Reviewerly/exHarmony huggingface dataset:
MDIFL: Robust Federated Learning Based on Malicious Detection and Incentives

Federated Learning (FL) is an emerging distributed framework that enables clients to conduct distributed learning and globally share models without requiring data to leave the local. In the FL process, participants are required to contribute data resources and computing resources for model training. However, the traditional FL lacks security guarantees and is vulnerable to attacks and damages by malicious adversaries. In addition, the existing incentive methods lack fairness to participants. Therefore, accurately identifying and preventing malicious nodes from doing evil, while effectively selecting and incentivizing participants, plays a vital role in improving the security and performance of FL. In this paper, we propose a Robust Federated Learning Based on Malicious Detection and Incentives (MDIFL). Specifically, MDIFL first uses a gradient similarity to calculate reputation, thereby maintaining the reputation of participants and identifying malicious opponents, and then designs an effective incentive mechanism based on contract theory to achieve collaborative fairness. Extensive experimental results demonstrate that the proposed MDIFL can not only preferentially select and effectively motivate high-quality participants, but also correctly identify malicious adversaries, achieve fairness, and improve model performance.
Leveraging Researcher Domain Expertise to Annotate Concepts Within Imbalanced Data

As more computational communication researchers turn to supervised machine learning methods for text classification, we note the challenge in implementing such techniques within an imbalanced dataset. Such issues are critical in our domain, where, in many cases, researchers attempt to identify and study theoretically interesting categories that can be rare in a target corpus. Specifically, imbalanced distributions, with a skewed distribution of texts among the categories, can lead to a lengthy and expensive annotation stage, forcing practitioners to sample and label large numbers of texts to train a classification model. In this paper, we provide an overview of the issue, and describe existing strategies for mitigating such challenges. Noting the pitfalls of previous solutions, we then provide a semi-supervised method – Expert Initiated Latent Space Sampling – that complements researcher domain expertise with a systematic, unsupervised exploration of the latent semantic space to overcome such limitations. Utilizing simulations to systematically evaluate our method and compare it to existing approaches, we show that our procedure offers significant advantages in terms of efficiency and accuracy in many classification tasks.
PIMFlow: Compiler and Runtime Support for CNN Models on Processing-in-Memory DRAM

Processing-in-Memory (PIM) has evolved over decades into a feasible solution to addressing the exacerbating performance bottleneck with main memory by placing computational logic in or near memory. Recent proposals from DRAM manufacturers highlighted the HW constraint-aware design of PIM-enabled DRAM with specialized MAC logic, providing an order of magnitude speedup for memory-intensive operations in DL models. Although the main target for PIM acceleration did not initially include convolutional neural networks due to their high compute intensity, recent CNN models are increasingly adopting computationally lightweight implementation. Motivated by the potential for the software stack to enable CNN models on DRAM-PIM hardware without invasive changes, we propose PIMFlow, an end-to-end compiler and runtime support, to accelerate CNN models on a PIM-enabled GPU memory. PIMFlow transforms model graphs to create inter-node parallelism across GPU and PIM, explores possible task- and data-parallel execution scenarios for optimal execution time, and provides a code-generating back-end and execution engine for DRAM-PIM. PIMFlow achieves up to 82% end-to-end speedup and reduces energy consumption by 26% on average for CNN model inferences.
Detecting Encrypted and Unencrypted Network Data Using Entropy Analysis and Confidence Intervals

The detection of clear and encrypted data that are transported through computer networks is of particular importance both for protecting the data and the users to whom they belong and to whom they are intended, as well as the networks through which they are transmitted. The proposed method consists of an algorithm that classifies the data it receives by testing the belongingness of their standard deviation values to established confidence intervals. Following the evaluation of the algorithm, an accuracy of 94.73% was obtained and it appears that the results can be used with certainty in subsequent analyses of the data detection.
Accelerating Deep Neural Networks on Mobile Multicore NPUs

