Reviewerly/exHarmony huggingface dataset:
Seizing Critical Learning Periods in Federated Learning

Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; however, it is still unclear how the training process affects the final test accuracy. Exacerbating this problem is the fact that FL executions differ significantly from traditional ML with heterogeneous data characteristics across clients, involving more hyperparameters. In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i.e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy. To further explain this phenomenon, we generalize the trace of the Fisher Information Matrix (FIM) to FL and define a new notation called FedFIM, a quantity reflecting the local curvature of each clients from the beginning of the training in FL. Our findings suggest that the initial learning phase plays a critical role in understanding the FL performance. This is in contrast to many existing works which generally do not connect the final accuracy of FL to the early phase training. Finally, seizing critical learning periods in FL is of independent interest and could be useful for other problems such as the choices of hyperparameters including but not limited to the number of client selected per round, batch size, so as to improve the performance of FL training and testing.
CODE: Contrastive Pre-training with Adversarial Fine-Tuning for Zero-Shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, and collaborators. However, its quality is suffered from lack of ample sources of expert information. This paper employs AMiner as the basis with an aim at linking any external experts to the counterparts on AMiner. As it is infeasible to acquire sufficient linkages from arbitrary external sources, we explore the problem of zero-shot expert linking. In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMinerand external sources to enhance the model’s transferability in an adversarial manner. For evaluation, we first design two intrinsic tasks, author identification and paper clustering, to validate the representation and matching capability endowed by contrastive learning. Then the final external expert linking performance on two genres of external sources also implies the superiority of adversarial fine-tuning method. Additionally, we show the online deployment of CODE, and continuously improve its online performance via active learning.
Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract)

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called “Learning to Ask,” which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.
A fast geometric prediction merge mode decision algorithm based on CU gradient for VVC

To address the problem of large computational redundancy in the geometric prediction merge mode with motion vector refinement (GPM with MMVD), a new decision algorithm based on CU gradient is proposed. By comparing the mean value of the gradient in four directions to determine whether GPM can be terminated early. The advance decision of GPM partition mode can be determined by the calculated gradient direction of CU. Meanwhile, the proposed algorithm saves coding time by reducing the number of candidate modes during the mode selection process.
Interpretable Learned Image Compression: A Frequency Transform Decomposition Perspective

Image compression is a key problem in this age of information explosion. With the help of machine learning, recent studies have shown that learning-based image compression methods tend to surpass traditional codecs. Image compression can be split into three steps: transform, quantization, and entropy estimation. However, the transform step in traditional codecs lacks flexibility because of the strict mathematical premise while the transform in most learning-based codecs neglects its intrinsic interpretation. After observing compression degradation degree varies on different frequency bands as illustrated as Fig. 1(a), we propose an end-to-end compression model from the frequency perspective with a frequency-pyramid transform and a frequency-aware fusion module. The right of the Fig. 1(a) displays each frequency layer&#x0027;s component of the proposed model from low to high-frequency splits. Intuitively, we can infer that the low-frequency part contains the global structure while the high-frequency part gets finer details, satisfying the feature of human visual system (HVS). The proposed model are detailedly shown in Fig. 1(b) that independent probability estimation models are set for each frequency split. Extensive experiments are conducted to demonstrate that our model outperforms all traditional codecs (e.g., JPEG, JPEG2000, HEVC, and VVC) on MS-SSIM metric on both Kodak and CLIC2020 professional test datasets. Taking BPG-4:4:4 as the anchor, our proposed model achieves 11.6&#x0025; BD-rate reduction under PSNR measurement, which is evaluated on the Kodak dataset.
REMOTE: Reinforced Motion Transformation Network for Semi-supervised 2D Pose Estimation in Videos

Existing approaches for 2D pose estimation in videos often require a large number of dense annotations, which are costly and labor intensive to acquire. In this paper, we propose a semi-supervised REinforced MOtion Transformation nEtwork (REMOTE) to leverage a few labeled frames and temporal pose variations in videos, which enables effective learning of 2D pose estimation in sparsely annotated videos. Specifically, we introduce a Motion Transformer (MT) module to perform cross frame reconstruction, aiming to learn motion dynamic knowledge in videos. Besides, a novel reinforcement learning-based Frame Selection Agent (FSA) is designed within our framework, which is able to harness informative frame pairs on the fly to enhance the pose estimator under our cross reconstruction mechanism. We conduct extensive experiments that show the efficacy of our proposed REMOTE framework.
Power Grid Monitoring Event Recognition Method Integrating Knowledge Graph and Deep Learning

