Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

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.

Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs:a controlled,optical coherence tomography angiography study

AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the study.One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular area.Radial peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device software.Automated parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB software.When the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded disc.RESULTS:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.

Measurement of in-vehicle volatile organic compounds under static conditions

The types and quantities of volatile organic compounds （VOCs） inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer （TD-GC/MS）. Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions （temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration）. Static vehicle testing demonstrated that although the amount of total volatile organic compounds （TVOC） detected within each vehicle was relatively distinct （4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C）, toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle＇s air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.

PERSISTENCE AND THE GLOBAL DYNAMICS OF THE POSITIVE SOLUTIONS FOR A RATIODEPENDENT PREDATOR-PREY SYSTEM WITH A CROWDING TERM IN THE PREY EQUATION

This paper deals with the global dynamical behaviors of the positive solutions for a parabolic type ratio-dependent predator-prey system with a crowding term in the prey equation, where it is assumed that the coefficient of the functional response is less than the coefficient of the intrinsic growth rates of the prey species. We demonstrated some special dynamical behaviors of the positive solutions of this system which the persistence of the coexistence of two species can be obtained when the crowding region in the prey equation only is designed suitably. Furthermore, we can obtain that under some conditions, the unique positive steady state solution of the system is globally asymptotically stable.

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.

Covalent Assembly of Penicillin Acylase in Mesoporous Silica Based on Macromolecular Crowding Theory

To improve the covalent immobilization of penicillin acylase （PA）, macromolecular crowding theory was applied to its immobilization. Influence of mass ratio of enzyme to the silica, as well as, activation time with glutaraldehyde on the activity of assembled PA, was studied. In the mesopores, the effect of fl-cyclodextrin （β-CD） on the immobilization of the enzyme was also investigated. It was remarkable that the coupled yield and relative activity reached 99.5% and 92.3%, respectively, when penicillin acylase assembled covalently in the mesopores. The results here indicate that mimicked macromolecule crowding could significantly ameliorate the performance of covalently immobilized PA.

Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique

Despite growing research for residential crowding effects on housing market and public health perspectives, relatively little attention has been paid to explore and model spatial patterns of residential crowding over space. This paper focuses upon analyzing the spatial relationships between residential crowding and socio-demographic variables in Alexandria neighborhoods, Egypt. Global and local geo-statistical techniques were employed within GIS-based platform to identify spatial?variations of residential crowding determinates. The global ordinary least squares (OLS) model?assumes homogeneity of relationships between response variable and explanatory variables?across the study area. Consequently, it fails to account for heterogeneity of spatial relationships. Local model known as a geographically weighted regression (GWR) was also employed using the same?response variable and explanatory variables to capture spatial non-stationary of residential?crowding. A comparison of the outputs of both models indicated that OLS explained 74 percent of?residential crowding variations while GWR model explained 79 percent. The GWR improvedstrength of the model and provided a better goodness of fit than OLS. In addition, the findings of this analysis revealed that residential crowding was significantly associated with different structural measures particularly social characteristics of household such as higher education and illiteracy. Similarly, population size of neighborhood and number of dwelling rooms were found to have direct impacts on residential crowding rate. The spatial relationship of these measures distinctly varies over the study area.

Changes in the crown angulation and dental arch widths after nonextraction orthodontic treatment: Model analysis of mild crowding with high canines

This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the effects of the orthodontic treatment for crowding with high canines on crown angulation and dental arch width in two patients. The results showed that the crown angulation was significantly increased, indicating distal tipping in the maxillary dental arch. This tendency was most commonly observed in the premolars among the lateral teeth. With respect to the dental arch width, the largest change was evident in the first molar and first premolar regions in cases 1 and 2, respectively. On the basis of these results, up-righting of mesially tipped lateral teeth and expansion of narrow dental arches could prove to be the keys to the success of space regaining or correction of high canines and mild crowding.

