Miracle-dz/UniFAR-FTUs huggingface dataset:
Nodule-Plus R-CNN and Deep Self-Paced Active Learning for 3D Instance Segmentation of Pulmonary Nodules

Accurate and automatic segmentation of pulmonary nodules in 3D thoracic Computed Tomography (CT) images is of great significance for Computer-Aided medical Diagnosis (CAD) of lung cancer. Currently, this important task remains challenging for lack of the voxel-level annotation and training strategies that balance target/background voxels in thoracic CT images. In this paper, a new region-based network, called Nodule-plus Region-based CNN, is proposed to detect pulmonary nodules in 3D thoracic CT images effectively while synchronously generating an instance segmentation mask for every detected instance. Our new network is constructed with a stack of convolutional blocks in which lateral connections are used to alleviate the difficulty of vanishing gradients. In addition, in order to reduce annotation workload and make best use of unannotated samples, we proposed a new Deep Self-paced Active Learning (DSAL) strategy by combining Active Learning (AL) and Self-Paced Learning (SPL) strategies. For the purpose of evaluating the performance of our proposed Nodule-plus R-CNN, we conduct a series of experiments on the public LIDC-IDRI dataset, and our model achieves 0.66 Dice and 0.96 TP Dice, which are state-of-the-art best results of pulmonary nodule segmentation. When the amount of available annotated samples is limited, our model trained with the DSAL strategy performs much better than that trained with the standard strategy.
Development of an 8-Bit AVR Microcontroller-based Network Measurement Probe

Current network measurement platforms are limited by either the consistency of intervals of data acquisition from software-based systems, or the distribution and logistics of field support for hardware-based systems. Embedded system-based platforms offer a solution for this. This paper presents an 8-bit AVR microcontroller-based platform that is able to perform Ping, Traceroute and Iperf for network measurement. Tests show that the probe is comparable with a Unix computer performing Ping and Traceroute. In our tests, the percentage difference average-wise on the RTTs measured by the probe from those measured by a Unix computer were 3.29%, 6.14%, and 1.45% for Ping on Facebook, Google, and Wikipedia respectively. It was also able to obtain the same network path obtained by a Unix computer through Traceroute. While capable of running UDP bandwidth measurements, it is restricted to 3 Mbps due to hardware limitations.
A Dual-Band CMOS Low-Noise Amplifier using Memristor-Based Tunable Inductors

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-µm RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.
Evaluation of Real-Time Ethernet with Time Synchronization and Time-Aware Shaper Using OMNeT++

Packet-switch Ethernet has been considered as a future communication standard for automotive and industrial applications because of its compatibility, scalability and high speed. However, many real-time systems in these domains need predictability which traditional packet-switch Ethernet does not support. Time-Sensitive Networking (TSN) is an upcoming set of standards aiming to provide deterministic services such as guaranteed packet transport with bounded low latency, low jitter, and low packet loss. In this paper, we present an OMNeT++ simulation model to simulate TSN time-based features. We provide a realistic clock-drift model and apply it to a distributed embedded system. Moreover, we utilize time synchronization and time-aware shaper to implement a high-accuracy scheduling. The simulation results show that the aforementioned protocols provide predictability for the system.
Improving the Generalizability of Robot Assembly Tasks Learned from Demonstration via CNN-based Segmentation

Kinesthetic teaching and Dynamic Movement Primitives (DMPs) enable fast and adaptable learning of robot tasks based on a human demonstration. A task encoded as a dynamic movement primitive can be reused with a different goal position, albeit with a resulting distortion in the approach trajectory with regards to the original task. While this is sufficient for some robotic applications, the accuracy requirements for assembly tasks in an industrial context, where tolerances are tight and workpieces are small, is much higher. In such a context it is also preferable to keep the number of demonstrations and of external sensors low. Our approach relies on a single demonstration and a single force-torque sensor at the robot tool. We make use of a Convolutional Neural Network (CNN) trained on the force-torque sensor data to segment the task into several movement primitives for the different phases: pickup - approach - insertion - retraction, allowing us to achieve better positional accuracy when generalizing the task primitives to new targets. To the best of our awareness, we are the first to utilize a CNN as a segmentation tool to improve the generalization performance of DMPs.
Generating Music Algorithm with Deep Convolutional Generative Adversarial Networks

With the extensive development of deep learning, automatic composition has become a vanguard subject exercising the minds of scientists in the area of computer music. This paper proposes an advanced arithmetic for generating music using Generative Adversarial Networks (GAN). The music is divided into tracks and the note segment of tracks is expressed as a piano-roll, through trained a gan model which generator and discriminator continuous zero-sum game to generate a wonderful music integrallty. In most cases, Although GAN excel in image generation, the model adopts a full-channel lateral deep convolutional network structure according to the music data characteristics in this paper, generate music more in line with human hearing and aesthetics.
System Requirements and Performance Predictions for L/S/C Band Geosynchronous SAR Interferometry