Neural processing units (NPUs) have become indispensable parts of mobile SoCs. Furthermore, integrating multiple NPU cores into a single chip becomes a promising solution for ever-increasing computing power demands in mobile devices. This paper addresses techniques to maximize the utilization of NPU cores and reduce the latency of on-device inference. Mobile NPUs typically have a small amount of local memory (or scratch pad memory, SPM) that provides space only enough for input/output tensors and weights of one layer operation in deep neural networks (DNNs). Even in multicore NPUs, such local memories are distributed across the cores. In such systems, executing network layer operations in parallel is the primary vehicle to achieve performance. By partitioning a layer of DNNs into multiple sub-layers, we can execute them in parallel on multicore NPUs. Within a core, we can also employ pipelined execution to reduce the execution time of a sub-layer. In this execution model, synchronizing parallel execution and loading/storing intermediate tensors in global memory are the main bottlenecks. To alleviate these problems, we propose novel optimization techniques which carefully consider partitioning direction, execution order, synchronization, and global memory access. Using six popular convolutional neural networks (CNNs), we evaluate our optimization techniques in a flagship mobile SoC with three cores. Compared to the highest-performing partitioning approach, our techniques improve performance by 23%, achieving a speedup of 2.1x over single-core systems.
Research on the design technology of mobile Mix-YOLOv5s model

Because the memory resources and computing resources of embedded devices are very limited, it is very difficult to deploy deep learning models on embedded devices. Therefore, how to efficiently and conveniently extract more feature maps from the limited feature maps in convolutional neural network has become a mainstream research direction. In the era of epidemic normalization, in order to deploy a mask face detection system more suitable for the actual scene in densely populated places such as communities, shopping malls, airports, railway stations and so on. This paper proposes the Mix-YOLOv5s model, which mainly combines the YOLOv5s model with the lightweight Ghost module, the CBAM attention module, Bidirectional Feature Pyramid Network(BiFPN), Mish activation function and Alpha-IoU loss function. They are used to promote the object detection ability of the model. We make quantitative and qualitative comparison between Mix-YOLOv5s and YOLOv5s. Compared with the YOLOv5s model, this model is able to improve the comprehensive performance by a small amount on the basis of greatly reducing Parameters and GFLOPs. Therefore, Mix-YOLOv5s model has great significance and advantages in the design of mobile terminal deep learning model, which can correctly judge whether the subjects wear masks, and has strong research value and broad application prospects.
Advantage policy update based on proximal policy optimization

In this paper, a novel policy network update approach based on Proximal Policy Optimization (PPO), Advantageous Update Policy Proximal Policy Optimization (AUP-PPO), is proposed to alleviate the problem of over-fitting caused by the use of shared layers for policy and value functions. Extended from the previous sample-efficient reinforcement learning method PPO that uses separate networks to learn policy and value functions to make them decouple optimization, AUP-PPO uses the value function to calculate the advantage and updates the policy with the loss between the current and target advantage function as a penalty term instead of the value function. Evaluated by multiple benchmark control tasks in Open-AI gym, AUP-PPO exhibits better generalization to the environment and achieves faster convergence and better robustness compared with the original PPO.
Formal Epistemology Meets Mechanism Design

Abstract This article connects recent work in formal epistemology to work in economics and computer science. Analysing the Dutch Book Arguments, Epistemic Utility Theory and Objective Bayesian Epistemology we discover that formal epistemologists employ the same argument structure as economists and computer scientists. Since similar approaches often have similar problems and have shared solutions, opportunities for cross-fertilisation abound.
An ENet Semantic Segmentation Method Combined with Attention Mechanism

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method.
Application of Ocean Bottom Seismometer: Study of the 4 Aug 2021 southernmost Okinawa Trough M6.1 event.

