Reviewerly/exHarmony huggingface dataset:
DACI: An Index Structure Supporting Attributed Community Queries

With the development of social networks, the problem of community query on attributed graphs has received increasing attention. Attributed communities models based on attributed k-cores, which are k-cores with vertices sharing keywords, have achieved great performance in finding high quality communities. However, existing solutions primarily rely on online pruning, resulting in low efficiency. We propose a dynamic three-layer index, called DACI, and a query algorithm based on it to efficiently find attributed k-cores. In the first layer, a hash table indexes attribute information. The second layer stores the coreness information in attributed k-cores while the third layer uses coreness to index corresponding vertices in attributed k-cores. Additionally, DACI dynamically stores the attributed k-cores calculated during the query process to improve the efficiency of subsequent queries. We conduct experiments and analyze results on four real datasets. The results indicate that the proposed solution has better query efficiency than existing solutions.
TSCMR:Two-Stage Cross-Modal Retrieval

Currently, large-scale vision and language models has significantly improved the performances of cross-modal retrieval tasks. However, large-scale models require a substantial amount of computing resources, so the execution of these models on devices with limited resources is challenging. Thus, it is paramount to reduce the model size and minimize computing costs of a model without sacrificing its performance. In this paper, we improved TERAN by dividing cross-modal retrieval into two stages: image-text coarse-grained matching and image-text fine-grained matching. Specifically, we present a novel approach called Two-Stage Cross-Modal Retrieval network(TSCMR). To reduce model size after model training, our approach utilized a new knowledge distillation method for Transformer-based models. Experiments have shown that our approach maintains a performance comparable to TERAN on the MS-COCO 1K test set, while being 2x smaller and 3.1x faster on inference.
A Novel TCM Prescription Recommendation Algorithm Based on Deep Crossing Neural Network

In this manuscript, a TCM prescription recommendation model based on deep crossed neural network was proposed, which aims to solve the problem caused by sparse discrete features in TCM prescription information obtained after encoding and feature automatic cross-combination through embedding layer and residual network. Use residual neural network to reduce overfitting and make convergence faster. Using the multi-level residual network, the combined feature vector of medical records and prescriptions is fully residual operation, so that the model can obtain more nonlinear feature information between medical records and prescriptions, and realize the corresponding treatment according to the name of the disease. The function of prescription to improve the level of clinical treatment.
STAD: Multivariate Time Series Anomaly Detection Based on Spatio-Temporal Relationship

Anomaly detection for multivariate time series is a very complex problem that requires models not only to accurately identify anomalies, but also to provide explanations for the detected anomalies. However, the majority of existing models focus solely on the temporal relationships of multivariate time series, while ignoring the spatial relationships among them, which leads to the decrease of detection accuracy and the defects of anomaly interpretation. To address these limitations, we propose a novel model, named spatio-temporal relationship anomaly detection (STAD). This model employs a novel graph structure learning strategy to discover spatial features among multivariate time series. Specifically, Graph Attention Networks (GAT) and graph structure are used to integrate each time series with its neighboring series. The temporal features of multivariate time series are jointly modeled by using Transformers. Furthermore, we incorporate an anomaly amplification strategy to enhance the detection of anomalies. Experimental results on four public datasets demonstrate the superiority of our proposed model in terms of anomaly detection and interpretation.
GANs-Based Model Extraction for Black-Box Backdoor Attack

Machine learning (ML) models, e.g., deep neural networks (DNNs), are susceptible to backdoor attack. Backdoor attack involves embedding concealed backdoors into deep neural networks during training, which are activated by attacker-specified triggers in malicious inputs, causing the model to produce erroneous outputs. In the context of backdoor attacks, neural network models may experience misjudgment, such as an instance where a traffic sign image denoting “stop” is mistakenly identified as a “proceed” sign by a traffic sign identification model under the influence of the attack. This misclassification poses a significant safety risk to the domain of autonomous driving. Traditional white-box attacks require to access the training samples. Different from white-box attacks, black-box attacks adopt the settings that the training set and model internals is inaccessible. In practice, the training dataset is usually not shared due to privacy or copyright concerns, therefore black-box attacks are more realistic than white-box ones. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. Our attack strategy consists in recovering a local model to substitute for the targeted DNN, using the local model to generate triggers, where inputs with the trigger will subsequently be misclassified by the targeted model. We incorporate Generative Adversarial Networks (GANs) to approximate the extracted model to the targeted model. All neural network models seems inherently possess “intrinsic” backdoors, even in the absence of attacks or poisoning. Grounded in this perspective, we generate triggers associated with the model’s “intrinsic” backdoors.
IMAGE QUALITY ASSESSMENT BY CONVOLUTIONAL NEURAL NETWORK USING THE TID2013 DATABASE

