Reviewerly/exHarmony huggingface dataset:
IPP-HF: An Identity Privacy Protection Scheme for Consortium Blockchain Hyperledger Fabric

The open and transparent ledger of blockchain makes the user’s identity and transaction details easily revealed in the blockchain network, and the privacy of individuals is seriously threatened, so the privacy protection issue gradually becomes a new challenge in blockchain. To solve the problem of identity privacy leakage during blockchain transactions, this paper proposes an identity privacy protection scheme based on the Hyperledger Fabric(IPP-HF). By making appropriate improvements to the certificateless signature and using the cooperation of long-term and short-term pseudo-identities, this paper designs an identity privacy protection scheme based on certificateless signature on the consortium blockchain Fabric, which makes the scheme satisfy the node identity anonymity and protects the privacy of user identity. Through theoretical analysis, it is proved that IPP-HF scheme can resist common public key replacement attacks and tampering attacks, and has the characteristics of signature unforgeability and guaranteed node anonymity. Finally, by comparing with other schemes, IPPHF scheme ensures high signature and verification efficiency while having strong anonymity.
Difficulty-aware Convolutional Knowledge Tracing for Student Performance Prediction

Predicting students’ performance in future learning activities is an important task in online education systems. It enables education researchers to understand students’ learning profiles and provide them with personalized teaching services based on this knowledge. Most existing knowledge tracing methods do not adequately model the difficulty of the exercise and instead model exercise information, student ability, and knowledge state features while ignoring their characteristics. To address these challenges, we propose a difficulty-aware CNN-based knowledge tracing model that extracts several factors effective in modeling exercise difficulty and models changes in students’ knowledge states and abilities at a microscopic level. Our model can simulate students’ learning processes more realistically and performs well on real-world datasets.
Egocentric Hand-object Interaction Detection

In this paper, we propose a method to jointly determine the status of hand-object interaction. This is crucial for egocentric human activity understanding and interaction. From a computer vision perspective, we believe that determining whether a hand is interacting with an object depends on whether there is an interactive hand pose and whether the hand is touching the object. Thus, we extract the hand pose, hand-object masks to jointly determine the interaction status. In order to solve the problem of hand pose estimation due to in-hand object occlusion, we use a multi-cam system to capture hand pose data from multiple perspectives. We evaluate and compare our method with the most recent work from Shan et al. [1] on selected images from EPIC-KITCHENS [2] dataset and achieve 89% accuracy on HOI (hand-object interaction) detection which is comparative to Shan’s (92%). However, for real-time performance, our method can run over 30 FPS which is much more efficient than Shan’s ($1\sim 2$ FPS). A demo can be found from https://www.youtube.com/watch?v=XVj3zBuynmQ.
BARM: Blockchain-based Anonymous Reward Mechanism for Medical Recommendation in Smart Healthcare

Medical Recommendation Service (MRS) can provide simple diagnostics and recommend a suitable doctor for patients when they input their disease symptoms by using AI and big data. However, people may be unwilling to share medical data due to data privacy issues and a lack of incentives, which makes it hard for MRS to continue to develop. Until now, there are no efficient solutions. To solve this, we propose a blockchain-based anonymous reward mechanism for medical recommendation (BARM) in smart healthcare. The proposed scheme uses accumulator, commitment, and Signatures of Knowledge (SoK) to provide anonymous rewards for users to mobilize more users to share data as much as possible. And we design a smart contract that can provide submit, query, and revoke functions to realize anonymous authentication of patients. The formal security analysis shows that the proposed scheme meets the required security requirements. Detailed performance analysis results show that the computation cost and communication cost of the scheme is feasible, which can be applied to the medical recommendation scenario of anonymous protection and promote the development of smart healthcare.
A Self-Supervised Representation Learning Method of Speech Recognition for Smart Grid

