Miracle-dz/UniFAR-FTUs huggingface dataset:
Android App Merging for Benchmark Speed-Up and Analysis Lift-Up

In the field of software analysis a trade-off between scalability and accuracy always exists. In this respect, Android app analysis is no exception, in particular, analyzing large or many apps can be challenging. Dealing with many small apps is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH. These particular benchmarks are not only used for the evaluation of novel tools but also in continuous integration pipelines of existing mature tools to maintain and guarantee a certain quality-level. Considering this latter usage it becomes very important to be able to achieve benchmark results as fast as possible. Hence, benchmarks have to be optimized for this purpose. One approach to do so is app merging. We implemented the Android Merge Tool (AMT) following this approach and show that its novel aspects can be used to produce scaled up and accurate benchmarks. For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy trade-off anymore. We show this throughout detailed experiments on DROIDBENCH employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark execution times are largely reduced without losing benchmark accuracy. Moreover, we argue why AMT is an advantageous successor of the state-of-the-art app merging tool (APKCOMBINER) in analysis lift-up scenarios.
End-to-end sensorimotor control problems of AUVs with deep reinforcement learning

This paper studies on sensorimotor control problems of Autonomous Underwater Vehicles (AUVs) using deep reinforcement learning. We design an end-to-end learning architecture mapping original sensor input to continuous control output without referring to the dynamics of vehicles. To avoid difficult and noisy underwater localization, we implement the learning without knowing the positions of AUVs by proposing novel state encoder and reward shaping strategies. Two distinct underwater tasks, obstacle avoidance with sonar sensor and pipeline following with visual sensor, are simulated to validate the effectiveness of proposed architecture and strategies. For the latter, we test the learned policy on realistic images of underwater pipelines to check its generalization ability.
Locating Deteriorated Links by Network-Assisted Multicast Proving on OpenFlow Networks

With the penetration of cloud computing and contents delivery networking, flexible and dynamic traffic engineering in a network connecting globally-distributed datacenters is demanded, which is often centrally managed based on OpenFlow technology. To maintain a high quality of service of the network, detecting and locating deteriorated (e.g., lossy) links is essential. Therefore, in this paper, a framework of actively monitoring both directions of all full-duplex (virtual) links of an OpenFlow-based network is proposed. By designing particular schemes of routing the probe packets along a multicast tree and ordering the statistics collection from selected switches, the proposed framework can promptly locate highly lossy links with a minimized load on both data-plane and control-plane incurred by the measurement. In addition, the proposed schemes are computationally-lightweight. Our proto-type OpenFlow implementation is tested on Mininet to show its feasibility. The numerical simulation also demonstrates the effectiveness of our proposal.
Latency-Constrained Cost-Minimized Request Allocation for Geo-Distributed Cloud Services

Latency to end-users and regulatory requirements push cloud providers to operate many datacenters all around the globe to host their cloud services. An emerging problem under such geo-distributed architecture is to assign each user request to an appropriate datacenter to benefit both cloud providers (e.g., low bandwidth cost) and end-users (e.g., low latency)—known as request allocation. However, prior request allocation solutions have significant limitations: they either focus only on optimizing the benefits for one entity (e.g., providers or users), or ignore some practical yet indispensable factors (e.g., heterogeneous latency requirements of different users and diverse per unit bandwidth cost among different datacenters) when optimizing benefits for both entities. In this paper, we study the problem of minimizing the total bandwidth cost for cloud service providers while guaranteeing the latency requirement for end-users. Specifically, we formulate an integer programming with consideration of the diversities in both the delay of requests and per unit bandwidth cost of datacenters. To efficiently and practically solve this problem, we first relax the integer programming into a continuous convex optimization and then take the advantages of random sampling to enforce the solution to be a feasible one for the original integer programming. We have conducted rigorous theoretical analysis to prove that our algorithm can provide a considerable good competitive ratio. Extensive simulations demonstrate that our proposed algorithm can reduce the total bandwidth cost by 30% while guaranteeing the latency requirements of all requests, as compared to conventional methods.
Development of Open-Source Motor Controller Framework for Robotic Applications

