Hierarchical learning using deep optimum-path forest

Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.     

Is that my hand? An egocentric dataset for hand disambiguation

With the recent development of wearable cameras, the interest for research on the egocentric perspective is increasing. This opens the possibility to work on a specific object detection problem of hand detection and hand disambiguation. However, recent progress in egocentric hand disambiguation and even hand detection, especially using deep learning, has been limited by the lack of a large dataset, with suitable variations in subject, activity, and scene. In this paper, we propose a dataset that simulates daily activities, with variable illumination and people from different cultures and ethnicity to address daily life conditions. We increase the dataset size from previous works to allow robust solutions like deep neural networks that need a substantial amount of data for training. Our dataset consists of 50,000 annotated images with 10 different subjects doing 5 different daily activities (biking, eating, kitchen, office and running) in over 40 different scenes with variable illumination and changing backgrounds, and we compare with previous similar datasets.Hands in an egocentric view are challenging to detect due to a number of factors, such as shape variations, inconsistent illumination, motion blur, and occlusion. To improve hand detection and disambiguation, context information can be included to aid in the detection. In particular, we propose three neural network architectures that jointly learn the hand and context information, and we provide baseline results with current object/hand detection approaches.     

User authentication in a tactile internet based remote surgery environment: Security issues,challenges, and future research directions

Recently, the Tactile Internet (TI) becomes a new era of the Internet. The TI provides ultra-reliable, ultra-responsive and intelligent network connectivity for delivering real-time control and physical haptic experiences from a remote location. The TI provides a different feeling to human–machine interaction by implementing the real-time interactive systems. In this review article, we discuss a generalized authentication model which can be used to perform authentication procedure among different communicating parties in order to secure remote surgery in the TI environment. By using the proposed authentication model, an authentication protocol can be designed so that an authenticated surgeon can use the robot/robotic arms to perform the surgery securely as well as remotely. Since the application is very critical, the important instructions provided by the surgeon to the robot/robotic arms must not be leaked in between the communication during the surgical procedure. To deal with this emerging research area, a secure mutual user authentication mechanism should be provided between a remote surgeon and the robot/robotic arms so that they can communicate securely using the established session key among them. Further, several security issues and challenges for such kind of communication are also discussed in this article. Finally, we discuss few points that need to be considered as future research works that are related to authentication for securing remote surgery in the TI environment.     

粒子群属性聚类的位置隐私保护

针对基于位置服务中连续查询情况下，用户自身属性信息很容易被攻击者获取，并通过关联获得用户位置隐私的情况，提出了一种利用粒子群聚类加速相似属性用户寻找，并由相似属性匿名实现用户位置泛化的隐私保护方法。该方法利用位置隐私保护中常用的可信中心服务器，通过对发送到中心服务器中的查询信息进行粒子群属性聚类，在聚类的过程中加速相似属性用户的寻找过程，由相似属性用户完成位置泛化，以此实现位置隐私保护。实验结果证明，这种基于粒子群属性聚类的隐私保护方法具有高于同类算法的隐私保护能力，以及更快的计算处理速度。     

Mechanical and thermal properties and microstructural evolution of Ta-doped Nb4AlC3

(Nb_1-xTa_x)₄AlC₃ (x = 0–0.5) ceramics were prepared by the hot press sintering method. The XRD results show that the second phase (Nb_1-xTa_x)C is formed when the Ta content increases to 25 mol%. The SEM micrographs show that (Nb_1-xTa_x)C has a core/rim structure, whose formation mechanism was also investigated. Substituting some Ta for Nb can significantly improve the mechanical properties of Nb₄AlC₃. (Nb_0.75Ta_0.25)₄AlC₃ exhibits an excellent fracture toughness of 8.3 ± 0.3 MPa m^1/2 at room temperature (RT). The highest Young's modulus (349 ± 16 GPa) and Vickers hardness (4.5 ± 0.3 GPa) at RT are exhibited by the (Nb_0.5Ta_0.5)₄AlC₃ sample, which correlate to increases of 18% and 80%, respectively, compared with those of Nb₄AlC₃. The flexural strengths of (Nb_0.5Ta_0.5)₄AlC₃ are 439 ± 18 MPa at RT and 344 ± 22 MPa at 1100 °C, which correlate to increases of 27% and 45%, respectively, compared with those of Nb₄AlC₃. The solid solution of Ta and the formation of (Nb_1-xTa_x)C are beneficial to the strengthening of Nb₄AlC₃. The coefficient of thermal expansion (CTE) increases slightly from 7.08 × 10⁻⁶ K⁻¹ for Nb₄AlC₃ to 7.24 × 10⁻⁶ K⁻¹ for (Nb_0.75Ta_0.25)₄AlC₃ at 25–1400 °C. The thermal conductivity of (Nb_0.75Ta_0.25)₄AlC₃ (28.4–29.8 W/m·K) is higher than that of Nb₄AlC₃ (18.1–21.2 W/m·K) over the whole test range (25–1000 °C). Owing to their excellent mechanical and thermal properties, Ta-doped Nb₄AlC₃ ceramics have good potential as structural materials.     

Human tracking from single RGB-D camera using online learning

Human detection and tracking is an essential component in several robotics applications, especially in indoor built environments where humans and robots are expected to coexist or collaborate. Due to their low cost and capability to capture both color and depth data, RGB-D cameras have shown significant promise in human detection and tracking for robotic applications. In this paper, a new human tracking method is proposed to detect and track a specific individual from a single RGB-D sensor using online learning classifiers with no ground plane assumption. Given a previous target human position, a candidate sampling method is designed to find potential positive samples while negative samples are obtained by a random sampling process. The kernelized Support Vector Machine (SVM) is employed as the online classifier to recognize the target human and updated using both the positive and negative examples. The experimental results on six RGB-D videos of a public dataset demonstrate that the proposed method achieves higher success rates compared to a 2D tracker and a 3D human detection method at a frame rate of 3.8 fps, and is capable of efficiently retrieving the target human following intermittent occlusion.     

A Channel Borrowing Approach for Cluster-based Hierarchical Wireless Sensor Networks

Mobile Networks and Applications - In Wireless Sensor Networks (WSNs), energy-efficient routing is required to conserve the scarce resources of these networks. Various energy-efficient routing...     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

Software and Systems Modeling - A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all...