Reviewerly/exHarmony huggingface dataset:
Multi-source Interaction Network for TextVQA

Text-based Visual Question Answering (TextVQA) is a topical and challenging task requiring reading, understanding, and reasoning ability. Existing models make full use of extensive information from the Optical Character Recognition (OCR) system. However, they all face the following challenges: 1) Interactions between multi modalities are hard to explore. 2) Input inconsistency during training and inference caused by teacher-forcing strategy expand the complexity. To better address the above difficulties, guided by prior information, we propose Multi-Source Interaction (MSI) network to comprehend multimodal features. Specifically, given the OCR words, firstly excavate potential relationships with question and image. Then, we reprocess multimodal features according to the sentence-level OCR relationship. In addition, we add noise to the answer features to adapt the different inputs in prediction. Experiments show that our model outperforms previous state-of-the-art models on TextVQA datasets.
A RDF Validation Tool Extended On Jena-ShEx

RDF is the core technology of the Semantic Web direction, and RDF is increasingly being used to represent and store information shared over the Web. When data consumers reuse RDF graphs created by others, it is necessary to understand the structure of the current data, and the current use of human-readable descriptions or ontologies to convey knowledge is often insufficient, so the correctness of the RDF becomes the key to the correct dissemination of knowledge in the knowledge graph. Based on Jena-ShEx, this paper optimizes the correctness test of RDF, draws on the idea of SHACL in the generation of validation report, and refines the test report of RDF, so that data consumers can quickly locate and modify data when conducting data verification. The improved tool passed all the test cases on the Shape Expression website and achieved good results.
Augmented Semi-supervised Learning for CNN Based Automatic Modulation Classification

The classification of signal modulation using machine learning algorithms requires considerable amount of labeled signal. In this paper, a semi-supervised learning framework for a CNN based modulation classifier is proposed to reduce the amount of labeled signal needed while achieving good classification performance. Various techniques have been adopted to improve upon the classic semi-supervised learning framework for improved performance in modulation classification. The results show that the proposed method achieves accuracy close to the supervised classifier with large amount labeled signal while far superior to the one with the same amount of labeled signal.
Federated Payload-based Anomaly Detection: An Investigation for Different Aggregation Algorithms

Payload-based anomaly detection (PAD) has been proven as a very effective solution to protect the Internet for a long period of time, but unfortunately rely on a big data of normal payload samples for model training, which potentially induces the privacy concerns for the data owners who usually hold their network payloads locally and are not willing to share in the public. To tackle the privacy issue, some pioneer research advocates the use of federated learning to build the PAD model (we call it federated PAD) by aggregating the local model's parameters rather than the local data directly, hence preserving the data privacy for PAD. We understand the federated PAD may sacrifice the detection performance to gain privacy benefits, while the trade-offs may vary quite largely in different federated aggregation algorithms. For this reason, we investigate the effectiveness of federated PAD with typical aggregation algorithms in this paper, which includes the weighted mean, trimmed mean, median, absolute mean, and Krum. We implement all of these algorithms in the condition of a typical PAD application, and also run extensive experiments on the real-world Internet payload datasets for comparison. Our results have successfully revealed the best practice for the use of federated aggregation for training the PAD.
Seeing Clear before Visual Tracking

In this paper, we propose a two-stages visual tracking method mainly based on two branches including image deblurring and visual tracking. Our main motivation is to achieve the robust visual tracking when the tracker is suffering fast motion blur. Firstly, we present the hierarchical model based on Spatial Pyramid Matching that performs the fine-to-coarse deblurring and exploits localized-to-coarse operations. After achieving the deblurred images, the proposed method use transformer framework with spatial and channel attention for extracting features in order to obtain the spatial and channel features simultaneously to obtain the fast visual tracking with the balance of accuracy and robustness. We first train the one-stage deblurring network in the dataset of Gopro. Then, we train the second stage visusal tracking branch. Lastly, we conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the outperforming results on large tracking benchmarks, we also validate the effectiveness of our method against the fast motion blurring.
FNet4Rec: A Simple and Efficient Sequential Recommendation with Fourier Transforms

