Reviewerly/exHarmony huggingface dataset:
Grounded theory-based model of the influence of digital communication on handicraft intangible cultural heritage

Digital technologies have altered social interaction and information transmission, creating new cultural communication media and models. Despite extensive investigations into the effect of digital communication on handicraft intangible cultural heritage (ICH), research on the relationship between the factors that influence the digital communication of handicraft ICH is relatively scarce. This study used open-ended interviews and textual analysis to obtain data concerning these influencing factors. A grounded theory approach found that five core factors have significant effects on the digital communication of handicraft ICH. These are digital communication awareness, cultural communication initiative, adaptability of digital technology, audience acceptance and cognition, and uncontrollable factors. Furthermore, a theoretical "awareness-behavior-attitude" model based on an analysis of the relationships between the five factors was constructed. Finally, the action mechanism of the theoretical model was identified and tested, revealing that the action modes of each influencing factor were inconsistent. Antecedent factors were directly proportional to awareness-behavior, situational factors were inversely proportional to awareness-behavior, and regulatory factors were inversely proportional to behavior-attitude. In addition, situational factors and regulatory factors were found to have a positive impact on antecedent factors.The online version contains supplementary material available at 10.1186/s40494-022-00760-z.
A content-based recommendation approach based on singular value decomposition

In the Internet era, where information and communication technologies (ICT) allow data exchange, new tools able to select the correct data are needed. In this field, Recommender Systems have a prime location. The recommendation methods take many forms based on the information exploited in order to provide rating forecasts. However, all of them can choose the correct information to support users. Indeed, these methods allow for overcoming the information overload problem. This paper introduces the theoretical bases of a novel recommendation method defined Rating Singular Value Decomposition (RSVD). RSVD is a Content-Based method that exploits the Singular Value Decomposition properties in order to calculate rating forecasts. This method aims to elaborate the users and items profile to obtain matrices related to ones obtained in Collaborative Filtering methods that exploit Singular Value Decomposition. The accuracy of RSVD is compared with the accuracy of Collaborative Filtering methods, and a study on the sparsity problem is performed. The results obtained are promising.
Incomplete multi-view clustering via virtual-label guided matrix factorization

Multi-view clustering methods have achieved appealing performance by exploring the data from multiple sources. It often assumes that the multiple-view data are complete. However, such rigorous assumption does not be satisfied in many real-world applications, in which only some views of data are available. The existence of the incomplete-view data would disable the current multi-view clustering algorithms. To address this challenge, in this article, we present a novel virtual-label guided matrix factorization (VLMF) to facilitate the incomplete multi-view clustering. Specifically, in VLMF, we first employ the graph regularization of each view to mine the geometrical structure of data. Then, we utilize the virtual label to guide matrix factorization. Furthermore, the clustering process and the consensus latent representation learning of data are integrated into a joint framework. Experimental results on several incomplete multi-view datasets illustrate the validity of the proposed method.
DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors

Camera-based 3D object detectors are welcome due to their wider deployment and lower price than LiDAR sensors. We first revisit the prior stereo detector DSGN for its stereo volume construction ways for representing both 3D geometry and semantics. We polish the stereo modeling and propose the advanced version, DSGN++, aiming to enhance effective information flow throughout the 2D-to-3D pipeline in three main aspects. First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features. Second, for grasping differently spaced features, we present a novel stereo volume - Dual-view Stereo Volume (DSV) that integrates front-view and top-view features and reconstructs sub-voxel depth in the camera frustum. Third, as the foreground region becomes less dominant in 3D space, we propose a multi-modal data editing strategy - Stereo-LiDAR Copy-Paste, which ensures cross-modal alignment and improves data efficiency. Without bells and whistles, extensive experiments in various modality setups on the popular KITTI benchmark show that our method consistently outperforms other camera-based 3D detectors for all categories. Code is available at https://github.com/chenyilun95/DSGN2.
A Deep Multimodal Adversarial Cycle-Consistent Network for Smart Enterprise System

Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data, which can capture the local structures from instance reconstructions and the global topology from adversarial games. Specifically, DMACCN is designed as an adversarial encoding-decoding architecture composed of the modality specific-encoder, the modality-common fusion network, the cycle-consistent modality-specific generator, and the modality-fusion discriminator, which can fully fuse complementary information of data. Then, an adversarial cycle-consistent loss is devised to guide the clustering pattern mining from complementary information of data, which can align semantics between modalities and capture clustering structures of instances. The two components collaborate in a seamless manner to capture accurate commercial patterns. Finally, extensive experimental results on four datasets show DMACCN greatly outperforms the comparison methods.
A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images

