Reviewerly/exHarmony huggingface dataset:
Adaptive KNN and graph-based auto-weighted multi-view consensus spectral learning

The multi-view learning is a fundamental problem in the multimedia analysis. However, most existing multi-view learning methods need to calculate a similarity matrix for each view. This operation has two drawbacks. Firstly, the construction of the similarity matrix needs a fixed function. But, in fact, the internal relationship between data points changes continuously. The fixed graph representation matrix is not conducive to fully consider the heterogeneous information about different views. Secondly, the similarity matrix of each view is used to participate independently in the subsequent calculation, which ignores the correlation and importance of different views. In our paper, an adaptive K Nearest Neighbor (KNN) and graph-based auto-weighted multi-view consensus spectral clustering (KGWMC) is proposed. An adaptive KNN method is used to calculate a similarity matrix for each view, which is updated in the subsequent iteration process . An adaptive joint optimization strategy is used to fuse multiple views for obtaining a consensus graph. In this step, instead of using a fixed function to calculate the weight of each view, we transform the weight calculation into an optimization problem for iteratively updating the weight of each view in a non-parametric form. The main novelty of KGWMC is the learning method of each similarity matrix and consensus graph in a mutual reinforcement manner. When the consensus graph converges, the spectral clustering algorithm is implemented to it for obtaining the final clustering . Finally, we demonstrate the effectiveness of our algorithm by comparing it with nine state-of-the-art algorithms on six real datasets.
Automated Process Knowledge Graph Construction from BPMN Models

Enterprise knowledge graphs are increasingly adopted in industrial settings to integrate heterogeneous systems and data landscapes. Manufacturing systems can benefit from knowledge graphs as they contribute towards implementing visions of interconnected, decentralized and flexible smart manufacturing systems. Process knowledge is a key perspective which has so far attracted limited attention in this context, despite its usefulness for capturing the context in which data are generated. Such knowledge is commonly expressed in diagrammatic languages and the resulting models can not readily be used in knowledge graph construction. We propose BPMN2KG to address this problem. BPMN2KG is a transformation tool from BPMN2.0 process models into knowledge graphs. Thereby BPMN2KG creates a frame for process-centric data integration and analysis with this transformation. We motivate and evaluate our transformation tool with a real-world industrial use case focused on quality management in plastic injection molding for the automotive sector. We use BPMN2KG for process-centric integration of dispersed production systems data that results in an integrated knowledge graph that can be queried using SPARQL, a standardized graph-pattern based query language. By means of several example queries, we illustrate how this knowledge graph benefits data contextualization and integrated analysis. In a broader context, we contribute towards the vision of a process-centric enterprise Knowledge Graph (KG). BPMN2KG is available at https://short.wu.ac.at/BPMN2KG , and the sample queries and results at https://short.wu.ac.at/DEXA2022 .
A novel locality-sensitive hashing relational graph matching network for semantic textual similarity measurement

Recent efforts adopt interaction-based models to construct the interaction of words between sentences, which aim to predict whether two sentences are semantically equivalent or not in semantic textual similarity (STS) task. However, these methods lack the global semantic awareness, which make it difficult to distinguish syntactic differences and also suffer from the inference time cost, primarily due to the calculation of the pair-interactions of words. A novel model called Locality-Sensitive Hashing Relational Graph Matching Network (LSHRGMN) is therefore proposed, which tackles these problems by syntactic dependency graph and locality-sensitive hashing (LSH). Specifically, syntactic dependency graph is aware of the global semantic information via rooting in each word to construct several trees and merging all the trees into one graph. LSH mechanism is introduced into pair-interactions of words for the inference efficiency problem. Extensive experiments are conducted on three real-world datasets, and the result shows that the proposed approach acquires higher accuracy and intriguing inference speed.
Situation-Aware Multivariate Time Series Anomaly Detection Through Active Learning and Contrast VAE-Based Models in Large Distributed Systems

