Reviewerly/exHarmony huggingface dataset:
LLU-Swin: Low-Light Image Enhancement with U-shaped Swin Transformer

Low-light image enhancement is a low-level task that aims to improve the brightness and contrast of underexposed images. State-of-the-art low-light methods for image enhancement, benefiting from the development of deep learning, have made great progress. The majority of existing approaches, however, are based on convolutional neural networks, and just a few attempts have been made with Transformers which could perform admirably on high-level vision tasks by readily building long-range dependencies and global context connections. In this paper, we propose LLU-Swin, a hierarchical encoder-decoder architecture, based on Swin Transformer and U-Net for low-light image enhancement. LLU-Swin is composed of several Residual Recovery Transformer Modules (RRTM), each of which contains several improved Swin Transformer layers with a residual connection. We also show Dilated Local-enhanced Window Transformer Block (DLTB), which uses non-overlapping window-based self-attention to offer tremendous efficiency and employs Dilated Locally-enhanced Feed-Forward Network(D-LeFF) to enhance the local sensory ability. Benefits from these two designs, LLU-Swin has great power to capture both global and local contexts. Experimental results demonstrate the LLU-Swin outperforms the state-of-the-art methods in image quality and inference speed.
Self-supervised Hypergraph Representation Learning

Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, and are often restricted to the transductive learning setting. Among others, a major hurdle for effective hypergraph representation learning lies in the label scarcity of nodes and/or hyperedges. To address this issue, this paper presents an end-to-end, bi-level pre-training strategy with Graph Neural Networks for hypergraphs. The proposed framework named HyperGRL bears three distinctive advantages. First, it is mainly designed in the self-supervised fashion which has broad applicability, and meanwhile it is also capable of ingesting the labeling information when available. Second, at the heart of the proposed HyperGRL are two carefully designed pretexts, one on the node level and the other on the hyperedge level, which enable us to encode both the local and the global context in a mutually complementary way. Third, the proposed framework can work in both transductive and inductive settings. When applying the two proposed pretexts in tandem, it can accelerate the adaptation of the knowledge from the pre-trained model to downstream applications in the transductive setting, thanks to the bi-level nature of the proposed method. Extensive experiments demonstrate that: (1) HyperGRL achieves up to 5.69% improvements in hyperedge classification, and (2) improves pre-training efficiency by up to 42.80% on average <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Verification of Sparsity in the Attention Mechanism of Transformer for Anomaly Detection in Multivariate Time Series

Anomaly detection in multivariate time series has been attracting attention in order to realize continuous stable operation of systems. As systems diversify and monitoring targets become more complex, the number and types of measurements obtained from sensors in the system have dramatically increased. It is necessary to instantly process a large amount of complex multivariate time series in order to determine anomalies with high detection accuracy. In this paper, we proposed a Transformer with a Discriminator for Anomaly Detection in multivariate time series (TDAD). Introducing an adversarial training and attention mechanisms has improved extractions of detailed loss and time series features during model training.We compare the performance of TDAD with five other deep learning methods on five publicly available datasets and demonstrate that it can determine anomalies with high accuracy. Furthermore, by proposing a TDAD with Sparse attention mechanism (called STDAD), we improved the interpretability of the patterns of time series and achieved better results by increasing the influence of strongly relevant data points in time series with long-term dependencies.
to Protect the Public Opinion Against New Types of Bots?

Automated accounts affect political discourse in online social networks and therefore pose a threat to public opinion. They manipulate the regular flow of discussions by, for example, spreading false news, polluting content, or changing the popularity of users/content. Meanwhile, 56% of online social network users express concern about online false news. Therefore, reliable detection of automated accounts, among other things, is crucial for protecting political discourse in our society. However, the task is complex and challenging. Recent studies show that state-of-the-art bot detection algorithms have severe generalization problems.In this paper, we study features for reliable generalized identification of bot behavior on Twitter. Therefore, we propose an ensemble model that combines multiple neural network architectures. In a preprocessing step, we vectorize tweet texts using BERTweet. Exploiting behavioral features, the model then uncovers patterns in the metadata via a feed-forward and a convolutional component and incorporates the vectorized tweet texts via a recurrent unit. Extensive Leave-One-Botnet-Out (LOBO) evaluations on 20 real-world bot data sets show state-of-the-art performance in all experiments and outperform related approaches in average and peak performance.
Energy Maestro–Transactive Energy Mechanism

