Reviewerly/exHarmony huggingface dataset:
Architecture and Key Technologies of Geographic Information System Highly Integrated with Power Grid Dispatching and Control System

In traditional power grid dispatching and control systems, GIS was embedded in the graphical browser by replacing graphical windows. This method has limitations such as poor interactivity between GIS and graphics, single display layout, and inability to flexibly support business scenario customization. This article proposes a GIS framework that is highly integrated with the power grid dispatching and control system, and discusses its key technologies such as human-machine modular GIS and multi activity geographic information services. Based on the actual application of engineering projects, the advantages and disadvantages of different GIS schemes were comprehensively compared, and the results showed that the research results have high practicality and convenience.
CNN Based Automatic Fault Detection in 3D Seismic Images

Fault detection in seismic images is important in interpreting seismic changes because the faults confirm unexpected subsurface topographical changes. In the traditional approach, faults are manually detected from post-stack seismic data and identified as reflection discontinuities, which is a very tedious process. To increase efficiency and reduce time consumption, a number of automatic fault detection techniques have been developed, of which techniques based on deep learning have proven to be efficient. This work proposed a deep learning based method to generate a seismic attribute called fault probability to highlight fault zones in the 3D seismic images. Since the technique detects the faults directly from seismic volumes, pre-computed attributes such as those used in coherence or edge detection methods are not strictly necessary. The proposed technique is instigated in two stages- training and prediction. In the training stage, a CNN model is trained with real data taken from 7 annotated seismic volumes, in which every point is labeled as fault or no-fault. Then in the prediction stage, the trained network is used to calculate the fault probability at every location in the new seismic image volumes. Both synthetic and real data sets are used to validate the proposed method. The obtained results proved that the proposed deep learning-based fault detection method outperforms some existing methods and also achieves effective performance compared to humans on an expert labelled seismic image dataset, much exactly predicting subtle faults which cannot annotate by an expert interpreter.
Learning for Spatio-temporal and Relational Data

The vast amounts of spatio-temporal data generated by a variety of devices are best utilized in conjunction with relational, tabular data rather than being referenced separately. To enhance the efficiency of data analysis, learned models are frequently used to approximate query results by increasing responsiveness at the cost of some accuracy. Machine learning techniques exist for spatio-temporal data and tabular data separately, but it is not straightforward to represent the combined data in a unified learned model. This paper explores the challenge of learning from heterogeneous data, particularly trajectory and tabular data, and proposes approaches for representation learning using probabilistic circuits and deep neural networks.
Fast Meta Failure Recovery for Federated Meta-Learning

In recent years, the field of distributed deep learning within the Internet of Things (IoT) or the edge has experienced exponential growth. Federated meta-learning has emerged as a significant advancement, enabling collaborative learning among source nodes to establish a global model initialization. This approach allows for optimal performance while necessitating minimal data samples for updating model parameters at the target node. Federated meta-learning has gained increased attention due to its capacity to provide real-time edge intelligence. However, a critical aspect that remains inadequately explored is the recovery of interim meta knowledge’s failure, which constitutes a pivotal key for adapting to new tasks. In this paper, we introduce FMRec, a novel platform designed to offer a fast and flexible recovery mechanism for failed interim meta knowledge in various federated meta-learning scenarios. FMRec serves as a complementary system compatible with different types of federated models and is adaptable to diverse tasks. We present a demonstration of its design and assess its efficiency and reliability through real-world applications.
Research on the Construction of a Knowledge Graph Information Search Engine in the web Domain

The article proposes a construction approach for a knowledge graph data search engine in the field of power grids. A detailed analysis is conducted on the construction, design, and operational implementation of the knowledge graph. A search engine system based on fuzzy algorithms and a search engine system integrating domain feature knowledge graphs are designed, which can recognize entities/assertions based on the Text Rank model, accurately search for external knowledge based on semantic methods, and set a global KG update strategy. The algorithm can achieve high entity/assertion recognition accuracy, automatically map domain knowledge to local KG, quickly update the service knowledge base online, and achieve high accuracy answers with low response delay. According to the testing results of the information and communication branch of the power company. The accuracy and recall of the keyword search algorithm in this method are 6.62%, 7.96%, and 2.24% higher, respectively. It can effectively overcome the ambiguity of power grid problem expression and accurately search for power grid knowledge.
Who’s in My Archive? An End-to-End Framework for Automatic Annotation of TV Personalities