Transcriptomic Profiling of the Immune Response to Crowding Stress in Juvenile Turbot (Scophthalmus maximus)

In this study,juvenile turbot Scophthalmus maximus were vaccinated with attenuated Edwardsiella tarda(EIBAV1)and reared at two different densities,low density(LD),(5.25±0.02)kg m−2,as control group and high density(HD),(20.53±0.05)kg m−2,as experimental group.Only density was considered as the variable.Five weeks after vaccination,the transcriptomes of spleen and head kidney from the turbot in two groups were analyzed with RNA-Seq technology.A total of 447 million reads were assembled into 41136 genes with an average length of 1274 bp and a N50 size of 2295 bp.A comparison of gene expression between HD and LD groups revealed 1155 differentially expressed genes(DEGs).Enrichment and pathway analysis of the 10 immune-related DEGs showed the centrality of toll-like receptor signaling pathway,cytosolic DNA-sensing pathway and platelet activation in the host immune responses.The 5 overexpressed inflammatory cytokines and 5 downregulated signal-regulated cytokines genes are covered by these immune-related DEGs.It was inferred that cells suffer damage and the immune response is restrained in turbot under crowding stress.

EXISTENCE AND UNIQUENESS OF THE POSITIVE STEADY STATE SOLUTION FOR A LOTKA-VOLTERRA PREDATOR-PREY MODEL WITH A CROWDING TERM

This paper deals with a Lotka-Volterra predator-prey model with a crowding term in the predator equation.We obtain a critical value λ1^D(Ω0),and demonstrate that the existence of the predator inΩ0 only depends on the relationship of the growth rateμof the predator and λ1^D(Ω0),not on the prey.Furthermore,whenμ<λ1^D(Ω0),we obtain the existence and uniqueness of its positive steady state solution,while whenμ≥λ1^D(Ω0),the predator and the prey cannot coexist inΩ0.Our results show that the coexistence of the prey and the predator is sensitive to the size of the crowding regionΩ0,which is different from that of the classical Lotka-Volterra predator-prey model.

Niching method using clustering crowding

This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that,the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.

Phase transition of DNA compaction in confined space: Effects of macromolecular crowding are dominant

With a view of detecting the effects of macromolecular crowding on the phase transition of DNA compaction confined in spherical space,Monte Carlo simulations of DNA compaction in free space,in confined spherical space without crowders and in confined spherical space with crowders were performed separately.The simulation results indicate that macromolecular crowding effects on DNA compaction are dominant over the roles of multivalent counterions.In addition,effects of temperature on the phase transition of DNA compaction have been identified in confined spherical space with different radii.In confined spherical space without crowders,the temperature corresponding to phase transition depends on the radius of the confined spherical space linearly.In contrast,with the addition of crowders to the confined spherical space,effects of temperature on the phase transition of DNA compaction become insignificant,whereas the phase transition at different temperatures strongly depends on the size of crowder,and the critical volume fraction of crowders pertains to the diameter of crowder linearly.

众包在证据合成中的实践应用研究——以Cochrane Crowd公民科学项目中的众包应用为例

[目的/意义]证据生成的及时性对于循证决策至关重要,而目前证据合成的效率通常不能满足决策者的需求。众包被认为是一种可以提高证据合成生产效率的潜在方法。本研究以Cochrane Crowd公民科学项目中的众包应用为例,总结众包在证据合成中的实践应用。[方法/过程]采用文献调研、网络调查、案例分析等方法,从众包者、志愿者、众包任务、Cochrane Crowd平台、质量评估5个维度分析了众包在Cochrane Crowd公民科学项目中的应用机制。[结果/结论]通过设置明确目标、激励措施、清晰任务,提供全面培训和适当的质量控制机制,可以应用众包为证据合成输出高质量结果。为未来针对不同领域证据合成中应用众包以及在证据合成的不同阶段使用众包的进一步研究提供参考。