Miracle-dz/UniFAR-FTUs huggingface dataset:
Microservice Aging and Rejuvenation

Due to ‘aging’, not only the service rate of the software decreases with time but the software itself experiences occasional crash/hang failures. Software rejuvenation involves occasional stopping the executing software, ‘cleaning’ the ‘internal state’ and restarting. Moreover, container technology promotes the process of fair and efficient allocation of physical resources among virtual machines. However, the emergence of distributed cloud platform undoubtedly increase the flexibility and complexity of the system. In this paper, we propose a method of predicting microservice [1] aging by deep learning, and a rejuvenation policy by the CVA architecture. From the perspective of container, it can provide a technology of vertical expansion and contraction of container resources, improve the utilization of resources in the clustered environment, and improve the availability of microservice system.
Formation Maneuvering of Mobile Inverted Pendulum Robots with Attitude Synchronization

In this paper, a formation maneuvering control is proposed for open loop unstable mobile inverted pendulum(MIP) robots to track a predefined trajectory while maintaining desired formation with synchronized attitudes. A connected undirected graph is used for inter-agent communication. A two loop control strategy is used to enhance the kinematic control to the actuator level. Simulation results are presented to showcase the performance of this approach.
Study on the Relationship between the Characteristics of the Acoustic and Electrical Signals Generated by Partial Discharges

The ultrasonic method (AE) is more and more widely used in partial discharge (PD) pattern recognition, however, it is difficult to detect the PD level by AE method. So it is necessary to find the relationship between the acoustic waves and the PD level for different PD patterns. In this paper, an approach to detect the degree of PD by AE method based on feature extraction and correlation degree analysis has been proposed. In this work, three typical discharge models were designed, then the discharge amount (Q) from electrical signals waves, and ultrasonic amplitude (A) duration time (DT) and ultrasonic energy (E) from ultrasonic signals waves are extracted as feature parameters. In the experiment, for each PD model, 50 signals of PD are recorded to find a relation between ultrasonic signal and electrical signals. Through analysis, it can be found that there are close mathematical relationships between the discharge amount Q and three characteristic parameters (A/DT/E) of ultrasonic signals. This provides a method for further research on the quantitative monitoring of partial discharge by ultrasonic method.
A Simple, Remote, Ultra-Sonic Based Personal Emergency Response System

Remote sensing of elderlies' call for help is of great interest to caregivers, the elderly's family, and, of course, to the elderly who most often doesn't want to be bothered with a wearable device or cannot physically reach the panic button. According to the center for disease control and prevention (CDC), 2.5 million elderlies are treated in emergency rooms due to fall injuries. Personal emergency response systems (PERS), also known as panic buttons or medical alerts, are very common in the industry. Some of them require the injured person to operate, some are passive but have to be on the injured person body in the form of a necklace or a wrist band. We propose a single transmitter (TX), single receiver (RX), fully remote ultrasonic radar system for classifying a call for help signal, which only requires the person to wave one of her/his hands for 3 seconds. A simple, yet robust algorithm is employed to classify the movement extracted out of the phase of the received signal emitted from a 40 kHz continuous-wave (CW) ultrasonic transmitter. The proposed algorithm is highly immune to thermal noise and employs a novel real time, optimal Doppler estimation without the use of spectral analysis algorithms such as the Fourier transform.
Approximation Algorithms for the Min-Max Cycle Cover Problem With Neighborhoods

