Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Prehensile Manipulation Learning through Self-Supervision

Manipulation is one of most emerging research and development areas in the field of robotics. Recently, state representation learning for control has been gaining attention. In this paper, we proposed a novel learning model based on neural networks in order to sample the actions of the robot to push objects to desired positions. Furthermore, an intuitive method was proposed to enable the robot to collect training data in an efficiently way. Specifically, a fully convolutional network encodes observations into latent space, and a mixture density network is implemented to infer an action distribution, since there are an infinite number of possible actions that may result in the same change of the state of the object. Through extensive experimental simulations and comparisons with the existing models, we demonstrated the efficiency of the proposed method applied to non-prehensile manipulation, such as pushing or rotating of small objects on the table.
A Method to Model the Volume Charge Density in a Multiple-Input Floating-Gate MOS Transistor

We present a methodology to model the volume charge density in the substrate of the Multiple-input Floating-Gate MOS (MIFGMOS) transistor. This method considers the MIFGMOS as an analog CMOS device. We based our procedure on Maxwell’s Equations through a Poisson Equation implementation on Matlab. The charges generated during the MIFGMOS polarization, which is made by applying voltages on the Control Gates (CG), are used to calculate electric fields, electric potentials, and finally, the volume charge density in the MIFGMOS transistor channel. Moreover, we introduce a method based on coordinates to generate a mathematical function to describe the electric-potential behavior on the substrate. Finally, we show the volume charge density behavior on three graphics, which allows us to predict regions in which will be a great amount of volume charge density, depending on the voltages of the terminals. The former reveals the relationship between control gates and volume charge density and will allow us to understand and analyze some superior order phenomena.
FollowAKOInvestor: Using Machine Learning to Hear Voices from All Kinds of Investors

As an increasing number of investors post their opinions or show their decisions on public platforms, a critical challenge is to make trading decisions by considering opinions from online investors. In this paper, by taking the real-world data from Stocktwits as an example, we develop FollowAKOInvestor, a systematic two-step framework to utilize sentiments from various kinds of investors to forecast stocks. First, FollowAKOInvestor divides investors into various groups according to their expertise levels and extract sentiments from different investor groups as features. Then, it uses machine learning techniques to make trading decisions by learning a prediction function to appropriately combine sentiments from different kinds of investors. The intuition of FollowAKOInvestor is that sentiments extracted from all kinds of investors (including experts and non-experts) can help us invest in stocks. Extensive data analysis and experiments show that FollowAKOInvestor generates high-yield portfolios.
Skin Lesion Segmentation Based on Deep Learning

The precise segmentation of the skin pathology area plays an important role in the determination of the skin disease type. This paper proposed the method in Deep learning, Mask R-CNN, to segment skin diseases, and introduced K-means clustering algorithm in the pre-processing of the data set. This method can ensure the accurate labeling of the data set itself, while accurately segmenting the skin pathology area. Experiment results based on ISIC (International Skin Imaging Collaboration) data set with 23906 images demonstrate that the segmentation effect on skin image and the segmentation accuracy of its test results is as high as 91.87%. Further more, It can segment lesions within a given skin image, even in the presence of fuzzy boundaries and complex textures.
Presenting Botmark: a Computer Benchmark for Service Robotics

Central to any robot is a computing system tasked with controlling it; acting as the robot's primary means of computation, its performance significantly impacts the performance of the robot as a whole. Accordingly, developing a rigorous and repeatable methodology to assess the performance of computers in this role is a important step towards improving the performance of robots. For this reason, we present Botmark in the first part of this paper: a benchmark suite designed exclusively for evaluating the performance of computers in the application of mobile service robotics. It comprises seven workloads representing common functionality of mobile service robots including path planning, SLAM, facial recognition, among others. In order to determine the functions to be included, we use the popular RoboCup@Home competition as a proxy for ‘real-world’ robotics applications and derive workloads for the benchmark from the tests in the competition. In the second part of this paper, we demonstrate the potential value of benchmarking tools by conducting an experiment evaluation using Botmark. We firstly make a comparison of various computing platforms representative of those commonly used in robotics and see a wide variability in the performance of each. We then look at the difference in performance between running the benchmark natively, in a virtual machine and in a container. To the extent of the authors' knowledge, this is the first computer benchmarking suite designed to address the area of mobile service robotics extensively. The benchmark is available for free use and we invite the community to use it to evaluate their platforms and submit their results to the authors for dissemination.
Network Slice Lifecycle Management Model for NFV-based 5G Virtual Mobile Network Operators

