Reviewerly/exHarmony huggingface dataset:
Influence maximization in social networks with privacy protection

With the explosive development of online social network platforms, how to find a small subset of users (seed nodes) across multiple social networks to maximize the spread of information is of great significance. In reality, different platforms need to consider not only the commercial value of data, but also the protection of data privacy. In this situation, these multiple social platforms can be treated as a system of “multiple private social networks”, which naturally arises a new problem: how to maximize the spread of information in multiple private social networks without breaking the protocol of privacy protection. In view of this, we propose an HE-IM algorithm to solve the problem from the perspective of cryptography. Specifically, we use the homomorphic encryption security protocol and the third-party servers to encrypt and decrypt the influence of nodes in each private network and update the set of seed nodes. The experimental results demonstrate that, by cooperatively fusing information from different private networks in a secret manner, our method can effectively find influential seed nodes to maximize influence in multiple private social networks. The performance of our method in maximizing influence range is much better than that of the baseline methods only considering the structure of single private network. Therefore, the method provides a new way for collaborative search of influential seed nodes in multiple private social networks. • An all-new problem is defined: how to maximize the spread of information in social networks with privacy protection. • An HE-IM method based on the homomorphic encryption protocol is designed to solve the problem. • The method can collaboratively fuse the structural information of different private networks in a secret manner. • Our method outperforms the baseline methods in maximizing the spread of influence.
Topic model for personalized end-to-end task-oriented dialogue

Constructing a personalized end-to-end task-oriented dialogue system is one of the most important and challenging tasks in natural language processing technology. Slot-filling has achieved success in a rule-based task-oriented dialogue system. However, building a rule-based task-oriented dialogue system for real conversations is time-consuming. We present a novel personalized end-to-end framework based on split memory for Memory Networks by using topic model in this paper. We analyze the drawbacks of existing end-to-end dialog systems based on Memory Networks and propose the architecture which consists of user profile and conversation history. User profile is constructed by topic words from personalized topic model. The test experiments on the public and real dataset demonstrate that our method achieves better performance than the baselines in end-to-end task-oriented dialogue system.
OntoHuman: Ontology-Based Information Extraction Tools with Human-in-the-Loop Interaction

This paper presents OntoHuman, a toolchain for involving humans in a process of automatic information extraction and ontology enhancement. Document Semantic Annotation Tool (DSAT) [13], a user interface of OntoHuman, offers an automatic function to extract information in the form of key-value-unit tuples from PDF documents based on ontologies. Additionally, it allows users to provide feedback to improve the ontologies used. Although the information extraction can be improved with the ontology, our use cases were previously limited to an area of space engineering. OntoHuman now tackles this shortcoming by allowing users to upload their customized ontologies. This entends usages to various domains and enables this shareable knowledge to be used cooperatively. Then we display the ontologies in a node-link representation so they are easier to understand. Another major improvement in OntoHuman is the graph data points extraction, which is still missing in the existing information extraction tools. The application of OntoHuman can be used for documents related to any engineering domain and makes the work with ontologies intuitive and collaborative for users.
MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion

Background: Code summarization automatically generates the corresponding natural language descriptions according to the input code to characterize the function implemented by source code. Comprehensiveness of code representation is critical to code summarization task. However, most existing approaches typically use coarse-grained fusion methods to integrate multi-modal features. They generally represent different modalities of a piece of code, such as an Abstract Syntax Tree (AST) and a token sequence, as two embeddings and then fuse the two ones at the AST/code levels. Such a coarse integration makes it difficult to learn the correlations between fine-grained code elements across modalities effectively. Aims: This study intends to improve the model’s prediction performance for high-quality code summarization by accurately aligning and fully fusing semantic and syntactic structure information of source code at node/token levels. Method: This paper proposes a Multi-Modal Fine-grained Feature Fusion approach (MMF3) for neural code summarization. The method uses the Transformer architecture. In particular, we introduce a novel fine-grained fusion method, which allows fine-grained fusion of multiple code modalities at the token and node levels. Specifically, we use this method to fuse information from both token and AST modalities and apply the fused features to code summarization. Results: We conduct experiments on one Java and one Python datasets, and evaluate generated summaries using four metrics. The results show that: 1) the performance of our model outperforms the current state-of-the-art models, and 2) the ablation experiments show that our proposed fine-grained fusion method can effectively improve the accuracy of generated summaries. Conclusion: MMF3 can mine the relationships between cross-modal elements and perform accurate fine-grained element-level alignment fusion accordingly. As a result, more clues can be provided to improve the accuracy of the generated code summaries.
Do Static Analysis Tools Affect Software Quality when Using Test-driven Development?

