Miracle-dz/UniFAR-FTUs huggingface dataset:
Grasping Objects Mixed With Towels

In logistics warehouse sorting, rubbish classification, and household services, scenarios exist in which rigid and soft objects are randomly piled together. In such situations, two major challenges arise in robotic picking tasks: the first is to distinguish rigid objects from soft objects, and the second is to grasp one object of each type at a time. In this study, we propose a novel robotic picking methodology for the grasping of objects mixed with towels. The proposed approach is based on a novel object detection method that can identify a rigid object placed in different directions using a rotational bounding box. Rigid objects can be separated from the mixed scene without object segmentation. Moreover, the grasping pose of a rigid object can be generated directly along its principal axis, without using a CAD model or specific pose detection method. The gripper opening width is determined according to the object size. Therefore, our method can detect whether other objects, particularly soft ones, exist around a rigid object. If no suitable grasping pose is available for the rigid objects, the grasping pose on a wrinkle of the towel is selected. The experiments demonstrate that our method can accomplish the picking task in scenes with mixed rigid and soft objects, thereby indicating its significance in robotic object detection and sorting.
Temporal Patterns Underlying Domestic Departure Passengers Behavior in the Airport

Air travelers’ behavior is closely related to the operational performance of any airport terminal. Much of previous research has focused on how airport operators balance the number of facilities in a terminal and the Level of Service (LOS), while the behavior of passengers is less considered. Not much is known, however, about passenger’s behavior during the entire departure process in an airport. In this study, we analyze empirical departure passenger’s data to gain an insight into the regular patterns of their activities in an airport. We find that there exist two distinguished temporal patterns during two discretionary periods— post check-in and pre-security check, post security check and pre-boarding. The time that departure passengers spend in these two periods is well approximated by a double power-law distribution and an exponential truncated power-law distribution respectively. The two distinguished distributions suggest that there may be different mechanisms underlying passengers’ behavior as indicated by previous studies on human mobility. We introduce a stochastic model that considers traveling experience and time pressure to capture the decision dynamics of human behavior. Simulation results suggest that traveling experience and time pressure dominate passenger’s decisions before and after security respectively. Our findings contribute to a better understanding of human dynamics, and also offer the potential for optimizing and simulation of airport terminal operation.
Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner. It uses its own predictions to assign labels to unlabeled data. For a self-training algorithm, the classification ability of the base learner and the estimation of prediction confidence are very important. The classical decision tree as the base learner cannot be effective in a self-training algorithm, because it cannot correctly estimate its own predictions. In this paper, we propose a novel method of node split of the decision trees, which uses weighted features to cluster instances. This method is able to combine multiple numerical and categorical features to split nodes. The decision tree and random forest constructed by this method are called FWCDT and FWCRF respectively. FWCDT and FWCRF have the better classification ability than the classical decision trees and forests based on univariate split when the training instances are fewer, therefore, they are more suitable as the base classifiers in self-training. What’s more, on the basis of the proposed node-split method, we also respectively explore the suitable prediction confidence measurements for FWCDT and FWCRF. Finally, the results of experiment implemented on the UCI datasets show that the self-training feature weighted clustering decision tree (ST-FWCDT) and random forest (ST-FWCRF) can effectively exploit unlabeled data, and the final obtained classifiers have better generalization ability.
Systematic Survey on Smart Home Safety and Security Systems Using the Arduino Platform

Smart home safety and security systems have gained much importance over the last few years owing to their notable impact in reducing and preventing losses in resources and human life caused by unwanted situations that could occur while homeowners are far away from their homes. To date, there is a lack of an in-depth literature analysis that could help researchers and developers better understand these systems and their applications in different contexts. It is therefore crucial that research evidence published in this area is presented. In this study, 63 research papers that examined smart home safety and security systems using the Arduino platform from popular literature databases were thoroughly surveyed to extract useful data. Then, the extracted data were analyzed to answer many research questions concerning state-of-the-art applications of these systems, their architectures, their enabling technologies, their components, etc. In addition, several challenges that these systems currently face and how future research could enable better implementation and use of these systems were discussed.
A suboptimal H2 clustering-based model reduction approach for linear network systems