The Southern Okinawa Trough (SOT) is a back-arc-basin and characterized by an active normal faulting system and magmatic activity. Most seismic activities beneath the Southern Okinawa Trough at shallow depths (&lt;30 km) are located about 50 km east of Ilan Plain (122.15&amp;#176;E - 122.55&amp;#176;E). &amp;#160;The seismic rate has increased significantly after May 2021. An earthquake struck offshore area of Ilan Plain on 4 August (0804 earthquake). Its magnitude of 6.1 at 7.0 km made it one of the rare, extremely powerful quakes ever before in the study area. However, the cause and mechanism are still unclear and worthy of further investigation. Furthermore, the previous study shows that submarine landslides have occurred in the northern continental margin of SOT. The frequent earthquakes will raise the risk of slope failure and may generate a local tsunami causing damage around the northeast coast of Taiwan. In order to explore the generation of 0804 earthquake, we have deployed an Ocean Bottom Seismometer (OBS) network to capture the seismicity around the study area. &amp;#160;In total, 852 events have been relocated and most of them are located in the high positive magnetic anomaly zone. The distributions of the earthquake show an NW-SE trending direction and may be related to the magmatic activity. &amp;#160;
Deep echo state networks in data marketplaces

Data Marketplaces are the digital platform for data buyers and data sellers to trade information as valuable products or items. The expectation taken for granted from the users of a data marketplace is the truth of the exchanged information. However, the trade of factual data also means the marketable product is no longer unique but a series of replicas. If every user within the data marketplace owns the same information, this data eventually becomes valueless. There are specific instances where the traded products are sought to be always unique, for instance predictions or digital art. This article applies Deep Echo State Networks (ESNs) in data marketplaces that map tradeable data into a larger dimensional space via the dynamics of reservoirs with fixed and non-linear properties. These reservoirs generate unique tradeable data products that cannot be replicated, therefore ensuring its exclusivity and commercial value. The validation results show that ESNs can also be applied to generate random tradeable products in different dimensional spaces. Specifically, the reservoir with its associated neural perturbation emulates a digital creator that generates unique and exclusive content based on 1D functions and 2D images.
Signal based Dilation Convolution CAM for Feature Extraction and Analysis in CNN Model

Abstract We propose a new kind of model interpretation and feature display method for quantitatively analysis of deep learning model’s working mechanism in physiological feature extraction and classification. Deep learning is faced with lack of interpretation and inner mechanism for its results, which has limited its application in some high-risk field, such as medical diagnosis. The method, designated as SDC-CAM, raised in this article was developed to display the features curve and key features during deep learning model process. And SDC-CAM can quantitatively show the tendency of specific data on different classification. A threshold value can be set to visually display the colored region as key features to show the working mechanism of convolutional neural network(CNN) model. We believe this method can provide positive effect for CNN interpretation and understanding.
Analysis of Chinese Named Entity Recognition Results Based on Different Paradigm

Abstract Named-entity recognition (NER) is a sub-task of information extraction. Currently, there are various approaches to achieve the NER task and the results are getting better. However, the internal structure of the models is becoming more and more complex, making them much less comparable. In view of the above reasons, we categorize NER methods and implement three different NER paradigms (classification-based model, sequence annotation-based model, and pointer network-based model) using the simplest structure for three Chinese NER datasets (PeopleDailyNER, FinanceSinaNER, and CLUENER). We find that the pointer network-based model works well for datasets with short sentence lengths and large data sizes; the classification-based model is good for datasets with large data sizes and numerous entity categories; in the vast majority of cases, the sequence annotation-based model has better entity recognition performance than the other two models.
Image detection of insulator defects in complex environments

