OpenRaiser/Intern-Atlas huggingface dataset:
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia. Generally, the information from each modality exhibits distinct representations and semantic information, which makes feature tends to be in separate latent spaces encoded with dual-tower architecture and makes it difficult to establish semantic relationships between modalities, resulting in poor retrieval performance. To address this issue, we propose a novel framework for cross-modal retrieval which consists of a cross-modal mixer, a masked autoencoder for pre-training, and a cross-modal retriever for downstream tasks.In specific, we first adopt cross-modal mixer and mask modeling to fuse the original modality and eliminate redundancy. Then, an encoder-decoder architecture is applied to achieve a fuse-then-separate task in the pre-training phase.We feed masked fused representations into the encoder and reconstruct them with the decoder, ultimately separating the original data of two modalities. In downstream tasks, we use the pre-trained encoder to build the cross-modal retrieval method. Extensive experiments on 2 real-world datasets show that our approach outperforms previous state-of-the-art methods in video-audio matching tasks, improving retrieval accuracy by up to 2 times. Furthermore, we prove our model performance by transferring it to other downstream tasks as a universal model.
Evaluating Deep Learning Assisted Automated Aquaculture Net Pens Inspection Using ROV
Venue: arXiv cs.CV 2023
Year: 2023

In marine aquaculture, inspecting sea cages is an essential activity for managing both the facilities' environmental impact and the quality of the fish development process. Fish escape from fish farms into the open sea due to net damage, which can result in significant financial losses and compromise the nearby marine ecosystem. The traditional inspection system in use relies on visual inspection by expert divers or ROVs, which is not only laborious, time-consuming, and inaccurate but also largely dependent on the level of knowledge of the operator and has a poor degree of verifiability. This article presents a robotic-based automatic net defect detection system for aquaculture net pens oriented to on-ROV processing and real-time detection. The proposed system takes a video stream from an onboard camera of the ROV, employs a deep learning detector, and segments the defective part of the image from the background under different underwater conditions. The system was first tested using a set of collected images for comparison with the state-of-the-art approaches and then using the ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing on embedded platforms.
Which is a better programming assistant? A comparative study between chatgpt and stack overflow
Venue: arXiv cs.SE 2023
Year: 2023

Programmers often seek help from Q\&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance.
Graph Edit Distance Learning via Different Attention
Venue: arXiv cs.AI 2023
Year: 2023

Recently, more and more research has focused on using Graph Neural Networks (GNN) to solve the Graph Similarity Computation problem (GSC), i.e., computing the Graph Edit Distance (GED) between two graphs. These methods treat GSC as an end-to-end learnable task, and the core of their architecture is the feature fusion modules to interact with the features of two graphs. Existing methods consider that graph-level embedding is difficult to capture the differences in local small structures between two graphs, and thus perform fine-grained feature fusion on node-level embedding can improve the accuracy, but leads to greater time and memory consumption in the training and inference phases. However, this paper proposes a novel graph-level fusion module Different Attention (DiffAtt), and demonstrates that graph-level fusion embeddings can substantially outperform these complex node-level fusion embeddings. We posit that the relative difference structure of the two graphs plays an important role in calculating their GED values. To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs. Based on DiffAtt, a new GSC method, named Graph Edit Distance Learning via Different Attention (REDRAFT), is proposed, and experimental results demonstrate that REDRAFT achieves state-of-the-art performance in 23 out of 25 metrics in five benchmark datasets. Especially on MSE, it respectively outperforms the second best by 19.9%, 48.8%, 29.1%, 31.6%, and 2.2%. Moreover, we propose a quantitative test Remaining Subgraph Alignment Test (RESAT) to verify that among all graph-level fusion modules, the fusion embedding generated by DiffAtt can best capture the structural differences between two graphs.
Learning Human-arm Reaching Motion Using IMU in Human-Robot Collaboration
Venue: arXiv cs.RO 2023
Year: 2023