The use of Network Function Virtualization (NFV) and Software-defined Networks (SDN) technologies in 5G networks provide Mobile Network Operators (MNOs) with new capacities to deploy and orchestrate network functions in a virtualized and programmable way, allowing them in turn to better provide and support new connectivity requirements demanded by users and vertical industries. The Network Slicing function provides the base functionality to virtualize the network and efficiently share it among customers. Besides, the extensive use of virtualization technologies in 5G provides a good support for the creation of virtual Mobile Network Operators (vMNO), that offer network services to its clients using the network resources provided by an MNO. However, the 3GPP architecture defined for the management of network slices fails to provide a good support for the vMNOs to have their own network slice management system. The main goal of the thesis work outlined in this paper is to address the integration of current technologies used by 5G networks into multi-domain and multi-client cloud computing scenarios, for the provision of vMNOs following a Network Slice as a Service (NSaaS) approach. The ongoing work focuses on defining mechanisms for the deployment, orchestration and management of diverse vMNOs available in the network slice ecosystem, proposing an extension to the network slice management capabilities proposed by 3GPP.
Mechanical and Computational Energy Estimation of a Fixed-Wing Drone

In this paper, we present a case study on the energy estimation of drones and derive a general modeling approach that estimates computational and mechanical energy separately. The mechanical energy model can easily be extended to other drones and is built using a Fourier series from a number of training flights. The computational energy model is more advanced as it handles heterogeneous hardware and incorporates a specification that defines the quality-of-service ranges for software components of the robotic system. The computational model is suitable for any mobile robot and is implemented in a modeling tool. The tool automatically generates an energy model from the specification by performing a set of empirical trials for selected configurations while approximating others. Information about the battery State of Charge is also included in the tool, hence allowing the evaluation of how different software configurations impact the battery. This approach can be used for dynamic mission assessment regarding different planning decisions. We here have demonstrated its ability to model the energy of a specific mission performed at varying levels of quality-of-service using a specific drone.
An auto contrast custom convolutional neural network to identifying Gram-negative bacteria

The process of identifying bacteria is an essential factor in the medical field. One of the germs that cause lung damage in pneumonia is gram-negative bacteria. The convolutional neural network method is the newest approach to machine learning because it has a high degree of accuracy. But the drawback is due to his in-depth knowledge, the computation time for the training process takes a long time. The method offered in this research is automatic contrast addition in the preprocessing stage and the use of custom layers. Also, augmentation data added to increase the variation in the amount of data in the training process. In using custom layers, the objective is to obtain minimal computational training time while maintaining maximum accuracy values. The results show that an average accuracy around 98.59% with average training time around 01 minutes 56 seconds, average MSE 0.0274, RMSE 0.1693, and MAE 0.0185.
Test Automation in Open-Source Android Apps: A Large-Scale Empirical Study

Automated testing of mobile apps has received significant attention in recent years from researchers and practitioners alike. In this paper, we report on the largest empirical study to date, aimed at understanding the test automation culture prevalent among mobile app developers. We systematically examined more than 3.5 million repositories on GitHub and identified more than 12,000 non-trivial and real-world Android apps. We then analyzed these non-trivial apps to investigate (1) the prevalence of adoption of test automation; (2) working habits of mobile app developers in regards to automated testing; and (3) the correlation between the adoption of test automation and the popularity of projects. Among others, we found that (1) only 8% of the mobile app development projects leverage automated testing practices; (2) developers tend to follow the same test automation practices across projects; and (3) popular projects, measured in terms of the number of contributors, stars, and forks on GitHub, are more likely to adopt test automation practices. To understand the rationale behind our observations, we further conducted a survey with 148 professional and experienced developers contributing to the subject apps. Our findings shed light on the current practices and future research directions pertaining to test automation for mobile app development.
Unsupervised Blink Detection and Driver Drowsiness Metrics on Naturalistic Driving Data

Driver drowsiness detection has always been center to research whether for accident risk minimization or recently for driver monitoring in the stages towards automated driving. In this work we analyse videos of visibly alert and less alert drivers collected within a naturalistic driving study in terms of different visual drowsiness metrics. The facial landmark method allows to compute the eye aperture remotely without additional wearables. From this an unsupervised blink detection algorithm is introduced that competes with other supervised methods on benchmark datasets. Common fatigue metrics such as blink rate are considered. We show that there is a significant difference in blink rate between different driver groups and also discuss fatigue levels during the course of a cruise. More importantly, we show that the distribution of eye aperture already displays valuable information on the driver’s blinking patterns without the actual need to derive a blink detection system in the first place.
RepoSkillMiner: Identifying software expertise from GitHub repositories using Natural Language Processing