Modeling users' current interests is one of the core tasks of a sequential recommendation system. The existing state-of-the-art methods like BERT4Rec mainly adopted BERT for learning the representation of user interests from their interacted data. However, the computational complexity of self-attention in BERT increases with the quadratic power of the input sequence length due to repetitive inference, which hinders their specific adoption in practice. To this end, in this paper, we propose a simple and efficient sequential recommender FNet4Rec. FNet4Rec employs the unparameterized Fourier Transform instead of self-attention to encode their sequential behaviors bidirectionally. Empirical results on two public datasets show that compared with BERT4Rec, FNet4Rec reduces memory usage by 21% and improves efficiency by 20%, while retaining more than 90% of its recommendation performance. Remarkably, when the sequence length increases, FNet4Rec becomes lighter and more efficient, moreover, the gap in recommendation performance is significantly closer or even negligible. We hope our work can give light on creating a lightweight and computationally efficient recommender that works well in some industrial recommendation scenarios where each user has a relatively long interaction sequence or some applications under constrained computational budgets (e.g. edge computing).
An Efficient Cross-Domain Lattice-Based Direct Anonymous Authentication Scheme

The existing lattice-based direct anonymous authentication (LDAA) scheme has not considered the cross-domain authentication problem, and it has the problem of low efficiency. In response to the above problems, this paper proposes an efficient cross-domain lattice-based direct anonymous authentication scheme. Firstly, we design a new cross-domain mechanism, which adopts the idea of “space for time” and stores the trust relationship in advance to shorten the time required for cross-domain authentication. Secondly, some steps in the sign process are optimized through a matrix transformation, which improves the overall efficiency of the authentication. Finally, through theoretical analysis and specific experiments, the security and superior performance of proposed scheme are verified.
A Survey of Data Mining Method in Heterogeneous Information Networks with Node Importance Evaluation

Various kinds of data can be abstracted into information networks, such as the connection between people in social communication, the publishing and published relationship between authors and papers in academic network, etc. Heterogeneous information networks composed of various types of nodes and edges contain more comprehensive and rich structural and semantic information. The node importance evaluation in heterogeneous information networks can not only be based on different types of heterogeneous information in the network, but also make the evaluated key nodes more meaningful and valuable. In recent years, the node importance evaluation in heterogeneous information networks has been widely concerned by academia and industry, and has become an important research topic of network analysis. However, at present, there is still a lack of work to comprehensively sort out the existing results, and it is difficult for relevant researchers to systematically understand the latest research progress and choose appropriate data mining methods in practical applications. Therefore, this paper comprehensively summarizes the data mining methods used in the evaluation of key nodes of heterogeneous information networks. This paper includes: the basic concept of heterogeneous information network; the latest research progress of data mining methods which used for node importance evaluation; the latest research progress of node importance evaluation of heterogeneous information network. Then we prospect the future development direction of node importance evaluation.
Semantic Segmentation Algorithm of Street View Image Via Deep Learning

Real-time semantic segmentation of street view images can be used as a visual aid system during autonomous driving. This paper proposes a multi-branch cross-layer fusion network (MCFNet). The model uses a multi-branch network to extract features, fuses the features of different stages of multiple branches for prediction, and uses a lightweight depthwise separable convolution to reduce the amount of computation. We use the Cityscapes dataset to train and evaluate the algorithm and get 61.6% mIoU and 115.3 fps.
A Trackable Privacy-Enhanced Machine Learning Classifier

Nowadays, the privacy protection of machine learning has been taken more and more attentions. Researchers are expected to protect the privacy of training data sets while ensuring the accuracy of machine learning results. This paper attempts to study the privacy protection of machine learning from another direction. A detailed traceable privacy-enhanced machine learning classifier is proposed which combines differential privacy and digital fingerprinting and introduces them together into a classifier algorithm. It can track training devices on the basis of providing more accurate classification results and protecting the privacy of training set. Particularly, privacy protection is achieved by training specific data through the classifier based on differential privacy. And the encoding of the training device identification information is used to specify the location of these specific data. It is the key to the tracking function of the classifier. We use real data to simulate the classifier. Combined with the simulation results, the feasibility, privacy and traceability of the classifier are analyzed. The results show that our classifier scheme can realize tracking training device while satisfying the privacy protection of training set.
An Improved Stereo Matching Network Based on Modified PSMNet

In order to solve the stereo matching problem better, the accuracy of the algorithm in the stereo matching task needs to be improved. To tackle the problem, we proposed a Dual Spatial Pyramid Pooling Stereo Matching Network with Attention Mechanism (DSANet), a stereo matching network with better multi-scale feature extraction capability based on the basic structure of Pyramid Stereo Matching Network (PSMNet). The network consists of two main modules: the dual spatial pyramid pooling module and the attention module. The former module extracts multi-scale features from different branches at the same time. After concatenating these different features, the latter module will optimize the fused features and generate the cost volume with different branches multi-scale feature. The proposed approach was evaluated on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) 2015 dataset. Our experiment results on the KITTI 2015 dataset show that the mismatch rate of our network is 3.02%. Compared with the basic structure of PSMNet, the proposed method improves the effect of stereo matching.
Dynamic Obstacle Avoidance Method of USV Based on Secure ACO with Hazard Index