The article is devoted to the problem of automatic image quality assessment by a convolutional neural network when using the common TID2013 image database for training the neural network. The TID2013 database was chosen for the reason that it contains 25 base real-world images, which were distorted from these images using 24 different distortion methods and with 5 distortion levels, creating a sufficiently large database of 3000 images for training the neural network. For each image, an average expert assessment of its quality is given. All input images for the neural network are divided into two groups - the training set and the validation set. We consider two options for solving the problem of building and training a neural network and the corresponding data structure. The first option is reduced to the task of numerical regression using the numerical values of expert assessments. The second option is the task of classifying the training and validation images into 5 classes according to their quality corresponding to distortion levels. Keras and TensorFlow software tools are used to build and study the neural network. The neural network structures and relevant parameters for training each layer are presented, as well as graphs of accuracy changes for training and validation images during training.
Lifting Wavelets with OGS for Doppler Profile Estimation

This article discusses the second-generation wavelet transform concept and technique and its application to the noise removal problem of MST radar data. Located near Gadanki in Andhra Pradesh, India, the MST radar is collecting data on climate change. To obtain weather data, the signal collected by the radar needs to be analyzed, which usually requires power spectrum estimation. Most parametric and non-parametric methods cannot predict Doppler at an altitude above 14 KM, which makes to search for introduction of new denoising methods. More research is predominantly done on many denoising algorithms and tested with the simulated signal with various thresholds. It is observed that Lifting wavelets (LWT) with OGS is more effective in denoising the signals. Split, predict, and update are the three phases of lifting transform which on application of these steps reduces noise effectively. The LWT with OGS is applied to MST radar data and the research results shows that the noise level is reduced at higher altitudes and the signal-to-noise ratio is improved.
Mobilenetv2-SSD Target Detection Method Based on Multi-scale Feature Fusion

Most of the deep learning networks used in target detection algorithms are very complex, which poses difficulties for edge devices with limited storage capacity and computing power, such as mobile terminals. A MobileNetv2-SSD target detection algorithm based on multi-scale feature fusion is proposed in this paper. We use MobileNetv2 to improve the SSD algorithm by replacing the original network’s backbone network VGG16 with the lightweight network MobileNetv2 to perform the model streamlining. The problem of accuracy loss due to model compression is improved by the designed feature fusion module and finally tested on the dataset. The experimental results show that the mAP and FPS are 90.37% and 50FPS, respectively, and the detection accuracy is improved by 3.24% compared with the original SSD network, the detection speed is 1.9 times of the original one, and the size of the model is only 28.5M, which is only 30.5% of the original model.
ProtoMix: Learnable Data Augmentation on Few-Shot Features with Vector Quantization in CTR Prediction

Click-Through Rate (CTR) prediction is a critical problem in recommendation systems since it involves enormous business interest. Most deep CTR model follows an Embedding & Feature Interaction paradigm. However, the feature interaction module cannot work well without a good embedding representation of features. Due to the long-tail phenomenon in real scenes, few samples are provided in the dataset for a large proportion of features. In this paper, we present ProtoMix, a model-agnostic framework for learnable data augmentation on few-shot features in CTR prediction. ProtoMix automatically extracts information from co-occurred features within the same instance to assign prototype embedding with vector quantization for few-shot features and further synthesize the embedding representation of the augmented virtual instance for training. Original embedding, feature interaction module, and the embedding generator are jointly trained on a well-designed objective in an end-to-end manner in ProtoMix. We experimentally validate the effectiveness and compatibility of ProtoMix by comparing it with baseline and other data augmentation methods on different deep CTR models and multiple real-world CTR benchmark datasets.
An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.
A long‐term memory enhanced echo state network and its optimization