Open-source designs are being increasingly used to develop robot hardware. When motor actuators were used for robot development in the past, it was difficult to find cost-effective commercial products. Open-source motor controllers, recently developed with the collaboration of many people, have exhibited phenomenal growth as their performance is now comparable to those of commercial products and their price is cheaper. In this study, we utilized an open-source Vedder electronic speed controller (VESC), originally developed for electric skateboards, as a general-purpose motor controller for robot control. The high-speed, high-torque, and high-output VESC was hardware-upgraded to enable more accurate current control, and advanced position and speed control algorithms were developed to produce Myongji-VESC (MJ-VESC), a general-purpose motor controller for multiaxis robot systems. To evaluate its current, speed, and position control performances, MJ-VESC was applied to an open-source planetary geared actuator that can be fabricated with a 3D printer. Based on this, we propose an open-source motor controller framework that can be universally used for multiaxis robot systems.
Common Dimensional Autoencoder for Learning Redundant Muscle-Posture Mappings of Complex Musculoskeletal Robots

It has been widely considered that a distinctive feature of musculoskeletal structures is that both the joint angle and stiffness can be changed by exploiting the agonistantagonist driving of the joint. However, musculoskeletal systems in animals and humans are typically highly complex, and the simple agonist-antagonist driving is rarely found. Therefore, in accordance with the increasing complexity of musculoskeletal robots, the feature that causes the robot to assume a posture with different stiffness values becomes difficult to achieve, owing to the difficulty in modeling the kinematics. Although datadriven approaches such as the neural network are regarded as suitable for modeling complex relationships, the training data are difficult to obtain because measuring joint stiffness is typically extremely difficult in contrast to measuring an actuator's state and posture. Hence, we herein propose the common dimensional autoencoder where the encoded feature exhibits identical dimensions to the original input vector. In the proposed network, in parallel with the original unsupervised training using the data of the actuators’ states, supervised training at part of the encoded features is performed using posture data. Consequently, features expressing the redundancy of inverse kinematics appear at the remaining part of the encoded features without using data such as joint stiffness. The validity of the proposed method was confirmed successfully through an experiment using a 10 degrees-of-freedom complex musculoskeletal robot arm driven by pneumatic artificial muscles.
Medical Multimedia Big Data Analysis Modeling Based on DBN Algorithm

With the development of medical multimedia analysis methods based on DBN, DBN models have gained the ability to surpass medical experts in the evaluation of multimedia in some clinical examinations. Firstly, based on the existing architecture of the Internet of Things, combined with the actual characteristics of the hospital, the medical multimedia data is accessed from the IoT support platform. Secondly, the medical multimedia data modeling and classification method based on DBN is studied and analyzed. Three network structure models, a deep belief network, a stacking automatic encoder and a convolutional neural network, were introduced and analyzed. The medical multimedia data classification modeling method based on DBN was proposed to further improve the accuracy of medical multimedia data classification. The experimental results show that compared with the traditional feature extraction based neural network classification method, the classification performance is better. Thirdly, the medical state assessment model is constructed based on the multivariate Gaussian distribution theory. To study how to use the multivariate Gaussian distribution theory to design an evaluation model that can evaluate the health status of users efficiently and accurately. Finally, using the MATLAB software platform, through the experiment and simulation of 40 groups of $8 \times 8064$ -dimensional physiological big data of 32 volunteers, First, determine the optimal parameters of a set of health assessment models; then use the model to learn the characteristics of physiological parameters; finally, the state assessment model to obtain the health assessment results. The experimental results show that the feature learning model based on convolutional neural network theory can effectively extract the deep features of medical multimedia big data. The health state assessment model based on multivariate Gaussian distribution theory can effectively evaluate the health status of human body.
A Robust Stereo Semi-direct SLAM System Based on Hybrid Pyramid

We propose a hybrid pyramid based approach to fuse the direct and indirect methods in visual SLAM, to allow robust localization under various situations including large-baseline motion, low-texture environment, and various illumination changes. In our approach, we first calculate coarse inter-frame pose estimation by matching the feature points. Subsequently, we use both direct image alignment and a multiscale pyramid method, for refining the previous estimation to attain better precision. Furthermore, we perform online photometric calibration along with pose estimation, to reduce un-modelled errors. To evaluate our approach, we conducted various real-world experiments on both public datasets and self-collected ones, by implementing a full SLAM system with the proposed methods. The results show that our system improves both localization accuracy and robustness by a wide margin.
Low Cost 2D Laser Scanner Based Indoor Mapping and Classification System