Geosynchronous synthetic aperture radar (GEO SAR) can provide daily repeat-pass and high coherent image pairs for interferometry. On the area mapped with thousands of kilometers width, GEO InSAR has some overwhelming advantages in disaster management, tectonic mapping and modeling, vegetation and soil moisture mapping. This paper provides an overview of system requirements for three potential bands (L/S/C) of GEO SAR interferometry applications, including orbit determination accuracy, orbit control accuracy, radar ranging accuracy and noise-equivalent backscattering coefficient. Under some appropriate GEO InSAR configurations and parameters, performance predictions of deformation measurement accuracy are given. Results show that lower band GEO InSAR has some lower requirements on satellite orbit measurement, control and maintain accuracy and a higher requirement on radar noise sensitivity instead. It is expected that the deformation measurement accuracy of 2~6 millimeters in local area and 1~3 centimeters in wide area will be available for the three potential bands of GEO InSAR.
A Low Correlation and Mutual Coupling MIMO Antenna

A new two-element multiple-input multiple-output (MIMO) antenna with low correlation and high port isolation is presented. First, two hybrid electromagnetic band gap (EBG) structures with the ability to support and stop surface wave propagation, respectively, are utilized simultaneously for achieving an extremely low envelope correlation coefficient (ECC). Then, based on studying of the ground current of the MIMO antenna with EBG structure, a new defected ground structure (DGS) is used to reduce the mutual coupling by controlling the polarization of the coupling field. The two antenna elements have an edge to edge spacing of <inline-formula> <tex-math notation="LaTeX">$0.13\lambda $ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is the free space wavelength at the resonant frequency. Finally, the rectangular slots are introduced to the patch to improve the cross polarization. Experimental results show that the ECC of the MIMO antenna is lower than 0.002. Furthermore, the maximum mutual coupling (MC) reduction of 22dB can be achieved within the working bandwidth. All of above make the MIMO antenna a potential candidate for mobile terminal-based MIMO antenna systems.
Embedding Model Design for Producing Book Recommendation

Internet services often recommend contents to users in order to maintain the interaction. Recommendation system plays a major role to formulate and produce a series of recommendation based on users’ behavior. Surprisingly, user-generated scoring or known as ratings are the main raw materials to learn the pattern of favorable contents of each user. As a part of collaborative filtering strategy, rating is considerable to be included in formulating recommended contents. In this research, the basic formulation to recommend books is discovered. The recommendation system has been tested on one random-picked user behavior and successfully has generated five recommended books by analyzing prior activities. The embedding model produced the recommended books with 59% of accuracy. This research is done to provide an insightful experience for developing content recommendation system in Binus University’s corporate learning system.
Limit of Achievable Information Rates in EDFA and Raman Amplified Transmission Systems Using Nonlinearity Compensation

Optical networks form an integral part of the world-wide communication infrastructure and nowadays over 95% of data traffic is carried over fibres. Erbium-doped fibre amplifiers (EDFAs) and Raman amplifiers have made it possible to extend the usable fibre bandwidth to increase the achievable capacity of optical communications in past decades to meet the ever-growing information rate demands. However, these amplification technologies are now viewed as limiting the accessible optical spectrum to ~5 THz and ~10 – 15 THz, respectively. Currently, the presence of Kerr effects in fibre channels has been largely regarded as the major bottleneck for enhancing achievable information rates of optical communications. Signal performance degradations due to fibre nonlinearities are more severe in the systems utilising larger transmission bandwidths, closer channel spacing and higher-order modulation formats. In this work, we will study the impact and compensation of Kerr effects to analyse the performance of long-haul optical fibre communication systems using EDFAs and Raman amplifiers. Achievable information rates of such ultra-wideband optical transmission systems will be discussed considering nonlinearity compensation and probabilistic shaping techniques.
The Impact of Political (in)Stability on the Innovation Performance: the Case of Bulgaria

The innovations are something that have the potential to support the growth of the enterprises and it is suggested that every entrepreneur would invest in innovations. Nevertheless, not everyone does so because there are many factors that influence the innovation performance. One of these factors is the political environment. The latter can affect the innovation performance in various ways. Political instability can have many forms and one of them is frequent change of governments. Political environment in Bulgaria in the period 2007-2017 is marked exactly by frequent change of governments. The authors of the current study aims at identifying the relationship between this form of political instability and the innovation performance of the business enterprises.
Prospective: A Data-Driven Technique to Predict Web Service Response Time Percentiles

