Reviewerly/exHarmony huggingface dataset:
Structural Image Anomaly Detection Method Based on Multi-scale Masking Feature Reconstruction

Anomaly detection have been increasingly applied to the task of workpiece surface defect detection. Such methods only need to use the images of qualified workpiece to train the network, which overcomes the problem that object detection require a large amount of defect data. Although such methods can reliably classify normal and abnormal workpieces, it is challenging to pinpoint abnormal areas, particularly on workpieces with complex structural backgrounds. To solve this issue, an anomaly detection method based on multi-scale masking feature reconstruction is proposed. First, the features of various scales are aligned, aggregated and concated to generate multi-scale features for local areas of the image. Then, the multi-scale features are separated into blocks and randomly masked in order to increase the precision of defect segmentation in the structural background image, and the masked features are reconstructed using a masked autoencoder in order to recover their original features. Finally, the feature differences before and after reconstruction are compared to detect abnormal areas in the image. The experimental results demonstrate that the method is adaptable and can successfully increase the fault segmentation accuracy of structural background workpieces while also achieving good detection results on unstructured background workpieces.
Convolutional Transformer for Radar Active Jamming Classification

Accurate classification of radar jamming signals can guide the application of effective anti-jamming policies to protect functions of the radar. In this paper, a novel method for radar active jamming classification based on time and range-Doppler domain is proposed. We design a lightweight convolutional Transformer network to process the data from two domains. Efficient convolutional blocks are employed to extract features from different domains. Then, the range gate tokenizer is proposed to operate a feature fusion and generate a sequence of tokens. Two Transformer encoders are responsible for further feature extraction and outputting final predictions. Simulation experiments show the proposed method has a high performance with 94% accuracy requiring only 42 ms latency on a Raspberry pie.
Generalized Zero-Shot Learning via Implicit Attribute Composition

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2.
Development of Trust Data Distribution Platform for Healthcare &amp; Medical Data

We have developed a trust data distribution platform for the health and medical data. In the health and medical fields, data quality, transparency, and reliability are important to data distribution. Especially, trustworthy data is essential for accurate predictions and diagnostic assistance using AI and machine learning. Our proposed system is based on nonfungible tokens (NFTs), which can enhance the reliability and value of data. Increased value of NFTs leads to increased value of data, which in turn becomes a valuable asset for patients and healthcare professionals. The system is designed to facilitate each stakeholder’s efforts to improve data reliability.
Neural Network-based Occupancy Map Joint Sampling for Video-based Point Cloud Compression

In video-based point cloud compression (V-PCC), a point cloud is projected as a texture video and a geometry video for the following compression. Besides, an occupancy map video is generated to indicate whether each pixel in the former two videos is connected to a real point in the point cloud. For bits saving purpose, the rough down-sampling before encoding and up-sampling after decoding impairs the accuracy of the map and therefore causes the quality loss of reconstructed point cloud. To tackle this problem, we propose a joint sampling network consisting of a compact resolution (CR) network and a super resolution (SR) network to replace the sampling process in V-PCC. We also modify the geometry video to make it more compatible with the new occupancy map video. Comprehensive experiments demonstrate that our sampling method for occupancy map video can bring performance improvement for point cloud. The BD-rate for geometry bits could be saved as much as 8.6 percents on D2 measurement.
Adaptive and Collaborative Multi-scale Alignment for Text-Based Person Search

Text-to-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are usually more informative and complete than text. It is crucial to establish semantic correlations between modalities and focus on task-relevant information in images. In this paper, we propose a novel Adaptive and Collaborative Multi-scale Alignment network (ACMA) for text-based person search that learns semantically consistent and information-aligned multi-modal representations. Firstly, we introduce a novel joint embedding module that adaptively integrates features of different pixels and words, thereby extracting semantically consistent multi-modal features at different scales. Second, we design a cross-modal fusion feature-based auxiliary visual branch to guide the extraction of key visual features that are beneficial for cross-modal matching. Extensive experiments validate that ACMA outperforms the state-of-the-art method.
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Streaming

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., High Efficiency Video Coding (HEVC), Alliance for Open Media Video 1 (AV1)) that lie below the predicted rate-distortion curve of the Advanced Video Coding (AVC) codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF score of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.
A Symbolic Execution-Based Approach for Smart Contract Vulnerability Detection

