Reviewerly/exHarmony huggingface dataset:
A Recommender Algorithm Based on Knowledge Graph Convolutional Network and Knowledge Reasoning Optimization

The collaborative filtering-based recommender algorithm suffers the interaction sparsity problem and cold start problem, experts have introduced knowledge graphs (KGs) and applied graph neural network models to achieve more personalized recommendations. However, potential links between entities, which can further extend the user interaction data within a KG and thus improve the performance of the recommender algorithm, are yet to be fully explored and mined. A novel knowledge graph convolutional network (KGCN) recommender algorithm based on knowledge reasoning optimization is proposed. Firstly, knowledge graph completion was conducted based on the knowledge reasoning algorithm as to obtain a KG with a better user interaction and richer semantic information. Secondly, the KGCN was applied to capture more higher-order features and enhance the performance of personalized recommendation in embedding and aggregating the completed KG. Recommendation experiments on movie, book and music datasets were conducted and reached the highest AUC, F1-score and recall rates of 0.986, 0.940 and 0.503, respectively. These results verify that our method is effective and indicate that it can facilitate the performance of recommender algorithms.
Neural Network Model Pruning without Additional Computation and Structure Requirements

In past work, deep learning researchers always designed hyperparameters such as model structure and learning rate first and then used the training set to train the weights in this model. While unrestricted model structure design leads to massive neuron redundancy in neural network models. By pruning these redundant neurons, not only can the storage be compressed effectively, but also the operation can be accelerated. In this paper, we propose a method to utilize the training set to prune the model structure during training: 1) train the initialized model and bring it to basic convergence; 2) feed the entire training set into the model and calculate the activations of neurons in each layer; 3) calculate the threshold for neuron pruning in each layer according to the pruning ratio, delete neurons whose activation value is lower than the threshold, and correspondingly delete the weights of the upper and lower layers; 4) further train the pruned model so that it eventually converges. This method of deleting redundant neurons not only greatly deletes the parameters in the model but also achieves model acceleration. We applied this method to some mainstream neural network models: VGGNet and ResNet, and achieved good results.
Blockchain-Assisted UAV Data Free-Boundary Spatial Querying and Authenticated Sharing

The flexibility and low cost of unmanned aerial vehicles (UAVs) offer great potential for them in areas such as disaster relief, energy line inspection, and traffic monitoring. Multiple UAVs form an airborne UAV network to share geo-tagged observation data for better collaborative missions. Blockchain can solve the security threats caused by the environment’s untrustworthiness and the UAV networks’ openness. However, the key to sharing data in blockchain-assisted UAV networks is identifying and understanding observational data and providing authentication query services in free boundary spatial. This paper proposes a blockchain-assisted UAV network data-sharing framework based on Non-Fungible Token (NFT). First, we design a marking and describing data method based on NFT to help geo-tagged data be effectively understood. Moreover, we propose a free-boundary spatial index tree to manage data and provide efficient queries. Furthermore, combined with the consensus mechanism and the blockchain transaction tree, the proposed sharing framework can provide query results authentication. Compared with the existing schemes, analysis and experiments demonstrate that our scheme could support the arbitrary expansion of UAV flight range and the random distribution of observation data in space, save at least 22% of storage overhead and reduce more than 36% of query time overhead.
Improving Adversarial Transferability with Heuristic Random Transformation

Deep neural network is very vulnerable to adversarial examples, which add subtle perturbations on the original image that are difficult for human to perceive, but can make network produce wrong classification results. The current advanced adversarial attack methods can achieve satisfactory results under the white-box setting, but when attacking the black-box model, especially for the defense models, they show poor transferability. It can mainly improve the transferability of adversarial attacks under black-box settings from two perspectives of gradient optimization and image transformation. We propose a new image transformation method, which is different from treating each pixel equally in previous works. We consider using the size of gradient value to reflect the importance of pixels, assigning different scaling factors to each gradient unit, and conducting heuristic random transformation on the images input in each iteration to achieve data enhancement, obtain more stable update direction and escape from local optimal values. Extensive experiments on ImageNet Dataset show that the proposed method has better performance than the existing methods. In addition, our method can also be combined with other attack methods to further improve the transferability of adversarial attacks. Besides, our approach also has excellent performance on the defense models.
A Self-Attention Based Task-Adaptive Integration of Pre-trained Global and Local Classifiers for Few-Shot Classification