Aiming at the problem that basic ants colony optimization (ACO) could not meet the requirements of safe obstacle avoidance for unmanned surface vessels (USV) in dynamic environment, a path planning method combining improved ACO and dynamic windows approach (DWA) was proposed. The improved heuristic information was proposed to solve the problem of low efficiency of basic ACO path search. In order to prevent the potential collision between USV and obstacles during navigation, a hazard index considering the inertia of USV and obstacle distance is designed to improve the probability of ACO. And the fusion of secure ACO and DWA improve the local safety obstacle avoidance ability of USV in dynamic environment. The experimental results show that the improved ACO can effectively improve the searching ability of the global path and ensure the safety of the planned path. The fusion algorithm can realize smooth dynamic obstacle avoidance based on the advantages of global path.
Fairness-improved UGAL Routing Scheme against Selfish Users in Dragonfly Networks

Due to the distinct advantages, including low diameter, low cost and high performance, the Dragonfly topology has revealed great potential in meeting the transmission requirements of modern data center networks. In particular, the paths in Dragonfly networks can be classified into minimal and non-minimal paths, and this path asymmetry issue makes load balancing extremely challenging. For uniform traffic pattern, minimal routing is better, while non-minimal routing is more suitable when minimal paths are congested under adversarial traffic pattern. To tackle this issue, the Universal Globally-Adaptive Load-balanced (UGAL) routing scheme, which combines both minimal routing and non-minimal routing schemes, was proposed to achieve high performance. However, due to that the selfish users prefer to select the flow with the shortest RTT (Round-Trip Time) to occupy faster paths, which results in the unfairness among users. In this paper, we develop a routing scheme called fairness-improved UGAL (F-UGAL), which aims to mitigate the unfairness in the network exacerbated by the presence of selfish behavior. F-UGAL routing decision is based on the ratio of the number of flows that have been routed to non-minimal paths to the minimal paths. Finally, the simulations show that F-UGAL can reduce the performance gained by selfish users from 59.4% to 38.9% on FCT under uniform traffic pattern, and from 43.1% to 32.3% under adversarial traffic pattern.
Enhanced Zero-Shot Learning Algorithm for Super-Resolution Reconstruction of Remote Sensing Images

Remote sensing images have been widely used in many scenarios. However, it is difficult to obtain high-resolution images from remote sensing images with the limitations of the sensors. The Zero-Shot Super-Resolution (ZSSR) method constructs the training set only by the input image itself, improving the performance while the blur kernels are unknown. The improvement is limited due to the limited information of the input image itself. In this paper, an enhanced zero-shot super-resolution is proposed to solve the problem of super-resolution(SR) reconstruction when the fuzzy kernel is unknown by downsampling the enhanced images of Content Adaptive Resampler(CAR) network to build the training set, and introducing the Convolutional Block Attention Module (CBAM) and the residual module to expand the network scale and optimizing the network structure. Experimental results on UCMerced_LandUses and other datasets show that, compared with ZSSR, the proposed method has better super-resolution reconstruction results in terms of image detail and image quality.
Local Semantic Feature-Based Hierarchical Matching Algorithm for Wide-Area Remote Sensing Images

Remote sensing image matching is an important branch of image matching in computer vision, and with the increasing variety of remote sensing images, the quality differences are also increasing. Aiming at the bottleneck problem of being unable to intelligently match wide-area remote sensing images with significant differences in scale information such as size and physical resolution, this paper proposes a remote sensing image hierarchical matching algorithm based on local semantic features, which solves the problem of low accuracy of remote sensing image matching in the case of large disparity in field of view. The experimental results show that the matching accuracy of the proposed regional hierarchical matching algorithm can be effectively improved compared with the direct matching algorithm of remote sensing images.
Error Correction of ASR in Air Traffic Control

Automatic Speech Recognition (ASR) for Air Traffic Control (ATC) is designed to convert ATC voice commands into text for Pilot-Controller Voice Communications (PCVCs) and other downstream tasks. Proper recognition of ATC instructions is critical to flight efficiency and flight safety. In order to improve the performance of speech recognition in air traffic control, we propose a transcription-based error correction method exploiting parallel Sequence-to-Sequence (S2S) models of Conformer and Transformer, which translate ASR output into corrected text. In this design, the models are trained by paired data of ASR hypothesis and ASR reference. To acquire ASR transcriptions with recognition errors, we perform data augmentation by applying time-frequency masking, adding various noises to the ATC speech, using n-best outputs of the ASR decoder, and adding correct text to the training data. The method also sets similarity threshold to ensure the correctability of the input and performs the output-checking to obtain reliable output. Experiments show that the proposed method reduces Character Error Rate (CER) and Sentence Error Rate (SER) by up to 1% and 8.3%, respectively.
Random Degradation Model for Deep Blind Image Super-Resolution