Recently, speech recognition has rapidly become a hot topic of machine learning and is widely applied in various domains. Despite impressive performance, speech recognition is still confronted with great challenges from different environmental factors in real world. To solve the challenges, a novel speech pattern recognition method for the pattern recognition task on speech data is proposed in the paper, which effectively combines machine learning techniques with acoustic model analysis. Specifically, the input speech data are first split into fragments, and then a clustering representation encoder network is utilized to extract the context representations of input speech data followed an aggregation module capturing clustering patterns. In the proposed method, the influence of environmental factors can be reduced as far as possible, and the robustness can be guaranteed effectively. Experimental results demonstrate the superior performance of the proposed method.
What Is Next? A Generative Approach for Service Composition Recommendations

Service recommendation is important in creating composite services, workflows, e-business solutions, etc. It often takes developers a long time to Figure out what the next service is. A lot of researchers have used collaborative filtering-based or content-based approaches to recommend services for developers. However, failing to model the co-occurrence relationships between services, current approaches cannot recommend the next services for service composition. This leads to a decrease in the accuracy of service composition recommendations. To tackle this problem, this paper proposes an Encoder-Decoder-based Recommender named EDeR, which transforms the service recommendation problem into a generation problem. First, we employ a self-supervised graph embedding method to fully learn the representation of each service according to both structural and descriptive information. Then, based on the co-occurrence relationships between services, we propose an encoder-decoder model to sequentially recommend services in a way that translates user requirements into a composite service. The results obtained from experiments conducted on a real-world dataset show that EDeR outperforms the state-of-the-art approaches significantly.
Technology for embedded GPU virtualization in the edge computing environment

With the rapid development of Internet of Things technology, the requirements for edge node data processing capability are increasing, and GPU processors are becoming more widely applied in edge nodes. Current research on GPU virtualization technology mainly focuses on cloud data centers, with little research on embedded GPU virtualization in scenarios with limited edge node resources. In contrast to cloud data centers, embedded GPUs in edge nodes typically do not have access to GPU utilization and video memory usage within each container. As a result, traditional GPU virtualization technologies are ineffective for resource virtualization on embedded devices. This paper presents a method to virtualize embedded GPU resources in an edge computing environment, called sGPU. We integrated edge nodes with embedded GPUs into Kubernetes via the device-plugin mechanism. Users can package GPU applications in containers and deploy them using Kubernetes on edge nodes with embedded GPUs. sGPU allows containers to share embedded GPU computing resources and divides a physical GPU into multiple virtual GPUs that can be allocated to containers on demand. To achieve GPU computing power division, we proposed a multi-container sharing GPU algorithm and implemented it in sGPU, which ensures the most accurate computing power segmentation under the competition of computing resources of a GPU used by multiple containers at the same time. According to the experimental results, the average overhead of sGPU is 3.28%. The accuracy of computing power segmentation is 92.7% when a single container uses GPU.
An Improved LightGBM Job Running Status Prediction Algorithm Integrating Combinatorial Feature Selection and Bayesian Hyperparameter Optimization on Spark

In recent years, Supercomputing Centers have played an increasingly important role in various fields of social and economic life. High-performance jobs often require large-scale computing, which will lead to a lot of computing costs. If the job running status can be predicted successfully, it will help to improve the resource management efficiency of the Supercomputing Centers. In recognition of this, we first propose a combinatorial feature selection method combining ReliefF and SelectKBest, which can obtain job running state features with better prediction accuracy. Then, an improved LightGBM algorithm based on Bayesian hyperparameter optimization is proposed. Thirdly, we propose a Supercomputing Center job running status prediction RS-BOLGBM algorithm on Spark. Finally, we evaluate our proposed RS-BOLGBM algorithm with five traditional machine learning classification algorithms: KNN, decision tree, random forest, SVM and GDBT. The experimental results show that our proposed RS-BOLGBM algorithm has good prediction accuracy on the common SWF (standard workloads format) dataset of Supercomputing Center. Our proposed RS-BOLGBM also has good running efficiency on Spark cluster.
PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.
A Privacy Preserving Video Surveillance System for Trauma Rooms

