Fabric-GC: A Blockchain-based Gantt Chart System for Cross-organizational Project Management

Large-scale production is always associated with more and more development and interaction among peers, and many fields achieve higher economic benefits through project cooperation. However, project managers in the traditional centralized approach cannot rearrange their activities to cross-organizational project management. Thanks to its characteristics, the Blockchain can represent a valid solution to the problems mentioned above. In this article, we propose Fabric-GC, a Blockchain-based Gantt chart system. Fabric-GC enables to realize secure and effective cross-organizational cooperation for project management, providing access control to multiple parties for project visualization. Compared with other solutions, the proposed system is versatile, as it can be applied to project management in different fields and achieve effective and agile scheduling. Experimental results show that Fabric-GC achieves stable performance in large-scale request and processing distributed environments, where the data synchronization speed of the consortium chain reached four times faster than a public chain, achieving faster data consistency

S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis

Stocks price prediction is a current hot spot with great promise and challenges. Recently, there have been many stock price prediction methods. However, the prediction accuracy of these methods is still far from satisfactory. In this paper, we propose a stock price prediction method that incorporates multiple data sources and the investor sentiment, which can be called S_I_LSTM. Firstly, we crawl multiple data sources on the Internet and preprocess them respectively. These data involve stock historical data, technical indicators, and non-traditional data sources, such as stock posts and financial news. Then, we use the sentiment analysis method based on convolutional neural network for the non-traditional data, which can calculate the investors' sentiment index. Finally, we combine sentiment index, technical indicators and stock historical transaction data as the feature set of stock price prediction and adopt the long short-term memory network for predicting the China Shanghai A-share market. The experiments show that the predicted stock closing price is closer to the true closing price than the single data source, and the mean absolute error can achieve 2.386835, which is better than traditional methods. We verified the effectiveness on the real data sets of five listed companies

Improving the Performance of OpenMP by Array

Abstract. The scalability of an OpenMP program in a ccNUMA system with a large number of processors suffers from remote memory accesses, cache misses and false sharing. Good data locality is needed to overcome these problems whereas OpenMP offers limited capabilities to control it on ccNUMA architecture. A so-called SPMD style OpenMP program can achieve data locality by means of array privatization, and this approach has shown good performance in previous research. It is hard to write SPMD OpenMP code; therefore we are building a tool to relieve users from this task by automatically converting OpenMP programs into equivalent SPMD style OpenMP. We show the process of the translation by considering how to modify array declarations, parallel loops, and showing how to handle a variety of OpenMP constructs including REDUCTION, ORDERED clauses and synchronization. We are currently implementing these translations in an interactive tool based on the Open64 compiler.

Recommending third-party APIs via using lightweight graph convolutional neural networks

Third-party APIs have been widely used to develop various applications. As the number of third-party APIs grows, it becomes increasingly challenging to quickly find suitable APIs that meet users’ requirements. Inspired by recommender systems, API recommendation methods have been proposed to address this issue. However, previous API recommendation methods are insufficient in utilising the high-order interactions between users and APIs, and thus have limited performance. Based on the model of lightweight graph convolutional neural network, this paper proposes an effective API recommendation method by exploiting both low-order and high-order interactions between users and APIs. It first learns the embedding of users and APIs from the user-API interaction graph, and then adopts a weighted summation operator to aggregate the embeddings learned from different propagation layers for API recommendation. Extensive experiments are conducted on a real dataset with 160,309 API users and 21,031 Web APIs, and the results show that our method has significantly better precision and recall than other state-of-the-art methods

Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism

Entity and relation extraction has been widely studied in natural language processing, and some joint methods have been proposed in recent years. However, existing studies still suffer from two problems. Firstly, the token space information has been fully utilized in those studies, while the label space information is underutilized. However, a few preliminary works have proven that the label space information could contribute to this task. Secondly, the performance of relevant entities detection is still unsatisfactory in entity and relation extraction tasks. In this paper, a new model GANCE (Gated and Attentive Network Collaborative Extracting) is proposed to address these problems. Firstly, GANCE exploits the label space information by applying a gating mechanism, which could improve the performance of the relation extraction. Then, two multi-head attention modules are designed to update the token and token-label fusion representation. In this way, the relevant entities detection could be solved. Experimental results demonstrate that GANCE has better accuracy than several competitive approaches in terms of entity recognition and relation extraction on the CoNLL04 dataset at 90.32% and 73.59%, respectively. Moreover, the F1 score of relation extraction increased by 1.24% over existing approaches in the ADE dataset

MS_HGNN: a hybrid online fraud detection model to alleviate graph-based data imbalance

Online transaction fraud has become increasingly rampant due to the convenience of mobile payment. Fraud detection is critical to ensure the security of online transactions. With the development of graph neural network, researchers have applied it to the field of fraud detection. The existing fraud detection methods will solve the class imbalance by sampling, but they do not fully consider the various imbalances in the heterogeneous graph, and the data imbalance will directly affect the performance of the model. This work proposes a hybrid graph neural network model for online fraud detection to address this issue. The three types of imbalance in online transactions are feature imbalance, category imbalance, and relation imbalance, and they are all addressed in the proposed model. The entities with the feature most closely related to the fraudsters will be determined for the feature imbalance, and samples will be taken for further identification in the subsequent training phase. The hybrid model then uses under-sampling in combination with the long-distance sampling to find nodes with high similarity of features for the category imbalance. Finally, we propose a reward/punishment mechanism based on reinforcement learning for relation imbalance, which uses the threshold created by training as the sampling weight between relations. This paper conducts experiments on the public datasets Amazon and Yelp. The experimental results show that the model proposed is 5.61% higher than the best model in the comparison model on Amazon dataset, and 1.58% higher on Yelp dataset

A novel approach for anti-pollution attacks in network coding

Network coding remarkably improves network performance and transmission efficiency for multi-cast. Nevertheless, as its inherent defect, it is vulnerable to pollution attacks, bringing in a severe decrease of the network performance. In the proposed work, a novel approach is put forwarded, which can rapidly identify and isolate the malicious nodes from the networks as early as possible. On the one hand, we raise a secure infrastructure, on the other hand, we advocate a secure transmission protocol to verify every received packet. Theoretical analysis and experimental results demonstrate that the proposed scheme has the optimal performance in terms of security, network delay and network throughput