A GitHub profile is becoming an essential part of a developer's resume enabling HR departments to extract someone's expertise, through automated analysis of his/her contribution to open-source projects. At the same time, having clear insights on the technologies used in a project can be very beneficial for resource allocation and project maintainability planning. In the literature, one can identify various approaches for identifying expertise on programming languages, based on the projects that developer contributed to. In this paper, we move one step further and introduce an approach (ac-companied by a tool) to identify low-level expertise on particular software frameworks and technologies apart, relying solely on GitHub data, using the GitHub API and Natural Language Processing (NLP)-using the Microsoft Language Understanding Intelligent Service (LUIS). In particular, we developed an NLP model in LUIS for named-entity recognition for three (3). NET technologies and two (2) front-end frameworks. Our analysis is based upon specific commit contents, in terms of the exact code chunks, which the committer added or changed. We evaluate the precision, recall and f-measure for the derived technologies/frameworks, by conducting a batch test in LUIS and report the results. The proposed approach is demonstrated through a fully functional web application named RepoSkillMiner.
Improving Discrimination Accuracy Rate Of DDoS Attacks and Flash Events

Millions of people across the world are using internet for their day to day activities. People are highly dependent on internet as they are using internet resources for their work in every field. It connects billions of people across the world. Internet Security has become a big issue and with passage of time. Among many threats, the Distributed Denial-of-Service (DDoS) attack is the most frequent threats in the networks. Consequences of these attacks are more powerful when launched during flash events which are legitimate traffic and cause denial of service. This paper focuses on improving discrimination accuracy rate of DDoS Attacks and Flash events. Random forest is used for classification. Symmetric uncertainty is used for feature selection. NSL KDD data set used to evaluate performance of classifier. Weka is used for implementing algorithms.
Using Machine Learning to Locate Gateways in the Wireless Backhaul of 5G Ultra-Dense Networks

A distributed wireless backhaul has emerged as an attractive solution for forwarding traffic to the core in 5G Ultra-Dense Networks (UDNs). It consists of a large number of small cells and a few of these cells, referred to as gateways, are linked to the core by high capacity fiber optic links. Each small cell is associated to one gateway and forwards its traffic to it directly or through multiple hops. The backhaul network capacity increases by decreasing the average number of hops. In this paper, we consider two machine learning-based clustering algorithms, namely, k-means and k-medoids, to find gateway locations that minimize the average number of hops. We compare their performance with a baseline approach at different small cell densities through extensive Monte Carlo simulations in terms of average number of hops. The results indicate that both clustering algorithms significantly outperform the baseline approach and k-medoids performs equal to or better than k-means.
Performance Analysis for SWIPT Based Cooperative Incremental Hybrid Decode-Amplify-Forward Relaying Protocol`

This work studies a simultaneous wireless information and power transfer based incremental hybrid decode-amplify-forward relaying (IHDAF) network. The relay can choose to keep silent or transmit message in the hybrid decode-and-forward (DF) or amplified-and-forward (AF) mode based on the quality of the channels among the source, relay and destination. The optimal relationship of the two important signal-to-noise ratio (SNR) thresholds at the relay and destination to decide whether cooperative mode is needed. The outage probability for the destination is derived. Numerical results compare the differences among the traditional DF, AF and proposed IHDAF protocols in terms of outage performance, and exhibit the superior performance of the proposed IHDAF protocol.
Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks

X-ray Computed Laminography (CL) is a well-known computed tomography technique to image the internal structure of flat objects. High-quality CL imaging requires, however, a large number of X-ray projections, resulting in long acquisition times. Reducing the number of acquired projections allows to speed up the acquisition process but decreases the quality of the reconstructed images. In this work, we investigate the use of Convolutional Neural Networks for processing volumes reconstructed from only four X-ray projections acquired at an inline CL scanning setup.
Identification of Lane-Change Maneuvers in Real-World Drivings With Hidden Markov Model and Dynamic Time Warping