Abstract This paper proposes a new type of echo state network called Long‐Term Memory Enhanced Echo State Network (LTME‐ESN). By extending the ideas of the forget gate and the input gate from the LSTM, LTME‐ESN modifies the equation for updating the state in a Leaky Integral Echo State Network (Leaky‐ESN). By regulating the accumulation of information, the construction of extended periods of dependency between states may be delayed, allowing for adaptive management of the data transported from the previous state to the present one. In order to optimize parameters by using Stochastic Gradient Descent (SGD), this research presents a necessary condition for LTME‐ESN to meet the properties of the echo state network. The study uses low‐frequency sinusoidal, high‐frequency sinusoidal, and chaotic time series to demonstrate the model's efficiency. Simulations show that LTME‐ESN outperforms Leaky‐ESN in prediction accuracy and variation.
MHRN-ST: Go Board Recognition With Multi-stage Highlight Removal Network Based On Swin Transformer

Artificial intelligence has generally surpassed humans in the game of Go, and the actual human-machine gaming environment is often more complex. In this environment, issues such as highlights, piece misplacement, and occlusions on the image can severely affect board recognition and reduce the intelligence level of the robot player. This paper proposes a multi-stage highlight removal network based on Swin Transformer, which can remove highlights on the image and recognize Go game information. Firstly, the image is processed by a multi-task detection network based on YOLOv5 to obtain highlight information. Then, the image is enhanced with the highlight information and input into the highlight removal network based on Swin Transformer for highlight removal. The output image is further processed by the detection network to obtain board information. We collected a total of 4242 images from publicly available datasets to train the detection network, which to some extent solves the problem of piece displacement. Additionally, we collected 1096 pairs of images for training the highlight removal network. Finally, the effectiveness of the proposed method was tested on a test set containing 56 images, achieving a recognition accuracy of 95.67%.
Image Encryption Algorithm Based on Quantum Chaotic Mapping and Wavelet Transform

Aiming at the problems of low security and slow encryption speed of traditional chaotic system, this paper designs a wavelet domain image encryption algorithm which combines quantum chaotic mapping and Lorenz hyper chaotic mapping. Firstly, the SHA-256 hash algorithm is used to generate the initial key, and the quantum chaotic sequence and Lorenz hyper chaotic sequence are generated iteratively. Then, the plaintext image matrix is decomposed by discrete wavelet, and the sub-band coefficient matrix of each component after decomposition is scrambled by quantum chaotic sequence, and then it is reconstructed by discrete wavelet to complete the scrambling. Finally, Lorenz hyper chaotic sequence is used to diffuse the scrambled images, and the cipher text images after secondary encryption are obtained and analyzed for security. The simulation results show that the chaotic sequence combined with quantum chaos and used for encryption can be associated with the plaintext image through SHA-256, and the way of wavelet domain scrambling can get a better encryption effect, good performance against all kinds of attacks, and has the characteristics of high security and fast computing speed.
Enhancing Robot Manipulation Skill Learning with Multi-task Capability Based on Transformer and Token Reduction

Learning skills from videos based on language instructions is an innate ability for humans. However, robots face significant challenge in establishing connections between language instructions and visual observations, exhibiting precise control, and retaining memory of previous actions. Additionally, multi-task learning is difficult due to potential interference between tasks. To address these issues, we propose an algorithm called CSATO, based on Transformer network architecture for robot skill learning from videos and achieving multi-task learning with given instructions. Our algorithm consists of a visual-language fusion network, a token reduction network, and a Transformer decoder network, which is trained by predicting actions for corresponding states. The fusion network facilitates the integration of information from different modalities, while the token reduction network reduces the number of tokens passed to the Transformer using channel and spatial attention mechanisms. Finally, the Transformer comprehensively models the relationships among instructions, current and historical visual observations, and generate autoregressive action predictions. To improve multi-task learning performance, learnable offsets parameters are introduced for each task in the final action prediction stage. The effectiveness of our approach is demonstrated through sereral continuous manipulation task, exhibiting the effectiveness of the proposed algorithm.
Image Compressed Sensing Reconstruction via Deep Image Prior with Feature Space and Texture Information