Abstract Aiming at the practical problems of low detection accuracy and low detection speed for the detection of power insulators and insulator defects in a complex environmental context, a method is proposed to improve the YOLOv4 algorithm for the detection of power insulator images and insulators with defects. By making a dataset of electric power insulators and insulators with defects, the K-means algorithm is used to cluster the electric power insulator image samples to obtain different sizes of prior box parameters; then a weighting factor is introduced by improving the balanced cross-entropy to increase the contribution degree of the loss function; finally, the depth of the network is deepened by adding convolution layers before and after the spatial pyramidal pooling structure. The experimental results show that the single detection time of the improved model is 3.27S, and the average detection accuracy for insulator defects is improved by 24.36% compared with the original YOLOv4 algorithm. Also the value of the mean average accuracy on the test set by the improved YOLOv4 algorithm is 84.05%, which is 17.83% better than the original YOLOv4 algorithm, fully demonstrating the ability to locate and identify the defects existing in power insulator images well.
External Attention Mechanism-Based Modulation Classification

Abstract This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7 % in terms of classification accuracy.
Supply chain quality level decision considering block-chain technology

Abstract Aiming at the problem of unqualified product quality, a two-level supply chain game model under two modes is established. The backward induction method is used to solve the decision variables to be analyzed and explore the application conditions of blockchain technology. We found that the supply chain with the smaller application cost, the smaller qualified rate of product quality and service quality, and the larger loss of reputation is the most suitable to use blockchain technology. Product quality, service quality and market price are improved. In addition, we also discuss the influence of sensitivity coefficient on product quality and service quality. The smaller the cost coefficient of product quality and the larger the importance of product quality are, the better the effect of product quality improvement is. The smaller the cost coefficient of service quality and the larger the importance of service quality are, the better the effect of service quality improvement is. It provides a reliable basis for better quality decision.
Neural Networks with the Correlative Layer for Multi-label Classification

Abstract Multi-label classification is a very significant but challenging task. Correlation between labels often exists, so recent works have paid much attention to using the correlation between labels to improve the classification performance. However, how to effectively learn the correlation is still a problem. In this paper, a general framework, i.e., the neural network with the correlative layer (CLNN), is proposed, where the correlative layer is used to express the dependencies between labels. Different from existing work, CLNN first trains a neural network without the correlative layer to obtain rough classification results and then trains the whole neural network to adaptively adjust all the weights including those of the correlation layer. Thus CLNN could learn both positive/negative and strong/weak relationships between labels. We test CLNN with three typical neural networks, and experimental results show that neural network can achieve better performance by adding the correlative layer, which demonstrates that the CLNN framework is very effective.
Research on non-time-series data filling methods based on feature evaluation

Abstract With the rapid development of the information age, a large amount of data is used in popular research areas such as data mining. Missing data has a very serious impact on both the process and the result of data mining, and it is important to find out how to fill the missing values accurately and efficiently. In this paper, we propose a method to optimally fill the missing values based on the backpropagation of evaluation functions for non-time-series data. Based on both the target value error and its own error after filling, four classical filling methods, namely mean, interpolation, model prediction, and K-nearest neighbor, are considered for selection. Finally, single-model padding and multi-model weighted padding schemes are compared, and the results show that the padding method with the highest fitness value is selected to work best for data with different degrees of missingness in different datasets.
Research and application of deep learning in image recognition

Abstract In recent years, deep learning technology has been one of the most important representatives of progress in the field of artificial intelligence technology in China. It has made great achievements in many fields, such as language recognition, natural language processing, image processing and video analysis. This paper further discusses the application research and practice of deep learning technology in modern digital image recognition. Deep learning technology has played a more and more important role in modern digital image recognition.
Research on transformer internal defect detection based on large scale model

Abstract Aiming at the problem that the traditional convolution network can not be used to detect and identify targets in the more complex target detection application scenarios in power grid business, a target detection scheme based on the ability of large model is proposed. The effects of convolution neural network layers, convolution kernel size and feature dimension on the recognition effect are systematically analyzed and compared. The differences between traditional convolution network and large model in data volume, accuracy and model construction efficiency are compared. The experimental results show that for the specific target detection business scenario downstream of power grid, the model constructed by this scheme has the advantages of fast and efficient, high accuracy and less data dependence, and the comprehensive performance is better than the traditional convolution network detection method.
Latent Space Cartography for Geometrically Enriched Latent Spaces

Abstract There have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds. Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.