In this paper we study the min-max cycle cover problem with neighborhoods, which is to find a given number of $K$ cycles to collaboratively visit $n$ Points of Interest (POIs) in a 2D space such that the length of the longest cycle among the $K$ cycles is minimized. The problem arises from many applications, including employing mobile sinks to collect sensor data in wireless sensor networks (WSNs), dispatching charging vehicles to recharge sensors in rechargeable sensor networks, scheduling Unmanned Aerial Vehicles (UAVs) to monitor disaster areas, etc. For example, consider the application of employing multiple mobile sinks to collect sensor data in WSNs. If some mobile sink has a long data collection tour while the other mobile sinks have short tours, this incurs a long data collection latency of the sensors in the long tour. Existing studies assumed that one vehicle needs to move to the location of a POI to serve it. We however assume that the vehicle is able to serve the POI as long as the vehicle is within the neighborhood area of the POI. One such an example is that a mobile sink in a WSN can receive data from a sensor if it is within the transmission range of the sensor (e.g., within 50 meters). It can be seen that the ignorance of neighborhoods will incur a longer traveling length. On the other hand, most existing studies only took into account the vehicle traveling time but ignore the POI service time. Consequently, although the length of some vehicle tour is short, the total amount of time consumed by a vehicle in the tour is prohibitively long, due to many POIs in the tour. In this paper we first study the min-max cycle cover problem with neighborhoods, by incorporating both neighborhoods and POI service time into consideration. We then propose novel approximation algorithms for the problem, by exploring the combinatorial properties of the problem. We finally evaluate the proposed algorithms via experimental simulations. Experimental results show that the proposed algorithms are promising. Especially, the maximum tour times by the proposed algorithms are only about from 80% to 90% of that by existing algorithms.
An Analysis on Automatic Performance Optimization in Database Management Systems

Maintaining database management systems under optimum condition is crucial for any enterprise application as it affects the overall system performance. However, the optimization process is a non-trivial task even for experienced database administrators due to many underlying challenges. Due to the importance of database optimization, this domain has been researched for the past several years. In this analysis starting off with discussing the importance of an optimum database for the overall performance, then we focus on the reasons behind the poor performance of database management systems. Database optimization is divided under two main categories as Physical design based optimization and Configuration parameter based optimization. While both of these will be discussed, the priority is given for the Configuration parameter optimization. Configuration based optimization comes with its own set of challenges as of 1) Large configuration parameter space, 2) Interdependency of configuration parameters, and 3) Configuration change with different types of workloads. These challenges will be elaborated while carrying out an analysis on the different approaches used in preexisting work to overcome the challenges. Finally, the paper would focus on foundations of optimization techniques which can be utilized for the configuration based database optimization process.
Short-range Robotic Navigation and Exploration Tasks via Deep Q-Networks for Biomedical Applications

This research is focused on the performance of a Deep Reinforcement Learning method on an agent (mobile robot) in a simulated virtual environment (Operating Room) for medical applications. The purpose of this research is to compare suitable decisive actions taken by the agent to achieve its goal target. Executing this goal requires the implementation of a reward-penalty system for observation and analysis. The agent’s accumulated reward is based on the best-navigated decision to avoid collisions; solely generating an intelligent agent system. We reviewed previous works on the impact of Deep Reinforcement Learning algorithms on an agent in areas of navigation and exploration. Adopting a Deep Reinforcement Learning method and a physical simulator, we trained and tested the agent using existing environments and our modeled operating room, respectively. Measuring the positive reward output of the experiment with different parameters of the algorithm such as the learning rate, maximum Q-value and the average time to attain its goal position, we presented our work with plots of the experiment and compared it with a widely known traditional method. Our experimental results indicated that the agent achieved a high positive reward of 3800 in our operating room environment with a learning rate of 0.5. Our research aimed at training an agent to make intelligent decisions in achieving its goal destination without prior experience and input data. Reinforcement Learning provides a structure for robotics to function effectively; utilizing and engaging a robot to navigate and explore in any given environment.
Cyber-Physical Integrated Intrusion Detection Scheme in SCADA System of Process Manufacturing Industry