Before deploying smart contracts to Ethereum, a crucial step is to review the contract code for potential security vulnerability. Symbolic execution is currently a prevalent method for detecting vulnerability in smart contracts, but it lacks robust support for arbitrary modifications by owners. Therefore, this paper, leveraging symbolic execution technology, investigates detection methods specifically addressing this type of vulnerability, and presents concrete implementation and experimental validation. In the initial phase, this paper conducts an in-depth study of vulnerable contracts by debugging the source code and Ethereum Virtual Machine (EVM) opcode instructions. The analysis encompasses opcode instructions and the contract’s global state, summarizing vulnerability characteristics, extracting crucial opcode instructions. Subsequently, based on symbolic execution technology, the paper proposes corresponding detection methods. Real-world smart contracts are employed in this study, categorized into a dataset of vulnerable contracts susceptible to attacks and a dataset of normal contracts. Experimental evaluations are conducted to assess the effectiveness and accuracy of the system’s detection capabilities. The results indicate that the system implemented in this paper achieves the intended design goals and enhances the efficiency of vulnerability detection.
Efficient Lightweight Attention Based Learned Image Compression

The CNN-based end-to-end learned image compression methods have already achieved a significant improvement in terms of coding efficiency. Moreover, with the capability of modeling long-range global correlation, the transformer-based image compression has further elevated the coding efficiency to outperform the latest Versatile Video Coding (VVC) standard. Nonetheless, the high computational burden of the self-attention mechanism in the transformer design present a significant obstacle for practical applications. To address this concern, we propose a relatively low-complexity end-to-end learned image compression approach by integrating an efficient lightweight attention module, which effectively mitigates the computational overhead associated with self-attention in the transformer design. The experimental results demonstrate that the proposed method achieves better RD performance than VVC. Furthermore, as compared to the prevailing state-of-the-art transformer-based approach, our method accelerates the coding speed by over 6 times while maintaining comparable coding efficiency.
FBL-BP: Byzantine-Resilient and Privacy-Preserving Federated Broad Learning

In response to the growing demand for clients' privacy protection, federated learning framework often needs appropriate privacy protection to protect clients' privacy better, such as the client uploading a blinded local model instead of the original real local model to the server, which makes the real value of the local model unobservable to the server. Although the privacy is protected, it will to be a huge challenge to the server to distinguish clients which are Byzantine clients. We propose a federated learning framework based on broad learning that can simultaneously achieve protection of clients' privacy and robustness against Byzantine attacks, i.e. FBL-BP. We apply differential privacy techniques to perturb the local models of the clients, which can protect clients' privacy. The server receives the perturbed local models and then guarantees the Byzantine-resilience of global model through an outlier removal mechanism based on cosine similarity. Finally, experimental results show that FBL-BP has significant Byzantine robustness and satisfactory accuracy, and possesses less time consumption than traditional methods.
Research on Millimeter Wave Communication System Based on Integrated Computer Artificial Intelligence Technology

The ADF4002 and two ADF5355 frequency modulation synthesizers are integrated to realize a millimeter wave communication system which is suitable for both medium and high frequency. The output power of MMW communication system is regulated by digital control of attenuator and amplifier circuit. The ADF5355’s PLL and frequency doubling circuit provides the RF module with an 8.4-11.2GHz bandwidth and 100 MHz step frequency tunable local oscillator signal. The channel modeling of the system with lateral axis migration is carried out based on the OAM mode migration characteristics. The system includes the theoretical derivation of the modal power distribution under the offset and the construction of the channel transmission matrix. Finally, the hardware structure of the system is analyzed and tested in detail, so that it has the advantages of low noise and low clutter. It is found that the lower the modal value of the selected mode set or the larger the step distance, the smaller the influence of the deviation on the system performance. The local oscillator components and transceivers are independent, which can effectively reduce problems such as source noise leakage and RF interference, reduce the PCB size of the transceiver, and make each component more flexible.
Social Occlusion Inference with Vectorized Representation for Autonomous Driving

Autonomous vehicles must be capable of handling the occlusion of the environment to ensure safe and efficient driving. The social occlusion inference task focuses on inferring occupancy from agent behaviors as a remedy for perceptual deficiencies. We identify visible trajectories, road context, and occlusion information as the three key environmental elements represented by vectors in our method. Therefore, this paper introduces a novel social occlusion inference approach that learns a mapping from three types of vectors to an occupancy grid map (OGM) representing the view of the ego vehicle. Specifically, vectorized inputs are encoded through the polyline encoder to aggregate vector-level features into polyline-level features. Since vehicles are constrained by the road and affected by other agents and occlusion areas, we exploit a transformer module that models the high-order social interactions of three types of polylines. Importantly, to address the inconsistency between input and output modalities and introduce prior knowledge of occlusion, occlusion queries are proposed to fuse polyline features and generate the OGM without the input of visual modality. We evaluate our approach on the INTERACTION dataset, which achieves on-par or better performance than the baselines. The ablation study demonstrates that three key elements of input can enhance the performance of our network.
Flexible Coding Order for Learned Image Compression

