Reviewerly/exHarmony huggingface dataset:
DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network

Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation systems. The results in previous research, however, suffer from problems such as data compression, information damage, and insufficient learning. Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item. In many recommendation systems, the goal is to maximize the number of clicks so the items returned to a user can be ranked by an estimated CTR. The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left and right parts; and the right one obtains a better item representation vector by the GCN. In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed. It used multiple comparison models and the MKR and FM_MKR algorithms as well as the DFM-GCN algorithm constructed in this paper. Having achieved a state-of-the-art performance, the experimental results of the AUC and f1 values verified by the CTR as well as the accuracy, recall, and f1 values of the top-k showed that the proposed approach was excellent and more effective when compared with different recommendation algorithms.
Incremental Graph Computations: Doable and Undoable

The incremental problem for a class \( {\mathcal {Q}} \) of graph queries aims to compute, given a query \( Q \in {\mathcal {Q}} \) , graph G , answers Q ( G ) to Q in G and updates ΔG to G as input, changes ΔO to output Q ( G ) such that Q ( G ⊕ ΔG ) = Q ( G )⊕ ΔO . It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q , ΔG and ΔO , which reduces the computations on possibly big G to small ΔG and ΔO . No matter how desirable, however, our first results are negative: For common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a) localizable , if its cost is decided by small neighbors of nodes in ΔG instead of the entire G ; and (b) bounded relative to a batch graph algorithm \( {\mathcal {T}} \) , if the cost is determined by the sizes of ΔG and changes to the affected area that is necessarily checked by any algorithms that incrementalize \( {\mathcal {T}} \) . We show that the incremental computations above are either localizable or relatively bounded by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.
Application of machine learning in intelligent encryption for digital information of real-time image text under big data

Abstract In the context of big data, the exploration of the application effect of machine learning in intelligent encryption for real-time image text digital information aims to improve the privacy information security of people. Aiming at the problem of digital information leakage of real-time image text, the convolutional neural network is introduced and improved by adding a preprocessing module to form AlexNet, to encrypt the digital information of real-time image text. Besides, to take into account both the security effect and the real-time performance of the system, the image text is encrypted by the chaotic sequence generated by a one-dimensional chaotic system called Logistic-Sine and a multi-dimensional chaotic system named Lorenz. In this way, a real-time image text encryption model is constructed by combining the chaotic function and AlexNet. Finally, a simulation experiment is performed to analyze the performance of this model. The comparative analysis indicates that the recognition accuracy of feature extraction of image text by the intelligent encryption model reaches 94.37%, which is at least 3.05% higher than that of other neural network models by scholars in related fields. In the security analysis of image text encryption, the information entropy of pixel values at (0, 0) of the proposed model is close to the ideal value 8. Meanwhile, the value of the number of pixels change rate is generally more than 99.50%, and the value of the unified average changing intensity is generally more than 33.50%. This demonstrates that the model has good security in resisting attacks. Therefore, the constructed model can provide good security guarantee under the premise of ensuring the recognition accuracy, which can provide experimental basis for improving the security performance of real-time image text data in the future.
CertificateChain: decentralized healthcare training certificate management system using blockchain and smart contracts

Abstract Objective Managing training certificates is an important issue in research that can lead to serious issues if not addressed properly. For institutions that currently do not have a dedicated management system for these training certificates, a central database is the most typical solution. However, such a system suffers from several risks, such as a single-point-of-failure. Materials and Methods To address this issue, we developed and evaluated CertificateChain, a decentralized training certificate management system by using peer-to-peer blockchain and automated smart contracts. We developed an efficient certificate dividing-and-merging algorithm to overcome the transaction size limit on blockchain. Results We performed experiments on the system to evaluate its performance, then created a web app and tested the system in a real-world scenario. CertificateChain scaled linearly in terms of time compared with the total number of certificates added and could be quickly queried for existing data stored on-chain. Discussion CertificateChain was able to store and retrieve the training certificates on the blockchain network, with limitations including a comparative analysis of other systems, evaluation of different consensus protocols, examining certificates off-chain, a thorough comparison with a centralized system, and the extension to the main public Ethereum network. Conclusion We believe that these results indicate that blockchain technology could be a viable decentralized alternative to traditional databases in this use case. Our software is publicly available at: https://doi.org/10.5281/zenodo.6257094.
A White-Box Testing for Deep Neural Networks Based on Neuron Coverage