Delivering fast response times for user transactions is a critical requirement for Web services. Often, a Web service has Service Level Agreements (SLA) with its users that quantify how quickly the service has to respond to a user transaction. Typically, SLAs stipulate requirements for Web service response time percentiles, e.g., a specified target for the ${95^{th}}$ percentile of response time. Violating SLAs can have adverse consequences for a Web service operator. Consequently, operators require systematic techniques to predict Web service response time percentiles. Existing prediction techniques are very time consuming since they often involve manual construction of queuing or machine learning models. To address this problem, we propose Prospective, a data-driven approach for predicting Web service response time percentiles. Given a specification for workload expected at the Web service over a planning horizon, Prospective uses historical data to offer predictions for response time percentiles of interest. At the core of Prospective is a lightweight simulator that uses collaborative filtering to estimate response time behaviour of the service based on behaviour observed historically. Results show that Prospective significantly outperforms other baseline techniques for a wide variety of workloads. In particular, the technique provides accurate estimates even for workload scenarios not directly observed in the historical data. We also show that Prospective can provide a Web service operator with accurate estimates of the types and numbers of Web service instances needed to avoid SLA violations.
Voltage Stability Analysis for AC/DC System With VSC-HVDC

AC/DC hybrid transmission has become one of the development directions for interconnecting large-area power grids. It is particularly important to perform static voltage stability analysis on AC/DC systems. In this paper, the continuous power flow method and the alternating iterative method with improved parameters are combined to calculate the load margin index to draw the PV curve. A polynomial interpolation prediction method and a strategy for automatically selecting the step length are proposed. This method has the advantages of accurate calculation, few iterations, and easy understanding and analysis. Considering the power constraints of the generator and the influence of different control methods on the voltage stability margin, it can realize the analysis of the voltage stability of the AC/DC system.Compared with the traditional algorithm, the modified IEEE30 node system indicates that this method has good applicability and robustness.
Green Job Shop Scheduling Problem with Machine at Different Speeds using a multi-objective grey wolf optimization algorithm*

Job shop scheduling problem (JSP) has been extensively researched by many scholars because of its importance in the field of industrial manufacturing. optimization criteria, such as makespan, tardiness and earliness, are usually adopted in most conventional scheduling problems. Green criteria have received wide concern with the advent of sustainable manufacturing. However, limited attention has been paid to green criteria in JSP. Therefore, this paper addresses a green JSP with machine at different speeds (JSMS). First, a new green scheduling mathematical model is formulated for this JSMS with objectives of minimizing makespan and total energy consumption. Then, a multi-objective grey wolf optimizer (MOGWO) is presented to deal with the JSMS. Finally, to prove the effectiveness of the proposed MOGWO, we compare it with other multi-objective genetic algorithm (e.g., MOGA) on some instances. The experimental results suggest that the proposed MOGWO outperforms its competitor on this JSMS.
Gamification for Employee Training Platform in Banking Industries

As one of the most recognized banks in Indonesia, Bank Mandiri has a mission to develop a perfect learning system that enables all of its staffs to face competition challenges and adapt to a sophisticated environment. The article takes the case study of gamification design in the IT department, where the staffs are expected to deliver advanced IT solutions. The article examines the gamification design with objectives to provide a reference for corporate training in the banking industry. The study uses qualitative methods in observation, and analysis the content of gamification implemented to the online learning platform. The outcome of the article is expected to introduce an attractive learning platform that can be accessed by all staffs in any time and any place with the presence of a virtual tutor, as an alternative to traditional learning.
Improved dynamic object detection within evidential grids framework

The deployment of autonomous robots/vehicles is increasing in several domains. To perform tasks properly, a robot must have a good perception about its environment while detecting dynamic obstacles. Recently, evidential grids have attracted more interest for environment perception since they permit more effective uncertainty handling. The latest studies on evidential grids relied on the use of thresholds for information management e.g. the use of a threshold, for the conflict characterized by the mass of empty set, in order to detect dynamic objects. Nevertheless, the mass of empty set alone is not consistent in some cases. Also, the thresholds used were chosen either arbitrary or tuned manually without any computational method. In this paper, first the conflict is composed of two parameters instead of mass of empty set alone, and dynamic objects detection is performed using a threshold on the evolution of this conflict pair. Secondly, the paper introduces a general threshold along with a mathematical demonstration to compute it which can be used in different dynamic object detection cases. A real-time experiment is performed using the RB1-BASE robot equipped with a RGB-D camera and a laser scanner.
A Labor-Efficient GAN-based Model Generation Scheme for Deep-Learning Defect Inspection among Dense Beans in Coffee Industry

