Reviewerly/exHarmony huggingface dataset:
Building knowledge graphs from technical documents using named entity recognition and edge weight updating neural network with triplet loss for entity normalization

Authors:
raw_author_name: Sung Hwan Jeon
raw_affiliation_string: Department of Industrial Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, Korea
raw_author_name: Hye Jin Lee
raw_affiliation_string: Department of Industrial Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, Korea
raw_author_name: Jihye Park
raw_affiliation_string: Department of Industrial Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, Korea
raw_author_name: Sungzoon Cho
raw_affiliation_string: Department of Industrial Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, Korea

Abstract: Attempts to express information from various documents in graph form are rapidly increasing. The speed and volume in which these documents are being generated call for an automated process, based on machine learning techniques, for cost-effective and timely analysis. Past studies responded to such needs by building knowledge graphs or technology trees from the bibliographic information of documents, or by relying on text mining techniques in order to extract keywords and/or phrases. While these approaches provide an intuitive glance into the technological hotspots or the key features of the select field, there still is room for improvement, especially in terms of recognizing the same entities appearing in different forms so as to interconnect closely related technological concepts properly. In this paper, we propose to build a patent knowledge network using the United States Patent and Trademark Office (USPTO) patent filings for the semiconductor device sector by fine-tuning Huggingface’s named entity recognition (NER) model with our novel edge weight updating neural network. For the named entity normalization, we employ edge weight updating neural network with positive and negative candidates that are chosen by substring matching techniques. Experiment results show that our proposed approach performs very competitively against the conventional keyword extraction models frequently employed in patent analysis, especially for the named entity normalization (NEN) and document retrieval tasks. By grouping entities with named entity normalization model, the resulting knowledge graph achieves higher scores in retrieval tasks. We also show that our model is robust to the out-of-vocabulary problem by employing the fine-tuned BERT NER model.
Unravelling ancient drilling techniques: A case of pottery repair in the Early European Neolithic

Authors:
raw_author_name: Bruno Vindrola-Padrós
raw_affiliation_string: Institut für Ur- und Frühgeschichte, (Institute of Prehistoric and Protohistoric Archaeology), Christian-Albrechts-Universität zu Kiel (Kiel University), Germany

Abstract: • Photogrammetric techniques detected diagnostic traces of drilling procedures. • Mixed use of drilling techniques for pottery repair observed at Méhtelek-Nádas. • Preference for repairing small or medium-sized open vessels unveils a regime of value. • Evidence of technical adaptation to vessels' damage state.
Sparse regularized correlation filter for UAV object tracking with adaptive contextual learning and keyfilter selection

Authors:
raw_author_name: Zhangjian Ji
raw_affiliation_string: Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
raw_author_name: Kai Feng
raw_affiliation_string: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, China; School of Computer & Information Technology, Shanxi University, Taiyuan, China
raw_author_name: Yuhua Qian
raw_affiliation_string: Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
raw_author_name: Jiye Liang
raw_affiliation_string: Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China

Abstract: Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e, boundary effect and filter corruption. To handle them, in this work, we propose a novel ℓ1 regularization correlation filter with adaptive contextual learning and keyfilter selection for UAV tracking. Firstly, we adaptively detect the positions of effective contextual distractors by the aid of the distribution of local maximum values on the response map of current frame which is generated by using the previous correlation filter model. Next, we eliminate inconsistent labels for the tracked target by removing one on each distractor and develop a new score scheme for each distractor. Then, we can select the keyfilter from the filters pool by finding the maximal similarity between the target at the current frame and the target template corresponding to each filter in the filters pool. Finally, quantitative and qualitative experiments on three authoritative UAV datasets show that the proposed method is superior to the state-of-the-art tracking methods based on correlation filter framework.
Dynamic receptive field adaptation for scene text recognition

Authors:
raw_author_name: Shu Tian
raw_affiliation_string: University of Science and Technology Beijing, Beijing, China
raw_author_name: Kang-Xi Zhu
raw_affiliation_string: University of Science and Technology Beijing, Beijing, China
raw_author_name: Hai-Bo Qin
raw_affiliation_string: University of Science and Technology Beijing, Beijing, China
raw_author_name: Chun Yang
raw_affiliation_string: University of Science and Technology Beijing, Beijing, China

Abstract: Scene text recognition methods of the Encoder–Decoder framework, generally assume that the proportion of characters in the same text instance are basically the same. However, this assumption does not always hold in the context of complex scene images. For adaptively revising the receptive field according to the different font in the scene text image, we propose a Dynamic Receptive Field Adaption Framework which consists of Memory Attention (MA) module and Dynamic Feature Adaptive (DFA) module. MA percepts the historical location information to adapt to the change of character position in the decoder. DFA selects the most distinguishing features from feature maps of different levels dynamically. Additionally, MA and DFA can be easily extended to the existing attention-based and transformer-based text recognition methods to improve their performance. With extension experiments on public benchmark datasets, including IIIT-5K, SVT, SVTP, CUTE80, RECTS, LSVT, and RCTW, our method has shown effectiveness and robustness.
RelJoin: Relative-cost-based Selection of Distributed Join Methods for Query Plan Optimization