We are developing energy ICT technology that aligns with government policies such as Energy 3020, Green New Deal, and carbon neutrality promotion strategy, and ETRI AI's implementation strategy to build an 'x+AI' innovation platform to realize Korea's intelligence. In particular, R&D was promoted to drive the spread of reliable AI utilization, focusing on AI utilization technology in the public sector of the energy industry. Therefore, in this paper, we would like to introduce the Energy Maestro–Transactive Energy Mechanism technology.
Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Cyber threats continue to grow with novel methods to attack computing systems, highlighting the need for sophisticated mechanisms and techniques to protect against such dynamic threats. Contemporary cyber defence mechanisms utilise a range of methods which rely on monitoring network or system-level events. However, with the growing use of the dark web by mal-actors to share exploits, breaches, and data leaks, the use of such information to strengthen defence mechanisms becomes an intriguing prospect. In this paper, we present our efforts to develop a text mining engine (Threat Miner) which analyses data from dark web forums and transforms it into actionable intelligence. Leveraging cutting-edge machine learning techniques and utilising a bespoke threat dictionary, Threat Miner extracts useful information from dark web forums into STIX form, enabling it to be used with threat intelligence platforms. We also present the results of a thorough evaluation of our scheme which was conducted with the CrimeBB dataset [1] to understand the feasibility of the approach as well as its effectiveness in strengthening defence capability against cyber threats.
Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium

On large-scale graphs, many graph neural networks are problematic in capturing long-range dependencies due to the oversmoothing problem. Recently, Graph Equilibrium Models (GEQs) arise as a promising solution to this issue. Their output is the equilibrium of a fixed-point equation, which can be seen as the result of iterating a GNN layer for infinite times, so that they inherently have global receptive fields. However, to find the equilibrium, GEQs require running costly full-batch root-finding algorithms from scratch during each model update, which leads to severe efficiency and scalability issues that prevent them from scaling to large graphs. To address these limitations, we propose VEQ, an efficient learning method to scale GEQs to large graphs. Instead of initializing the equilibrium from scratch in full-batch training, VEQ uses the latest equilibrium of in-batch nodes and their 1-hop neighbors (dubbed Virtual Equilibrium) to accelerate and calibrate the root-finding process in mini-batch training. With virtual equilibrium as an informative prior, VEQ is able to reach the equilibrium in fewer steps while still capturing global dependencies. Theoretically, we provide convergence analysis for the forward and backward pass of VEQ. Empirically, VEQ significantly outperforms existing GEQs by a large margin (more than 1.5%) on all benchmark datasets, with much less training time and memory. Also, VEQ achieves competitive and even superior performance to many highly engineered explicit GNNs on large-scale benchmark datasets like ogbn-arxiv and ogbn-products. VEQ shows that after we resolve the efficiency and scalability issues, GEQs are indeed favorable on large graphs due to their advantage of capturing long-range dependencies.
GAPS: Generality and Precision with Shapley Attribution

In an age of the growing use of Machine-learning, it has become an imperative task to be able to explain the processes behind the functions of many "black box" models. The explainability feature of artificial intelligence is key to building trust between humans and computers' algorithmic predictions. One of the main ways to generate this interpretability is through attribution methods, which produce importance values of each feature for a single instance in a dataset. There are many different ways of attribution for various Machine-learning models, including ones designed for specific models or "model agnostic" attribution methods—ones that do not require a specific model to achieve importance values. These attribution methods are valued because of their easily understood nature. While evaluation procedures exist such as generality and precision for rule-based explanation methods, these have not been used on attribution methods until recently. A recent experiment by Ratul et al. [1] proved that the two most popular local model-agnostic attribution methods, LIME and SHAP, have poor precision and generality. In this paper, we propose a new attribution method, the Generality and Precision Shapley Attributions (GAPS). To evaluate these models, we use the generality and precision equations used previously to evaluate the other models. We present our findings that GAPS produces higher generality and precision scores than the existing LIME and SHAP models.
CH3: A Mixed Workload Benchmark for Scalable NoSQL

Database management systems that support hybrid workloads (i.e., HTAP or HOAP) first arose in the relational world. Such hybrid data management support in the document database (NoSQL) world is also gaining popularity in both commercial and research arenas. The CH2 benchmark was proposed in 2021 to evaluate such hybrid NoSQL platforms. In addition to operational and analytical services, full-text search is a key component of NoSQL platforms that provides a search engine-like query processing capability on JSON documents. In this paper, we present CH3, a mixed workload benchmark for evaluating scalable NoSQL platforms with OLTP, OLAP, and full-text search (FTS) workloads. Like CH2, the CH3 benchmark borrows from and extends both TPC-C and TPC-H. However, CH3 generates meaningful text content and includes FTS indexes and FTS queries on these indexes to model an FTS workload. This paper presents the required extensions from CH2 to address FTS workloads, the detailed design of CH3, and performance results obtained by running the CH3 benchmark against Couchbase Server (which offers Query, Analytics, and Search Services). The results provide insight into the performance of the Search Service, the performance isolation among OLTP, OLAP and FTS workloads, and the horizontal scalability of Couchbase Server as well as the effectiveness of CH3 for evaluating the mixed workload performance of such NoSQL platforms.
Flexible Partitioning of Geographical Information based on GPS Coordinates for k-Anonymity