Knowledge about the presence of people in a video is a valuable source of information in many applications, such as video annotation, retrieval and summarisation. The contribution of this paper goes in the direction of demonstrating how AI-based face processing technologies can be profitably used to perform video annotation of television content. To validate our vision, we developed the Face Management Framework (FMF), which implements an end-to-end pipeline for face analysis and content annotation based on few-shot or zero-shot face embedding extraction models. The results of the test campaign of the system show that the key performance indicators that we defined were exceeded by a wide margin, demonstrating how media workflows could greatly benefit from the tool and the efficiency improvements it brings.
Blockchain-Based Federated Learning-Convolutional Neural Network for Preserving Data Privacy and Security

The Internet of Things (IoT) and Industrial cloud computing have completely transferred the healthcare sector due to the rapid rise of distributed healthcare data. The security and privacy of healthcare data are critical issues facing in the healthcare sector. In this research, a BlockChain-based Federated Learning-Convolutional Neural Network (BC-FL-CNN) is proposed for preserving electronic health data privacy by integrating the BC and Deep Learning (DL) approach. CNN is used to classify normal and abnormal users in the processed dataset. The abnormal users were then processed and removed from the database as well as access to the health data utilizing BC with FL approach. Existing methods such as Convolution Neural Network-BlockChain-Cryptography-Federated Learning (CNN-BC-Cryp-FL), FL, and FL Risk-based Authorization Middleware for Healthcare (FRAMH) are used to compare with the proposed BC-FL-CNN approach. The proposed BC-FL-CNN achieves better accuracy of 98.25%, precision of 97.23%, recall of 97.56%, and 97.16% f1-score when compared to the existing approaches like CNN-BC-Cryp-FL, FL, and FRAMH respectively.
Machine-Learning-Based Multidimensional Big Data Analytics over Clouds via Multi-Columnar Big OLAP Data Cube Compression

This paper proposes a new theory on combining innovative Multidimensional Big Data Analytics with well-known Machine Learning (ML) in order to magnify the expressive power and the accuracy of knowledge insights discovery from massive big datasets. At the level of enabling technology, with the goal of fully supporting this novel paradigm, the issue of managing and mining big OLAP data cubes over Clouds arises. Due to computational complexity requirements, the latter challenge is addressed by proposing an innovative solution for (1) representing big OLAP data cubes over Clouds via a multi-column-based representation, and (2) compressing the deriving multi-column representations for achieving the desired effectiveness and efficiency. This paper introduces the fundamental model of Machine-Learning-Based Multidimensional Big Data Analytics, along with a reference architecture implementing it.
LM-DiskANN: Low Memory Footprint in Disk-Native Dynamic Graph-Based ANN Indexing

Approximate Nearest Neighbor (ANN) search has become a fundamental operation in numerous applications, including recommendation systems, computer vision, and natural language processing. The advent of Large Language Models (LLMs) arouses new interest in developing more efficient ANN algorithms, which will be the core functionality of vector databases as long-term memory of LLM. Multiple types of index structures, such as hashing-based, tree-based, and quantization-based, have been developed for ANN, and recently, graph-based algorithms have become the SOTA paradigm with the best trade-off between recall rate and query latency. However, almost all the existing graph-based index structures can only be hosted in memory due to the otherwise frequent I/O operations during searching if the graph-based index is stored on disk. The problem follows that for extremely large datasets, it is infeasible to accommodate the whole graph-based index in memory, and furthermore, it is difficult to build the whole index in memory at once. Thus, it is favorable if the graph-based index can be stored purely on disk and loaded into memory on demand during searching on the graph. There are existing efforts, such as DiskANN, which try to store graph-based index structure on disk while still keeping a compressed version of the dataset in memory to reduce disk I/O and speed up distance calculation. In this paper, we introduce LM-DiskANN, a novel dynamic graph-based ANN index that is designed specifically to be hosted on disk while keeping a low memory footprint by storing complete routing information in each node. By conducting extensive experiments on multiple benchmark datasets, we demonstrate that LM-DiskANN achieves a similar recall-latency curve while consuming much less memory compared with SOTA graph-based ANN indexes. Furthermore, its scalability and adaptability make it a promising solution for future big data applications.
Cost-Efficient Cross-Region Copy in Cloud Data Warehouse: An Evolutionary Approach