Authors:
raw_author_name: Francis C.M. Lau
raw_affiliation_string: Department of Computer Science, The University of Hong Kong, Hong Kong, China
raw_author_name: Heming Cui
raw_affiliation_string: Department of Computer Science, The University of Hong Kong, Hong Kong, China
raw_author_name: Yupeng Li
raw_affiliation_string: Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China
raw_author_name: Bing Lin
raw_affiliation_string: College of Physics and Energy, Fujian Normal University, Fuzhou, China; Key Laboratory of Quantum Manipulation and New Energy Materials, Fuzhou, China; School of Computer Science, Peking University, Bejing, China
raw_author_name: Xiping Hu
raw_affiliation_string: Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China; Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China

Abstract: Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different network communication patterns in the data exchange phase generate varying network communication workloads and significantly affect the distributed join performance. However, most cost-based query optimizers focus on the local computing cost and do not precisely model the network communication cost. We propose a cost model for various distributed join methods to optimize join queries in DISC platforms. Our method precisely measures the network and local computing workloads in different execution phases, using information on the size and cardinality statistics of datasets and cluster join parallelism. Our cost model reveals the importance of the relative size of the joining datasets. We implement an efficient distributed join selection strategy, known as RelJoin in SparkSQL, which is an industry-prevalent distributed data processing framework. RelJoin uses runtime adaptive statistics for accurate cost estimation and selects optimal distributed join methods for logical joins to optimize the physical query plan. The evaluation results on the TPC-DS benchmark show that RelJoin performs best in 62 of the 97 queries and can reduce the average query time by 21% compared with other strategies.1
Experimental evaluation of RISC-V micro-architecture against fault injection attack

Authors:
raw_author_name: Maryam Esmaeilian
raw_affiliation_string: School of Computer Engineering, Iran University of Science and Technology (IUST), Iran
raw_author_name: Hakem Beitollahi
raw_affiliation_string: School of Computer Engineering, Iran University of Science and Technology (IUST), Iran

Abstract: Today, the use of embedded processors is increasing dramatically and they are used in all aspects from our daily life to security applications. Physical access to hardware has made the hardware security a major concern. Hardware attacks compromise the hardware security by physically accessing target devices. Among the available techniques for hardware attacks, Fault Injection Attacks (FIAs), such as clock glitching, are one of the most harmful types of non-invasive attacks that can disrupt the operation of an embedded system. Thus, it will be important and fundamental to evaluate embedded software programs before using them in critical applications and check their vulnerability against fault injection attacks. However, it is often difficult for software developers to assess vulnerabilities. In this paper, an easy-to-use platform is presented to facilitate the process of evaluating the vulnerability of programs running on embedded processors against clock glitching attacks. Our experimental results show the vulnerability window of RISC-V micro-architecture for different high-level C-functions. The results of this research can help the developers of embedded systems that are used in security applications to evaluate their system against clock glitching attacks with the least cost in a short time.
The evaluation of innovation ability of China's information technology application innovation enterprises based on cloud modeling

Authors:
raw_author_name: Yuya Liu
raw_affiliation_string: School of Economics and Management, Huanghuai College, Zhumadian, 463000, China; School of Economics and Management, Shihezi University, Shihezi, 832000, China
raw_author_name: Sheng Mai
raw_affiliation_string: School of Economics and Management, Shihezi University, Shihezi, 832000, China

Abstract: This paper adopts the theoretical method of the cloud model to establish the evaluation index system. It tests it through examples, and the results show that the cloud model has a flexible conversion function in qualitative description and quantitative expression, and the evaluation results obtained can be visualized through graphs. It is concluded that the evaluation of the technology application innovation industry (ITAI) enterprise's innovation power by using the cloud modeling method can reflect the current level of the ITAI enterprise's innovation power more realistically, which is helpful to find out the innovation path of the ITAI enterprise.
Swarm Self-supervised Hypergraph Embedding for Recommendation

Authors:
raw_author_name: Meng Jian
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, China
raw_author_name: Yulong Bai
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, China
raw_author_name: Jingjing Guo
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, China
raw_author_name: Lifang Wu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, China

Abstract: The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users’ requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users’ interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation.
Distributional Learning for Network Alignment with Global Constraints

Authors:
raw_author_name: Hui Xu
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Liyao Xiang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Xiaoying Gan
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Luoyi Fu
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Xinbing Wang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Chenghu Zhou
raw_affiliation_string: Chinese Academy of Sciences, China

Abstract: Network alignment, pairing corresponding nodes across the source and target networks, plays an important role in many data mining tasks. Extensive studies focus on learning node embeddings across different networks in a unified space. However, these methods have not taken the large structural discrepancy between aligned nodes into account and, thus, are largely confined by the deterministic representations of nodes. In this work, we propose a novel network alignment framework highlighted by distributional learning and globally optimal alignment. By modeling the uncertainty of each node by Gaussian distribution, our framework builds similarity matrices on the Wasserstein distance between distributions and applies Sinkhorn operation, which learns the globally optimal mapping in an end-to-end fashion. We show that each integrated part of the framework contributes to the overall performance. Under a variety of experimental settings, our alignment framework shows superior accuracy and efficiency to the state-of-the-art.
Enhancing 6G network security: GANs for pilot contamination attack detection in massive MIMO systems

