Reviewerly/exHarmony huggingface dataset:
Energy-Efficient Online Node Cooperation Strategy for Hierarchical Federated Learning

With the popularity of Internet of Things (IoT), a large amount of data is generated at the network edge. Federated Learning (FL) can use this data for joint Cloud-Client model training. The overhead of uploading data in FL is high, and using edge servers for partial model aggregation in Hierarchical Federated Learning (HFL) can reduce the latency and energy cost. In HFL, opportunistic communication and D2D provide opportunities for node cooperation. In this work, we optimize the node cooperation strategy using opportunistic communication with the objective to minimize energy cost under the delay constraint. We design an online node cooperation strategy (OSRN) based on the optimal stopping theory. Through theoretical analysis, we prove the NP-hardness of the problem investigated. We conduct through simulation experiments and find that the proposed algorithm outperforms the random selection algorithm with 18.09% reduction in energy cost.
Impact of changes in chatbot’s facial expressions on user attention and reaction time

Communication within online platforms supported by chatbots requires algorithms, language processing methods, and an effective visual representation. These are crucial elements for increasing user engagement and making communication more akin to natural conversation. Chatbots compete with other graphic elements within websites or applications, and thus attracting a user's attention is a challenge even before the actual conversation begins. A chatbot may remain unnoticed even with sophisticated techniques at play. Drawing attention to the chatbot area localized within the periphery area can be carried out with the use of various visual characteristics. The presented study analyzed the impact of changes in a chatbot's emotional expressions on user reaction. The aim of this study was to observe, based on user reaction times, whether changes in a chatbot's emotional expressions make it more noticeable. The results showed that users are more sensitive to positive emotions within chatbots, as positive facial expressions were noticed more quickly than negative ones.
Capsule Network Based on Multi-granularity Attention Model for Text Classification

Text classification is a challenging task aimed at identifying categories of text. In order to improve the feature extraction capability of existing shallow text classification models and extract text information hierarchically from bottom to top, a multi-granularity attention model-based capsule network (LMAC) is proposed. This paper adopts a stacked dilated convolutional structure as an encoding module from the perspective of local correlation and long-term dependency. Capturing multi-granular semantic features of text. Combined with capsule network to build a classification model. A multi-granularity attention model based on dilated convolution is proposed, which fully mines multi-granularity semantic features of text by weighting and summing semantic features of different granularities. The effectiveness of the method is validated on 7 datasets for text classification tasks. Compared with the existing research work, the performance is improved significantly. It shows that the LMAC model can mine text semantic features more comprehensively and deeply, and improve the performance of text classification.
Topology-Aware Quantization Strategy via Personalized PageRank for Graph Neural Networks

Graph neural networks (GNNs) have shown strong performance on many tasks with graph-structured data. However, GNNs also face challenges as existing typical GNNs follow a neighborhood aggregation strategy, which leads to a very high complexity of GNNs thus limiting their deployment on resource-limited devices such as mobile devices. Thus, an efficient GNN is essential. Quantization is a very effective and important technique for inference acceleration in deep neural networks (DNNs), but very limited research works have been carried out to explore quantization algorithms for GNNs. In this paper, we propose a Topology-aware Quantization strategy via Personalized PageRank (TQPP) that can topologically perceive importance of nodes in overall structure of the graph. Furthermore, the protective masks generated based on the importance level ensure that sensitive nodes perform full-precision operations while the rest of the nodes are quantized. With this mixing precision approach, efficient partitioning of different nodes for quantization or keeping full-precision operations can be achieved, leading to greater acceleration and less memory footprint while retaining better model performance. We validate our algorithm on three different datasets and we demonstrate the gains of up to 27.5% over the base quantization baseline as well as a speedup of up to $3.6 \times$ on CPU deployment.
Brisk-Yolo: A Lightweight Object Detection Algorithm for Edge Devices