The massive amounts of monitoring data in network applications bring an urgent need for intelligent operation in large distributed systems. The key problem is precisely detecting anomalies in multivariate time series (MTS) monitoring metrics with the awareness of different application scenarios. Unsupervised MTS anomaly detection methods aim at detecting data anomalies from historical MTS without considering the out-of-band information (including user feedback and background information like code deployment status), which leads to poor performance in practice. To take advantage of the out-of-band information, we propose ACVAE, an MTS anomaly detection algorithm through active learning and contrast VAE-based detection models, which simultaneously learns MTS data’s normal and anomalous patterns for anomaly detection. We also use a learnable prior to capture system status from the background information. Moreover, we propose a query model for VAE-based methods, which can learn to query labels of the most useful instances to train the detection model. We evaluate our algorithm on three different monitoring situations in eBay’s search back-end systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ACVAE</i> achieves a range F1 score of 0.68~0.96 with only 3% labels, significantly outperforming the best competing methods by 0.18~0.50, and even better than a supervised ensemble method designed by domain experts in eBay.
Formation of digital competence of CS bachelors in the use of cloud-based learning environments

The article clarifies the concept of digital competence of CS bachelors in the use of cloud-based learning environments. The criteria (value-motivational, cognitive, operational-activity, research, didactic), indicators, and levels (low, medium, sufficient, high) of the formation of digital competence of CS bachelors in the use of cloud-based learning environments (CBLE) are specified. The results of the pedagogical experiment were analyzed, demonstrating an increased level of digital competence formation of CS bachelors in the use of CBLE, and thus the pedagogical efficiency of adopting the methodical system of using CBLE in the training of CS bachelors.
Federated learning with workload-aware client scheduling in heterogeneous systems

Federated Learning (FL) is a novel distributed machine learning, which allows thousands of edge devices to train models locally without uploading data to the central server. Since devices in real federated settings are resource-constrained, FL encounters systems heterogeneity, which causes considerable stragglers and incurs significant accuracy degradation. To tackle the challenges of systems heterogeneity and improve the robustness of the global model, we propose a novel adaptive federated framework in this paper. Specifically, we propose FedSAE that leverages the workload completion history of clients to adaptively predict the affordable training workload for each device. Consequently, FedSAE can significantly reduce stragglers in highly heterogeneous systems. We incorporate Active Learning into FedSAE to dynamically schedule participants. The server evaluates the devices' training value based on their training loss in each round, and larger-value clients are selected with a higher probability. As a result, the model convergence is accelerated. Furthermore, we propose q-FedSAE that combines FedSAE and q-FFL to improve global fairness in highly heterogeneous systems. The evaluations conducted in a highly heterogeneous system demonstrate that both FedSAE and q-FedSAE converge faster than FedAvg. In particular, FedSAE outperforms FedAvg across multiple federated datasets - FedSAE improves testing accuracy by 22.19% and reduces stragglers by 90.69% on average. Moreover, holding the same accuracy as FedSAE, q-FedSAE allows for more robust convergence and fairer model performance than q-FedAvg, FedSAE.
Governing the Digital Economy: An Exploration of Blockchains with Chinese Characteristics

In 2019 President Xi Jinping called for the prioritisation of blockchain technology as part of China’s next phase of development. In China, blockchain technologies have been experimentally deployed in various areas including court records, securities exchanges, finance, and food supply chains. The emergence of blockchain as a governmental technology raises numerous questions, including: (i) what are the conditions of emergence and existence of China’s interest in blockchain technology? (ii) what are the features of “blockchains with Chinese characteristics”? (iii) what implications are there for the post-COVID-19 pandemic world, in which technological issues are likely to be pivotal points of contention? This article seeks to examine these questions and frame a research programme that can shed light on how blockchains may impact the evolution and shape of China’s social and economic structure and the interaction of China with the rest of the world, including the prospect of “decoupling” and “deglobalisation.” Examples of blockchain innovation are drawn from food and pharmaceutical supply chains, the Healthy China 2030 policy, the digital RMB/Yuan and the Belt and Road Initiative with attention to the emerging legal and institutional frames that support the application of blockchain technologies.
Cervical Cell Segmentation Method Based on Global Dependency and Local Attention

The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods.
Improving adversarial robustness of traffic sign image recognition networks