“Eventization” of power grid monitoring is an effective way to deal with massive alarm information. The existing event recognition method adopts the method of text information mining, and the overall recognition accuracy is not high. Therefore, this paper proposes a power grid monitoring event recognition method integrating knowledge graph and deep learning. First, the method constructs the knowledge graph of monitoring equipment and uses the improved GraphSAGE (graph sample and aggregate) algorithm to perform representation learning on the graph, and integrate the structural characteristics of monitoring equipment into the generated alarm vectors. Then, the GRU (Gated Recurrent Unit) neural network trains the alarm vectors and related events. In addition, this paper combines the proposed method with the existing monitoring expert system, and puts forward a monitoring event recognition strategy. Finally, through the case analysis and comparison of the actual data of the power grid, the effectiveness of the proposed method and strategy is verified, which further improves the accuracy of monitoring event recognition.
Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation

Recommendation for cold-start users who have very limited data is a canonical challenge in recommender systems. Existing deep recommender systems utilize user content features and behaviors to produce personalized recommendations, yet often face significant performance degradation on cold-start users compared to existing ones due to the following challenges: (1) Cold-start users may have a quite different distribution of features from existing users. (2) The few behaviors of cold-start users are hard to be exploited. In this paper, we propose a recommender system called Cold-Transformer to alleviate these problems. Specifically, we design context-based Embedding Adaption to offset the differences in feature distribution. It transforms the embedding of cold-start users into a warm state that is more like existing ones to represent corresponding user preferences. Furthermore, to exploit the few behaviors of cold-start users and characterize the user context, we propose Label Encoding that models Fused Behaviors of positive and negative feedback simultaneously, which are relatively more sufficient. Last, to perform large-scale industrial recommendations, we keep the two-tower architecture that de-couples user and target item. Extensive experiments on public and industrial datasets show that Cold-Transformer significantly outperforms state-of-the-art methods, including those that are deep coupled and less scalable.
HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images

The segmentation and visualization of liver vessels in 3D CT images are essential for computer-aided diagnosis and preoperative planning of liver diseases. Due to the irregular structure of liver vessels and image noise, accurate extraction of liver vessels is difficult. In particular, accurate segmentation of small vessels is always a challenge, as multiple single down-sampling usually results in a loss of information. In this paper, we propose a hierarchical progressive multiscale learning network (HPM-Net) framework for liver vessel segmentation. Firstly, the hierarchical progressive multiscale learning network combines internal and external progressive learning methods to learn semantic information about liver vessels at different scales by acquiring receptive fields of different sizes. Secondly, to better capture vessel features, we propose a dual-branch progressive 3D Unet, which uses a dual-branch progressive (DBP) down-sampling strategy to reduce the loss of detailed information in the process of network down-sampling. Finally, a deep supervision mechanism is introduced into the framework and backbone network to speed up the network convergence and achieve better training of the network. We conducted experiments on the public dataset 3Dircadb for liver vessel segmentation. The average dice coefficient and sensitivity of the proposed method reached 75.18% and 78.84%, respectively, both higher than the original network. Experimental results show that the proposed hierarchical progressive multiscale network can accurately segment the labeled liver vessels from the CT images.
Transformation classification of human squat/sit-to-stand based on multichannel information fusion

In existing rehabilitation training, research on the accuracy of recognizing completed actions has achieved good results; however, the reduction in the misjudgment rate in the action conversion process needs further research. This article proposes a multichannel information fusion method for the movement conversion process of squat/sit-to-stand, which can help online movement conversion classification during rehabilitation training. We collected a training dataset from a total of eight subjects performing three different motions, including half squat, full squat, and sitting, equipped with plantar pressure sensors, RGB cameras, and five inertial measurement units. Our evaluation includes the misjudgment rate for each action and the time needed for classification. The experimental results show that, compared with the recognition of a single sensor, the accuracy after fusion can reach 96.6% in the case of no occlusion and 86.7% in the case of occlusion. Compared with the complete time window, the classification time window is shortened by approximately 25%.
A Photovoltaic Power Predicting Model Using the Differential Evolution Algorithm and Multi-Task Learning