Video surveillance in trauma room can improve the security, patient safety, healthcare standards, and hygiene monitoring, but video surveillance system can cause privacy breach by showing and storing sensitive information of patients and staff. Head blurring can protect privacy, and we can still able to know most actions of people with blurred head. But when postures of people in the videos are complex or when there are many people, using real-time detection program to detect and blur heads is challenging, with a high missed detection rate. OpenPose is a state-of-the-art human body skeletons estimation framework. And we build a head detection and blurring technique based on OpenPose to solve this problem. We first use OpenPose to extract 18 human key skeleton points, and find 5 key skeleton points which are used to describe head. Finally, we apply the image obfuscation techniques to the detected heads. The experiment results show that our proposed techniques can ensure privacy protection and reduces the missed detection rate of the output results. Our experiment results prove that our proposed technique has a high value in privacy protection applications.
STAR-FC: Structure-Aware Face Clustering on Ultra-Large-Scale Graphs

Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are inefficient for inference due to the use of numerous overlapped subgraphs. Previous approaches fail to tackle these two challenges simultaneously. To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method. Specifically, we design a structure-preserving subgraph sampling strategy to explore the power of large-scale training data, which can increase the training data scale from 105 to 107. On this basis, a novel hierarchical GCN training paradigm is further proposed for better capturing the dynamic local structure. During inference, the STAR-FC performs efficient full-graph clustering with two steps: graph parsing and graph refinement. And the concept of node intimacy is introduced in the second step to mine the local structural information, where a calibration module is further proposed for fairer edge scores. The STAR-FC gets 93.21 pairwise F-score on standard partial MS1M within 312 seconds, which far surpasses the state-of-the-arts while maintaining high inference efficiency. Furthermore, we are the first to train on an ultra-large-scale graph with 20 M nodes, and achieve superior inference results on 12 M testing data. Overall, as a simple and effective method, the proposed STAR-FC provides a strong baseline for large-scale face clustering.
Graph Representation Learning on Noise and Sparse Labels

Graph Neural Networks(GNNs) learn node representations of graphs through topological structure neighborhood propagation and aggregation. It has achieved great success in graph learning tasks and is widely used in node classification tasks. However, graphs in the real world are usually sparse and noisy, and noise can also affect surrounding nodes through topological structure, which negatively affects the performance of GNNs. In this paper, we propose a new framework to deal with these problems. Specifically, first, we design an edge generator to densify the sparse graph using node similarity and reduce the weight of noisy edges. Then, adaptive label propagator generates pseudo-labels based on topological structure, and the dynamic and adaptive weighting strategy of the adaptive label propagator can overcome the shortcomings of GNNs on noisy labels. Finally, we use the topology information and node information of the obtained new graph for final classification and pseudo-label reweighting through GNNs. Experimental results on public datasets indicate the superiority of our method over existing baselines, demonstrating the robustness of our method to sparse and noisy graphs.
Reinforcement Learning-Based Explainable Recommendation over Knowledge Graphs with Negative Sampling

Introducing knowledge graphs (KGs) into the recommender systems not only improves their performance but also enhances the interpretability. However, most KG-based recommendation methods have the problem of inefficiency and ex-post explanation, which reinforcement learning (RL) methods can solve properly. Most existing RL-based methods for explainable recommendations only consider positive rewards when designing the reward part of the RL environment, which is defective and misleads the policy of the RL agent. To address this problem, we propose Reinforced Knowledge Graph Reasoning with Reinforced Negative Sampling (RKGR-RNS) by introducing a negative sampling method into RL-based recommendation, which refines the reward mechanism to help optimize the agent’s policy. And a judge module is proposed to improve the performance of the recommender system further. Experiments on three real datasets demonstrate that our method is better than the state-of-the-art baseline.
Utility-Aware Data Anonymization Model for Healthcare Information

The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients’ information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients’ health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients’ information before releasing it for research purposes according to certain requirements specified by the data user (researcher).
A Lightweight Homomorphic Encryption Federated Learning Based on Blockchain in IoV

Artificial intelligence combined with the Internet of Vehicles (IoV) can improve the performance of automatic driving and service quality of vehicles. However, data privacy protection of IoV has become a challenging problem. In order to effectively protect user data security and reduce resource consumption, we propose a lightweight homomorphic encryption federated learning framework based on blockchain. Firstly, we combine federated learning with blockchain to train a global model collaboratively without sharing their raw data. Meanwhile, the aggregation of shared models is conducted in the on-chain nodes of the blockchain instead of a single server with traditional federated learning. Secondly, considering the further security of the sharing model on the chain, we design a lightweight homomorphic encryption approach because of the high computational cost with the existing homomorphic encryption federated learning. Finally, we conducted comparison experiments with existing homomorphic encryption federated learning schemes, and the experimental results show that our scheme can effectively protect data privacy and reduce computational costs and storage space.
Design pattern recommendations for building decentralized healthcare applications