The few-shot image classification task aims to train a model to correctly classify unlabeled samples when only a few example images are available. Most current metric-based approaches consider only a single task and ignore the variability between tasks. In this paper, we extract potential information of images from both local and global levels by training feature extractors sensitive to global and local features. Then, the multilevel features are optimized further by employing a self-attention mechanism to assign suitable weights based on the target tasks’ characteristics. Extensive experiments on several benchmark datasets show that the proposed model has better generalization ability for different tasks, and our strategy improves by 4%~6% over the baseline, showing satisfactory results. Our code is available at: https://github.com/XiangLi0503/MTnet.
A Cooperative Two-stage Migrating Birds Optimization Algorithm Based on Quasi-Entropy

A cooperative two-stage migrating birds optimization (CTMBO) algorithm based on quasi-entropy is proposed to solve complex continuous numerical optimization problems. First, a two-stage cooperative mechanism including different mutation and crossover operators is employed to generate neighborhood solutions. Second, a quasi-entropy index is proposed as the judgment condition to balance exploration and exploitation dynamically. The elitist-guided evolutionary strategy, which is a co-evolution tactic from elite individuals and individuals in the following flock, is applied to strengthen global search capability. Meanwhile, the capability of local search is enhanced by the individual cooperation strategy, which is designed based on a co-evolution tactic among individuals in the following flock. The performance of CTMBO is evaluated on CEC 2017 benchmark suite by comparing it with certain state-of-art algorithms. The experimental results indicate the effectiveness of CTMBO on continuous complex problems.
Dynamic Generation of Immersive CAVE Environments: Using Digital Game Mechanisms

The virtualization of natural environments is becoming increasingly common in contemporary society, and it has found even more widespread application due to social restrictions, practical considerations, and economic factors. This study presents a dynamic generator of immersive collaborative environments in CAVE (Cave Automatic Virtual Environment), which employs stereoscopy to project 3D images onto a simulated CAVE. The environment supports real-time interaction with the projected elements, and the outcome of the project is a system that can dynamically generate virtual environments in CAVE of varying sizes and formats, rendering elements that can be projected onto a real model.
Spatial-temporal Consistency based Crowd Emotion Recognition

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation.
A Robust Framework for Fixing The Vulnerability of Compressed Distributed Learning

Nowadays, as a prevailing paradigm for large-scale machine learning, distributed learning has been faced with two challenges, communication bottleneck and limited robustness. For the communication challenge, compression is widely-used as a solution. For the robustness challenge, some robust defence methods have been proposed. However, previous works that simultaneously consider these two challenges are limited. Through an experiment on the w8a dataset, we found that compressed distributed learning with rand-K is vulnerable to poisoning attacks. Therefore, in this paper, we propose a robust compressed distributed framework for distributed learning settings. Experimental evaluations on a9a and w8a datasets have shown the effectiveness of our proposed framework, which markedly decreases the average optimality gap from 1.47E −2 and 2.15E −2 to 3.98E −4 and 4.33E −4 respectively.
Efficient Federated Learning Aggregation Protocol Using Approximate Homomorphic Encryption

