Reviewerly/exHarmony huggingface dataset:
Research on Denoising of Multiple Compressed Image Based on Structure-Texture Decomposition

At present, most of the researches on denoising of compressed images are one-time compression. But in practice, the image will be compressed more than once. Therefore, a denoising method combining deep learning and traditional methods was proposed for multiple compressed images. First, the data set was compressed twice through singular value decomposition (SVD) to obtain noisy images after multiple compressions. Secondly, the noisy image was decomposed to obtain the noisy structure image and texture image. Then denoised them separately, the noisy structure image was used the feed-forward denoising convolutional neural network (DnCNN), and the noisy texture image was used the selected mean method. Finally, the denoised structure image and texture image were combined to obtain the denoised multiple compressed image. The Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) of this method are approximately improved by 0.9~1.5dB and 0.02~0.06, respectively. Moreover, the texture image is extracted and targeted for denoising, retaining more detailed information and achieving a clearer visual effect.
A Fully Connected Network Based on Memory for Video Anomaly Detection

The study of video anomaly detection (detecting abnormal events in videos) has attracted a lot of attention in the fields of computer vision and deep learning. In general, auto-encoders based on memory architecture are the mainstream anomaly detection methods. The model records the diversity of normal samples by introducing a memory module with multiple memory items. These items are used to record the different features, and participate in the reconstruction phase of the video frame. Since the reconstructed frame is mainly implemented by the convolutional layers in auto-encoder, and the Convolutional Neural Network has powerful representation capacity so that abnormal frames can also be reconstructed well by auto-encoder. By analyzing the advantages of the fully connected layers in Convolutional Neural Network, we propose an unsupervised learning method termed fully connected network based on memory for video anomaly detection. In order to reduce the representation capacity of Convolutional Neural Network, we introduce the improved Fully Connected Network that is based on the memory module. The training of the Fully Connected Network relies on the training results of the memory module, so we use a two-step scheme to train our model. Experimental results proved that our method outperforms state-of-the-art methods.
Dependency and Association Enhanced Finding Similar Exercises in Online Education System

Finding Similar Exercises (FSE) is a tough nut to crack in online education systems, especially among large scale exercise corpus. Given exercises with associated attributes, contents (texts and images) and knowledge concepts, most solutions learn exercise representations in isolation, where only contents or concepts are exploited. Though a few works try to combine concept-exercise associations for capturing long-range similarity among exercises, they just utilize a single relation among concepts but neglect the multiple dependencies. In this paper, we propose a novel Dependency and Association enhanced Finding Similar Exercises framework, namely DAFSE, which can consider both the concept-exercise associations and the multiple dependency relations among concepts, simultaneously. Specifically, we first build a concept-exercise-content multi-layer graph and embed each concept, exercise and content node through an Embedding Layer. Then we introduce Attentive Combination Layer to effectively preserve the long-range similarity as well as rich domain knowledge to exercise representations by propagating on the multi-layer graph. Finally, we use a Similarity Score Layer to assess the similarity of exercise pairs. Extensive experiments on real-world datasets demonstrate the effectiveness of DAFSE.
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Existing studies for representation learning of homogeneous information networks cannot be applicable to academic heterogeneous information networks (HINs) with multi-type nodes and multi-relationships because of the lack of ability to issue heterogeneity. Meanwhile, due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of academic HINs and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated.
An Efficient End-to-End CNN Network for High-voltage Transmission Line Segmentation

Automation detection of power transmission lines is of great importance for intelligent power inspection, which could well serve the route programming and motion guidance of examination platforms. However, due to complex factors, such as complex natural environment, illumination change, image noise, efficient detection of transmission lines still frontages great challenges. Lately, deep learning has exhibited a good detection effect among different segmentation tasks. Nevertheless, it still has a few disadvantages in high-precision image segmentation, like inadequate detection, information loss caused by multiple pooling operations, etc. To realize automatic and accurate pixel-level extraction, an attention fusion segmentation network is put forward to provide an end-to-end segmentation module. Considering the the problem of class imbalance, a global attention model is introduced to make the module focus more on the target region and suppress the unimportant features. Meanwhile, aimed at the semantic gap, residual path is also proposed to achieve effective usage of local information. In addition, to solve information loss issue which arise from plenty of pooling processing, an attention fusion block is put forward to realize effective feature aggregation of multi-scale features and improve the detection ability of segmentation network on multi-scale objects. Experiments exhibit that the attention fusion segmentation network has a good extraction capacity compared with other classical segmentation network.
Adaptive Weight of Unreliable Relation Module for Semi-supervised Multi-label Image Recognition