The robustness of deep neural networks is an increasingly essential issue as they become more and more prevalent in several real-world applications like autonomous vehicles. If traffic signs turn to adversarial examples, an autonomous vehicle will probably be misled and cause fatal accidents. To improve adversarial robustness, a new cost function for training convolutional neural recognition networks is proposed in this paper. Recent works proved that by employing the classifier probabilities on the complement (incorrect) classes as well as the ground-truth class in Softmax Cross Entropy, the model achieves better performance on adversarial inputs. In this paper, we show that in addition to using the information from Softmax layer, the extracted features from convolutional layers also enhance the robustness. In our new cost function, Regularized Guided Complement Entropy (RGCE), by decreasing the output of convolutional layers' activation functions alongside utilizing Softmax layer output in training phase, we reach better model performance on adversarial attacks. Our proposed algorithm is evaluated on CIFAR-10 and GTSRB datasets. The performances of different convolutional neural networks on clean and adversarial images are reported and compared with other methods.
Constructing High-Resolution (10 km) Daily Diffuse Solar Radiation Dataset across China during 1982–2020 through Ensemble Model

Diffuse solar radiation is an essential component of surface solar radiation that contributes to carbon sequestration, photovoltaic power generation, and renewable energy production in terrestrial ecosystems. We constructed a 39-year (1982–2020) daily diffuse solar radiation dataset (CHSSDR), using ERA5 and MERRA_2 reanalysis data, with a spatial resolution of 10 km through a developed ensemble model (generalized additive models, GAM). The validation results, with ground-based measurements, showed that GAM had a high and stable performance with the correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE) for the sample-based cross-validations of 0.88, 19.54 Wm−2, and 14.87 Wm−2, respectively. CHSSDR had the highest consistency with ground-based measurements among the four diffuse solar radiation products (CERES, ERA5, JiEA, and CHSSDR), with the least deviation (MAE = 15.06 Wm−2 and RMSE = 20.22 Wm−2) and highest R value (0.87). The diffuse solar radiation values in China range from 59.13 to 104.65 Wm−2, with a multi-year average value of 79.39 Wm−2 from 1982 to 2020. Generally, low latitude and low altitude regions have larger diffuse solar radiation than high latitude and high altitude regions, and eastern China has less diffuse solar radiation than western China. This dataset would be valuable for analyzing regional climate change, photovoltaic applications, and solar energy resources. The dataset is freely available from figshare.
An integrated spatiotemporal-based methodology for deepfake detection

Abstract Rapid advances in deep learning models have made it easier for public and crackers to generate hyper-realistic deepfake videos in which faces are swapped. Such deepfake videos may constitute a significant threat to the world if they are misused to blackmail public figures and to deceive systems of face recognition. As a result, distinguishing these fake videos from real ones has become fundamental. This paper introduces a new deepfake video detection method. You Only Look Once (YOLO) face detector is used to detect faces from video frames. A proposed hybrid method based on proposing two different feature extraction methods is applied to these faces. The first feature extraction method, a proposed Convolution Neural Network (CNN), is based on the Histogram of Oriented Gradient (HOG) method. The second one is an ameliorated XceptionNet CNN. The two extracted sets of features are merged together and fed as input to a sequence of Gated Recurrent Units (GRUs) to extract the spatial and temporal features and then individuate the authenticity of videos. The proposed method is trained on the CelebDF-FaceForencics++ (c23) dataset and evaluated on the CelebDF test set. The experimental results and analysis confirm the superiority of the suggested method over the state-of-the-art methods.
Measuring dynamics of solar energy resource quality: Methodology and policy implications for reducing regional energy inequality