Authors:
raw_author_name: Jyoti Deshwal Yadav
raw_affiliation_string: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201309, U.P., India
raw_author_name: Vivek K. Dwivedi
raw_affiliation_string: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201309, U.P., India
raw_author_name: Saurabh Chaturvedi
raw_affiliation_string: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201309, U.P., India

Abstract: Accurate channel estimation at the base station (BS) is crucial for reliable, high-throughput communication in massive multiple input multiple output (MIMO) systems. However, the process of estimating channel matrices in massive MIMO systems is prone to pilot contamination attacks. These attacks not only degrade the efficiency of channel estimation but also increase the probability of information leakage. In this paper, a method is being proposed for detecting pilot contamination signals using a deep learning model, namely a generative adversarial network (GAN), at the BS of a massive MIMO system. GAN comprises two interconnected models, the first model is the generator, which creates synthetic samples, and the second model is the discriminator, which distinguishes between real and generator-generated samples. Residual block-based convolutional neural networks (CNNs) like ResNet and reverse ResNet are implemented as discriminator model of GAN for improving the detection of pilot-contaminated signals. The generator, structured as an autoencoder, generates artificial data resembling real data from noisy signals. The simulation results demonstrate that the proposed model effectively identifies the pilot-contaminated attacks; this effectiveness is evaluated through performance matrices called accuracy and loss function. Furthermore, the performance of the model is observed by varying the number of BS antennas and pilot lengths.
Using Neural and Graph Neural Recommender systems to Overcome Choice Overload: Evidence from a Music Education Platform

Authors:
raw_author_name: Michalis Vlachos
raw_affiliation_string: Faculty of Business and Economics, University of Lausanne, Switzerland
raw_author_name: Ahmad Ajalloeian
raw_affiliation_string: Faculty of Business and Economics, University of Lausanne, Switzerland
raw_author_name: Ninghao Liu
raw_affiliation_string: University of Georgia, USA
raw_author_name: Johannes Schneider
raw_affiliation_string: University of Liechtenstein, Liechtenstein

Abstract: The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music. Our research is based on data from Tomplay, a music platform that offers sheet music with professional audio recordings, enabling users to discover and practice music content at varying levels of difficulty. Through our analysis, we emphasize the distinct interaction patterns on educational platforms like Tomplay, which we compare with other commonly used recommendation datasets. We find that interactions are comparatively sparse on educational platforms, with users often focusing on specific content as they learn, rather than interacting with a broader range of material. Therefore, our primary goal is to address the issue of data sparsity. We achieve this through entity resolution principles and propose a neural network (NN)-based recommendation model. Further, we improve this model by utilizing graph neural networks (GNNs), which provide superior predictive accuracy compared to NNs. Notably, our study demonstrates that GNNs are highly effective even for users with little or no historical preferences (cold-start problem). Our cold-start experiments also provide valuable insights into an independent issue, namely, the number of historical interactions needed by a recommendation model to gain a comprehensive understanding of a user. Our findings demonstrate that a platform acquires a solid knowledge of a user’s general preferences and characteristics with 50 past interactions. Overall, our study makes significant contributions to information systems research on business analytics and prescriptive analytics. Moreover, our framework and evaluation results offer implications for various stakeholders, including online educational institutions, education policymakers, and learning platform users.
A fully secure lattice-based signcryption with designated equality test in standard model

Authors:
raw_author_name: Xinjian Chen
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
raw_author_name: Hongbo Li
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
raw_author_name: Jianye Huang
raw_affiliation_string: University of Wollongong, Wollongong, NSW 2500, Australia
raw_author_name: Willy Susilo
raw_affiliation_string: University of Wollongong, Wollongong, NSW 2500, Australia
raw_author_name: Qiong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China; Guangzhou Key Laboratory of Intelligent Agriculture, Guangzhou 510642, China

Abstract: In hospital information systems, large volumes of electronic diagnostic records (EDRs) take up most of the storage space. While cloud server providers can reduce the local storage burden on hospitals and provide data-sharing services, the potential threat of sensitive data leakage and non-traceability of diagnoses prevents hospitals from uploading patients' diagnostic records to remote cloud servers directly. Therefore, to address security and traceability issues, we propose a new construction of post-quantum secure signcryption scheme with a designated equality test based on lattices (LB-SCDET) in this paper. Our LB-SCDET scheme allows a designated tester to perform equality tests on signcrypt-ciphertexts of EDRs without leaking what the EDRs actually are. Compared to the recent LB-SCET scheme proposed by Le et al., our LB-SCDET scheme implements a designated mechanism and is secure against offline message recovery attacks (OMRA). The comparison shows that our scheme enjoys a higher level of security, albeit the ciphertext size is slightly larger. Finally, we prove the scheme to be secure under the hardness of the Learning-with-Errors problem and unidirectionality of the Short-Integer-Solution problem. To be of independent interest, we show that the LB-SCET scheme fails to achieve the claimed IND-CCA security.