With the introduction of neuron coverage as a testing criterion for deep neural networks (DNNs), covering more neurons to detect more internal logic of DNNs became the main goal of many research studies. While some works had made progress, some new challenges for testing methods based on neuron coverage had been proposed, mainly as establishing better neuron selection and activation strategies influenced not only obtaining higher neuron coverage, but also more testing efficiency, validating testing results automatically, labeling generated test cases to extricate manual work, and so on. In this article, we put forward Test4Deep, an effective white-box testing DNN approach based on neuron coverage. It is based on a differential testing framework to automatically verify inconsistent DNNs’ behavior. We designed a strategy that can track inactive neurons and constantly triggered them in each iteration to maximize neuron coverage. Furthermore, we devised an optimization function that guided the DNN under testing to deviate predictions between the original input and generated test data and dominated unobservable generation perturbations to avoid manually checking test oracles. We conducted comparative experiments with two state-of-the-art white-box testing methods DLFuzz and DeepXplore. Empirical results on three popular datasets with nine DNNs demonstrated that compared to DLFuzz and DeepXplore, Test4Deep, on average, exceeded by 32.87% and 35.69% in neuron coverage, while reducing 58.37% and 53.24% testing time, respectively. In the meantime, Test4Deep also produced 58.37% and 53.24% more test cases with 23.81% and 98.40% fewer perturbations. Even compared with the two highest neuron coverage strategies of DLFuzz, Test4Deep still enhanced neuron coverage by 4.34% and 23.23% and achieved 94.48% and 85.67% higher generation time efficiency. Furthermore, Test4Deep could improve the accuracy and robustness of DNNs by merging generated test cases and retraining.
Attention-Enhanced One-Stage Algorithm for Traffic Sign Detection and Recognition

Detection and recognition of traffic signs are the keys to creating advanced driving assistance systems. Making highly precise maps also requires the identification and extraction of such road elements as traffic signs. Traditional detection and recognition methods no longer meet today’s needs, and object recognition algorithms based on deep learning have become the mainstream solution. However, current algorithms have limitations. The recognition speed of one-stage strategy algorithms is fast, but recognition accuracy is not satisfactory especially for small objects. The accuracy of two-stage algorithms is higher, but the recognition speed is extremely slow. This paper solves these problems with a proposed parallel attention convolution module, a channel attention pyramid network, and a loss function diagonal and center point IoU based on the YOLOv3 algorithm. The improved models in this paper are compared with SSD, YOLOv3, and Faster RCNN. Experimental results show that the proposed models have some improvement over the above models: the mAP of the models with PACM, CAFPN, and DCPIoU was 76.02%, compared with SSD300, SSD500, Faster RCNN, and YOLOv3, which had improvements of 9.27%, 6.93%, 2.94, and 5.3%, respectively. And the FPS of the improved model is basically the same as the original YOLOv3, without reducing the real-time performance.
Why is a document relevant? Understanding the relevance scores in cross-lingual document retrieval

Modern cross-lingual document retrieval models are capable of finding documents relevant to the query. However, they do not have the capabilities for explaining why the document is relevant. This paper proposes a novel learning-to-rank model named LM-EMD that uses the multilingual BERT language model and Earth Mover's Distance (EMD) to measure the document's relevancy to the input query and provide interpretable insights into why a document is relevant. The model uses the query and document token's contextual embeddings generated with multilingual BERT to measure their distances in the embedding space, which are then used by EMD to calculate the document's relevance score and identify which document tokens contribute the most to its relevancy. We evaluate the model on five language pairs of varying degrees of similarity and analyze its performance. We find that the model (1) performs similar as the best performing comparing model on high-resource languages, (2) is less effective on low-resource languages, and (3) provides insight into why a document is relevant to the query.
Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles

Currently, autonomous vehicles and intelligent robots are used in an extensive set of industrial applications. However, in the recent past, the frequency with which small obstacles arise on highways has risen dramatically, which will lead to severe incidents on highways. Hence, small obstacle detection is critical for increasing the efficiency of autonomous vehicles to enable their safe navigation to avoid road accidents. On the other hand, these small-size obstacles are of varying sizes, shapes, and colors and are difficult to be detected under low lighting and illumination conditions. Existing research studies suggest deep-learning-assisted semantic segmentation models; however, an optimal model along with improved performance is of greater necessity. From this perspective, we have suggested transfer learning-based approaches using state-of-the-art semantic segmentation models namely UNet++, PSPNet, PANNet, LinkNet, and DeepLabV3+ for the detection of small-size obstacles under strict lighting and illumination conditions. Furthermore, we used images of road scenes with extremely small-size obstacles, which were neglected by past research studies, since these obstacles were not included in the databases that they were using in their findings and may have failed to effectively address the problem of obstacle detection. Moreover, for faster and better convergence, we have modified the backbone architectures of these models with Residual-Network (ResNet)-18 and ResNet-34 trained on ImageNet weights. It is observed that DeepLabV3 + ResNet-18 as backbone architecture shows the highest results with a mean intersection-over-union score of 64% along with a 95% value of accuracy.
Lossless Data Hiding in Encrypted Images Compatible With Homomorphic Processing