Semi-supervised multi-label classification is a challenging task due to the insufficient training guidance and unknown label co-occurrence probabilities. For papers in recent years, relation modules are widely utilized to explore the potential label relationships, but the severe frequency-biased issue between a global relationship and local images significantly degrades their effectiveness. Besides, the difference in data distribution between training and testing sets further affects the performance, especially when the labeled data is limited. To address these problems, we propose a simple selective relation module to learn an adaptive weight of relation module for each image and enforce the consistency between relation-based predictions and initial predictions. In addition, we exploit the augmentation-based consistency loss to generate more confident relation-based pseudo-labels and more robust relation-importance predictions. Our methods can be added to a variety of relation-based multi-label classification methods and we show our improvements in the classification accuracy on Pascal VOC dataset.
Survey of images semantic segmentation based on deep learning

In recent years, inspired by deep learning, the performance of semantic segmentation has been greatly improved. According to the research status of semantic segmentation based on deep learning, this paper firstly combs the semantic segmentation method based on convolutional neural network and the new method based on Transformer respectively, and briefly introduces their core algorithms. Then, the performance of these methods on different datasets is compared and analyzed. Finally, the semantic segmentation methods and the future development trend are summarized.
A Multimodal Fusion Scene Graph Generation Method Based on Semantic Description

For the scene graph generation task, a multimodal fusion scene graph generation method based on semantic description is proposed considering the problems of long-tail distribution and low frequency of high-level semantic interactions in the dataset. Firstly, target detection and relationship inference are performed on the image to construct an image scene graph. Second, the semantic descriptions are transformed into semantic graphs, which are fed into a pre-trained scene graph parser to construct semantic scene graphs. Finally, the two scene graphs are aligned for display and the information of nodes and edges are updated to obtain a fused scene graph with more comprehensive coverage and more accurate semantic interaction information.
Design and Implementation of Sewage Pipeline Cleaning Robot Based on Beidou Positioning

With the continuous advancement of urbanization, the use of underground pipelines is gradually increasing. At present, they have been widely used in domestic plumbing supply, oil transportation, natural gas transmission, sewage discharge, etc. However, with the increase of service time, deposits and internal diseases (deformation, cracks, etc.) will appear in the pipeline, which brings potential hidden dangers to the normal transportation of the pipeline. Traditional manual cleaning and inspection is costly and time-consuming, and some liquid and gas are harmful to people’s health, which poses great challenges to pipeline cleaning and disease detection.In response to this phenomenon, we designed and implemented a sewage pipe cleaning robot based on Beidou navigation system, which can help clean the garbage and dirt in the sewage pipe, and can also inspect the diseases inside the pipe. The hardware part of the robot mainly consists of car chassis with a robotic arm, a high-definition camera, a Beidou positioning chip and other parts, while the software part mainly includes an object recognition algorithm based on OpenCV, a robotic-arm grasping control algorithm and a Beidou positioning algorithm. The robot has the functions of grasping foreign objects, accurately positioning, monitoring the internal environment of the pipelines in real time and uploading data to the cloud platform. In the actual pipeline environment, the robot’s functions such as grasping and collecting the foams which treated as foreign objects, real-time positioning and position uploading, and crack detection on the inner wall of the pipeline are tested, and the test results verified the effectiveness of all functions of the robot designed in this paper.
Global Interest Transfer Guided Session-based Recommendation