The rapid growth of photovoltaic installed capacity exacerbates the power management challenges faced by photovoltaic power stations, emphasizing the importance of accurate and stable photovoltaic generation forecasting. As a result, researchers conducted research and developed several photovoltaic power prediction models. However, many prediction models focus exclusively on the algorithm structure in order to improve model accuracy, oblivious to how the dataset is constructed and divided for the prediction model. This paper proposes a comprehensive model to address this gap. To be more precise, the differential evolution algorithm is constantly looking for optimal values between different populations and determining the best way to construct datasets for prediction tasks. Multi-task learning enables the transfer of knowledge between related tasks via parameter sharing layers, referring to the accuracy and stability of prediction models. Overall, the proposed model achieves high prediction accuracy and stability. The prediction error of the proposed model is less than 450W in RMSE, NRMSE is less than 2.5%, and R-Square is greater than 99% in multiple prediction tasks. Additionally, when compared to other single-task prediction models with an R-Square greater than 96%, the proposed model further reduces the root mean squared error by an average of 28% and the standard deviation of root mean squared error by 54%.
Blockchain in Petroleum Industry

Blockchain technology has been developed for more than ten years and has become a trend in various industries. As the oil and gas industry is gradually shifting toward intelligence and digitalization, many large oil and gas companies were working on blockchain technology in the past two years because of it can significantly improve the management level, efficiency, and data security of the oil and gas Industry. This paper aims to let more people in the oil and gas industry understand the blockchain and lead more thinking about how to apply the blockchain technology. To the best of our knowledge, this is one of the earliest papers on the review of the blockchain system in the oil and gas industry. This paper first presents the relevant theories and core technologies of the blockchain, and then describes how the blockchain is applied to the oil &amp; gas industry from four aspects: trading, management and decision making, supervision, and cyber security. Finally, the application status, the understanding level of the blockchain in the oil and gas industry, opportunities, challenges, and risks and development trends are analysed.
A Neural Model for Compositional Word Embeddings and Sentence Processing

We propose a new neural model for word embeddings, which uses Unitary Matrices as the primary device for encoding lexical information.It uses simple matrix multiplication to derive matrices for large units, yielding a sentence processing model that is strictly compositional, does not lose information over time steps, and is transparent, in the sense that word embeddings can be analysed regardless of context.This model does not employ activation functions, and so the network is fully accessible to analysis by the methods of linear algebra at each point in its operation on an input sequence.We test it in two NLP agreement tasks and obtain rule like perfect accuracy, with greater stability than current state-of-the-art systems.Our proposed model goes some way towards offering a class of computationally powerful deep learning systems that can be fully understood and compared to human cognitive processes for natural language learning and representation.
Oil Tank Detection With Improved EfficientDet Model

Rapid detection of oil tank targets has become a topic of significant and increasing interest because the quick acquisition of the distribution and volume of oil storage tanks has high economic and military value. However, research in the literature still faces many challenges in different scenarios. This paper presents a new oil tank detection approach for improved EfficientDet model. The proposed approach has three primary operations: First, adding three-dimensional deformable convolution to the EfficientDet model as pre-detection to limit detection in the smaller area. Second, loading an attention mechanism appropriate for Oil Tank detection on the myNet model highlights tiny target information. Lastly, training the myNet model repeatedly by focal loss function to seek better results. The result shows that the overall accuracy of mean average precision in the proposed approach increased by at least 8.25% compared with that of the tested conventional approaches. Therefore, it provides advantageous capabilities for monitoring Oil Tanks in high-resolution remote sensing imagery.
Optimum structural design of spatial truss structures via migration-based imperialist competitive algorithm