The “CRC” service optimizes cross-region data copying for enhanced cost efficiency, progressing through three distinctive phases. During phase 1, an essential pipeline is established to fulfill functional prerequisites while ensuring robust security standards. In phase 2, cost-efficiency is amplified by decreasing execution frequency, a strategy encompassing subphases: a) exclusion of unaltered tables, b) deferred scheduling facilitated by modification timestamp logging, and c) projection of execution durations to guarantee data timeliness. In phase 3, the spotlight shifts to minimizing scanned data for partitioned tables, leveraging phase 2’s algorithmic expansion from tables to partitions. Evaluation results underscore the service’s tangible impact, displaying significant cost reduction across frequently accessed datasets.
Enhancing Representation Learning with Label Association for Multi-Label Text Classification

Multi-label text classification (MLTC) is an important task in the field of natural language processing (NLP). Suffering from limited input length, most existing models learn text representation and label representation separately, leading to the overlook of correlations between texts and labels. To this end, we introduce a comprehensive model for the MLTC task. Under the same representation space, our model, which is equipped with Graph Convolutional Network (GCN) layer, attention mechanism, and contrastive learning objective, learns representations of texts and labels jointly. To tackle the issue caused by the input length limitation, we develop a two-stage label reduction method via the application of label merging and association. Our method’s effectiveness is validated through extensive experiments on various MLTC datasets, unraveling the intricate correlations between texts and labels.
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection

Object detection has achieved remarkable advancements through the utilization of deep neural networks; however, its heavy reliance on extensive labeled data remains a significant challenge. We first delve into active learning strategies that leverage the power of uncertainty estimation and detector resilience. Departing from approaches biased toward high-performing classes, we present an innovative solution known as Hierarchical Uncertainty Aggregation and Emphasis Loss (HUALE). This approach introduces hierarchical uncertainty aggregation, which effectively retrieves and ranks unlabeled images for precise image selection. Secondly, we introduce the Emphasis loss, a novel loss augmentation to object detection training and combine it with the EIoU loss and Synchronic IoU-Localization loss to elevate the accuracy of bounding box localization and address non-convexity challenges. Our method, built upon the Faster R-CNN framework, consistently outperforms the baseline (random selection) with average mAP (%) improvements of 4.53/3.44/1.57 across the PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO datasets.
Document-Level Event Argument Extraction Based on Bidirectional Span Detection

In this paper, we propose a document-level event argument extraction model called BSDM based on bidirectional span detection, mainly to address the problems of insufficient use of known information and overlooked correlations between span boundaries in document-level event argument extraction tasks. Firstly, in order to fully utilize known information, we design event types and event roles as event templates in the data preprocessing stage. Then, we integrate trigger and event type information into event roles in the information fusion layer, and identify argument boundaries under the guidance of role information. Secondly, in order to establish correlations between span boundaries, we design forward/backward decoders for the model, decoding argument spans from starting/ending boundaries respectively. Instead of using independent span boundary recognition methods, our model uses de-coded argument boundaries to recognize pending argument boundaries. Finally, considering the core role of trigger in events, we regard event detection as an auxiliary task for document-level event argument extraction. Through multitask joint training, the model will have better performance. We conducted sufficient experiments on the RAMS dataset. The results show that when using BERT-base as the encoder, BSDM achieved an F1 score 0.7% higher than the current SOTA result, and when using BERT-large as the encoder, it showed performance only second to the current SOTA model.
Deep Reinforcement Learning based Data Placement optimization in Data Center Networks