Session-based recommendation aims to predict the next item based on the anonymous user’s clicked item sequence. Users’ interest in different content shifts regularly, and almost all of the current session based recommendation methods can’t capture the transfer relationship between interests, which can guide our prediction of the next item. This paper proposes an innovative method called Global Interest Transfer Guided Session based Recommendation(GITG), which uses global information to learn interest representations and transfer rules between interests to help the recommendation. In GITG, we parse sessions from two perspectives: (i)Interest: we learn the items’ interest representation by using the global neighbor set and learn the interests transfer relationship in the interest graph. (ii)Session: we learn the local embedding in the session graph and combine it with the global-post embedding. From these two perspectives, we can obtain interest representation and session representation, which provide high-value information for recommendation. Experiments show that GITG performs well on three real-world datasets.
Minimizing the Average Job Completion Time for Acceleration Systems in Cloud Computing

With the development of computation intensive applications, such as deep neural network inference and deep packet inspection, the conventional computation resources are exhausted by these computing tasks, which results in large application response time. To improve the user experience, more and more providers deploy accelerators in their computing clusters. Accordingly, there is a problem arising: how should we schedule the non-preemptive jobs such that the average job completion time can be minimized. To answer this question, we first formulate the problem to be a mathematical programming model. Based on solid analysis, we find that the problem we need to solve is NP-hard. Due to the hardness of this problem, we propose a (6 – 2/M)-approximation algorithm to solve it efficiently, where M is the number of accelerator servers in the system. Through extensive simulations, we find that the proposed algorithm outperforms the conventional scheduling algorithms, FIFO and Shortest Job First (SJF), by 24.24% and 29.07%, respectively.
FRETA-D: A Toolkit of Automatic Annotation of Grammatical and Phonetic Error Types in French Dictations

Dictation is considered as an efficient practice for testing French as a Foreign Language (FFL) learners’ language proficiency. However, in-class dictation and teachers’ manual correction greatly reduce teaching efficiency. An existing dictation platform can only partly resolve these problems by providing instant error correction. To pursue better pedagogical feedback, this study develops an annotation toolkit called FRETA-D (FRench Error Type Annotation for Dictation), with an aim to provide more detailed error-type information for both FFL students and teachers. With parallel “learner input - reference text” sentences as input, FRETA-D can automatically identify error boundaries as well as classify and annotate the errors into fine-grained error types. Designed especially for French dictation, FRETA-D features a data-set-independent classifier based on a framework with 25 main error types, which is generated from French grammar rules and incorporates the characteristics of FFL learners’ common dictation errors. Five French teachers are invited to evaluate the appropriateness of the automatically predicted error types of 147 randomly chosen “error-correction” span pairs, and the acceptance rate reached more than 85%.
CNXA: A Novel Attention Mechanism Aided Convolution Network

Recently, a new generation of convolutional networks, namely ConvNeXt, is proposed, which has the same number of model parameters as Swin-Transformer and superiority in terms of accuracy. This paper designs a ConvNeXt-base network based on the attention mechanism (CNXA). Specifically, we first embed the channel attention mechanism, the spatial attention mechanism, and the combination of both into ConvNeXt-base to improve the performance of the network to recognize targets in images. Then, a new large kernel-filling attention mechanism is proposed based on the above attention mechanisms. Experiments are finally designed to evaluate the proposed CNXA network. We can see that its TOP-1 accuracy on the ImageNet-100 dataset is about 0.4% higher than that of ConvNeXt-base. Moreover, the experimental part verifies the robustness of the model. Open source code is available in https://github.com/ZJieX/cnxa.
Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion

Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method (FFACR) to achieve text-to-video matching, ranking and searching. The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator. Generator and discriminator are trained alternately based on adversarial learning, so that the data obtained by the feature mapping network is semantically consistent with the original data and the modal features are eliminated. Experimental results demonstrate that the proposed method performs better in text-to-video search on the self-built datasets of technology videos.
RoFace: A robust face representation approach for accurate classification

The recent development of technological applications has made it inevitable to replicate human eyesight talents artificially and the issues requiring particular attention in the ideas of solutions increase in proportion to the number of applications. Facial classification in admittance restriction and video inspection is typically amidst the open-ended applications, where suitable models have been offered to meet users' needs. While it is true that subsequent efforts have led to the proposal of powerful facial recognition models, limiting factors affecting the quality of the results are always considered. These include low-resolution images, partial occlusion of faces and defense against adversarial attacks. The aspect of the input image, the verification of the presence of face occlusion in the image, the motive derived from the image, and the ability to fend off adversarial attacks are all examined by the RoFace formal representation of the face, which is presented in this paper as a solution to these issues. To assess the impact of these components on the classification/recognition accuracy, experiments have been conducted.
Electromagnetic Waveform Characterization for Side-Channel Attacks on AES Encryption