In this paper we propose a (suboptimal) H2 model reduction method for a class of linear network systems that describe diffusively coupled networks. To preserve a network structure, we form a reduced-order model by using the characteristic matrix of a graph clustering so that the reduced-order model has less number of vertices. Then, we formulate the model reduction problem as a nonconvex optimization problem with binary variables, aiming for a graph clustering that minimizes the H2-norm of the approximation error. Based on the controllability and the observability Gramians of the error system we derive an optimization problem with mixed-binary variables and then we propose a convex relaxation of the binary variables, leading to a smooth optimization formulation. For this new optimization problem we derive an explicit expression for the gradient of the objective function and then we employ a projected gradient algorithm for solving the optimization problem with mathematical guarantees on its convergence.
A Fusion of CNNs and ICP for 3-D Point Cloud Registration*

3-D point cloud registration appears to be one of the principal techniques to estimate object pose in 3-D space and is critical to object picking and assembly in automated manufacturing lines. Thereby, this paper proposes an effective registration architecture with the aim of estimating the transformation between a data point cloud and the model point cloud. Specifically, in the first registration stage, a trainable Convolutional Neural Network (CNN) model is developed to learn the pose estimation between two point clouds in the case of a full range of orientation from −180° to 180°. In order to generate the training data set, a descriptor is proposed to extract features which are employed to train the CNN model from point clouds. Then, based on the rough estimation of the trained CNN model in the first stage, two point clouds can be further aligned precisely in the second stage by using the Iterative Closest Point (ICP) algorithm. Finally, the performance of the proposed two-stage registration architecture has been verified by experiments in comparison with a baseline method. The experimental results illustrate that the developed algorithm can guarantee high precision while significantly reducing the estimation time.
Contact Force based Balancing and Tracking Control of a Ballbot using Projected Task Space Dynamics with Inequality Constraints

Since the ball-balancing robot has been researched, many kinds of platforms and control methods have been developed for the ballbot. Even though the behavior of the robot has been achieved by previous studies, there are few studies considering the contact forces between the robot and ball. In this paper, we propose balancing and tracking control of the ballbot, with unilateral constraints. Using an adequate task transformation matrix, the task space dynamics can be divided into the dynamics of the robot and the ball respectively. This decomposition has advantages to obtain the input torque through the ball task dynamics with constraint forces. Through the proposed formalism, the contact force can be computed from the ball task space dynamics with the quadratic programming(QP) with inequality constraints such as unilateral constraints and friction constraints. The obtained contact force is used in the robot task space dynamics to get control input. In addition, the balancing force is computed using CoM reflex as a reference. Hence, using the synthesized controller, the contact force based balancing and tracking control simulation is performed.
Attraction domain estimation of linear controllers for the attitude control of VTOL vehicles: P/PI control of a quadrotor

In this paper, we present a numerical method based the Lyapunov theory to estimate the attraction domain of a class of nonlinear systems. This problem is motivated by the analysis of linear attitude controllers for the control of Vertical Take-Off and Landing (VTOL) vehicles such as quadrotors. These linear controllers are typically designed in order to ensure local stability around the hover point. The purpose of this work is to estimate their attraction domain around this point. The proposed attraction domain estimation method requires to solve a convex optimization problem involving parameter-dependent Linear Matrix Inequalities (LMI). This problem is generally difficult to solve as it is an infinite dimensional optimization problem. However, we reveal that the specific structure of the VTOL vehicles attitude model can be exploited to make this LMI problem finite dimensional and thus numerically solvable.
Strict Lyapunov functions for consensus under directed connected graphs