This paper presents a new hybrid algorithm generated by combining advantageous features of the Imperialist Competitive Algorithm (ICA) and Biogeography Based Optimization (BBO) to create an effective search technique. Although the ICA performs fairly well in the exploration phase, it is less effective in the exploitation stage. In addition, its convergence speed is problematic in some instances. Meanwhile, the BBO method's migration operator strongly emphasizes local search to focus on promising solutions and finds the optimum solution more precisely. The combination of these two algorithms leads to a robust hybrid algorithm that has both exploratory and exploitative functionalities. The proposed hybrid algorithm is named Migration-Based Imperialist Competitive Algorithm (MBICA). To validate its performance, MBICA is used to optimize a variety of benchmark truss structures. Compared to some other methods, this algorithm converges to better or at least identical solutions by reducing the number of structural analyses. Finally, the results of the standard BBO, ICA, and other recently developed metaheuristic optimization methods are compared with the results of this study.
Verification of Multiple Models of a Safety-Critical Motor Controller in Railway Systems

Motor controllers, such as the ones used in signalling systems, include critical embedded software. Alstom is a company that produces such embedded systems, which must follow complex certification processes that require formal modelling and analysis. The formal analysis of these real-time systems have to balance between including enough details to be useful and abstracting away enough details to be verifiable. This paper describes our work in the context of the European VALU3S project to integrate the analysis of such systems with the Uppaal model checker during the development cycle, involving both developers from Alstom and academic partners. We use special Excel tables to configure the underlying Uppaal models and requirements, bridging these two stakeholders. We follow Software Product Line Engineering principles, e.g., allowing features to be turned on and off and periodicities to be changed, and verify different properties for each of such configuration. We automate the instantiation and verification in Uppaal of a set of selected configurations via an open-source prototype tool named Uppex.
$\bm {P}^{\bm {3}}$: Privacy-Preserving Prediction of Real-Time Energy Demands in EV Charging Networks

Real-time and accurate prediction of charging pile energy demands in electric vehicle (EV) charging networks contributes significantly to load shedding and energy conservation. However, existing methods usually suffer from either data privacy leakage problems or heavy communication overheads. In this article, we propose a novel blockchain-based personalized federated deep learning scheme, coined <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> , for privacy-preserving energy demands prediction in EV charging networks. Specifically, we first design an accurate deep learning-based energy demands prediction model for charging piles, by making use of the CNN, BiLSTM, and attention mechanism. Second, we develop a blockchain-based hierarchical and personalized federated learning framework with a consensus committee, allowing charging piles to collectively establish a comprehensive energy demands prediction model in a low-latency and privacy-preserving way. Last, a CKKS cryptosystem based secure communication protocol is crafted to guarantee the confidentiality of model parameters while model training. Extensive experiments on two real-world datasets demonstrate the superiorities of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> scheme in accurately predicting real-time energy demands over state-of-the-art schemes. Further, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> scheme can achieve reasonably low computational costs, compared with other homomorphic-based schemes, such as Paillier and BFV.
Visual Interpretation of CNN Prediction Through Layerwise Sequential Selection of Discernible Neurons

In recent years, researchers have been working to interpret the insights of deep neural networks in the pursuit of overcoming their opaqueness and so-called 'black-box' tag.In this work, we present a new posthoc visual interpretation technique that finds out discriminative image regions contributing highly towards networks' prediction.We select most discernible set of neurons per layer and engineer the forward pass operation to gradually reach most discriminative image locations.While searching for discernible neurons, existing approaches either end up with low-resolution visualization maps, or suffer lack of neuron discriminativity in the way.Moreover, some methods concentrate only on current layer information overlooking meaningful information from adjacent layers, limiting the overall scope of selection.We address these issues by exploring the layer-to-layer connected structure of a neuron and obtaining contributions from its current layer along with its adjacent layers, e.g., succeeding and preceding layer.We introduce a score function where such contributions are assembled with appropriate priorities.Layerwise discernible neurons are then selected based on top scores, ensuring a reliable and credible selection.We validate our proposal through objective and subjective evaluations by examining its performance in terms of models' faithfulness and human-trust, where we visualize its efficacy over other existing methods.We also perform sets of sanity check experiments on our method to show its overall reliability as a visualization map.