Background. Test-Driven Development (TDD) is an agile software development practice, which encourages developers to write “quick-and-dirty” production code to make tests pass, and then apply refactoring to “clean” written code. However, previous studies have found that refactoring is not applied as often as the TDD process requires, potentially affecting software quality.
On the Relationship Between Story Points and Development Effort in Agile Open-Source Software

Background: Previous work has provided some initial evidence that Story Point (SP) estimated by human-experts may not accurately reflect the effort needed to realise Agile software projects. Aims: In this paper, we aim to shed further light on the relationship between SP and Agile software development effort to understand the extent to which human-estimated SP is a good indicator of user story development effort expressed in terms of time needed to realise it. Method: To this end, we carry out a thorough empirical study involving a total of 37,440 unique user stories from 37 different open-source projects publicly available in the TAWOS dataset. For these user stories, we investigate the correlation between the issue development time (or its approximation when the actual time is not available) and the SP estimated by human-expert by using three widely-used correlation statistics (i.e., Pearson, Kendall and Spearman). Furthermore, we investigate SP estimations made by the human-experts in order to assess the extent to which they are consistent in their estimations throughout the project, i.e., we assess whether the development time of the issues is proportionate to the SP assigned to them. Results: The average results across the three correlation measures reveal that the correlation between the human-expert estimated SP and the approximated development time is strong for only 7% of the projects investigated, and medium (58%) or low (35%) for the remaining ones. Similar results are obtained when the actual development time is considered. Our empirical study also reveals that the estimation made is often not consistent throughout the project and the human estimator tends to misestimate in 78% of the cases. Conclusions: Our empirical results suggest that SP might not be an accurate indicator of open-source Agile software development effort expressed in terms of development time. The impact of its use as an indicator of effort should be explored in future work, for example as a cost-driver in automated effort estimation models or as the prediction target.
Fuzzy Inference System-like Aggregation Operator for Fuzzy Signatures

This paper deals with a novel fuzzy aggregation operator. This aggregation operator is suggested to such fuzzy signatures, where the correlation between the leafs or branches can not be handled by classical operators as weighted relevance aggregation operator or weighted generalized mean, more with fuzzy rules. This paper presents the suggested Fuzzy inference system-like aggregation operator (FISAO), shows its axiomatic conformity and depicts its use through an experimental example.
Traffic Sign Detection Based on Driving Sight Distance in Haze Environment

To explore the relationship between traffic sign detection performance and driving sight distance in haze environment, this paper proposed a UV correlation model among sight distance, haze grade and traffic sign detection performance. First, the German traffic sign data set (GTSDB) is synthesized into experimental data set according to three levels of light haze, haze and dense haze. The Faster R-CNN model is utilized to detect the traffic signs after dehazing by Guided Filter Dehazing Algorithm. The detection accuracy is as high as 95.11%, which shows that the model has strong generalization ability and adaptability. Second, the weight is determined by haze, taking the driving sight distance as U layer and the detection result of Faster RCNN model as V layer, establishing the UV correlation model. Finally, KM algorithm is used to solve the correlation model, and the best matching result between UV layers is gained. The experimental results show the haze level significantly affects the driving sight distance, and then affects the detection accuracy of traffic signs. When the driving Sight distance threshold is 300 meters, 100 meters and 50 meters in light haze, haze and dense haze, the KM algorithm obtains the detection accuracy levels of A (higher than 93%), B (88%-93%) and C (85%-88%), respectively.
MShNet: Multi-scale feature combined with h-network for medical image segmentation