Solar energy utilization has been widely recognized as the most promising solution for achieving the global and regional carbon neutral targets. While existing literature on assessment of the solar energy resource mostly focuses on the current availability of energy quantity, the dynamic quality characteristics of reliable energy supplies are always neglected. This research presents a holistic spatiotemporal assessment methodology to quantify the solar energy resource quality (SERQ) from a quantity and quality coupled perspective. Specifically, we used the time-series daily meteorological reanalysis data, annual land use/cover, and nighttime light data during 1992–2018 to investigate the dynamic properties of SERQ at the 0.125° × 0.125° grid cell level across China. The validation process based on exiting solar power farms was conducted to analyze the applicability of proposed method in site selection. Our findings revealed that the annual solar energy potential in China was estimated at 66 PWh with the high resource quality level (SERQI>0.6). Furthermore, the outcomes of resource quality evaluation are more sensitive to the power load accessibility index. The involvement of energy quality dimension would reduce regional energy inequality in China. Thus, we highlight the necessity of improving the long-distance power transmission and energy storage infrastructures. Mapping the SERQ at the country level could provide investors and decision makers with useful insights to develop appropriate energy planning and incentive policies.
$O^{2}$-SiteRec: Store Site Recommendation under the O2O Model via Multi-graph Attention Networks

The emergence of Online-to-Offline (O2O) stores based on delivery platforms (e.g., Uber Eats, DoorDash, and Eleme) provides great convenience to people's lives. In the O2O model, one of the essential problems for merchants is to select a suitable store site, i.e., store site recommendation problem. We argue that the existing works for the traditional brick-and-mortar stores cannot address this problem due to two unique factors in the O2O model including (i) dynamic supply caused by courier capacity and dispatching strategies and (ii) various customer demands caused by delivery distance and customer preferences. To incorporate these new factors, we design <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O^{2}$</tex> SiteRec, a store site recommendation method under the O2O model via multi-graph attention networks, which consists of (i) a courier capacity model based on a multi-semantic relation graph attention network to capture courier capacity; (ii) a heterogeneous multi-graph based recommendation model, where the courier capacity, customer preferences, and context features are fused. We evaluate our method based on one-month real-world data consisting of 39,465 stores and 23.6 million orders from one of the largest O2O platforms in China. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.
Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan

The National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the NIH's role as a key steward of United States biomedical research and information and seeks to enhance that stewardship through systematic recommendations for the preservation and sharing of research data generated by funded projects. The policy is effective as of January 2023. The recommendations include a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding applications, and while no strict template was provided, the NIH has released supplemental draft guidance on elements to consider when developing a plan. This article provides 10 key recommendations for creating a DMSP that is both maximally compliant and effective.
Weighted Correlation Embedding Learning for Domain Adaptation

Domain adaptation leverages rich knowledge from a related source domain so that it can be used to perform tasks in a target domain. For more knowledge to be obtained under relaxed conditions, domain adaptation methods have been widely used in pattern recognition and image classification. However, most of the existing domain adaptation methods only consider how to minimize different distributions of the source and target domains, which neglects what should be transferred for a specific task and suffers negative transfer by distribution outliers. To address these problems, in this paper, we propose a novel domain adaptation method called weighted correlation embedding learning (WCEL) for image classification. In the WCEL approach, we seamlessly integrated correlation learning, graph embedding, and sample reweighting into a unified learning model. Specifically, we extracted the maximum correlated features from the source and target domains for image classification tasks. In addition, two graphs were designed to preserve the discriminant information from interclass samples and neighborhood relations in intraclass samples. Furthermore, to prevent the negative transfer problem, we developed an efficient sample reweighting strategy to predict the target with different confidence levels. To verify the performance of the proposed method in image classification, extensive experiments were conducted with several benchmark databases, verifying the superiority of the WCEL method over other state-of-the-art domain adaptation algorithms.
Similarity Measurement of Segment-Level Speaker Embeddings in Speaker Diarization

In this paper, we propose a neural-network-based similarity measurement method to learn the similarity between any two speaker embeddings, where both previous and future contexts are considered. Moreover, we propose the segmental pooling strategy and jointly train the speaker embedding network along with the similarity measurement model. Later, this joint training framework is further extended to the target-speaker voice activity detection (TS-VAD), with only slight modification in the network architecture. Experimental results of the DIHARD II, DIHARD III and VoxConverse datasets show that our clustering-based system with the neural similarity measurement achieves superior performance to recent approaches on all three datasets. In addition, the segment-level TS-VAD method further improves the clustering-based results and achieves DER of 16.48&#x0025;, 11.62&#x0025; and 4.39&#x0025; on the DIHARD II, DIHARD III and VoxConverse datasets, respectively.