Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.

Memetics clarification of abnormal behavior

AIM： Biological medicine is hard to fully and scientifically explain the etiological factor and pathogenesis of abnormal behaviors; while, researches on philosophy and psychology （including memetics） are beneficial to better understand and explain etiological factor and pathogenesis of abnormal behaviors. At present, the theory of philosophy and psychology is to investigate the entity of abnormal behavior based on the views of memetics. METHODS： Abnormal behavior was researched in this study based on three aspects, including instinctive behavior disorder, poorly social-adapted behavior disorder and mental or body disease associated behavior disorder. Most main viewpoints of memetics were derived from ＂The Meme Machine＂, which was written by Susan Blackmore. When questions about abnormal behaviors induced by mental and psychological diseases and conduct disorder of teenagers were discussed, some researching achievements which were summarized by authors previously were added in this study, such as aggressive behaviors, pathologically aggressive behaviors, etc. RESULTS： The abnormal behaviors mainly referred to a part of people＇s substandard behaviors which were not according with the realistic social environment, culture background and the pathologic behaviors resulted from people＇s various psychological diseases. According to the theory of ＂meme＂, it demonstrated that the relevant behavioral obstacles of various psychological diseases, for example, the unusual behavior of schizophrenia, were caused, because the old meme was destroyed thoroughly but the new meme was unable to establish; psychoneurosis and personality disorder were resulted in hard establishment of meme; the behavioral obstacles which were ill-adapted to society, for example, various additional and homosexual behaviors, were because of the selfish replications and imitations of ＂additional meme＂ and ＂homosexual meme＂; various instinct behavioral and congenital intelligent obstacles were not significance for memetics. CONCLUSION： Generation of abnormal behavior is not only caused by complexly biological factors, also by philosophical and psychological entities.

Integrated ribosome and proteome analyses reveal insights into sevoflurane-induced long-term social behavior and cognitive dysfunctions through ADNP inhibition in neonatal mice

A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.

Blockchain abnormal behavior awareness methods: a survey

With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users’ assets and information. Many researchers have worked on blockchain abnormal behavior awareness in respond to these threats. We summarize respectively the existing public blockchain and consortium blockchain abnormal behavior awareness methods and ideas in detail as the difference between the two types of blockchain. At the same time, we summarize and analyze the existing data sets related to mainstream blockchain security, and finally discuss possible future research directions. Therefore, this work can provide a reference for blockchain security awareness research.

On the Precursory Abnormal Animal Behavior and Electromagnetic Effects for the Kobe Earthquake (M~6) on April 12, 2013

After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitude (M) ~6) EQ happened on April 12 (UT), 2013 at a place close to the former 1995 Kobe EQ (M~7), so we have tried to find whether there existed any precursors to this EQ, especially abnormal animal behavior (milk yield of cows), observed at Kagawa, Shikoku, near the EQ epicenter. The milk yield of cows has been continuously monitored at Kagawa, and it is found that the milk yield exhibited an abnormal depletion about 10 days before the EQ. This behavior has been extensively compared with the former electromagnetic precursors (ULF radiation, ionos-pheric perturbation). This leads to the discussion on the sensory mechanism of unusual behavior of mild yield of cows, and it may be suggested that ULF radiation among different electromagnetic precursors is a mostly likely driver, at least, for this EQ.

A Fast Detection Method of Network Crime Based on User Portrait

In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct the abnormal behavior rule base on various kinds of abnormal behaviors in advance,and construct the user portrait including basic attribute tags,behavior attribute tags and abnormal behavior similarity tags for network users who have abnormal behaviors.When a network crime occurs,firstly get the corresponding tag values in all user portraits according to the category of the network crime.Then,use the Naive Bayesian method matching each user portrait,to quickly locate the most likely network criminal suspects.In the case that no suspect is found,all users are audited comprehensively through matching abnormal behavior rule base.The experimental results show that,the accuracy rate of using this method for fast detection of network crimes is 95.9%,and the audit time is shortened to 1/35 of that of the conventional behavior audit method.

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed.First,detecting vehicles based on deep learning is implemented,and Kalman filtering and feature matching are used to track vehicles.Subsequently,the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine,and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene.The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis.The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems.In addition,the implementation and analysis process show the usability,generalization,and effectiveness of the proposed framework.

ANALYSIS OF VISCOSITY ABNORMALITIES OF POLYELECTROLYTES IN DILUTE SOLUTIONS

It was found that the interface effects in viscous capillary flow influenced the process of viscosity measurement greatly, and the abnormal viscosity behaviors of polyelectrolytes as well as neutral polymers in dilute solution region were ascribed to interface effect. According to this theory, we have reviewed the previous viscosity data of derivatives of poly-2- vinylpyridine reported by Maclay and Fuoss first. Then, the abnormal viscosity behaviors of a series of sodium polystyrene sulfonate samples with various molecular weights in dilute aqueous solutions were studied further. The solute adsorption behaviors and structural information of polymers have been discussed carefully.

Review of Some Advances and Applications in Real-time High-speed Vision: Our Views and Experiences

The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.