Blockchain and distributed ledger technologies (DLT) are emerging decentralized infrastructures touted by researchers to improve existing systems that have been limited by centralized governance and proprietary control. These technologies have shown continued success in sustaining the operational models of modern cryptocurrencies and decentralized finance applications (DeFi). These applications has incentivized growing discussions in their potential applications and adoption in other sectors such as healthcare, which has a high demand for data liquidity and interoperability. Despite the increasing research efforts in adopting blockchain and DLT in healthcare with conceptual designs and prototypes, a major research gap exists in literature: there is a lack of design recommendations that discuss concrete architectural styles and domain-specific considerations that are necessary for implementing health data exchange systems based on these technologies. This paper aims to address this gap in research by introducing a collection of design patterns for constructing blockchain and DLT-based healthcare systems that support secure and scalable data sharing. Our approach adapts traditional software patterns and proposes novel patterns that take into account both the technical requirements specific to healthcare systems and the implications of these requirements on naive blockchain-based solutions.
ME-SFP: A Mixture-of-Experts-Based Approach for Software Fault Prediction

In the last two decades, many machine-learning-based works have been presented to build software fault prediction (SFP) models. The assessment of all these works showed that none of the machine learning classifiers could be generalized as the best-performing classifier in all prediction contexts. However, these techniques showed complementary behavior among them, which suggests combining their learning for improved performance. Various ensemble models explored in SFP have a major concern with the static weights assigned to the base learners for combining their decisions. In this article, we present a Mixture-of-Experts (MoE)-based approach named ME-SFP that uses the experts generated using a learning technique and a Gaussian mixture model as a gating function and a data partition technique. For the experimentation using the presented approach, we have shown the use of decision trees (DTs) as well as multilayer perceptrons (MLPs) as experts. We call them ME-SFP[DT] and ME-SFP[MLP]. We conduct a set of experiments on 35 publicly available software project datasets (PROMISE, JIRA, AEEEM, and Eclipse datasets) for SFP and measure the performance of built fault prediction models by using different measures such as F1-score, precision, recall, area under ROC curve (AUC), probability of false alarm, Mathews correlation coefficient, and G-means. Additionally, we perform Friedman's test and the Wilcoxon signed-rank sum test between the presented models, ensemble methods, baseline method, and individual learning techniques (DT and MLP). Results showed that ME-SFP[DT] and ME-SFP[MLP] produced improved results compared to individual techniques and ensemble methods.
Predicate-Augmented Personalized PageRank for Entity Typing in Knowledge Graphs

Knowledge graph entity typing aims to find relevant types for a given entity, which has numerous applications such as link prediction, knowledge graph completion and entity disambiguation. Personalized PageRank (PPR) is a well-known relevance measure that can be used for finding relevant types. However, it utilizes the contextual structure for measuring relevance scores, and the semantics of different relations are not considered enough, which is insufficient for accurately typing entities as the connections via the underlying semantics are neglected. In this paper, we propose a predicate-augmented Personalized PageRank, namely PrePPR, for effectively finding underlying entity types in knowledge graphs. We derive a predicate augmented graph by using predicates of relations between entities, which helps enrich the graph information without handling the complex predicates directly. For discriminating the importance of different links, we compute the weights based on link frequency and co-occurrence probabilities. We adopt PPR to compute relevance between entities and types in a predicate augmented graph instead of the original knowledge graph. We show that PrePPR inherits the basic philosophy of PPR without destroying the original philosophy, and lots of existing optimization techniques on PPR are applicable to the relevance computation. And then, we adopt forward push algorithm to speed up the relevance computation by local updates. We conduct extensive experiments on two real datasets, which show the performance of the proposed PrePPR through comparison with the-state-of-the-art approaches.