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity.
MTE: Learned Image Compression with a Merge-Then-Estimate Entropy Model

Recent advancements in learning-based image compression methods have shown promising results. The success of these methods heavily relies on the entropy model, which predicts the probability distribution of the quantized latent representation of the image based on available knowledge. However, most existing entropy models follow an estimate-then-merge pipeline, leading to two potential issues: limited flexibility in modeling spatial context and inadequate fusion of different prior sources. In this paper, we propose a novel approach called the MergeThen-Estimate (MTE) entropy model. Our method addresses these issues by first uniformly merging available priors into a ’prior token’ using a Prior Embedding Module for each spatial location in the quantized latent representation. Next, we introduce a Content-aware Context Model to dynamically capture the dependencies of the currently coding representation on its neighboring available priors. Experiments on the Kodak dataset demonstrate the superiority of our proposed MTE entropy model.
One-class Classification Fault Detection Method Based on Manhattan Distance and Bagging

In response to the difficulty of obtaining fault samples in avionics equipment fault detection, a bagging ensemble classification algorithm based on Manhattan distance is proposed by combining the Bagging ensemble learning framework with similarity score. This algorithm achieves avionics component fault detection under only normal sample training conditions. Based on normal data samples, an algorithm flow for bagging ensemble classification based on Manhattan distance was proposed, and classification experiments were conducted on avionics equipment detection data. The final results demonstrate that the proposed method has better detection performance.
Management-Oriented Operating Systems: Harnessing the Power of DAOs and Foundation Models

This paper presents Management-Oriented Operating Systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social Systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.
Image Coding for Machines based on Non-Uniform Importance Allocation

In the Internet era, the explosive growth of media data processing poses significant challenges for the research of Image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens of data storage and transmission. Existing ICM methods face challenges in achieving sufficient generalization ability when developing a single codec to handle diverse downstream tasks. To address these issues, we propose a unified ICM framework that facilitates diverse downstream tasks with a novel importance allocation mechanism. Equipped with a spatially variable-rate image compression codec, we introduce two options: online updating and offline predicting the non-uniform quality map, which governs the quality distribution of reconstructed images based on specific downstream tasks. Our proposed method is rigorously evaluated through extensive experiments on diverse and comprehensive fine-grained image classification datasets. The experiment results conclusively demonstrate the effectiveness of the proposed method in achieving a superior rate-distortion trade-off for ICM.
An Efficient ConvNet for Learned Image Compression with Transformer-Style Architecture

Recently, transformer-based and convolution-based methods have achieved significant results in learned image compression. By comparing the design of convolutional network (convnet) and transformers, we replace the self-attention with convolution to capture spatial and channel adaptability. We propose a simple attention module (SAM) with transformer style. Combining the proposed SAM with channel-wise and checkerboard entropy model, we propose an efficient end-to-end learned image compression method. It is a simple method but obtains strong result and efficient coding speed. Experiments demonstrate that our method achieves competitive results by comparing with previous learning-based methods and conventional image codecs.
DiscSumm: A Discourse Based Saliency Model to Generate Abstract Summaries for Conversation

Summarization of large corpus of text is an important and hard problem. The amount of text information is increasing at an exponential rate which makes it consumption and organization difficult. This data also involves information obtained from transcription, chat along with traditional long form content such as books, periodicals, web pages etc. In this paper, we focus on long conversations obtained via transcription of multi speaker audio calls. Most of the traditional works text summarization focus on structured text summarization, however, we focus on multi turn conversation summarization. Many recent works perform model finetuning which improves the abstractive summarization by multi folds, it can still be improved further by incor-porating text into the encoder framework. Therefore, in this work we have studied the discourse-based saliency model that identifies the important segments within a text conversation and combines them adaptively to generate summary of the conversations. We demonstrate that the proposed method achieves state of the art re-sults on public benchmarks.
Automated Netlist Generation from Offline Hand-Drawn Circuit Diagrams

This paper introduces a novel method for the precise and efficient interpretation of hand-drawn circuit diagrams. This method addresses the challenge of understanding electrical drawings by not only classifying and localizing electronic components, as previous works have done, but also recognizing textual labels and their associations with electronic components. The method addresses the complexities of recognizing decimal points, grouping textual labels, and associating the grouped textual labels with the corresponding electronic components. These complexities arise due to the relative locations of individual textual labels, the orientation of textual labels, and the location of electronic components in the circuit. To evaluate the proposed method, we tested it on a set of 100 randomly selected images. The method correctly identified the objects in 87% of the images and accurately generated the Netlist for 82% of the images. This resulted in an accuracy rate of 94.24% for the object identification algorithm and an overall accuracy rate of 82% for the method.