The Poisson noise is added to the image during the acquisition of medical X-Ray images. The distorted image due to this noise makes it difficult for physicians to diagnose the disease. Although there are various approaches for filtering Poisson noise, these approaches have disadvantages such as excessive smoothing of the image, distorting the texture information, reducing the image quality and high computational cost. In this study, a novel method that removes Poisson noise from medical X-Ray images is proposed by overcoming the above mentioned disadvantages. In the proposed method, the Wiener filter is modified using the FIR filter embedded in the standard Wiener algorithm. The FIR filter design was carried out using the ASO optimization algorithm. Optimum local mean and optimum local variance values are calculated using the optimization matrix corresponding to the FIR filter coefficients and transferred to the standart Wiener filter layer as parameter inputs. The proposed method showed superior performance in synthetic images and medical X-Ray images in terms of PSNR, MSE, SSIM metrics and image quality metrics such as luminous intensity, Contrast index, Entropy and Sharpness. The time consumption of the proposed method is much less. The clinical usage of the proposed method may help doctors to be able to diagnose the disease more accurately by interpreting the X-ray images. Besides, the proposed method can also have a positive effect on the CAD performance by using it at the pre-processing stage of CAD systems.
Artificial bee colony algorithm based on adaptive neighborhood topologies

During the past few years, many neighborhood-based ABC variants have been developed to utilize the valuable information of neighbors for guiding searches instead of using the best individual or elite individuals. However, neighbor selection is determined by the neighborhood topology, and different neighborhood topologies are suitable for different problems. Unfortunately, previous neighborhood-based ABC variants have often used a single type of neighborhood topology, which significantly affects algorithm performance. Hence, to take advantage of different neighborhood topologies, we propose a new neighborhood-based ABC variant using adaptive neighborhood topologies, called ABC-ANT. In ABC-ANT, to determine which type of neighborhood topology should be selected, the fitness distance correlation technique is first used to identify the feature of the fitness landscape for a given problem. Then, according to the identified feature, the most suitable neighborhood topology is adaptively selected, which is beneficial to adapt the search to the fitness landscape. Moreover, in ABC-ANT, the scout bee phase is modified by developing a dual-elite search strategy to save the search experience. Extensive experiments are conducted on two test suites, i.e., CEC2013 and CEC2017, and one real-world optimization problem, i.e., the dynamic economic dispatch problem. Seven ABC variants and six non-ABC variants are included in the performance comparison. The results verify that ABC-ANT has very competitive performance.
Physics-infused Machine Learning for Crowd Simulation

Crowd simulation acts as the basic component in traffic management, urban planning, and emergency management. Most existing approaches use physics-based models due to their robustness and strong generalizability, yet they fall short in fidelity since human behaviors are too complex and heterogeneous for a universal physical model to describe. Recent research tries to solve this problem by deep learning methods. However, they are still unable to generalize well beyond training distributions. In this work, we propose to jointly leverage the strength of the physical and neural network models for crowd simulation by a Physics-Infused Machine Learning (PIML) framework. The key idea is to let the two models learn from each other by iteratively going through a physics-informed machine learning process and a machine-learning-aided physics discovery process. We present our realization of the framework with a novel neural network model, Physics-informed Crowd Simulator (PCS), and tailored interaction mechanisms enabling the two models to facilitate each other. Specifically, our designs enable the neural network model to identify generalizable signals from real-world data better and yield physically consistent simulations with the physical model's form and simulation results as a prior. Further, by performing symbolic regression on the well-trained neural network, we obtain improved physical models that better describe crowd dynamics. Extensive experiments on two publicly available large-scale real-world datasets show that, with the framework, we successfully obtain a neural network model with strong generalizability and a new physical model with valid physical meanings at the same time. Both models outperform existing state-of-the-art simulation methods in accuracy, fidelity, and generalizability, which demonstrates the effectiveness of the PIML framework for improving simulation performance and its capability for facilitating scientific discovery and deepening our understandings of crowd dynamics. We release the codes at https://github.com/tsinghua-fib-lab/PIML.
EdgeWatch: Collaborative Investigation of Data Integrity at the Edge based on Blockchain