YOLOv5 is a fast and accurate object detection algorithm which has been widely adopted in many industrial scenarios. However, it still has too much parameters and high computational costs for some edge and low-power devices. Thus, it is necessary to explore a more efficient object detection algorithm for cheap deployment. In this paper, we realize a more lightweight and efficient object detection algorithm based YOLOv5s, called Brisk-Yolo. Firstly, we construct a lightweight backbone based MobileNet V2, by introducing a sand glass block to replace inverted residual block at deep layers, which make the backbone more lightly with negligible loss of accuracy. Meanwhile, we use non-local block to replace SPPF block to help the model capture a wide range of spatial relationships with less parameters. Secondly, we propose an efficient and lightweight multi-scale feature fusion method “Cross-FPN”, which combines FPN and an improved cross resolution weighting unit to promote the communication between multi-scale features. It gains accuracy improvement than original FPN and considerably reduces the number of parameters than PAFPN. Finally, a quality focal loss is introduced to boost accuracy further. Brisk-Yolo has only 3.68M parameters and 9.3 GFLOPS, which reduces about 49% parameters and 44% computation complexity than original YOLOv5s. Meanwhile, it achieves 34.1 mAP and 54.5 AP50 on COCO va12017 dataset, with a competitive trade-off between accuracy and model size.
Relational Context Enhanced Dual Path Reasoning for Fact Prediction in Knowledge Graph

Path-based knowledge graph reasoning approaches, which typically encode paths from the head entity to the tail entity for fact prediction, show good performance. Previous work demonstrates that leveraging entity type information can bring in benefits for learning path representations. However, many entities are not typed or loosely typed, resulting in inaccurate understanding of entity semantics. In addition, prior methods tend to model paths only from the forward direction through sequential networks like LSTM. Thus, they can neither observe the path information after the current encoding step nor capture new path patterns from the reverse direction. To address the above problems, we introduce a Relational Context enhanced Dual Path Reasoning (RCDPR) model in this paper. First, the model utilizes relational contexts, namely the relations linked from the given entity, to derive reliable entity semantics. Second, Transformer-based path encoder is designed to obtain path representations, so as to look into the information of the whole path and apply self-attention to emphasize important entity semantics. A dual path encoding method is presented to enhance the path representation by additionally encoding the new patterns embedded in the reverse path, which are combined with the forward path representation for prediction. Experimental results on two benchmark datasets, WN18RR and FB15k-237, show that our approach outperforms state-of-the-art methods on the fact prediction task, which obtains absolute MAP improvements of 2.62% and 3.66%, respectively, compared to path-based methods. Furthermore, adequate experiments illustrate the effectiveness of utilizing entity relational contexts and dual path encoding for inference.
Multi-view clustering method based on graph attention autoencoder

Multi-view graph clustering has received deep attention in fields such as data mining and computer vision, and is widely used in recommendation systems. Although some research has been done on multi-view diagram clustering and achieved good performance, most clustering methods use shallow models to deal with the complex relationships within multi-view diagrams, which may severely limit the ability to model multi-view diagram information. To explore the topology of the data, many clustering methods embedded in graphs are now developed, but they all do not take into account the community-specific distribution of node representations, resulting in low clustering results. Our proposed approach based on graph attention autoencoder is to reconstruct multiple graph views using one information graph view and content data to learn node embedding. Meanwhile, a$\ell$1,2-norm penalty is adopted to constrain the community-specific distribution of node representations, so that node representations of the same community have the same distributions in the dimensional space. The method is integrated into the recommender system, and a user model that is more suitable for the user’s preference needs is constructed to improve the performance of the recommender system and user satisfaction. Extensive experimental results show that our proposed method outperforms several state-of-the-art methods.
MGSF: Towards Multi-Graphs Semantic Fusion for Multi-behavior Recommendation

In real life, users’ behavior preferences reflect their interests. The purpose of the multi-behavior recommendation is to model users’ preferences based on their multiple behavior characteristics and make accurate recommendations. However, existing approaches exploit weights to reflect the significance of different behaviors, which are unable to fully extract the semantic information carried by various behaviors of users. In addition, these models employ functions independent of node type on the user and item side, which ignores the inherent differences between nodes. To alleviate these problems, we propose a novel multi-behavior recommendation framework based on GNN, namely MGSF, which is capable of making Multi-Graphs Semantic Fusion under a specific interactive environment. In particular, we devise a heterogeneous message encoding layer that incorporates different behavior semantic information into the node representation. Then, we design the implicit message passing mechanism to deliver high-order supervisory information of the same type. In addition, we utilize separate transformation and aggregation operations to respectively handle two different types of nodes for users and items. Experiments on two real datasets show that our method outperforms the baseline models.
Multi-view Self-attention Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has been applied by many Internet companies to enhance user travel experience. The state-of-the-art deep learning methods in next POI recommendation advocate the self-attention mechanism to model the user long-term check-in sequence. However, the existing methods ignore the interdependence between POI and POI category in the historical interaction. The POI and POI category sequences can be regarded as multi-view information of user check-in behaviors. This paper proposes a multi-view self-attention network (MVSAN) for next POI recommendation. Firstly, MVSAN uses a self-attention layer to update the feature representation of POI and POI category respectively. Then it generates the importance of POI under the condition of the POI category through a co-attention module. To make better use of geospatial information, we design a spatial candidate set filtering module to help the model improve recommendation performance. Experiments on two real check-in datasets show that MVSAN yields outstanding improvements over the state-of-the-art models in terms of recall.
Safety Boundary in Virtual Reality: An Approach Based on User Motion Analysis and Prediction