It is known that for consensus of systems interconnected under a general directed graph topology a necessary and sufficient condition for consensus is that there exist at least one rooted spanning tree. In this paper we present an original statement of linear algebra that serves to characterise the spanning-tree condition for directed graphs in terms of a Lyapunov equation involving the graph’s Laplacian. Our results apply to the case of systems described by simple first and second order integrators. As a result, we provide strict Lyapunov functions that ensure, via direct constructive proof, global exponential stability of the consensus manifold.
One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications

Autonomy is increasingly demanded by industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., robot dynamics compensation) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a userdefined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in 125 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved.
ColPri: Towards a Collaborative Privacy Knowledge Management Ontology for the Internet of Things

User privacy preferences management is a nontrivial task. In the context of the Internet of Things (IoT), where a huge amount of data is generated, transferred and stored via various local and cloud architectures, privacy protection becomes complex and hard to manage. Indeed, privacy management is a time-consuming activity that requires a lot of knowledge which most of IoT system users often lack or are not keen on acquiring due to its complexity. The knowledge dimension has often been neglected, by both researchers and industry. In this article, we focus on the privacy protection knowledge management aspect. We produce a first version of ColPri, an ontology that sets the basis for a collaborative extensible privacy protection knowledge management system that is able to collaboratively produce diagnosis of IoT stakeholders privacy policies. This paper aims to investigate collaborative privacy knowledge management in the IoT and how non-technical users could benefit from it to easily configure their privacy policies. It allows an open exchange of privacy-related knowledge. We propose ColPri, a collaborative privacy ontology after specifying design requirements that guided our choices during the ontology creation process. This ontology lays out the use of a privacy community to create and develop privacy-related information within a user-centric privacy architecture. Then, we show how to use this ontology through a use case scenario. Finally, we describe future research based on this work.
A Secure Enhanced Key-Policy Attribute-Based Temporary Keyword Search Scheme in the Cloud

Attribute-based keyword search (ABKS), as an important type of searchable encryption, has been widely utilized for secure cloud storage. In a key-policy attribute-based temporary keyword search (KP-ABTKS) scheme, a private key is associated with an access policy that controls the search ability of the user, while a search token is associated with a time interval that controls the search time of the cloud server. However, after a careful study, we uncover that the only existing KP-ABTKS construction [1] is not secure. Through two carefully designed attacks, we first show that the cloud server can search the ciphertext in any time. As a result, their scheme cannot support temporary keyword search. To address this problem, we present an enhanced KP-ABTKS scheme and prove that it is selectively secure against chosen-keyword attack in the random oracle model. The proposed scheme achieves both fine-grained search control and temporary keyword search simultaneously. In addition, the performance evaluation indicates that our scheme is practical.
Full Band Compact Power Arm Subsystem With High Directive Sample

An output arm is the first subsystem in the microwave components chain connected immediately after the power amplifier. These arms are responsible for transferring the signal from the output port of the power amplifier to the standard line of the system. The second objective is to obtain directive samples for both forward and backward signals. The typical design process for such a subsystem is carried out through the design of separate components, independently. This limits the achievable performance of the entire system and introduces excessive losses at intermediate flanges. In this work, we present a novel approach to design such a subsystem using a global optimization procedure. Specifically, we address both compactness and full-band operation of output arms deployed in radar applications. Analysis and design of each component in the proposed subsystem are illustrated. The presented subsystem achieves a deep matching level of 19 dB within an overall length below $2\lambda _{g}$ . Moreover, the directive sample obtained through the presented design has an ultra-flat response of 20 dB ± 0.5 dB over the entire band of operation, while the directivity of the samples exceeds 18 dB. The measured results of the fabricated prototype have an excellent agreement with the simulations.
Inspecting Intrusion Prevention System Signatures for False Blocking using Set Theory