Most Intrusion Detection System(IDS) used in Supervisory Control and Data Acquisition (SCADA) systems now are focused on the cyber field but ignored the process states in physical field of the plants. Attacks aiming at the protocol traffics can be detected but attacks aiming at the processes are difficult to be detected such as the Man In The Middle (MITM) attacks and the Replay attacks. We propose a scheme in both cyber way and physical way to detect the above attacks. Validation of process states is used to detect malicious behaviors to prevent the physical components from being damaged which can be caused by MITM, Replay, and Zero-day attacks. Nonparallel hyperplane based fuzzy classifier is presented to realize the classification of branching shaped data sets which are difficult to be classified by two parallel hyperplane of Support Vector Machine(SVM) to detect attacks caused by DoS (SYN flood) and other attacks in cyber field. Modbus/TCP traffic data are used to test the algorithm and simulation process states are used to test the validation part and the performance of this hybrid scheme is excellent.
Single-Stage Three-Phase Grid-Tied Isolated SEPIC-Based Differential Inverter With Improved Control and Selective Harmonic Compensation

The simple circuit based on DC-DC converters is the main attractive feature of the differential inverter topologies. It has a single-stage and provides modularity and scalability. However, the Negative Sequence Harmonic Component (NSHC) generated at the output terminal may hinder its practical applications. This paper presents a single-stage three-phase isolated differential inverter based on three High-Frequency Link (HFL) transformer-based DC-DC SEPIC converters. The utilized SEPIC converters perform voltage step-up/ down capability with galvanic isolation, which is essential for Renewable Energy Sources (RES). It mitigates the Common-Mode Voltage (CMV) and Electro-Magnetic Interference (EMI). Moreover, this paper proposes a two-loop based d-q synchronous frame grid-current control to mitigate its NSHC. A Type-II compensator and simple NSHC detection circuit are proposed to enhance the inverter’s stability and compensate phase-delay of the utilized SEPIC converters. NSHC detection is developed using three cascaded Low Path Filters (LPFs). A 1.6kW inverter prototype was set to validate the performance of the proposed inverter and its control. The control is implemented by the MWPE3 C6713A Expert III DSP board. The proposed topology has a maximum efficiency of 89.744 at 700W output power and 86.4% at full power. The proposed control decreases the NSHC from 40.6 % to 1.614%, which shows its accuracy and precision. Furthermore, THD is reduced from 35.61% to 4.087% and satisfy the recent grid codes (<5%). The simulation results using PSIM software, power loss distribution, and a comparison study of the proposed inverter with similar topologies are also presented.
Human Motion Recognition Based on Radar Micro-Doppler Features Using Bayesian Network

Human target is a kind of widely concerned target, this paper aims at walk, run and jump three kinds of human activities to identify. In the simulation experiment, first of all, using the single frequency continuous-wave radar measures walk, run and jump three kinds of activities to obtain the corresponding echo signal. Secondly, the time-frequency analysis applied to echo signals, and the spectrograms of three kinds of action are obtained respectively. Then can extract the required micro-Doppler features from spectrograms. Finally, the human motion can be identified by using Bayesian network based on fusion micro-Doppler features.
Non-Volatile Field-Effect Transistors Enabled by Oxygen Vacancy-Related Dipoles for Memory and Synapse Applications

Exploring the high-performance non-volatile memories for realizing energy-efficient memory in complementary metal–oxide–semiconductor (CMOS) circuits, memory-in-computing, and the artificial synapse is the key to the rapid growth in data markets. One of the significant aspects is the development of non-volatile field-effect transistor (NVFET), which possesses the advantage of decoupling the “write” and “read” functions using the third terminal. In this work, building on a semiconductor channel integrated with an amorphous Al<sub>2</sub>O<sub>3</sub> gate insulator, we report a ferroelectric-like NVFET memory and analog synapse that differ from those utilizing polycrystalline-doped HfO<sub>2</sub> films. Switchable polarization (<inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>) is demonstrated in TaN/Al<sub>2</sub>O<sub>3</sub>/TaN, TaN/Al<sub>2</sub>O<sub>3</sub>/Si, and TaN/Al<sub>2</sub>O<sub>3</sub>/Ge stacks, which is attributed to the voltage-modulation of the oxygen vacancy and negative charge dipoles in gate insulator. A TaN/Al<sub>2</sub>O<sub>3</sub>/Ge capacitor achieves over 10<sup>10</sup> cycles endurance of polarization-voltage measurement. A memory window (MW) of 0.85 V is obtained in the NVFET integrated with Al<sub>2</sub>O<sub>3</sub> insulator under ±3 V at 100 ns program/erase (P/E) condition, and the P/E voltage can be reduced to ±1.6 V. A NVFET analog synapse is demonstrated to have a dynamic range above 100 [asymmetry (<inline-formula> <tex-math notation="LaTeX">$\vert \alpha _{p} - \alpha _{d} \vert $ </tex-math></inline-formula>)< 0.1] with ±2 V/100 ns potentiation/depression pulses. These results can be extended universally to other amorphous oxides and show promise for 3-D (fin-shaped) NVFETs with very small fin pitch.
Analyzing Fault Tolerance Behaviour in Memristor-based Crossbar for Neuromorphic Applications