To release microdata of medical information for secondary use, it is necessary to anonymize it to protect the individual’s privacy in that data. In k-anonymity, we generalize identifying attributes of each individual to partition the data into groups of more than k records that take the same values for those identifying attributes. However, generalizing geographical information on individuals based on a domain- level hierarchy leads to anonymized data of low data utility because the population densities of regions vary significantly in Japan. Therefore, we develop a new technique of recursively partitioning regional information based on GPS coordinates. Our experimental results show that the proposed method adjusts the granularity of geographical information flexibly such that the resulting groups of records for k-anonymity possess much better uniformity in size than those with the conventional generalization method.
Metadata Verification: A Workflow for Computational Archival Science

Researchers seeking to apply computational methods are increasingly turning to scientific digital archives containing images of specimens. Unfortunately, metadata errors can inhibit the discovery and use of scientific archival images. One such case is the NSF-sponsored Biology Guided Neural Network (BGNN) project, where an abundance of metadata errors has significantly delayed development of a proposed, new class of neural networks. This paper reports on research addressing this challenge. We present a prototype workflow for specimen scientific name metadata verification that is grounded in Computational Archival Science (CAS), report on a taxonomy of specimen name metadata error types with preliminary solutions. Our 3-phased workflow includes tag extraction, text processing, and interactive assessment. A baseline test with the prototype workflow identified at least 15 scientific name metadata errors out of 857 manually reviewed, potentially erroneous specimen images, corresponding to a ∼0.2% error rate for the full image dataset. The prototype workflow minimizes the amount of time domain experts need to spend reviewing archive metadata for correctness and AI-readiness before these archival images can be utilized in downstream analysis.
Face detection and mask detection based on convolutional neural network

In the covid-19 pandemic is not completely over today, wearing masks travel is still a necessary means to reduce the risk of transmission of the virus. To solve the problem of low efficiency and low accuracy in face mask detection, a convolutional neural network detection model is proposed in this paper. The paper first gives a brief introduction to the basic principles of convolutional neural network, and then builds on the MTCNN network structure, combining the popular Python language, Keras and tensorflow-gpu, this paper constructs a reasonable and effective neural network model for face detection, and then completes the detection of whether a face wears a mask, finally, the simulation results show that the proposed model has good performance and robustness in the presence of occlusion, different angles, dim light and different expressions.
Improved SSD based pedestrian detection algorithm for forklift active warning system

Forklifts are the common transport vehicles used in the industrial production widely. The accidents related to forklifts such as collisions between people and vehicles, speeding, and overturning often occur. With the rise of deep learning technology, target detection algorithms based on deep learning have also been applied to the detection of vehicles and pedestrians in traffic scenarios. This paper takes pedestrian detection as a research angle, and studies how to make the algorithm not only have a high detection speed, but also ensure the detection accuracy in practical scene applications. In addition, due to the fact that the algorithm is used in practical application scenarios, the road environment is often complicated and the vehicles are dense, and it may also encounter some extreme weather factors such as night, rain, cloudy, etc. These complex traffic conditions will increase Pedestrian detection difficulty. In order to solve the problem of low detection accuracy of pedestrian target detection in complex road conditions, this paper improves the existing target detection algorithm and designs a pedestrian target detection algorithm based on deep learning, so as to accurately detect pedestrians in more complex road scenes The goal is to reduce traffic accidents and ensure driving safety. The validity of the clustering-based anchor parameter setting method is verified by experiments.
BP Neural Network Transparency and Structure Reduction Algorithm Based on Weight Contribution Rate

Neural networks have excellent nonlinear mapping approximation ability, but the neural network modeling method belongs to the “black box” method. The obtained model lacks transparency and the interpretability of each variable is poor. In this paper, the single layer neural network model is transparently studied, combining the neural network paraphrasing map, the connection weight method and the improved randomization test, this method can be further extended to provide a reference method for more complex simplification of deep or multi-layer network models. Through the research on the numerical simulation of neural network and the classification of double crescent data, the results show that the simplified method obtains the internal information of process variables and greatly improves the model's “understandable” ability. Therefore, this study provides a good way for the transparency of the neural network model and the streamlining of the network structure in deep learning.
Detection of abnormal power consumption patterns of power users based on machine learning