This paper presents a low-cost indoor mapping and classification system using cheap RPLidar 2D laser scanners. In this work, a combination of two laser scanners mounted orthogonally on a trolley or backpack has been used to generate 3D map of the surveyed indoor vicinity and to classify it. The generated map has been estimated using Simultaneous Localization and Mapping (SLAM) technique while classification has been done using Random Sampling and Consensus (RANSAC) based segmentation technique. In order to completely map the indoor environment, the proposed hardware system has been required to move manually along the surveyed vicinity and all online sensors measurements have been recorded using Robot Operating System (ROS). Later, the recorded data has been playback and desired mapping and classification techniques have been applied to generate results in offline mode. Multiple tests have been conducted using proposed system and results have been found accurate and nearer to ground truth if compared using the standard manual measuring devices available in the local market.
Energy-Efficient Locomotion Strategies and Performance Benchmarks using Point Mass Tensegrity Dynamics

This work introduces a novel 12-motor paired-cable actuation scheme to achieve rolling locomotion with a spherical tensegrity structure. Using a new point mass tensegrity dynamic formulation which we present, we utilize Model Predictive Control to generate optimal state-action trajectories for benchmark evaluation. In particular, locomotive performance is assessed based on the practical criteria of rolling speed, energy efficiency, and directional trajectory-tracking accuracy. Through simulation of 6-motor, 12-motor paired cable, and 24-motor fully-actuated policies, we demonstrate that the 12-motor schema is superior to the 6-motor policy in all benchmark categories, comparable to the 24-motor policy in rolling speed, and is over five times more energy efficient than the fully-actuated 24-motor configuration.
HRV Analysis Using Complexity Indices

This paper presents a theoretical study about Heart Rate Variability analysis using information theory-based complexity indices as entropies and Lempel-Ziv complexity. The main idea is to try to find a relation between signal complexity and possible nonlinear dynamics of Heart Rate Variability which are unseen at first investigation. The proposed procedures are applied on test signals which are taken from specified databases.
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes.
Limbs Movement and Motor Imagery: an EEG Study

In this paper, we used EEG recordings to investigate event-related desynchronization (ERD) and event-related synchronization (ERS) in 30 subjects, in order to differentiate between motor execution and motor imagery for hands and feet movement and imagination, respectively. ERD and ERS measurements were calculated by ERSP with the gain model. We analyzed Theta, Alpha and Beta bands in pre-motor, motor and sensorimotor regions. In the experimental procedure, for hand movement and imagination it was possible to identify an Alfa desynchronization during motor execution and a Beta synchronization during motor imagery. However, for feet movement, no significant ERD was observed whatsoever in any bands. On the other hand, during motor imagery of the feet, a synchronization occurred in Alfa band at centro-parietal channels (sensorimotor region) contralateral to the imagined foot movement. We believe these findings could be considered when designing a Brain Computer Interface (BCI) systems based on the motor imagery paradigm.
Learning to Grasp Arbitrary Household Objects from a Single Demonstration

Upon the advent of Industry 4.0, collaborative robotics and intelligent automation gain more and more traction for enterprises to improve their production processes. In order to adapt to this trend, new programming, learning and collaborative techniques are investigated. Program-bydemonstration is one of the techniques that aim to reduce the burden of manually programming collaborative robots. However, this is often limited to teaching to grasp at a certain position, rather than grasping a certain object. In this paper, we propose a method that learns to grasp an arbitrary object from visual input. While other learning-based approaches for robotic grasping require collecting a large dataset, manually or automatically labeled in a real or simulated world, our approach requires a single demonstration.We present results on grasping various objects with the Franka Panda collaborative robot after capturing a single image from a wrist mounted RGB camera. From this image we learn a robot controller with a convolutional neural network to adapt to changes in the object’s position and rotation with less than 5 minutes of training time on a NVIDIA Titan X GPU, achieving over 90% grasp success rate.
Information Flow Modeling and Performance Evaluation of Communication Networks Serving Power Grids