In 2021, with the popularity of the concept of the Metaverse, Virtual Reality (VR) technology has gradually developed, which will play an indispensable role in promoting the construction of the Merse. However, due to the immature technology, the safety of users cannot be fully guaranteed. While users have a deeper sense of immersion in the virtual world, they are likely to lose their perception of the surrounding physical environment. The purpose of this study is to solve the pain point that users still cause body collisions within the safety boundary in the VR scene. We recruited 62 participants to play VR games and trigger particular events in the virtual reality scene. Three basic motion event categories were defined according to the common motions of users. We collected participants’ characteristic data through questionnaires and recorded the user’s motion information using the headset and controllers. After acquiring the participants’ motion data, we segmented the three-dimensional coordinates, extracted the motion range features, and constructed the relational data set. Furthermore, we used statistical methods to mine the correlation information between user characteristics and user motion range categories and explore and analyze the possibility of optimizing the dataset. Finally, we used the machine learning methods to train the multi-classification prediction model and deployed our safety boundary systems in the virtual reality environment.
SRDF: Single-Stage Rotate Object Detector via Dense Prediction and False Positive Suppression

Oriented object detection has made astonishing progress. However, existing methods neglect to address the issue of false positives caused by the background or nearby clutter objects. Meanwhile, class imbalance and boundary overflow issues caused by the predicting rotation angles may affect the accuracy of rotated bounding box predictions. To address the above issues, we propose a Single-stage Rotate object detector via Dense prediction and False positive suppression (SRDF). Specifically, we design an Instance-level False Positive Suppression Module (IFPSM), IFPSM acquires the weight information of target and non-target regions by supervised learning of spatial feature encoding, and applies these weight values to the deep feature map, thereby attenuating the response signals of non-target regions within the deep feature map. Compared to commonly used attention mechanisms, this approach more accurately suppresses false positive regions. Then, we introduce a hybrid classification and regression method to represent the object orientation, the proposed mothed divide the angle into two segments for prediction, reducing the number of categories and narrowing the range of regression. This alleviates the issue of class imbalance caused by treating one degree as a single category in classification prediction, as well as the problem of boundary overflow caused by directly regressing the angle. In addition, we transform the traditional post-processing steps based on matching and searching to a two-dimensional probability distribution mathematical model, which accurately and quickly extracts the bounding boxes from dense prediction results. Extensive experiments on Remote Sensing, Synthetic Aperture Radar, and Scene Text benchmarks demonstrate the superiority of the proposed SRDF method over state-of-the-art rotated object detection methods. Our codes are available at https://github.com/TomZandJerryZ/SRDF.
PATVD:Vulnerability Detection Based on Pre-training Techniques and Adversarial Training

Software vulnerability detection has attracted more and more attention. Traditional vulnerability detection methods require a lot of expertise to define the vulnerability features, whereas the deep learning based methods can perform the feature extraction automatically. These deep learning based techniques handle the problem directly by regarding the code fragments as a series of tokens or a single graph structure, which makes them perform well on synthetic or semi-synthetic datasets. However, for real-world scenarios, the existing deep learning based techniques still suffer from some limitations. In this work, we propose PATVD, a vulnerability detection method combining pre-training techniques and adversarial training. We first use the pre-training model to combine the source code fragments and the corresponding data flow graph, such that both the characteristics of code structures and the token sequence can be integrated to learn the more complex vulnerability information contained in the real-world datasets. Then, to address the imbalance in the distribution of real-world datasets, we incorporate adversarial training on the code embedding space as a way of regularization to improve model generalization. Finally, the code representation added to the adversarial training is given to the classification model. To verify the validity of our method, we perform a set of comparative experimental evaluations on both artificially synthesized datasets and the latest published real-world datasets. The experimental results show that PATVD has achieved better results than the state-of-the-art methods in both synthetic datasets and real-world datasets. The adversarial training module can also make the model attain additional gains on real-world datasets.
Few-Shot Node Classification on Attributed Networks Based on Prototypical Network