A network intrusion prevention system (NIPS) is a network security application that detects and blocks cyberattacks to protect information assets. However, it sometimes generates false positives during its blocking mode operation, which leads to blocking legitimate user inputs and reducing service availability. Thus, such false blockings need to be reduced. Machine learning methods for reducing false positives or NIPS evaluation methods for detecting NIPS signatures that cause false blockings have been proposed, but they do not always remove all false blockings. We propose a method using set theory to detect all signatures that cause false blockings. The method checks whether a set of user inputs representing as a regular expression and a set of signatures are mutually exclusive in set theory. Therefore, false blockings are eliminated by tuning the signatures judged as not mutually exclusive with the proposed method. We implemented and evaluated the proposed method, and results indicate that it not only detected all imperfect signatures but was also executed faster than a conventional method.
Vehicle Classification and Speed Estimation Based on a Single Magnetic Sensor

The integration of Internet of things (IoT) and intelligent transportation system (ITS) is expected to improve the traffic efficiency and enhance the driving experience. However, due to the dynamic traffic environment and various types of vehicles, it is a challenge to perform vehicle classification and speed estimation with a single magnetic sensor. In this paper, based on a single low-cost magnetic sensor, a scheme is proposed to achieve vehicle classification and speed interval estimation by designing a two-dimensional convolutional neural network (CNN). Specifically, we extract the magnetic field data of each vehicle and then convert the collected data into a two-dimensional grayscale image. In this way, the images of vehicle signals with different types and driving speeds can be used as the input data to train the designed CNN model. With the designed CNN model, we classify the vehicles into 7 types and estimate the speed interval of each vehicle, where the speeds in the range of 10km/h-70km/h are divided into 6 intervals of size 10km/h. The performance of the proposed vehicle classification and speed estimation scheme is evaluated by experiments, where the experimental results show that the accuracy of vehicle classification and the accuracy of speed interval estimation are 97.83% and 96.85%, respectively.
R3MAT: A Rapid and Robust Graph Generator

One of the main problems when developing graph-based applications is the availability of large and representative datasets. The lack of real graphs has motivated the development of software tools for generating synthetic graphs. R-MAT is a data generation method that was designed to produce synthetic graphs whose characteristics resemble those occurring in real networks. Although the generation model defined by R-MAT is easy to understand, its implementation is not trivial and it has intrinsic memory restrictions that makes the generation of very large graphs difficult. This paper studies the practical implementation of R-MAT. We discuss the issues of the original implementation which works with the adjacency matrix of the graph and analyze the size of the resulting graph obtained with the R-MAT model. Then, we introduce and experimentally evaluate R<sup>3</sup>MAT, an alternative implementation for R-MAT based on an array of degrees. These experiments show that <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> our R<sup>3</sup>MAT is able to generate graphs of hundred million nodes and billion edges in a single machine, <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> our method preserves the characteristic power-law distribution of the edge degrees present in real-world graphs, and <inline-formula> <tex-math notation="LaTeX">$(iii)~\text{R}^{3}$ </tex-math></inline-formula>MAT has the best performance in the current state of the art, when considering a single modest computer in a sequential fashion.
Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST–T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST–T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST–T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST–T segment waveforms in ECG-based automated CAD detection.
The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN

Named Data Networking (NDN) is one of the main research projects of the information center network (ICN), and its efficient forwarding mechanism attaches the attention of researchers. Like other networks, NDN also faces the threat of cyber attacks. With the assistance of the colluding server, the Collusive Interest Flooding Attacks (CIFA) can use the defects of the NDN’s internal forwarding mechanism to send malicious interest packets in the form of pulses. It affects the normal requests of legitimate users and reduces the quality of NDN network services in this way. By analyzing the characteristics of network traffic and CIFA model, a new CIFA detecting method based on the prediction error between particle filter and one-step prediction algorithm is proposed. This scheme samples the network traffic and judges whether the network is under attack by comparing the normalized error value of the one-step prediction and the estimate of the particle filter. Experimental analysis shows that the detection scheme in this paper has higher detection rate than the existing detection schemes.