For the introduction of new automated driving functions, the systems need to be verified extensively. A scenario-driven approach has become an accepted method for this task. But, to verify the functionality of an automated vehicle in the simulation in a certain scenario such as a lane-change, relevant characteristic of scenarios need to be identified. That, however, requires to extract these scenarios from real-world drivings accurately. For that purpose, this work proposes a novel framework based on a set of unsupervised learning methods to identify lane-changes on motorways. To represent various types of lane-changes, the maneuver is split up into primitive driving actions with a Hidden Markov Model (HMM) and Divisive Hierarchical Clustering (DHC). Based on this, lane-change maneuvers are identified using Dynamic Time Warping (DTW). The presented framework is evaluated with a real-world test drive and compared to other baseline methods. With a F1 score of 98.01% in lane-change identification, the presented approach shows promising results.
Modeling and Analysis of Wireless Power Transfer System in Metal Environment

Magnetically-coupled resonant wireless power transfer (MCR-WPT) technology is one of the most promising technologies to power embedded sensors. In many practical situations, embedded sensors are located in a metal environment. The nearby metal materials will affect the performance of the MCR-WPT system. In this paper, the equivalent circuit model of the MCR-WPT system in metal environment is established and the factors affecting the transmission efficiency is analyzed. Then, simulation analysis is performed using Maxwell software and Simplorer software. Two cases are considered in the simulation study: the coil is close to a metal plate and the coil is close to a metal shaft. Simulation results show that metal materials will change the coil inductance and interfere with the magnetic field distribution of the MCR-WPT system, which in turn affects the transmission efficiency of the system.
UAV/Drone Fast Wireless Charging FRP Frustum Port for 85-kHz 50-V 10-A Inductive Power Transfer

UAVs or drones have been employed in lots of applications, including various industrial purposes such as social infrastructure monitoring, precision agriculture, and small scale logistics. Since large industrial drones usually convey large batteries, a fast charger with large-current output is desired. Although wireless charging stations for drones have recently been reported in the literature, the charging currents in those reports are several amperes or less. This paper reports a fast wireless charging port for an 85-kHz band 50-V 10-A inductive power transfer system, which is made of fiber reinforced plastic (FRP) to be used out of doors. The unique frustum structure of the charging port, which has been proposed by authors and their colleague, is advantageous on high coupling between transmit and receive coils, light weight of receive coil, prevention of misalignment even by gusting wind. The measurement of the whole 85-kHz band inductive charging system including the FRP charging port and the receive coil mounted to the legs of commercial drone shows that received dc current is more than 10 A. This result indicates the potential of the proposed wireless charging frustum port for fast wireless charging for mid-sized and large drones.
A novel concept for validation of pre-crash perception sensor information using contact sensor

Perception sensors4 are presently used in advanced driver assistance and pre-crash warning systems. They are extensively investigated for different applications in upcoming highly automated vehicles. These sensors have the capability to provide important pre-crash information that could be used in sophisticated safety strategies. A validation sensor is required to check the plausibility of crucial information from these sensors used in integrated safety systems. This paper describes the implementation of a two resistive element contact sensor as a validation sensor, which works on a principle of change in resistance on the occurrence of contact during the crash. The sensor has the technical ability to detect the first contact and provide information about the position of impact (position of contact) and also the overlap (width of contact). The goal is to predict the required safety strategies based on the pre-crash information and activate and/or select the best-suited safety strategies based on the validation. The results from the crash test show that the impact position can be determined at 1.4 milliseconds using the proposed sensor. Finally, a novel methodology based on the simplified geometry is discussed, which can be employed to validate the relative velocity and approach angle.4context of this research work, ‘Perception sensors’ refers to RADAR, LiDAR and Camera
Mixed Massive Random Access

In this paper, the massive random access scenario that a part of users transmit both common and user-specific information and the others transmit only common information is considered, and a mixed massive random access scheme is proposed. In the proposed scheme, the common information is transmitted by unsourced random access (URA) technique, and the user-specific information by index-modulated (IM)-aided sourced random access (SRA). Specifically, the concatenated scheme consisting of inner compressed sensing codebook and outer parity-check code is employed in URA, and IM-aided non-coherent grant-free non-orthogonal multiple access scheme is employed in SRA. The URA and SRA signals are superposed at the transmitter of users who transmit both common and user-specific information. At the receiver, first, the covariance-based non-Bayesian multiuser detection is employed to estimate the indices of active codewords related with common information in each block, and both user activity and transmit data related with user-specific information. Further, the estimated indices of active codewords in different blocks are stitched based on the outer-code parity to recover the common information. Finally, simulation results show that the proposed mixed scheme can work well in the system without channel state information including large- and small-scaling fading coefficients at the receiver.