One major operation in neuromorphic computing is vector-matrix multiplication (VMM), which is required during training and inference phases, and is expensive in terms of power consumption and latency. Hardware accelerators using emerging technologies like memristor crossbars can be used to speed up the process. Various faults in the crossbar can introduce errors in VMM computation. Existing methods to handle faults using retraining and remapping incur overheads in terms of hardware, power and delay. In this paper the impact of faults on memristor-based crossbars are explored to analyze the overall accuracy of VMM operations. It has been observed that in presence of limited number of faults, the accuracy is not significantly affected. However, as the number of faults increases, the error in computation also increases. The proposed approach works in two phases, high-level fault detection and low-level fault detection. In the first phase, the percentage of stuck-at faults in the crossbar is identified, and if it lies below a threshold the second phase is skipped. In the second phase, an efficient method to identify the exact location of the faults is used. The approach required less number of read/write operations as compared to existing works in the literature.
An Ultralow Loss N-channel RB-IGBT with P-drift Region

A novel ultralow loss N-channel reverse-blocking (RB) IGBT with the P-drift region is proposed (PD RB-IGBT). In the blocking state, the symmetrical five-layer structure (P+/N-FS/Pdrift/N-CS/P-body) of the PD RB-IGBT enables high bidirectional blocking capabilities for the device. During the turn-off transient, the parasitic SCR structure of the PD RB-IGBT introduces the regenerative feedback mechanism to accelerate the excess carrier extracting speed, thus reducing the turn-off time. In addition, the PD RB-IGBT also optimizes the turn-on performance by suppressing the gate self-charging effect. Simulation results show that, when compared with the BEFE RB-IGBT, the PD RB-IGBT delivers similar blocking capability and on-state voltage drop while reducing turn-off loss by 42%. At a typical reverse recovery dV/dt of 10 kV/μs, the turn-on loss and reverse recovery loss of the PD RB-IGBT could be also reduced by 35%. Another advantage of the PD RB-IGBT lies in its commercial CSTBT-compatible fabrication process, which makes the device a promising candidate for power applications.
Fungal Hyphae and Structural Stability of Soil

This review describes the possible mechanisms by which fungal hyphae, especially those of vesicular-arbuscular mycorrhizal (VA) fungi, bind microaggregates of soil (~0.25 mm diameter) into stable macroaggregates (>O. 25 mm), and suggests a model of the formation of a stable macroaggregate by fungal hyphae. Factors which affect the growth of VA mycorrhizal hyphae in soil and their production of extracellular polysaccharides are discussed in relation to stable macroaggregates. The interactions between clay particles and hyphae are described. The review suggests areas of future research on stabilization of aggregates by VA mycorrhizal hyphae.
Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management