Mobile edge computing (MEC) offers the infrastructure for improving data caching performance structurally by deploying edge servers at the network edge within users' close geographic proximity. Popular data like viral videos can be cached on edge servers to serve users with low latency. Investigating the integrity of these edge data is critical and challenging as edge servers often suffer from unreliability and constrained resources. Meanwhile, EDI (edge data integrity) investigation must be performed by edge servers collaboratively at the edge to avoid excessive backhaul network traffic. There are two main challenges in practice: 1) there is a lack of Byzantine-tolerant collaborative investigation method; and 2) edge servers may be reluctant to collaborate without proper incentives. To tackle these challenges systematically, this paper proposes a novel scheme named EdgeWatch to enable robust and collaborative EDI investigation in a decentralized manner based on blockchain. Under EdgeWatch, edge servers collaborate on EDI investigation following a novel integrity consensus. A blockchain system comprises of three main components is built as the infrastructure to facilitate integrity consensus: 1) an incentive mechanism that motivates edge servers to participate in EDI investigation; 2) a reputation system that elects reliable leaders for block consensus; and 3) a leader randomization technique that protects leaders from targeted attacks. We evaluate it against three representative schemes experimentally. The results demonstrate the high precision, efficiency, and robustness of EdgeWatch.
Multi-view learning with privileged weighted twin support vector machine

By using inter-class and intra-class K-Nearest Neighbors (KNNs), weighted twin support vector machine (WLTSVM) mines as much potential similarity information in samples as possible to improve the common short-coming of nonparallel hyperplane classifiers. Multi-view learning (MVL) has a lot of potential due to the multi-modal datasets that are becoming available. In this paper, we propose a new multi-view learning with privileged weighted twin support vector machine (MPWTSVM). It not only inherits the advantages of WLTSVM but also has its characteristics. Firstly, it enhances generalization ability by mining intra-class information from the same perspective. Secondly, it reduces the redundant constraints with the help of inter-class information, thus improving the running speed. Most importantly, it can follow both the consensus and the complementary principles simultaneously. The consensus principle is realized by minimizing the coupling items of different views in the original objective function. The complementary principle is achieved by establishing privileged information paradigms and MVL. Compared with state-of-the-art MVL methods: SVM-2K, MVTSVM, MCPK, PSVM-2V, MVRDTSVM and MVTHSVM-2C, our model has better accuracy and classification efficiency. Experimental results on numerous datasets prove the effectiveness of the proposed algorithm.
Genet

As deep reinforcement learning (RL) showcases its strengths in networking, its pitfalls are also coming to the public's attention. Training on a wide range of network environments leads to suboptimal performance, whereas training on a narrow distribution of environments results in poor generalization.
GSO-simulcast

We present GSO-Simulcast, a new architecture designed for large-scale multi-party video-conferencing systems. GSO-Simulcast is currently deployed at full-scale in Alibaba's Dingtalk video conferencing that serves more than 500 million users. It marks a fundamental shift from today's Simulcast, where a media server locally decides how to switch and forward video streams based on a fragmented network view. Instead, GSO-Simulcast globally orchestrates the publishing, subscribing, as well as the resolution and bitrate of video streams for each participant using a centralized controller that is aware of all network constraints in a meeting. The controller automatically modifies stream configurations to meet the participants' real-time network changes and updates. In doing so, GSO-Simulcast achieves multiple goals: (1) reducing video and network mismatch, (2) less path congestion, and (3) automated stream policy management. With the deployment of GSO-Simulcast, we observed more than a 35% reduction in the average video stall, 50% reduction in the average voice stall, and 6% improvement in the average video framerate. We describe the principle, design, deployment, and lessons learned.
LFS-AS: Lightweight Forward Secure Aggregate Signature for e-Health Scenarios