Federated Learning (FL) is a novel machine learning paradigm that enables multiple participants to collaboratively train a machine learning model by aggregating local gradients from each client without sharing sensitive train data with each other. The clients’ gradients plaintext transmission makes FL systems vulnerable to inference attacks, which aim to infer clients’ data from their model updates. Masking local gradients with additive homomorphic encryption (especially the Paillier scheme) before gradients aggregate is a straightforward way to ensure security. Unfortunately, this method needs traversing and encrypting gradients element-wisely, resulting in low computation efficiency and high communication cost. In this paper, we present BatchAgg, an efficient aggregation protocol for FL utilizing ciphertext packing technique provided by approximate homomorphic encryption scheme. Instead of encrypting gradients individually, BatchAgg encrypts a gradient vector into one ciphertext and homomorphically computes batch operations. Specifically, BatchAgg is built on the federated averaging protocol. In detail, we implement a federated image classification model for datasets horizontal split as a baseline and replace the Paillier-based aggregation protocol with BatchAgg to accelerate model training with lower communication cost. Evaluation results show that BatchAgg achieves 60× training speedup while reducing the communication cost by 57% compared with Paillier under the same security level. Moreover, BatchAgg can be easily integrated into existing models while assuring security throughout federated training without causing performance loss.
Computation of Mobile Phone Collaborative Embedded Devices for Object Detection Task

In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.
Research on False Comment Recognition Algorithm Integrating crazy fan base Subculture Phenomenon

Nowadays, online shopping has gradually become the mainstream, and the importance of user comments has gradually increased. Due to the operation of “playing” and “controlling comments”, the fake “brainless blowing” comments that overtout the products endorsed by stars have poured out in large numbers. These fake reviews are easy to lead users to make wrong purchase decisions, damage the interests of users, and also easily cause nonfan group users’ aversion, affecting the promotion and sales of merchants. Therefore, it becomes very important to identify fake comments from a large number of comments, but it is very difficult for users to identify fake comments quickly and accurately in a large number of comments. In response to this problem, this paper collects the semantic and emotional characteristics of the text content of user reviews, combines the behavior characteristics of users when making purchase decisions, and builds a false recognition model for user reviews based on LSTM (Long Short-term Memory) to assist users in identifying false reviews. So that users can make accurate decisions and purchase products that meet their needs. Based on the F1 value, accuracy rate, and recall rate as evaluation indicators, after comparison experiments with eight commonly used models, the algorithm in this paper has the best effect.
Optimal Datacenter Selection for Cloud Services Using Swarm Intelligence

Cloud computing has become one of the most influential technologies in the computer science field. In the modern-day world, people are dependent on cloud services in every aspect of life. Cloud services process a vast number of user requests through various data centers. So often, data center selection plays a vital role in user satisfaction and the success of cloud services. Researchers are working relentlessly to use the behaviors of nature to solve real-world problems. In this paper, we used swarm intelligence to find optimal data centers for userbases. The swarm intelligence algorithms use their experience and knowledge of their neighbors to direct the algorithm toward an optimal solution. We have designed a particle swarm optimization-based data center selection policy inspired by swarm intelligence. To explore the accuracy and performance of the algorithm, we simulated it with different real-world scenarios using CloudAnalyst. The simulation results of response time and data processing time of the cloud environment exhibit that the proposed data center selection policy outperforms the traditional data center policy, such as optimized response time and closest data center policy.
FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings.
Research on Medical Data Storage and Secure Sharing Scheme Based on Blockchain

With the explosive development of technology and Internet communication, it has become an inevitable trend to realize the secure storage and sharing of electronic medical data among hospitals. In recent researches, there are also many problems in realizing secure storage and sharing of electronic medical data, such as "data silos", leakage of patient sensitive information due to data sharing and having no reliability about the original data uploaded by patients. To solve the above problems, we propose a blockchain-based medical data storage and secure sharing scheme. In the scheme, we utilize IPFS-based Web3.Storage for medical data storage, propose a sensitivity classification and access control strategy for sensitive data leakage and present a blockchain-based original data reliability checking strategy to check the reliability of the original data. Our scheme is explained in detail in the paper, and the performance analysis of this scheme is carried out to prove the feasibility of this scheme.
SynCPFL:Synthetic Distribution Aware Clustered Framework for Personalized Federated Learning