Intelligent data placement in data center networks for big data applications is a constructive approach to reduce the data movement delay, which can further improve the user service latency. Traditionally, data placement methods have relied on the study of the prior distribution of data requests discovered via network trace data analysis. Data placement in the network is an online decision-making problem. Despite the available machine learning architectures, how to deal with dynamic network conditions and user requests that vary over time is still an unanswered question. Toward this goal, this research formulates a Markov Decision Process (MDP) model based on deep reinforcement learning (RL) that takes into account the delay associated with data movement and service latency. The proposed method presents a novel intelligent data placement approach that leverages two state-of-the-art RL techniques; Deep Q-Network (DQN) and Soft Actor-Critic (SAC). Additionally, a prioritized butter is incorporated to replay important transitions more frequently and therefore, learn more efficiently. Extensive experiments and analyses conducted on Microsoft Research Cambridge (MSR) traces show the effectiveness and superior performance of the proposed approach for minimizing overall user service latency. The results highlight that our model outperforms the existing techniques in terms of the overall reward, achieving optimal data placement in real-world scenarios.
FOCAL: A Cost-Aware Video Dataset for Active Learning

In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOChL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOChL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two performance-cost metrics and show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours. The code associated with this paper can be found at this link. The data can be downloaded at this location.
Ensemble Machine Learning Based Blockchain Model for Privacy Preserving of Medical data in Fog Computing

The advent of COVID-19 highlights the need for big data-driven medical applications, the Internet of Medical Things, and smart healthcare. The biological information collected is strictly private. This enormous quantity of biological information is, however, beyond the capacity of current health care systems. Therefore, cloud computing has become the norm for archiving and sharing data. The combined information is then put to use in a variety of ways, including research and the identification of previously unknown facts. Textual forms (such as test results, prescriptions, and diagnoses) are the norm for biological data. Unfortunately, there are a number of security dangers and assaults that may be made against such data, including infringements on privacy and confidentiality. The security of biological data has come a long way, yet the majority of current methods still cause considerable delays and cannot support real-time replies. To improve the healthcare system, this study suggests a unique fog-enabled blockchain based privacy-preserving approach that makes use of machine learning. The suggested model efficiently carries out Medical Entity Recognition as it is built on four different machine learning models. In terms of detection rate and accuracy, the suggested model is 97% and 98%, respectively, better than the state-of-the-art replicas, as shown by the experiments. The sanitization approach outdoes the state-of-the-art by 8 percentage points when it comes to preserving utilities.
Fine-Tuned BERT Based Multilingual Model for Named Entity Recognition in Native Indian Languages

Accurate recognition of named entities is essential in real-world applications such as information extraction, document classification, sentiment analysis, and machine translation.The fine-tuned BERT model demonstrates exceptional performance in multilingual NER tasks by leveraging language-specific opti-mization, cross-lingual transfer, and context understanding. In contrast, other models applied to multilingual data may exhibit limitations. The research utilizes the WikiAnn dataset, including languages like Tamil, Malayalam, Bengali, and Marathi, to train the fine-tuned BERT model.The system achieved a precision of 95.3% and a recall of 94.8% for the Bengali language, outperforming the generic model used for Indian languages.It demonstrates superior accuracy and effectiveness in deciphering Bengali text.This research provides a robust solution for accurate named entity recognition across multiple languages, benefiting real-world applications.
Energy Efficiency and Green Computing in Large-scale Data Centers

With the development of energy efficiency measurement methods, the target demand for energy utilization in data centers is constantly improving, and the study of green energy efficiency (EE) factors in large-scale data centers based on DEA (Data envelopment analysis) is becoming increasingly important. In the construction of the entire EE factor analysis model, how to improve the green energy efficiency of the improved model and improve the efficiency of system power usage is currently a key issue that needs to be urgently solved. This article used traditional research methods on factors affecting green energy efficiency, analyzed the hierarchical composition of EE in large-scale data centers (DC), and calculated the four major energy efficiency indicators in DC. Based on the data results, the following conclusions were drawn through discussion. Through simulation experiments, six data center samples from different regions were selected, and the DEA based green energy efficiency factor analysis model was applied. Compared with traditional schemes, the green energy utilization rate has increased, with a comprehensive average improvement of 11.1%. At the same time, the power utilization efficiency has also improved, with a comprehensive average decrease of about 7.7%. This indicates that the large-scale data center green energy efficiency factor analysis model based on DEA has good results in practical applications. This study provides strong guidance and reference for the sustainable development of future data centers, and innovative solutions to address the growing digital demand and environmental challenges.