Objective: Medical image segmentation is the key foundation of medical image analysis. However, the uncertainty of the size, shape and location of the lesion greatly affect the segmentation accuracy. To solve the above problems, the multi-scale feature combined with the h-network (MShNet) is proposed in this paper. Methods: Firstly, a network framework, which is similar in shape to the letter "h" and consists of an encoder and two decoders is built to obtain stronger feature expression ability. The first decoder is responsible for obtaining the preliminary segmentation information of the image, and the second decoder enhances the feature expression of the nodule by fusing the information learned by the first decoder. Secondly, an enhanced down-sampling module is constructed in the encoder to reduce the information loss caused by down-sampling. In addition, to further reinforce the generalization ability of the model, the fusion convolutional pyramid pooling is designed to realize multi-scale feature fusion. Results: In the internal dataset of thyroid nodules, the DSC is 0.8721 and the HD is only 0.9356; DSC in the public dataset (DDTI,TN3K,ISIC and BUSI) also reached the optimal levels of 0.7580, 0.7815, 0.8853 and 0.7501 respectively and the HD for the last segmentation (Kvasir-SEG) is 16.5197. Conclusion: A large number of experimental results show that MShNet effectively improves the segmentation performance with less parameters, and achieves the most advanced performance in robustness and efficiency. Significance: The proposed algorithm provides a deep learning segmentation procedure that can segment thyroid nodule in ultrasound images effectively and efficiently.
Differentially Private Set Intersection for Asymmetrical ID Alignment

Private Set Intersection (PSI) is typically used to achieve ID alignment with protection of IDs in the preparation phase of Vertical Federated Learning (VFL). However, existing PSI approaches are limited to protecting IDs that are outside the intersection of participants, and most ignore the sensitivity of intersection for a weak party in an asymmetrical ID alignment. Since the set size of the strong party is much greater than the weak party’s in an asymmetrical federation, and the intersection usually accounts for a substantial part of the weak party set, the weak party’s sensitive sample IDs would be severely compromised through sharing the intersection. To address this issue, we propose Differentially private PSI Cardinality and PSI (DPSI-CA, DPSI) protocols, which protect the intersection cardinality and sensitive IDs inside the intersect ion for the weak party, respectively. First, DPSI-CA encodes IDs in binary notation, and combines them with the GM encryption, to perform the ID-matchmaking by executing bitwise plaintext XOR. Then, the encrypted matching results are independently perturbed using randomized responses to produce differentially private outputs for PSI-CA, and its unbiased estimate is added to remove the deviation brought by the randomization. Furthermore, DPSI fuses Pseudo-Random Function (PRF)-based zero sharing, garbled Bloom filter, and Oblivious PRF (OPRF)-based shares reconstruction, to successfully reconstruct the shares corresponding to sampled IDs in the intersection. Meanwhile, a randomized response is used to sample the inputs and perturb the outputs of the OPRF-based shares reconstruction, producing a randomly sampled intersection for the weak party and differentially private intersection for the strong party. Finally, the privacy analysis shows that our protocols provide differential privacy for the weak party’s sensitive sample IDs, and extensive experiment results illustrate the feasibility of the asymmetrical ID alignment involving millions of IDs.
Design and Implementation of a Secure RISC-V Microprocessor

Secret keys can be extracted from the power consumption or electromagnetic emanations of unprotected devices. Traditional countermeasures have a limited scope of protection and impose several restrictions on how sensitive data must be manipulated. We demonstrate a bit-serial RISC-V microprocessor implementation with no plain-text data. All values are protected using Boolean masking. Software can run with little to no countermeasures, reducing code size and performance overheads. Unlike previous literature, our methodology is fully automated and can be applied to designs of arbitrary size or complexity. We also provide details on other key components, such as clock randomizer, memory protection, and random number generator (RNG). The microprocessor was implemented in 65-nm CMOS technology. Its implementation was evaluated using NIST tests and side-channel attacks. Random numbers generated with our RNG pass on all NIST tests. The side-channel analysis on the baseline implementation extracted the advanced encryption system (AES) key using only 375 traces, while our secure microprocessor was able to withstand attacks using 20M traces.
Dynamic Graph Message Passing Networks for Visual Recognition

Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive. In this paper, we propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph. This is achieved by adaptively sampling nodes in the graph, conditioned on the input, for message passing. Based on the sampled nodes, we dynamically predict node-dependent filter weights and the affinity matrix for propagating information between them. This formulation allows us to design a self-attention module, and more importantly a new Transformer-based backbone network, that we use for both image classification pretraining, and for addressing various downstream tasks (e.g. object detection, instance and semantic segmentation). Using this model, we show significant improvements with respect to strong, state-of-the-art baselines on four different tasks. Our approach also outperforms fully-connected graphs while using substantially fewer floating-point operations and parameters. Code and models will be made publicly available at https://github.com/fudan-zvg/DGMN2.
Temporal Multimodal Graph Transformer With Global-Local Alignment for Video-Text Retrieval