Degradation models are widely explored in Super-resolution (SR) field. The traditional degradation model, which mainly involves blur and downsampling degradation, cannot well simulate the degradation in real-world scenarios. Although some degradation models have made good progress by using a series of complex degradation types, they ignore some simple cases in reality. They, therefore, cannot cover the various degradations of real images. To address this problem, we propose a novel random degradation model. Specifically, our degradation model consists of various degradation types, including blur, noise, multi-step sampling, rotation and JPEG compression. Moreover, a novel random and dropout strategy is employed to generate the degradation sequence of different lengths to simulate various real-world degradations better. Based on the random degradation model, we extend the powerful ESRGAN to a practical super-resolver (namely, RDGAN). To verify the effectiveness of the new degradation model, we have made extensive comparisons on synthetic and real datasets. The results have shown that the new degradation model helps to improve the ability of the super-resolver significantly, thus benefiting existing deep super-resolution networks in practical use.
Ontology-Based Knowledge Graph Construction and Application for Large Workpiece Forging

Due to the advent of the big data era and the redundancy of industrial data, people use a series of information technologies to continuously promote the transformation of traditional industry, and the development of industry is bound to move into the era of intelligence. The article carries out the construction of process knowledge mapping on the existing industrial process design scheme, constructs a process ontology model through industrial specifications, starts from acquiring data from industrial archives, identification of industrial entities, extraction of relationships between industrial entities and fusion of process knowledge, and constructs a process assembly knowledge mapping in the forging field by storing the acquired process data into the graph database, and finally The visualization display of data storage based on database Neo4j is realized. The experimental results have verified the feasibility of the designed diagram.
Proactive Edge Computing for Video Streaming: A Mutual Conversion Model for Varying Requirements on Representations

The vast proliferation of video streaming traffic imposed unprecedented challenges to origin servers in the Core Network (CN), further exacerbated by the diversity of requirements on video encoding format, resolution, and frame rate. Multi-access Edge Computing (MEC) has been introduced to mitigate the problem by storing and transcoding videos at the network edges to reduce traffic to CN. Traditional approaches hardly convert videos mutually or successively, limiting the overall performance of edge servers. By applying the space-time video super-resolution (STVSR) algorithm and transcoding on edge servers, this work proposes a novel mutual conversion model with a conversion graph among representations. The model measures and estimates the qualities of conversion outputs to maintain high Quality of Experience (QoE). Moreover, an off-peak proactive cache replacement algorithm is introduced to utilize remaining resources by prefetching and pre-converting popular representations, catering to the changing interests of clients. Transmission history is also collected to predict band-width and playback buffer length. The experimental results demonstrate that the proposed approach significantly reduces traffic to the origin server while achieving high QoE.
A Group-Correlated Privacy Protection Trajectory Publishing Method Based on Differential Privacy

The group relationship (Community Relation) contained in the trajectory data can be used for hot spot exploration, community governance, and traffic diversion, which has broad application prospects. Trajectory group association privacy refers to the user relationship with a similar movement mode in the trajectory data. Publishing trajectory data to analysts without protection will cause the leakage of such privacy. Recently, trajectory correlation privacy has attracted the attention of researchers, proposing solutions based on differential privacy. Still, existing methods are limited to protecting the motion patterns of two users and cannot be used in multi-user scenarios. Moreover, existing methods use heuristic strategies to reconstruct trajectories, which have excessive noise increase and large loss of published trajectory availability. Because of the above problems, we design a probability differentiation tree (PDT) structure to describe the user's movement pattern, then define the probability differentiation tree similarity function. A noise probability differentiation tree generation algorithm (NPDT) is proposed to realize the trajectory of user-associated privacy protection by adding Laplace noise to the probability value of PDT. We also propose the trajectory reconstruction algorithm (TRA) to reconstruct each user trajectory through the noise probability differentiation tree, noise trajectory number distribution, and noise trajectory length distribution to form the final published trajectory data set. Theoretical analysis and experimental results show that the proposed privacy protection method effectively maintains the availability of trajectory data while improving the privacy protection intensity of group association.