Coffee beans are one of most valuable agricultural products in the world, and defective bean removal plays a critical role to produce high-quality coffee products. In this work, we propose a novel labor-efficient deep learning-based model generation scheme, aiming at providing an effective model with less human labeling effort. The key idea is to iteratively generate new training images containing defective beans in various locations by using a generative-adversarial network framework, and these images incur low successful detection rate so that they are useful for improving model quality. Our proposed scheme brings two main impacts to the intelligent agriculture. First, our proposed scheme is the first work to reduce human labeling effort among solutions of vision-based defective bean removal. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time. The above two advantages increase the degree of automation to the coffee industry. We implement the prototype of the proposed scheme for conducting integrated tests. Testin. results of a case study reveal that the proposed scheme ca] efficiently and effectively generating models for identifyin defect beans.Our implementation of the proposed scheme is available a https://github.com/Louis8582/LEGAN.
Long-term Tracking Based on Kernelized Correlation Filtering

Aiming at the problems of kernelized correlation filter tracking algorithm such as scale change, severe occlusion and tracking failure under similar target tracking, a long-term target tracking algorithm based on KCF is proposed. The algorithm introduces color information into the tracker, and the scale information makes the algorithm adapt to the more complex situation when tracking. It is worth noting that the trackers train two models, one to represent coordinates and the other to characterize confidence. In addition, training online support vector machine classifiers is also crucial. This article uses a multi-expert tracking strategy to enable longterm tracking to be re-detected in the event of a failed tracking condition. The validity of the proposed algorithm is verified by the OTB-2013 [1] evaluation sequence. Compared with several other classical algorithms, this algorithm has been significantly improved. In the target occurrence scale, occlusion, deformation and other interference cases with strong robustness.
Automatic Segmentation of the Left Ventricle From Cardiac MRI Using Deep Learning and Double Snake Model

The left ventricle segmentation (LVS) is of great important for the evaluation of cardiac function. This study aimed to establish new segmentation algorithms that can enhance the accuracy and robustness of automatic LVS on magnetic resonance images. The datasets involved 45 subjects, including 12 heart failure patients with ischemia, 12 heart failure patients without ischemia, 12 hypertrophy patients and 9 normal individuals. The experiments consisted of three important steps. At first, deep learning was employed for the coarse LVS on myocardial images. Next, a double snake model was applied to assess the endo- and epi-cardial boundaries. Finally, the optimal epicardial boundary was obtained by adopting radial region growing method. Additionally, the performance of the developed LVS method was evaluated by the previously established software. Furthermore, the developed LVS method was validated by applying the datasets of 45 subjects. The results showed that the good contours, overlapping dice metric and average perpendicular distance of both epi- and endo-cardial contours were approximately 97%, 0.97 and 1.8 mm respectively. The regression coefficient and coefficient of determination between the proposed method and clinical experts were 0.96 and 1.039, respectively for ejection fraction, while 0.92 and 0.994 for left ventricle mass. These findings reveal that the developed method can enhance the accuracy and robustness of LVS. This novel LVS approach exhibits outstanding performance and possesses promising potential to increase the reliability of computer-aided imaging detection system for cardiovascular disease.
Flow Splitter: A Deep Reinforcement Learning-Based Flow Scheduler for Hybrid Optical-Electrical Data Center Network

Hybrid optical-electrical switching based data center network (HOE-DCN) has been regarded as a promising architecture for the next generation data center network (DCN). To achieve traffic optimization, the main superiority of HOE-DCN is its capability to offload the long-lived ‘elephant’ flows by optical interconnections, and transmit the latency-sensitive ‘mice’ flows by electrical switching. However, most previous works identify and schedule the flows according to a fixed flow size threshold, which can hardly handle the highly dynamic network conditions in recent DCN. In order to achieve more effective flow scheduling in HOE-DCN, in this paper, we propose Flow Splitter (FS), a deep reinforcement learning (DRL) based flow scheduler which enables HOE-DCN to make instant flow scheduling according to the runtime network conditions. To train a more effective DRL agent, we upgrade the DRL method named Deep Deterministic Policy Gradient (DDPG) and propose DDPG-FS, which is capable of learning a high-performance flow scheduling policy in the complex network environment. Through simulation, we prove that our FS can significantly improve the performance of HOE-DCN. Compared with the recent flow scheduling approaches for HOE-DCN, our FS can obviously reduce the average flow complete time of arrival flows, especially the latency-sensitive mice flows.