A cyber-physical system (CPS) represents the coupling network between a communication network and a power grid. Information flow (IF) as the medium of communication networks has an important influence on the performance assessment of this type of coupling network. However, traditional IF models cannot be applied to CPS directly because they do not take CPS distinctive coupling effect and associated operational mechanism into account. In this paper, we propose an information flow model (IFM) based on graph theory and traffic dynamics considering the functional architecture and transmission mode in realistic CPS to bridge the gap mentioned above. In the IFM, congestion rate and loss rate are employed to assess the performance of communication networks serving power grids under different transmission strengths and modes. Moreover, an auxiliary model about CPS topology is constructed, which can generate more realistic CPS topology. Case studies on an IEEE 39-bus system reveal the transmission performance of CPS in detail. The IFM is exploited to detect critical nodes and compare the effects of node enhancement on IF transmission of networks. In addition, IFM is also applied to compare the transmission performances of six network topologies, including three basic complex networks and three community-based networks. The results demonstrate the effectiveness of the proposed model, and some suggestions are provided on performance improvement of IF transmission for CPS.
Context Based Trust Formation Using Direct User-Experience in the Internet of Things(IoT)

The Internet of Things (IoT) is converting the real world into cyber network. Security becomes a challenging task in IoT. For that reason, trust is used as an essential security measure in IoT devices. Trust Management System (TMS) is designed to mitigate the risks of uncertainty between the communicating parties. Direct interactions with servers based on the user experiences in various contexts can be considered as a solution for trust formation. The aim of this paper is to develop a context based trust management system that uses direct experiences of users while being connected with servers in various contexts. The technique is based on the Naïve Bayesian classification method. The context based direct user experiences are filtered, classified and then used as a trust formation procedure to formulate and update trust with the passage of time. The proposed protocol has been evaluated using web services datasets and results show that trust formation considering contextual information provides a promising solution.
Compression of PPG Signals with SONG Adaptive Delta Modulation

In this paper, results of a case study focused on the implementation of the SONG adaptive delta modulation (SADM) for compression of photoplethysmography (PPG) signals are summarized. The performance of the SADM is optimized for operation with PPG signals with respect to the signal-to-noise ratio and the normalized percentage root-mean-square difference. By using of this 1 bit per sample algorithm compression ratios of 1:N could be achieved when an N-bit analog-to-digital convertor is used for the PPG data acquisition. The presented results are obtained through computer simulations using real-world PPG signal databases. The possible applications of this contribution include the fields of medical sensor networks, telemedicine, data storage etc.
A Novel 4-DoF Robotic Link Mechanism with E-CoSMo: Kinematics Based Torque Analysis

In this study, a novel four degrees of freedom (DoF) robotic link mechanism and its torque analysis based on extended coaxial spherical joint module (E-CoSMo) are introduced. The E-CoSMo is a previously developed 4-DoF point-centered rotational parallel mechanism with coaxial actuators. The use of the E-CoSMo on robotic system is expected to improve dynamic performance since all actuators are mounted on fixed base. Furthermore, output torque is amplified in a specific axial direction, due to a coaxial parallel mechanism. Torque analysis of the mechanism is derived based on kinematics of a virtual 4-DoF serial linkages and the E-CoSMo. A virtual force-based position control simulation is performed for verifying the torque analysis. Due to the valid torque analysis, the norm of position error is less than 0. 7mm. It is confirmed that the output torque is amplified compare to actuator torques, which is a special feature of the E-CoSMo. Control experiments using prototype are also carried out to validate the feasibility.
A Capacity-Aware User Recruitment Framework for Fog-Based Mobile Crowd-Sensing Platforms

Mobile Crowd-Sensing and Fog Computing are fundamental Internet of Things technologies tailored for smart cities. The former enables user’s devices to collect and share data in urban environments. The latter shifts the computation close to end users, lightening the work that their devices have to perform to communicate sensed data in the Cloud. In a fog-based MCS campaign a large number of devices with heterogeneous resources executes sensing tasks generally distributed by remote servers. A careful selection of some of these users’ devices for sensing operations can bring benefits to the whole platform in terms of computational costs and energy saving. In this paper, we propose a novel users’ recruitment model based on distance, computational capacity, and residual battery of devices. The selection process is carried out in a scenario where devices of the MCS campaign periodically share their battery and Central Processing Unit status to fog nodes through their short-range communication interfaces. Based on this information, fog nodes select devices suitable for performing specific tasks. To verify the effectiveness of the proposed model, we compare our solution with a selection model based only on distances, using an MCS simulator suitably modified for fog-based scenarios as testbed. Results show that our model is able to achieve a more accurate task resolution and a more effective recruitment selection, detecting those devices that can perform sensing operations better than others, thus, guaranteeing an overall average saving of computational and energy resources.