In today’s society, the interactions between various entities form complicated attributed networks. The node classification task as the core task of analyzing attribute networks has gained a considerable amount of attention from researchers. The existing graph neural network-based node classification approaches all demand lots of labeled data, but a high percentage of node classes in practical scenarios contain only restricted labeled data. So their classification effect is not good. In response to this problem, we put forward an improved graph prototypical network (IGPN) to enhance the few-shot node classification’s accuracy. In particular, we start by obtaining the embedding representations of nodes on the attributed network through a graph attention network. Then combined with the node’s degree information and neighbor information, the importance coefficient of all nodes in the support set is learned. After that, we can obtain the prototype representation of every class. Finally, the node classification can be completed by the distance of the query nodes’ representation from all class prototypes. Our method is preferable to the baseline method on three datasets according to a large number of experiments.
Aspect-Level Sentiment Classification Based on Self-Attention Routing via Capsule Network

Aspect-level sentiment classification task aims at determining the sentiment polarity towards each aspect in a sentence. Although existing models have achieved remarkable performance, they always ignore the semantic relationship between aspects and their context, resulting in the lack of syntax information and aspect features. Therefore, the paper proposes a novel model named ASC based on the Self-Attention routing combined with the Position-biased weight approach, ASC-SAP in short. First, the paper utilizes the position-biased weight approach to construct an aspect-enhanced embedding. Furthermore, the paper develops a novel non-iterative but highly parallelized self-attention routing mechanism to efficiently transfer the aspect features to the target capsules. In addition, the paper utilizes pre-trained model bidirectional encoder representation from transformers (BERT). Comprehensive experiments show that our model achieves excellent performance on Twitter and SemEval2014 benchmarks and verify the effectiveness of our models.
Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models

Securing their users’ privacy is a central duty of Online Social Networks (OSN), but the complex non-linear effects of social media content on privacy is not well understood. We propose a novel framework that integrates XGBoost, Latent Dirichlet Allocation (LDA) topic models and Generalized Additive Models (GAM) to perform statistical inference about the complex non-linear relationship between the topics and privacy of tweets. First, XGBoost is used to predict the privacy of tweets. Then, the predictions are improved by analyzing the classified tweets with LDA topic models. Finally, we model the nonlinear relationship between topics and privacy with GAMs by using (penalized) splines. Instead of being limited to predictive modeling, our approach enables us to model the non-linear relationship between latent topics and the privacy of tweets.
Learning Cyber Threat Intelligence Knowledge Graph Embedding with Heterogeneous Relation Networks Based on Multi-Head Relational Graph Attention

Cyber threat intelligence (CTI) Knowledge Graphs Embedding (KGE) complete the tasks such as KG complementation by retaining the structural information of the graph and mining the implicit relations between entities, which can help security organizations analyze potential threat information and improve the cybersecurity protection capabilities. However, learning graph data in CTI is challenging for the data sparsity and their heterogeneity, it is critical to consider how to deal with complex graph data and aggregate multiple types of semantic information simultaneously. To solve the heterogeneity of KGEs in CTI, we propose a heterogeneous Cyber Threat Intelligence GNNs model Relational-Multi-Head-Graph-Attention-Networks (R-MGAT). Specifically, the importance of different relations is firstly learned through a multi-head attention mechanism. Then the neighbor features of different entities are learned under each relation. Finally, the weight entities and the relation-based feature are aggregated to generate the embedding representation. In this way, our model could capture various types of semantic information and selectively aggregate informative features. What’s more, we manually constructed a CTI KG by annotating APT reports in response to the lack of public datasets in CTI fields. We use entity classification and link prediction to verify our R-MGAT model. A series of benchmark experiments illustrate that RMGAT can generate competitive performance in KGE.
Keycrux: A New Design of Distributed and Convenient Blockchain Digital Wallet

With the wide application of blockchain in all walks of life, the management of blockchain keys has become increasingly important. At present, the key management of blockchain digital wallet mostly relies on mnemonics which are still hard to memorize. In this paper, we propose a new design of blockchain digital wallet. We generate the key into a code which later is separated into n shared images based on our visual secret sharing scheme. The shared images can be further presented in different formats including QR. codes, paper, clothes and audio watermarking, which is convenient for key recovery. Moreover, the wallet updates new shared images each time in hot media ensuring the security. Additionally, we provide a visual secret sharing scheme by combinatorial structures and solving a group of linear equations. Once the visual secret sharing scheme is safe, the wallet is safe without additional initial trust settings, which excels on security and convenience.