Reversible data hiding in ciphertext has potential applications for privacy protection and transmitting extra data in a cloud environment. For instance, an original plain-text image can be recovered from the encrypted image generated after data embedding, while the embedded data can be extracted before or after decryption. However, homomorphic processing can hardly be applied to an encrypted image with hidden data to generate the desired image. This is partly due to that the image content may be changed by preprocessing or/and data embedding. Even if the corresponding plain-text pixel values are kept unchanged by lossless data hiding, the hidden data will be destroyed by outer processing. To address this issue, a lossless data hiding method called random element substitution (RES) is proposed for the Paillier cryptosystem by substituting the to-be-hidden bits for the random element of a cipher value. Moreover, the RES method is combined with another preprocessing-free algorithm to generate two schemes for lossless data hiding in encrypted images. With either scheme, a processed image will be obtained after the encrypted image undergoes processing in the homomorphic encrypted domain. Besides retrieving a part of the hidden data without image decryption, the data hidden with the RES method can be extracted after decryption, even after some processing has been conducted on encrypted images. The experimental results show the efficacy and superior performance of the proposed schemes.
AR-Assisted Surgical Guidance System for Ventriculostomy

Augmented Reality (AR) is increasingly used in medical applications for visualizing medical information. In this paper, we present an AR-assisted surgical guidance system that aims to improve the accuracy of catheter placement in ventriculostomy, a common neurosurgical procedure. We build upon previous work on neurosurgical AR, which has focused on enabling the surgeon to visualize a patient&#x0027;s ventricular anatomy, to additionally integrate surgical tool tracking and contextual guidance. Specifically, using accurate tracking of optical markers via an external multi-camera OptiTrack system, we enable Microsoft HoloLens 2-based visualizations of ventricular anatomy, catheter placement, and the information on how far the catheter tip is from its target. We describe the system we developed, present initial hologram registration results, and comment on the next steps that will prepare our system for clinical evaluations.
DeepLib: Machine translation techniques to recommend upgrades for third-party libraries

To keep their code up-to-date with the newest functionalities as well as bug fixes offered by third-party libraries, developers often need to replace an old version of third-party libraries (TPLs) with a newer one. However, choosing a suitable version for a library to be upgraded is complex and susceptible to error. So far, Dependabot is the only tool that supports library upgrades; however, it targets only security fixes and singularly analyzes libraries without considering the whole set of related libraries. In this work, we propose DeepLib as a practical approach to learn upgrades for third-party libraries that have been performed by similar clients. Such upgrades are considered safe, i.e., they do not trigger any conflict, since, in the training clients, the libraries already co-exist without causing any compatibility or dependency issues. In this way, the upgrades provided by DeepLib allow developers to maintain a harmonious relationship with other libraries. By mining the development history of projects, we build migration matrices to train deep neural networks. Once being trained, the networks are then used to forecast the subsequent versions of the related libraries, exploiting the well-founded background related to the machine translation domain. As input, DeepLib accepts a set of library versions and returns a set of future versions to which developers should upgrade the libraries. The framework has been evaluated on two real-world datasets curated from the Maven Central Repository. The results show promising outcomes: DeepLib can recommend the next version for a library as well as a set of libraries under investigation. At its best performance, DeepLib gains a perfect match for several libraries, earning an accuracy of 1.0.
Geometric Graph Representation Learning via Maximizing Rate Reduction

Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation learning methods (e.g., based on random walk and contrastive learning) are limited to maximizing the local similarity of connected nodes. Such pair-wise learning schemes could fail to capture the global distribution of representations, since it has no explicit constraints on the global geometric properties of representation space. To this end, we propose Geometric Graph Representation Learning (G2R) to learn node representations in an unsupervised manner via maximizing rate reduction. In this way, G2R maps nodes in distinct groups (implicitly stored in the adjacency matrix) into different subspaces, while each subspace is compact and different subspaces are dispersedly distributed. G2R adopts a graph neural network as the encoder and maximizes the rate reduction with the adjacency matrix. Furthermore, we theoretically and empirically demonstrate that rate reduction maximization is equivalent to maximizing the principal angles between different subspaces. Experiments on real-world datasets show that G2R outperforms various baselines on node classification and community detection tasks.
Near-real-time detection of co-seismic ionospheric disturbances using machine learning

SUMMARY Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Measurements of co-seismic ionospheric disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in near-real-time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning-based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform data set show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.
Securing Smart Homes Using Face Recognition

In today’s era of automation and smart devices, there is a crucial need to modify our current security system. With the help of technologies like IoT and machine learning, we can not only improve the current door lock security system but also seamlessly integrate it in our daily lives. The machine learning algorithm uses highly sophisticated algorithm in recognizing faces. Only the person whose face is matched can be able to unlock the door. So, limitation of managing keys will be resolved. This technology opens door to a vast number of possibilities where high security is required. Most importantly, this technology is highly affordable so that everyone can have access to maximum security at minimum cost.
Dynamic Spectrum Access for Internet-of-Things Based on Federated Deep Reinforcement Learning

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.