The advancement of Internet of Medical Things (IoMT) leading to the proliferation of electronic healthcare scenarios, with an expanding trend of hospitals and healthcare organizations employing Electronic Medical Records (EMRs) with uploading to the cloud for sharing. However, security challenges exist during the generation and uploading of medical records, such as record tampering, key attacks, etc., which will become the maximum bottleneck restricting the development of e-Health scenarios in the near future. Some scholars consider digital signature techniques, such as Attribute-based Signature, to address the security challenges but ignoring its overwhelming efficiency. In this paper, we propose a lightweight forward secure aggregate signature for e-Health scenarios. We devise a lightweight secure aggregate signature to provide unforgeability and forward security for medical records, which is the first aggregate signature scheme that enables forward security in e-Health scenarios. Furthermore, our scheme offers superior lightweight properties, requiring only 5.78ms for aggregation and 4.85ms for verification of 1000 signatures, which is significantly lower than existing schemes, especially suitable for medical systems with enormous data volume. Security analysis and experimental evaluation indicate that our scheme assures correctness, unforgeability and forward security, while fulfilling lightweight demands that outperform existing schemes.
An Improved Arithmetic Optimization Algorithm and Its Application to Determine the Parameters of Support Vector Machine

The arithmetic optimization algorithm (AOA) is a new metaheuristic algorithm inspired by arithmetic operators (addition, subtraction, multiplication, and division) to solve arithmetic problems. The algorithm is characterized by simple principles, fewer parameter settings, and easy implementation, and has been widely used in many fields. However, similar to other meta-heuristic algorithms, AOA suffers from shortcomings, such as slow convergence speed and an easy ability to fall into local optimum. To address the shortcomings of AOA, an improved arithmetic optimization algorithm (IAOA) is proposed. First, dynamic inertia weights are used to improve the algorithm’s exploration and exploitation ability and speed up the algorithm’s convergence speed; second, dynamic mutation probability coefficients and the triangular mutation strategy are introduced to improve the algorithm’s ability to avoid local optimum. In order to verify the effectiveness and practicality of the algorithm in this paper, six benchmark test functions are selected for the optimization search test verification to verify the optimization search ability of IAOA; then, IAOA is used for the parameter optimization of support vector machines to verify the practical ability of IAOA. The experimental results show that IAOA has a strong global search capability, and the optimization-seeking capability is significantly improved, and it shows excellent performance in support vector machine parameter optimization.
Novel blockchain transaction provenance model with graph attention mechanism

• Novel blockchain transaction provenance model with graph attention mechanism is proposed. • The triple data structure is used to describe the provenance information. • The proposed method is used to improve the traceback accuracy. • The effectiveness of the proposed method is verified based on real large-scale Bitcoin dataset. • The experiment shows that the proposed method achieves state of the art result. With the maturity of the blockchain technology, more and more blockchain digital currencies including the Bitcoin, the Ethereum and the Ripple have been developed. Meanwhile, the security problem of the blockchain digital currency has become increasingly serious. It is necessary to build a provenance model for a large number of blockchain transaction, which can be used to find out which link has trouble and who is responsible once a problem occurs. However, the existing blockchain transaction data analysis methods have low traceback accuracy for data provenance. Therefore, a novel blockchain transaction provenance model with graph attention mechanism is proposed in this paper. The graph attention mechanism is used to identify the traders. Then, a triplet data structure is built to record provenance information and express the relationship between transaction amount and traders. Finally, the smart contract is used to implement the proposed provenance model. The provenance information can be searched through the interface provided by the smart contract when the traceback is required. In addition, the real large-scale Bitcoin transaction dataset Elliptic is used to compare the proposed method with other existing methods. The experiment show that the proposed method achieves state of the art results of the traceback accuracy.
Multiple Communication Channels in Literary Texts

The statistical theory of language translation is used to compare how a literary character speaks to different audiences by diversifying two important linguistic communication channels: the "sentences channel" and the "interpunctions channel".The theory can "measure" how the author shapes a character speaking to different audiences, by modulating deep-language parameters.To show its power, we have applied the theory to the literary corpus of Maria Valtorta, an Italian mystic of the XX-century.The likeness index L I , ranging from 0 to 1, allows to "measure" how two linguistic channels are similar, therefore implying that a character speaks to different audiences in the same way.A 6-dB difference between the signal-to-noise ratios of two channels already gives 0.5 L I ≈ , a threshold below which the two channels depend very little on each other, therefore implying that the character addresses different audiences differently.In conclusion, multiple linguistic channels can describe the "fine tuning" that a literary author uses to diversify characters or distinguish the behavior of the same character in different situations.The theory can be applied to literary corpora written in any alphabetical language.