Many tasks performed by two humans require mutual interaction between arms such as handing-over tools and objects. In order for a robotic arm to interact with a human in the same way, it must reason about the location of the human arm in real-time. Furthermore and to acquire interaction in a timely manner, the robot must be able predict the final target of the human in order to plan and initiate motion beforehand. In this paper, we explore the use of a low-cost wearable device equipped with two inertial measurement units (IMU) for learning reaching motion for real-time applications of Human-Robot Collaboration (HRC). A wearable device can replace or be complementary to visual perception in cases of bad lighting or occlusions in a cluttered environment. We first train a neural-network model to estimate the current location of the arm. Then, we propose a novel model based on a recurrent neural-network to predict the future target of the human arm during motion in real-time. Early prediction of the target grants the robot with sufficient time to plan and initiate motion during the motion of the human. The accuracies of the models are analyzed concerning the features included in the motion representation. Through experiments and real demonstrations with a robotic arm, we show that sufficient accuracy is achieved for feasible HRC without any visual perception. Once trained, the system can be deployed in various spaces with no additional effort. The models exhibit high accuracy for various initial poses of the human arm. Moreover, the trained models are shown to provide high success rates with additional human participants not included in the model training.
FAM: fast adaptive federated meta-learning
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model, which can then be personalized locally on individual clients. Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. Nonetheless, learning suffers when data distributions diverge. There is a need to learn a global model that can be adapted using client's specific information to create personalized models on clients is required. MRI data suffers from this problem, wherein, one, due to data acquisition challenges, local data at a site is sufficient for training an accurate model and two, there is a restriction of data sharing due to privacy concerns and three, there is a need for personalization of a learnt shared global model on account of domain shift across client sites. The global model is sparse and captures the common features in the MRI. This skeleton network is grown on each client to train a personalized model by learning additional client-specific parameters from local data. Experimental results show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the sparse parameter set to be communicated during federated learning drastically reduced communication overhead, which makes the scheme viable for networks with limited resources.
Towards Autonomous Multi-Modal Mobility Morphobot (M4) Robot: Traversability Estimation and 3D Path Planning
Venue: arXiv cs.RO 2023
Year: 2023

This thesis enhances the autonomy of the M4 (Multi-Modal Mobility Morphobot) robot, designed for Mars and rescue missions. The research enables the robot to autonomously select its locomotion mode and path in complex terrains. Focusing on walking and flying modes, a Gazebo simulation, and custom perception-navigations pipelines are developed. Leveraging deep learning, the robot determines optimal mode transitions based on a 2.5D map. Additionally, an energy efficient path planner based on 2.5D mapping is implemented and validated in simulations. The contributions demonstrate scalability for future mode integrations. The M4 robot showcases intelligent mode switching, efficient navigation, and reduced energy consumption, bringing us closer to fully autonomous multi-modal robots for exploration and rescue missions. This work paves the way for future advancements in autonomous robotics, with the ultimate vision of deploying the M4 robot for exploration and rescue tasks, making a significant impact in the quest for intelligent and versatile robotic systems.
Image Coding for Machines with Object Region Learning
Venue: arXiv cs.CV 2023
Year: 2023

Compression technology is essential for efficient image transmission and storage. With the rapid advances in deep learning, images are beginning to be used for image recognition as well as for human vision. For this reason, research has been conducted on image coding for image recognition, and this field is called Image Coding for Machines (ICM). There are two main approaches in ICM: the ROI-based approach and the task-loss-based approach. The former approach has the problem of requiring an ROI-map as input in addition to the input image. The latter approach has the problems of difficulty in learning the task-loss, and lack of robustness because the specific image recognition model is used to compute the loss function. To solve these problems, we propose an image compression model that learns object regions. Our model does not require additional information as input, such as an ROI-map, and does not use task-loss. Therefore, it is possible to compress images for various image recognition models. In the experiments, we demonstrate the versatility of the proposed method by using three different image recognition models and three different datasets. In addition, we verify the effectiveness of our model by comparing it with previous methods.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
Venue: arXiv cs.CV 2023
Year: 2023

Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which are expected to have similar styles. However, due to the diversity and complexity of Chinese font styles, the synthesized glyphs of existing methods usually exhibit visible artifacts, such as missing details and distorted strokes. In this paper, we propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes. Furthermore, our VQ-Font leverages the inherent design of Chinese characters, where structure components such as radicals and character components are combined in specific arrangements, to recalibrate fine-grained styles based on references. This process improves the matching and fusion of styles at the structure level. Both modules collaborate to enhance the fidelity of the generated fonts. Experiments on a collected font dataset show that our VQ-Font outperforms the competing methods both quantitatively and qualitatively, especially in generating challenging styles.
Channel Estimation By Transmitting Pilots From Reconfigurable Intelligent Surface
Venue: arXiv cs.IT 2023
Year: 2023

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.
Multi-Subdomain Adversarial Network for Cross-Subject EEG-based Emotion Recognition
Venue: arXiv cs.SD 2023
Year: 2023

The individual difference between subjects is significant in EEG-based emotion recognition, resulting in the difficulty of sharing the model across subjects. Previous studies use domain adaptation algorithms to minimize the global domain discrepancy while ignoring the class information, which may cause misalignment of subdomains and reduce model performance. This paper proposes a multi-subdomain adversarial network (MSAN) for cross-subject EEG-based emotion recognition. MSAN uses adversarial training to model the discrepancy in the global domain and subdomain to reduce the intra-class distance and enlarge the inter-class distance. In addition, MSAN initializes parameters through a pre-trained autoencoder to ensure the stability and convertibility of the model. The experimental results show that the accuracy of MSAN is improved by 30.02\% on the SEED dataset comparing with the nontransfer method.
DETDet: Dual Ensemble Teeth Detection
Venue: arXiv cs.CV 2023
Year: 2023