Deep learning has yielded remarkable achievements in compressed sensing image reconstruction in recent years. However, when the sampling rate is low, the reconstruction image is blurred and lacks texture details. In this paper, we design a dual-path compressed sensing reconstruction network based on image feature space information flow and texture information, which can solve the above problems to a certain extent. The feature space information flow path employs proximal gradient descent algorithms to map essential structural information from the pixel space to the feature space, effectively reducing data dimensionality and redundancy. On the other hand, after combining the attention mechanism, the texture information path restores the texture details of the image in the frequency domain, which can effectively enhance the quality and visual effects of the reconstruction image. During training, a unified loss function guides the alternating optimization of both paths. Evaluation of prominent benchmark datasets, including Set5, Set11, and BSD68, reveals that our proposed method outperforms traditional iterative approaches and existing deep learning-based methodologies in terms of both reconstructed image quality and network robustness under low sampling rates.
Russian National Public Library for Science and Technology. The past, present and future. (The 65-th anniversary)

The foundation, development and activities of the Russian National Public Library for Science and Technology (RNPLS&amp;T) are examined in the historical aspect. The precedent events, the origins of the library and development from the initial library collections in science and technology in the early 20-th century are described. The RNPLS&amp;T (the USSR State Public Library for Science and Technology, SPLS&amp;T USSR, at the time of its establishment) has inherited the legacy and, partially, the collections of the State Science Library. The authors focus on the evolution of the SPLS&amp;T USSR as the head sci-tech library of the nation providing information support of new developing industrial technologies as well as prospective scientific disciplines. The authors offer information on current RNPLS&amp;T activities, their vectors and performances; characterize the library collections, e-catalogs, digital archives and RNPLS&amp;T’s e-library, as well as library and information services, educational activities and its large-scale international scientific and educational events. The publication is prepared on the occasion of the RNPL&amp;T’s 65-th anniversary
KGRL: A Method of Reinforcement Learning Based on Knowledge Guidance

Reinforcement learning usually starts from scratch. Due to the characteristic of receiving feedback from interactions with the environment, starting from scratch often leads to excessive unnecessary exploration. In contrast, humans have a lot of common sense or prior knowledge when learning. The existence of prior knowledge accelerates the learning process and reduces unnecessary exploration. Inspired by this, in this article, we propose a knowledge-guided approach to learning reinforcement learning policies. We use prior knowledge to derive an initial policy and guide the subsequent learning process. Although prior knowledge may not be completely applicable to new tasks, the learning process is greatly accelerated because the initial policy ensures a fast start to learning and guidance in the middle allows for avoiding unnecessary exploration. This is a new framework that combines human prior suboptimal knowledge with reinforcement learning. We refer to it as KGRL - Knowledge-Guided Reinforcement Learning. The KGRL framework includes defining fuzzy rules based on prior knowledge, learning an initial policy based on fuzzy rules, and guiding the new policy through knowledge-guided encoding. We conducted experiments on four tasks in OpenAI Gym and PLE, and the results show that our algorithm combines prior knowledge and reinforcement learning effectively. By utilizing knowledge-guided encoding, it improves the performance of reinforcement learning, trains faster, and increases rewards by approximately 12.7 $$\%$$ .
FACE RECOGNITION METHODS IN VIDEO SURVEILLANCE SYSTEMS USING MACHINE LEARNING

The article is dedicated to the investigation of face identification methods and aims to determine the most suitable one for a security system based on facial recognition from surveillance cameras. The time costs of these methods and their robustness against geometric scale distortions and rotations in various planes have been analyzed. Custom datasets have been generated for experimentation purposes. Special attention has been given to striking a balance between the speed and accuracy of the examined methods for their utilization as the initial stage of a security system based on facial recognition in a video stream. The conducted research has revealed that the most effective methods are RetinaFace-MobileNet0.25, FaceBoxes, SCRFD500MF, and CenterFace; RetinaFaceResNet125, DSFD, and RetinaFaceMobile0.25 which are resilient to facial rotations. Furthermore, when selecting the most optimal facial recognition method for application within a security system, the presence of informative facial parameters was taken into account, as well as the fact that the recognition methods used in the subsequent stage have their limitations concerning resilience to affine transformations.