This work presents a device waveform characterization for electromagnetic side-channel attacks on AES encryption, linking current research to side-channel attack methodologies and mathematical software driven analysis by detailing the electromagnetic emanations of an ATMEGA8 microcontroller while performing AES encryption. Additionally, we present a methodology for analyzing data collected while performing side-channel attacks and also demonstrate some defensive counter-measures against side-channel attacks and their impact on the characterization.
Automated Structure Matching of Printed Circuit Boards

We address the challenge of matching photos of printed circuit boards (PCBs) that have the same layout. A simple approach to this problem is to use an off-the-shelf computer vision (CV) machine learning (ML) model to embed images into vectors and then calculate the similarity between those embeddings. However, this approach is prone to diversion by visual differences between photos that do not affect PCB layout. With a large, paired dataset of matching PCBs with visual differences, it would be possible to train a ML model to ignore these visual differences, but such a dataset does not exist and is expensive to create. We develop an approach that alleviates this problem by considering only component bounding boxes and classes, as outputted by an object detection ML model, forcing it to ignore irrelevant visual features. Specifically, we use a structure matching algorithm that compares the type, shape, and relative position of the components on the PCB. The algorithm uses the layout of the components as a proxy for the overall PCB layout. We demonstrate that our method can be used in conjunction with visual embedding similarity in order to take advantage of both visual similarities and structural similarities between PCB images. Our approach also has two unique capabilities. First, it finds the optimal alignment of the images such that matching components are overlaid. Second, in addition to photos, it can be directly applied to component data without the need for an object detector. This leaves the application of this algorithm open to manually inputted or modified component structures or schematics. We additionally open-source our evaluation and object detection code, model weights, and manually-gathered matched dataset used for evaluation at https://github.com/twosixlabs/pcb_structure_matching.
System Integrity – A Cautionary Tale

One of the vital security requirements in information systems is integrity of information that is processed. Current security practices place more emphasis on protection of data with less focus on protection of the system processing that data, with inadequate security requirements specification throughout the system development life cycle. The consequence of this practice is a potential system dysfunction or degraded performance thus compromising system integrity. This paper explores the current practices for providing system integrity protection and the limitations thereof which can potentially undermine system integrity.
Possibilistic Reject-Classification Based on Contrastive Learning in Vector Quantization Networks

In this paper, while considering rejection as an option, we attempt to tackle the problem of multiclass classification and the uncertainty that arises from the class possibility assignment of data. To address the challenge of classification based on possible class assignments, we use the likelihood ratio, which helps us develop a holistic approach that considers all the positive and negative effects of assigning a particular class as opposed to others to a data point. To this end, we propose a possibilistic variant of the contrastive-learning function, inspired by RSLVQ [20], and a class-wise decision rule based on it. The latter is used to define the total cost function. In addition, with the help of likelihood ratio, an error-rejection trade-off inspired by Chow [3], is proposed. Finally, modification of the cost function and integration of rejection into it result in an interpretable model whose capabilities in both aspects (classification/rejection) are demonstrated by application to different data sets.
Deep Structured Graph Clustering Network

Shallow clustering methods adopt linear or simple nonlinear projections to reduce the feature dimensions, which may suffer from the weak representation capability. Contrastively, deep clustering methods have the advantages on representing the sample characteristics. However, most deep clustering models focus on preserving feature information of samples and ignore the important intrinsic structures of samples. Besides, large amounts of neural network parameters should be optimized in deep clustering models, but no proper semantic supervision can be used in the unsupervised clustering process. To alleviate these problems, in this paper, we propose a unified deep structured graph clustering network to guide the unsupervised deep clustering process with a theoretically ideal cluster structure. Specifically, we simultaneously learn the discriminative feature representation of samples, and the similarity graph of samples with well clustering structure by automatically assigning proper neighbors to each sample. Experiments on several public testing datasets demonstrate the effects of the proposed method.