The field of dentistry is in the era of digital transformation. Particularly, artificial intelligence is anticipated to play a significant role in digital dentistry. AI holds the potential to significantly assist dental practitioners and elevate diagnostic accuracy. In alignment with this vision, the 2023 MICCAI DENTEX challenge aims to enhance the performance of dental panoramic X-ray diagnosis and enumeration through technological advancement. In response, we introduce DETDet, a Dual Ensemble Teeth Detection network. DETDet encompasses two distinct modules dedicated to enumeration and diagnosis. Leveraging the advantages of teeth mask data, we employ Mask-RCNN for the enumeration module. For the diagnosis module, we adopt an ensemble model comprising DiffusionDet and DINO. To further enhance precision scores, we integrate a complementary module to harness the potential of unlabeled data. The code for our approach will be made accessible at https://github.com/Bestever-choi/Evident
FaceCoresetNet: Differentiable Coresets for Face Set Recognition
Venue: arXiv cs.CV 2023
Year: 2023

In set-based face recognition, we aim to compute the most discriminative descriptor from an unbounded set of images and videos showing a single person. A discriminative descriptor balances two policies when aggregating information from a given set. The first is a quality-based policy: emphasizing high-quality and down-weighting low-quality images. The second is a diversity-based policy: emphasizing unique images in the set and down-weighting multiple occurrences of similar images as found in video clips which can overwhelm the set representation. This work frames face-set representation as a differentiable coreset selection problem. Our model learns how to select a small coreset of the input set that balances quality and diversity policies using a learned metric parameterized by the face quality, optimized end-to-end. The selection process is a differentiable farthest-point sampling (FPS) realized by approximating the non-differentiable Argmax operation with differentiable sampling from the Gumbel-Softmax distribution of distances. The small coreset is later used as queries in a self and cross-attention architecture to enrich the descriptor with information from the whole set. Our model is order-invariant and linear in the input set size. We set a new SOTA to set face verification on the IJB-B and IJB-C datasets. Our code is publicly available.
A comprehensive review on Plant Leaf Disease detection using Deep learning
Venue: arXiv cs.CV 2023
Year: 2023

Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning. The advantages and limitations of different deep learning models including Vision Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD), Disease Detection Network (DDN), and YOLO (You only look once) are described in this review. The review also shows that the studies related to leaf disease detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies.
Detecting Language Model Attacks with Perplexity
Venue: arXiv cs.CL 2023
Year: 2023

A novel hack involving Large Language Models (LLMs) has emerged, exploiting adversarial suffixes to deceive models into generating perilous responses. Such jailbreaks can trick LLMs into providing intricate instructions to a malicious user for creating explosives, orchestrating a bank heist, or facilitating the creation of offensive content. By evaluating the perplexity of queries with adversarial suffixes using an open-source LLM (GPT-2), we found that they have exceedingly high perplexity values. As we explored a broad range of regular (non-adversarial) prompt varieties, we concluded that false positives are a significant challenge for plain perplexity filtering. A Light-GBM trained on perplexity and token length resolved the false positives and correctly detected most adversarial attacks in the test set.
P3LI5: Practical and Confidential Lawful Interception on the 5G Core
Venue: arXiv cs.CR 2023
Year: 2023

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., network identifiers for tracking suspects. With the privacy-enhancements of network identifiers in the 5th generation of mobile networks (5G), LEAs need to interact with CSPs for network identifier resolution. This raises new privacy issues, as untrusted CSPs are able to infer sensitive information about ongoing investigations, e.g., the identities of their subscribers under suspicion. In this work, we propose P3LI5, a novel system that enables LEAs to privately query CSPs for network identifier resolution leveraging on an information retrieval protocol, SparseWPIR, that is based on private information retrieval and its weakly private version. As such, P3LI5 can be adapted to various operational scenarios with different confidentiality or latency requirements, by selectively allowing a bounded information leakage for improved performance. We implement P3LI5 on the 5G LI infrastructure using well known open-source projects and demonstrate its scalability to large databases while retaining low latency. To the best of our knowledge, P3LI5 is the first proposal for addressing the privacy issues raised by the mandatory requirement for LI on the 5G core network.
Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools
Venue: arXiv cs.CL 2023
Year: 2023

Generative models (foundation models) such as LLMs (large language models) are having a large impact on multiple fields. In this work, we propose the use of such models for business decision making. In particular, we combine unstructured textual data sources (e.g., news data) with multiple foundation models (namely, GPT4, transformer-based Named Entity Recognition (NER) models and Entailment-based Zero-shot Classifiers (ZSC)) to derive IT (information technology) artifacts in the form of a (sequence of) signed business networks. We posit that such artifacts can inform business stakeholders about the state of the market and their own positioning as well as provide quantitative insights into improving their future outlook.