performances on specific tasks, but face a particularly challenging problem of modality mismatch because of diversity of input modalities and their fixed structures. In this paper, we present \textbf{Switch-BERT} for joint vision and language representation learning to address this problem. Switch-BERT extends BERT architecture by introducing learnable layer-wise and cross-layer interactions. It learns to optimize attention from a set of attention modes representing these interactions. One specific property of the model is that it learns to attend outputs from various depths, therefore mitigates the modality mismatch problem. We present extensive experiments on visual question answering, image-text retrieval and referring expression comprehension experiments. Results confirm that, whereas alternative architectures including ViLBERT and UNITER may excel in particular tasks, Switch-BERT can consistently achieve better or comparable performances than the current state-of-the-art models in these tasks. Ablation studies indicate that the proposed model achieves superior performances due to its ability in learning task-specific multimodal interactions.
PrimaDNN': A Characteristics-aware DNN Customization for Singing Technique Detection
Venue: arXiv cs.SD 2023
Year: 2023

Professional vocalists modulate their voice timbre or pitch to make their vocal performance more expressive. Such fluctuations are called singing techniques. Automatic detection of singing techniques from audio tracks can be beneficial to understand how each singer expresses the performance, yet it can also be difficult due to the wide variety of the singing techniques. A deep neural network (DNN) model can handle such variety; however, there might be a possibility that considering the characteristics of the data improves the performance of singing technique detection. In this paper, we propose PrimaDNN, a CRNN model with a characteristics-oriented improvement. The features of the model are: 1) input feature representation based on auxiliary pitch information and multi-resolution mel spectrograms, 2) Convolution module based on the Squeeze-and-excitation (SENet) and the Instance normalization. In the results of J-POP singing technique detection, PrimaDNN achieved the best results of 44.9% at the overall macro-F measure, compared to conventional works. We also found that the contribution of each component varies depending on the type of singing technique.
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc
Venue: arXiv cs.CV 2023
Year: 2023

The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.
Feature Adversarial Distillation for Point Cloud Classification
Venue: arXiv cs.CV 2023
Year: 2023

Due to the point cloud's irregular and unordered geometry structure, conventional knowledge distillation technology lost a lot of information when directly used on point cloud tasks. In this paper, we propose Feature Adversarial Distillation (FAD) method, a generic adversarial loss function in point cloud distillation, to reduce loss during knowledge transfer. In the feature extraction stage, the features extracted by the teacher are used as the discriminator, and the students continuously generate new features in the training stage. The feature of the student is obtained by attacking the feedback from the teacher and getting a score to judge whether the student has learned the knowledge well or not. In experiments on standard point cloud classification on ModelNet40 and ScanObjectNN datasets, our method reduced the information loss of knowledge transfer in distillation in 40x model compression while maintaining competitive performance.
HOKEM: Human and Object Keypoint-based Extension Module for Human-Object Interaction Detection
Venue: arXiv cs.CV 2023
Year: 2023

Human-object interaction (HOI) detection for capturing relationships between humans and objects is an important task in the semantic understanding of images. When processing human and object keypoints extracted from an image using a graph convolutional network (GCN) to detect HOI, it is crucial to extract appropriate object keypoints regardless of the object type and to design a GCN that accurately captures the spatial relationships between keypoints. This paper presents the human and object keypoint-based extension module (HOKEM) as an easy-to-use extension module to improve the accuracy of the conventional detection models. The proposed object keypoint extraction method is simple yet accurately represents the shapes of various objects. Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces adaptive graph optimization and attention mechanism, accurately captures the spatial relationships between keypoints. Experiments using the HOI dataset, V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by a large margin.