With the development of China's power industry, the data of power consumption is increasing, and the illegal stealing of electricity by high-tech means is increasing. How to effectively detect abnormal power consumption behavior has become an important problem that power companies need to solve. Firstly, the principal component analysis (PCA) is used to reduce the dimension of the user's electricity consumption behavior to improve the efficiency of model detection; The abnormal detection of residential power consumption is realized by double algorithm judgment. Aiming at the problem that it is difficult to determine the abnormal proportion of LOF, DBSCAN is used to determine the abnormal value proportion, realize the optimization of LOF algorithm, and obtain the detection results of abnormal power consumption of residents; Then the Isolated Forest model is used to detect the abnormal electricity consumption of residents, and the users of abnormal electricity are selected; Finally, an example is given to verify the feasibility of the anomaly detection model, which improves the accuracy of abnormal power consumption detection.
Power Grid Fastener Defect Detection Based on Dual Context Information Fusion

The working state of fasteners is closely related to the safe and stable operation of the power grid. However, the performance of the existing fastener defect detection models is not enough to meet the requirements, because the size of object is small, the environment is complex, the sample size is unbalanced and so on. Therefore, we design a simple but effective feature fusion method, called Double Context Information Enhancement Module. The model can fully learn the features of fasteners by expanding the receptive field of the feature map during the construction of the feature pyramid, and the feature expression ability of fasteners can be enhanced by learning the features of adjacent layers after the construction of the feature pyramid. Through experiments, we proved that DCEM can improve about 8.8AP compared with the original model, and at the same time, the effectiveness and advancement of DCEM are proved by comparing with SOTA.
Intelligent Decision Support System for Distribution Network Planning Based on Artificial Intelligence and GIS Technology

With the rapid development of urban economy, urban power supply demand has increased significantly. State Grid Corporation has made great efforts to develop UHV power grid, and at the same time has accelerated the construction and transformation of power grid. Therefore, reasonable distribution planning has become particularly important. This paper mainly studies the design and research of intelligent decision support system for distribution network planning based on artificial intelligence and GIS technology. This paper first analyzes the application of GIS technology in planning decision support system, and builds GIS distribution network planning intelligent decision support system based on this. The performance test of the system is carried out in this paper. Through the results of the performance test, it can be known that the system built in this paper meets the needs of daily use.
Signal analysis and processing method of HF digital communication waveform

This paper introduces five key links in the signal analysis and processing of HF digital communication waveform, including signal preprocessing, signal parameter analysis, signal blind demodulation, signal protocol analysis, signal synchronous demodulation, and analyzes and applies them in combination with specific HF digital communication waveform signals.
A lightweight neural network model for security inspection targets

Although the application of the Yolo-V3 algorithm in target detection has a good performance, it is difficult to deploy the model to mobile platforms such as embedded devices due to the large network model and many redundant parameters. Aiming at the above limitations, this paper proposes a cyclic pruning algorithm based on O-T (Optimal Thresholding) threshold selection, prunes the original Yolo-V3 model, and obtains a lightweight model for security inspection target detection. Firstly, a local sparse strategy is proposed in the sparse training part to make the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma$</tex> factor on which pruning depends more distinguishable. Secondly, in the threshold selection of the pruning process, the O-T threshold algorithm is introduced to make the selection of the pruning threshold more rational. Finally, in the fine-tuning part, an improved knowledge distillation strategy is proposed to improve the effect of precision recovery. The experimental results show that the pruning algorithm can achieve a higher pruning rate while ensuring a small loss of accuracy, and obtain a more lightweight security inspection target detection model.
End-to-End Speech Recognition Methods Based on Convolutional Neural Networks and Bidirectional Gating Recurrent Unit

In view of the problem that the traditional acoustic model is complex and cannot be trained uniformly, and the data must be pre-aligned, this paper proposes a Chinese end-to-end speech recognition model based on convolutional neural network (CNN) and bidirectional gating recurrent unit (Bi GRU). This model uses the one-dimensional convolutional neural network to model the acoustics model, which can effectively extract the high-level abstract features with the context information, reduce the gradient disappearance, and uses the CTC beam search algorithm to realize the end-to-end training and decoding. Experimental results on the open-source dataset AISHELL-1 show a significant improvement in speech recognition performance compared to the baseline model, with 15.8% character error rate (CER) on the test set.