Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Fraud cases have been a risk in society and people’s property security has been greatly threatened.In recent studies,many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis.These algorithms are also suitable for fraudulent phone text recognition.Compared to these tasks,the semantics of fraudulent words are more complex and more difficult to distinguish.Recurrent Neural Networks(RNN),the variants ofRNN,ConvolutionalNeuralNetworks(CNN),and hybrid neural networks to extract text features are used by most text classification research.However,a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively.Therefore,a new model is proposed in this paper.In the fraudulent phone text,the knowledge that can be learned by the model includes the sequence structure of sentences,the correlation between words,the correlation of contextual semantics,the feature of keywords in sentences,etc.The new model combines a bidirectional Long-Short Term Memory Neural Network(BiLSTM)or a bidirectional Gate Recurrent United(BiGRU)and a Multi-Head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden layer.BiLSTM or BiGRU is used to build the encoding and decoding layer.Multi-head attention mechanism module with convolution(MHAC)enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this paper.The THUCNews data sets and fraudulent phone text data sets are used in experiments.Experiment results show that compared with the baseline model,the proposed model(LMHACL)has the best experiment results in terms of Accuracy,Precision,Recall,and F1 score on the two data sets.And the performance indexes on fraudulent phone text data sets are all above 0.94.

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Humanity in Monstrosity: Monsters in Frankenstein and The Island of Doctor Moreau

Monsters are commonly stereotyped as horrible and grotesque creatures. But in Frankenstein and The Island of Doctor Moreau, Shelly and Wells both delineate some complicated but meaningful monster characters. These monsters’ features and natures represent their creator’s intention and purpose. In both texts, monsters are ugly but benevolent, while their creators are eccentric and monstrous. The relationship between men and monsters allows us to view the definition of humanity from a more critical and objective perspective.

威灵与恶力:唐宋精怪世界中红赤系色彩的文化意象考论

红赤系色彩是唐宋志怪文学中刻画精怪形象的重要手段。文化象征源于火焰的红赤系色彩与鲜血的文化意象相叠加,衍生出两元文化象征:一方面红赤色成为唐宋精怪世界中位高而权重、辟邪与降福的威灵象征;另一方面红赤色在唐宋精怪世界中也具有灾异与不祥、邪祟与厄运的恶力象征。这种两元文化象征属性,使红赤色精怪成为唐宋志怪文学中的重要主体。

艺术与品牌:探析品牌艺术传播的策略——以GENTLE MONSTER为例

社会发展对品牌的推广产生巨大影响。一些品牌开始突破传统传播形式探,其中艺术作为一种品牌传播新型手段之一,在品牌形象塑造中发挥重要作用。文章以GENTLE MONSTER品牌艺术传播的策略为研究对象,采用个案研究法探讨品牌艺术传播,在品牌传播中应突破传统的方式,借用多元的艺术形式进行传播。

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host.Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack entities.This paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity interactions.Firstly,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these semantics.Then,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack samples.At last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT detection.Evaluation results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack activities.Compared to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.

陕西神怪类皮影造型在纺织品中的应用研究

有关皮影表演的完整记录最早是在宋代,此后,皮影艺术在中华大地生根发芽、茁壮成长。陕西神怪类皮影造型更是别具一格,不仅展现了皮影艺术历朝历代的审美风格,还表现出皮影艺术的多方面价值。文章通过对陕西神怪类皮影造型的来源进行整理归纳和分析,结合民俗文化和宗教文化背景,对陕西神怪类皮影的材料、造型类别、特色及其与民俗艺术的联系进行深入探究。

A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.

回返《捉妖记》的生活世界:一种实践民俗学的考察

从实践民俗学的视角考察《捉妖记》发现,影片中的好妖被赋予逍遥、纯真、干净的意义,符合现代社会大众心理认知和审美要求,因此获得广泛认可。影片解构了以人为中心的世界,将妖置于与人平等的地位,表达了影片对现代人的生活、行为方式的态度,即追求人与万物的和谐,不被物质利益诱惑,在逍遥与无争中发挥人的能动性。

凡神幽冥之间:淮阴高庄战国墓刻纹图像新考

淮阴高庄战国墓刻纹图像是复原吴越艺术与精神世界的重要材料,所见“凡俗”与“神怪”不同题材的图像内核其实一脉相承,并与薄壁刻纹铜器的明器性质相配适,皆是为墓主构建的死后空间,即幽冥世界。前人认为神怪图像属于“山海图”实际缺乏足够证据。“凡俗”与“神怪”题材的流行年代略有先后,其转变主要在于晋楚文化影响消长的背景下,吴越地区的丧葬观念由“事死如生”向“信巫鬼,重淫祀”的更迭。

京极夏彦《姑获鸟之夏》中的女性形象浅析

日本作家京极夏彦于1994年出版了第一本推理小说《姑获鸟之夏》,不仅开启了京极夏彦的作家生涯,更成为了日本推理史上的不朽名著。从《姑获鸟之夏》中所出现的女性角色出发,对女性角色的形象进行具体的分析研究,从而归纳出京极夏彦笔下的女性角色的形象特征:女性一般是妖怪的化身;女性集“恶”与“美”于一身;女性和男性相比,是需要被拯救的人。

农村青年女性代内被剥削的形成机制研究——基于“扶弟魔”现象的田野调研

女性作为一个被社会构建的性别群体,长期以来在各个领域都面临着挑战和限制。研究发现,农村“扶弟魔”女性在原生家庭场域、自我认知场域以及核心家庭场域下存在不同的日常话语形式。为了厘清女性代内被剥削的发生逻辑,构建责任—文化—权力框架讨论青年女性代内被剥削现象,可从以下三方面入手:关注父辈权力和女性权力相互博弈的平衡点;对于农村女性身份认同困境,需要建立农村和城市、原生家庭和核心家庭的平衡点;现有理论存在知识缺口,建议审视并构建农村本土化的理论体系。

基于Multi-head Attention和Bi-LSTM的实体关系分类

关系分类是自然语言处理领域的一项重要任务,能够为知识图谱的构建、问答系统和信息检索等提供技术支持.与传统关系分类方法相比较,基于神经网络和注意力机制的关系分类模型在各种关系分类任务中都获得了更出色的表现.以往的模型大多采用单层注意力机制,特征表达相对单一.因此本文在已有研究基础上,引入多头注意力机制(Multi-head attention),旨在让模型从不同表示空间上获取关于句子更多层面的信息,提高模型的特征表达能力.同时在现有的词向量和位置向量作为网络输入的基础上,进一步引入依存句法特征和相对核心谓词依赖特征,其中依存句法特征包括当前词的依存关系值和所依赖的父节点位置,从而使模型进一步获取更多的文本句法信息.在SemEval-2010 任务8 数据集上的实验结果证明,该方法相较之前的深度学习模型,性能有进一步提高.