Traditional dialogue policy needs to be trained independently for each dialogue task. In this work, we aim to solve a collection of independent dialogue tasks using a unified dialogue agent. The unified policy is parallelly trained using the conversation data from all of the distributed dialogue tasks. However, there are two key challenges:(1) the design of a unified dialogue model to adapt to different dialogue tasks; (2) finding a robust reinforcement learning method to keep the efficiency and the stability of the training process. Here we propose a novel structured actor-critic approach to implement structured deep reinforcement learning (DRL), which not only can learn parallelly from data of different dialogue tasks but also achieves stable and sample-efficient learning. We demonstrate the effectiveness of the proposed approach on 18 tasks of PyDial benchmark. The results show that our method is able to achieve state-of-the-art performance.
Machine Learning based DDOS Detection

One of a high relentless attack is the crucial distributed DoS attacks. The types and tools for this attacks increases day-to-day as per the technology increases. So the methodology for detection of DDoS should be advanced. For this purpose we created an automated DDoS detector using ML which can run on any commodity hardware. The results are 98.5 % accurate. We use three classification algorithms KNN, RF and NB to classify DDoS packets from normal packets using two features, delta time and packet size. This detector mostly can detect all types of DDoS such as ICMP flood, TCP flood, UDP flood etc. In the older systems they detect only some types of DDoS attacks and some systems may require a large number of features to detect DDoS. Some systems may work only with certain protocols only. But our proposed model overcome these drawbacks by detecting the DDoS of any type without a need of specific protocol that uses less amount of features.
A Hash Method for Calculating Rough Set Approximations

Rough set theory (RST) contributes a practical mathematic framework for knowledge discovery and uncertainty reasoning. Hash tables, often employed in data mining, are one of important data models in data structure and can fast access an object according to its key. The computation of rough set approximations plays a crucial role as a basic step of knowledge discovery or relevant tasks under RST. The calculation process of approximations, however, is very time-consuming. To address this issue, through integrating generic information system with highly efficient hash table, the concept of hash information systems (HIS) is firstly introduced and some of their properties are explored. Then, A hash-information-system-based method is proposed for the calculation of rough-set approximations. Finally, This study offers an example for illustrating the validity of the presented approach.
Predicting Breast Cancer using effective Classification with Decision Tree and K Means Clustering technique

Breast Cancer has been an emerging form of threat to life of women. It leads to death in many cases. It is important to work towards the preventive and precautionary measures for the same. Thus, as a precautionary measure to predict this breast cancer a model has been created. The proposed model thus uses two algorithms namely K-means and Decision Trees to predict Breast Cancer Cells in the human body by using large datasets available.
Characterizing Radar Network Traffic: a first step towards spoofing attack detection

An Air Traffic Management (ATM) Surveillance System is used to provide services to perform Air Traffic Control (ATC) (e.g., horizontal separation between aircraft). This sytem carries messages containing aircraft's position from a collection of radars of an Air Navigation Service Provider (ANSP) through its network. Then Radar traffic is one of the most important sources of information for this system. The format of the radar messages is defined by a specific application-layer protocol entitled ASTERIX. The evolution of the security policy and technologies used makes existing radar systems, once considered safe, now potentially open to attack. Both safety and security of ATM system could be impacted by any kind of attack into the network traffic, who could maliciously modified information about aicrafts, in particular thanks to Spoofing Attack. To counter this risk, there is need to detect intrusion and then to have anomaly detection modules for this safety-critical network traffic, that can be deployed in a security appliance. In order to design this module, we did a statistical analysis to have an overview of the traffic to better know what we need to protect. Specifically, we studied radar network traffic in order to extract high level statistic characteristics of normal radar traffic. This allowed us to identify a trend in the evolution of this traffic. We were then able to inject a spoofing attack (when a malicious party impersonates another device or network user for the purpose of altering the data) into this traffic to modify the nominal traffic. Thereafter, we were able to detect this attack using our method, which consists of the use of a machine learning detection method, using a Long-Short Term Memory (LSTM) mechanism. This is the subject of our paper, an overview of radar traffic and a method to detect spoofing attack in this traffic. This would help to develop an ATM IDS especially as this type of attack could remain invisible for air traffic controller.