Video-text retrieval is a crucial task that has been a powerful application for multi-media data analysis and attracted tremendous interest in the research area. The core steps are feature representations and alignment to overcome the heterogeneous gap between videos and texts. Existing methods not only take advantage of multi-modal information in videos but also explore local alignment to enhance retrieval accuracy. Although performing well, these methods seem deficient at three perspectives: a) The semantic correlations between different modal features are not considered, which introduces irrelevant noise in feature representations. b) The cross-modal relations and temporal associations are ambiguously learned by a single self-attention manipulation. c) The training signal to optimize the semantic topic assignment for local alignment is missing. In this paper, we proposed a novel Temporal Multi-modal Graph Transformer with Global-Local Alignment (TMMGT-GLA) for video-text retrieval. We model the input video as a sequence of semantic correlation graphs to exploit the structural information between multi-modal features. Graph and temporal self-attention layers are leveraged on the semantic correlation graphs to effectively learn cross-modal relations and temporal associations respectively. For local alignment, the encoded video and text features are assigned to a set of shared semantic topics, and the distances between residuals from the same ones are minimized. To optimize the assignments, a minimum entropy-based regularization term is proposed for training the overall framework. Experimental results are carried out on the MSR-VTT, LSMDC, and ActivityNet Captions datasets. Our method outperforms previous approaches by a large margin and achieves state-of-the-art performance.
High Fidelity Quantum State Transfer by Pontryagin Maximum Principle

High fidelity quantum state transfer is an essential part of quantum information processing. In this regard, we address the problem of maximizing the fidelity in a quantum state transformation process satisfying the Liouville-von Neumann equation. By introducing fidelity as the performance index, we aim at maximizing the similarity of the final state density operator with the one of the desired target state. Optimality conditions in the form of a Maximum Principle of Pontryagin are given for the matrix-valued dynamic control systems propagating the probability density function. These provide a complete set of relations enabling the computation of the optimal control strategy.
A Scalable Blockchain-based Smart Contract Model for Decentralized Voltage Stability Using Sharding Technique

Blockchain technologies are one possible avenue for increasing the resilience of the Smart Grid, by decentralizing the monitoring and control of system-level objectives such as voltage stability protection. They furthermore offer benefits in data immutability and traceability, as blockchains are cryptographically secured. However, the performance of blockchain-based systems in real-time grid monitoring and control has never been empirically tested. This study proposes implementing a decentralized voltage stability algorithm using blockchain-based smart contracts, as a testbed for evaluating the performance of blockchains in real-time control. We furthermore investigate sharding mechanisms as a means of improving the system's scalability with fixed computing resources. We implement our models as a proof-of-concept prototype system using Hyperledger Fabric as our blockchain platform, the Matpower library in MATLAB as our power system simulator, and Hyperledger Caliper as our performance evaluation tool. We found that sharding does indeed lead to a substantial improvement in system scalability for this domain, measured by both transaction success rates and transaction latency.
Decentralized Health Data Distribution: A DLT-based Architecture for Data Protection

The management, protection and sharing of sensitive data such as those associated with the health domain are crucial in enabling personal care and contributing to worldwide medical advancements. Distributed Ledger Technologies (DLTs) allow for data protection compliant solutions in untrusted contexts that guarantee data immutability, protection and transparency when needed. This paper proposes an architecture based on DLTs, Smart Contracts and Distributed File Storage (DFS), enabling user data sovereignty, confidentiality and secure access control. A use case on health data is presented, where we apply a combination of DLT, DFS and an access control mechanism to allow users to distribute their data. Finally, we show an experimental evaluation of the overall architecture to demonstrate the feasibility of implementing practical DLT-based healthcare solutions. The results are collected through independent tests, available opensource, that verify the system's response time in each of its functions and as the load increases. The results are promising and show that the system is feasible and can scale as the load increases.
Light-Field Depth Estimation Using RNN and CRF

Convolutional Neural Networks (CNNs) have recently been successfully applied to depth estimation from light field. Different from those CNN-based methods, we utilize the sequence characteristics of Epipolar Plane Images (EPIs) and introduce a novel light-field depth estimation method based on the Recurrent Neural Network (RNN). Our network builds upon two-stages architectures, involving a local depth estimation and a depth refinement part. In the first part, we regard an EPI patch as a vector sequence which is fed into the RNN to obtain a local depth value. Then, guided by the theory of Conditional Random Field (CRF), we globally optimize the depth map in the second part. Our network was trained in the disparity truth values provided by the synthetic light-field dataset. Experimental results show that our method allows to estimate high-quality disparity results.