Federated Learning (FL) is a promising machine learning paradigm for collaborative training on cross-soils in a privacy-protected manner. However, the existence of non-IID data causes problems such as performance degradation and thus becomes one of the key challenges in FL recently. To address this problem, we propose a clustered personalized federated learning method named as SynCPFL. SynCPFL groups clients sharing with the similar data distribution together, thereby facilitating collaboration and producing a better-personalized model for each client. In contrast to existing clustered federated learning methods, SynCPFL does not require multiple rounds of interaction between clients and server, so that the communication overhead is reduced a lot, thereby saving resources of clients. We evaluate SynCPFL on benchmark datasets, the experimental results demonstrate that SynCPFL outperforms existing methods.
Time-Aware POI Recommendation Based on Multi-Grained Location Grouping

The task of point-of-interest (POI) recommendation aims to recommend locations to users in location-based applications. Among them, the task of time-aware POI recommendation aims to capture the user’s preferences that change dynamically over time, so as to make more accurate recommendations to users at a specific time. While existing works take into account the spatial, temporal and category context of POIs, they cannot capture user preferences that are more fine-grained than the category granularity. Additionally, RNN-based methods suffer from the problem of long-term dependency when capturing a user’s check-in patterns. To address these challenges, we propose a novel model with POI multi-grained grouping method which captures the user’s co-visit patterns and weekly patterns, to obtain finer-grained POI groups. The model also utilizes the transformer model to capture the user’s check-in preference patterns. We evaluate our model on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed model.
Logic Event Graph Enhanced Narrative Generation

Narrative generation aims at producing fluent long texts from input data, which is widely used in event prediction, story generation and other fields. Previous work mainly relied on text data, picture data, knowledge graphs, etc. Unfortunately, these static data only focused on entities and their relationships, cannot reflect the dynamic development of events in the real world. In addition, because the language model itself does not have the ability of logical reasoning, the generated results often lack logical reasoning. To address these challenges, several studies incorporate logic event graphs into the task of narrative generation. The Logic Event Graph(LEG) is essentially an event logic knowledge base, which describes the event evolution law and development modes. With the help of the LEG, the narrative generation system will acquire the human "reasoning" ability, and generate narratives that better reflect the objective evolution laws of human behaviors and events. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we—(1)analyze the context of narrative generation; (2)discuss the advantages of joining the LEG in narrative generate; (3)review common strategies in narrative generation; (4)summarize the evaluation metricse of narrative generate, and the advantages and disadvantages of each evaluation metrics are discussed; (5)present current challenges and future research directions. This survey can guide researchers and practitioners to understand the research progress in these areas.
Discriminative Feature Focus via Masked Autoencoder for Zero-Shot Learning

Zero-shot learning (ZSL) is an important research area in computer-supported cooperative work in design, especially in the field of visual collaborative computing. ZSL normally uses transferable semantic features to represent the visual features to predict unseen classes without training the unseen samples. Existing ZSL models have attempted to learn region features in a single image, while the discriminative attribute localization of visual features is typically neglected. To handle the mentioned problem, we propose a pre-trained Masked Autoencoders(MAE) based Zero-Shot Learning model. It uses multi-head self-attention in Transformer blocks to capture the most discriminative local features from a partial perspective by considering both positional and contextual information of the entire sequence of patches, which is consistent with the human attention mechanism when recognizing objects. Further, it uses a Multilayer Perceptron(MLP) to map visual features to the semantic space for relating visual and semantic attributes, and predicts the semantic information, which is used to find out the class label during inference. Both quantitative and qualitative experimental results on three popular ZSL benchmarks show the proposed method achieves the new state-of-the-art in the field of generalized zero-shot learning and conventional zero-shot learning. The source code of the proposed method is available at https://github.com/yangjingqi99/MAE-ZSL