Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

Contribution à la validation d’un outil informatique pour l’évaluation des surfaces corporelles brûlées

INTRODUCTION L’évaluation de la surface corporelle brûlée, essentielle pour établir la réanimation volémique et statuer sur le pronostic, se base actuellement sur la transposition des brûlures observées sur des diagrammes en papier représentant des mannequins standard. Le but de ce projet est de contribuer à la validation d’un outil pour l’évaluation des ratios brûlés. Deux outils ont été proposés, un numériseur portable et les mesures anthropométriques. MÉTHODES Quatre mannequins commerciaux avec différentes morphologies ont été sélectionnés. Chaque mannequin a été numérisé 10 fois avec un numériseur portable et une fois avec un numériseur de référence. Les surfaces corporelles totales ont été calculées et comparées. Dans un deuxième temps, 14 volontaires ont estimé la surface coporelle brûlée en utilisant les diagrammes papier et en dessinant sur un logiciel. RÉSULTATS Les valeurs des surfaces corporelles totales obtenues avec le numériseur portable sont 0,812; 1,581; 1,711 et 1,916 m2 pour les mannequins enfant, femme I, femme II et homme. Les résultats du numériseur de référence sont 0,815; 1,588; 1,716 et 1,918 m2. Les ratios des surfaces brûlées des mannequins correspondent à 23,33; 22,67, 32,63 et 26,07 % pour les mannequins. Les estimations des surfaces brûlées obtenues avec les diagrammes en papier représentent 29,9; 32,5; 40,4 et 35,7 %. Les résultats obtenus avec le logiciel sont 23,5; 22,6; 32,1 et 25,1 %. CONCLUSION Le numériseur portable reproduit avec précision et exactitude la surface corporelle totale. L’utilisation des données anthropométriques pour le calcul du pourcentage brûlé représente un outil plus précis et exact que les diagrammes papier.PURPOSE Total body surface area burned assessment is currently based on the transpositon of burns seen on paper charts representing standard models. The purpose of this project is to contribute to the validation of a tool for TBSA assessment. A handheld 3D scanner and anthropometrical measurements have been proposed. METHODS Four commercial mannequins with different body shapes were selected. Each model was scanned 10 times with a handhed, white light scanner and once with a gold standard scanner. The images were transferred to a modelling software allowing to assess the total body surface. Total body surfaces were calculated and compared. Secondly, 14 volunteers estimated the total burned surface using paper charts and drawing on 3D virtual models. RESULTS Values of the total body surface obtained with the handheld scanner are 0.812; 1.581; 1.711 and 1.916 m2 for the child, female I, female II and male mannequins. The results of the gold standard scanner are 0.815 ; 1.588 ; 1.716 and 1.918 m2. Burn extent ratios correspond to 23.33 ; 22.67 ; 32.63 and 26.07 % for the same mannequins. Estimated burned surfaces obtained with paper diagrams are the following : 29.9 ; 32.5 ; 40.4 and 35.7 %. Results obtained with the software are 23.5 ; 22.6 ; 32.1 and 25.1 %. CONCLUSION The handheld scanner is a precise and accurate tool for the assessment of the total surface. Use of anthropometric data for the calculation of the body burned surface is a more accurate tool and less variable than the conventional paper charts

Modelo de aprendizaje de máquinas para reducir las fallas de instanciación en composiciones de servicios

Este artículo, presenta un modelo de aprendizaje de maquinas orientado a reducir las fallas que se generan al asumir instancias de datos incorrectas por parte del planificador al momento de realizar el proceso de composición de servicios Web. Para ello, se  enfatiza en la adquisición de información del mundo real a través de la ejecución del servicio y por medio de árboles de decisión, predecir la mejor regla de aprendizaje en función a la ejecución de un servicio.  ABSTRACT This paper, show a machine learning model to reduce failures generated by assuming incorrect information instances by the planner during Web service composition process. For it, we emphasize in the acquisition of real-world information through the Web service execution and through decision trees, predicting the best learning rule according to the execution each service.

View and clothing invariant gait recognition via 3D human semantic folding

A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2-dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness

A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks