Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Features Extraction Using Autoencoder in Intrusion Detection System

Intrusion Detection System (IDS) can detect attacks by analysing the patterns of data traffic in the network. With a large amount of data that is processed in the IDS, then need to do a feature extraction to reduce the computational cost of processing raw data in IDS. Feature extraction will transform features to the lower dimension to accelerate the learning process and improve the accuracy. This research on automatic feature extraction using simple autoencoder and SVM to classify attacks on IDS. We use various functions activation and loss to see how far this feature extraction feature can improve accuracy. We use Datasets KDD Cup‘ 99 NSL-KDD and to evaluate the effectiveness of the mechanisms of detection after extraction features process. In the proposed model, the activation functions autoencoder hyperparameter ReLU activation and loss function cross-entropy gives best accuracy value than other functions.
Learning MPC: System stability and convergent identification under bounded modelling error

The dual-control problem of simultaneously regulating a plant and identifying its dynamics is addressed in a linear model predictive control (MPC) framework. We propose and study an approach where two optimal control problems are solved in series, online: the first is a standard regulating linear quadratic MPC problem, which achieves exponential nominal stability; the second is an ancillary optimization problem that promotes persistency of excitation, in order to facilitate online learning of the system dynamics. Maximization of the minimum eigenvalue of predicted information matrix increments, in a recursive least squares (RLS) scheme, is used. Our main result, achieved by employing inherent robustness from exponentially stabilizing MPC, establishes a bound on the permitted magnitude of excitation applied by the secondary optimization; in terms of the system model error. Satisfaction of this bound guarantees stability of the closed-loop uncertain system, despite the exciting perturbations.
A Review of Serverless Frameworks

Serverless computing is a new computing paradigm that promises to revolutionize the way applications are built and provisioned. In this computing kind, small pieces of software called functions are deployed in the cloud with zero administration and minimal costs for the software developer. Further, this computing kind has various applications in areas like image processing and scientific computing. Due to the above advantages, the current uptake of serverless computing is being addressed by traditional big cloud providers like Amazon, who offer serverless platforms for serverless application deployment and provisioning. However, as in the case of cloud computing, such providers attempt to lock-in their customers with the supply of complementary services which provide added-value support to serverless applications. To this end, to resolve this issue, serverless frameworks have been recently developed. Such frameworks either abstract away from serverless platform specificities, or they enable the production of a mini serverless platform on top of existing clouds. However, these frameworks differ in various features that do have an impact on the serverless application lifecycle. To this end, to assist the developers in selecting the most suitable framework, this paper attempts to review these frameworks according to a certain set of criteria that directly map to the application lifecycle. Further, based on the review results, some remaining challenges are supplied, which when confronted will make serverless frameworks highly usable and suitable for the handling of both serverless as well as mixed application kinds.
Hydrodynamic forces on a cylinder with anode

Jackets are offshore structures which have anodes distributed on them to prevent corrosion of the structure by seawater. The anodes project from the surface of the jacket, altering the drag coefficient and projected area, thereby increasing the wave load. They are distributed according to the surface area contribution of each submerged bay of the jacket and those near the surface will attract more wave loads because of their proximity to surface waves. The orientation of the anode with respect to the direction of wave will also affect the wave load generated. Anodes which project outwards from the jacket members will generate more wave load than an anode shielded by the jacket leg. In the industry, the general practice is to increase the overall wave loading due to presence of anodes by a factor, using thumb-rules and experience. This is a conservative practice and may also be an over-estimation of the loading attributed to anodes. A good understanding and estimation of the increase in wave loads due to anodes will lead to a more economical design of the structure. Existing information in present literature is limited. In this regard, detailed experimental investigations were carried on a vertical cylinder fitted with an anode, as existing information in present literature is found to be lacking. Experiments were conducted on a bare cylinder and a cylinder fitted with an anode to observe the variation in hydrodynamic coefficients and wave load for different angles of wave attack. Regular wave tests were carried out in a wave flume in the Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, India, capable of simulating intermediate and deep water conditions. From the limited set of experiments, inferences are drawn on the increase in wave load and variation of hydrodynamic coefficients of the cylinder with anode.
Performance Analysis of Ad-Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) Under Black Hole Attacks in Mobile Ad Hoc Network (MANET)

MANET consists of a collection of nodes which dynamically can be made anywhere without using a fixed network infrastructure such as a base station so that MANET becomes vulnerable to network attacks. The black hole attack is one of several types of attacks that occurs in MANET. A black hole attack is an attack that causes packets around the attacking node to disappear as of the network loses several information and can reduce its performances. Choosing the right routing protocol is one of the efforts to minimize the impact of black hole attacks in MANET. This study was done to compare which is better between AODV and DSR routing protocol during black hole attack in MANET. From the simulation results, the AODV routing protocol shows better values compared to DSR routing protocol from several QoS parameters such as throughput, delay, and packet loss; either before being hit by black hole attacks or after being hit by single black hole and collaborative black hole attacks.
Crosstalk Noise Detection and Removal in Multi-beam Sonar Images Using Convolutional Neural Network

With its long operating range and ability to be used in a turbid environment, sonar sensor is mainly used to explore underwater environment. As a result, many algorithms based on the sonar have been developed. However, in the case of a multi-beam sonar, its mechanism causes crosstalk noise around the underwater object, which degrades the accuracy of these algorithms. In this paper, we propose a method to remove crosstalk noise from a sonar image by detecting the region where the crosstalk noise occurred using a convolutional neural network and filling the detected region with adjacent pixel values. The proposed method could accurately and effectively detect and remove crosstalk noise in a given sonar image. Therefore, the accuracy of the sonar-based algorithms such as a 3-D reconstruction of underwater terrain can be improved.
Analyzing Of Different Features Using Haar Cascade Classifier

Technology always developed all the time. It makes many humans utilize the technology for doing everything. Not only used for doing something good, but there are many humans use it for doing criminal, such as robbing. On the other hand, there is something found for preventing that problem. It is a tool for detecting face. Face recognition is made for identifying a face by digital image. This paper presents a research on security of a room. The room can only be opened by identifying ad matching the face of the one that has been inputted to the data base of the system. Haar cascade classifier is used in this research. The processes of identifying of the objects were discussed in this paper. From the experimental test, it can be concluded that the proposed method is effective enough in processing the features of the objects.
Data-Driven Vessel Operational Profile Based on t-SNE and Hierarchical Clustering

Measurements from the on-board monitoring systems of marine vessels are increasingly available for data analysis and are growing in importance as the ship industry enters a phase of digitalization. Sensor data can be applied to establish a real vessel operational profile that is often required by ship owners’ and designers. Today, the vessel operational profile is created based on engineering and ship owner experience from similar vessel designs involving information regarding how the vessel has been commonly operated. Engineering knowledge applied to establish task definitions based on available signals gives an overview on what the vessel performed in reality. However, to fully utilize the data and receive a more detailed operational profile, we propose to apply data-driven techniques to find vessel operational modes. Data-driven techniques can be utilized for establishing finer resolution vessel tasks, for example, modes, and to find a more rigorous definition versus human definition. In this paper, we present a workflow to find vessel operational modes based on available signals involving machine learning techniques. We show that the data-driven techniques can be used to complement the engineering mode definitions.
Analysis of Deadline Miss Rates for Uniprocessor Fixed-Priority Scheduling

Timeliness is an important feature for many embedded systems. Although soft real-time embedded systems can tolerate and allow certain deadline misses, it is still important to quantify them to justify whether the considered systems are acceptable. In this paper, we provide a way to safely over-approximate the expected deadline miss rate for a specific sporadic real-time task under fixed-priority preemptive scheduling in uniprocessor systems. Our approach is compatible with the existing results in the literature that calculate the probability of deadline misses either based on the convolution-based approaches or analytically. We demonstrate our approach by considering randomly generated task sets with an execution behavior that simulates jobs that are subjected to soft errors incurred by hardware transient faults under a given fault rate. To empirically gather the deadline miss rates, we implemented an event-based simulator with a fault-injection module and release the scripts. With extensive simulations under different fault rates, we evaluate the efficiency and the pessimism of our approach. The evaluation results show that our approach is effective to derive an upper bound of the expected deadline miss rate and efficient with respect to the required computation time.
Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

Diabetic retinopathy (DR) is a common eye disease and a significant cause of blindness in diabetic patients. Regular screening with fundus photography and timely intervention is the most effective way to manage the disease. The large population of diabetic patients and their massive screening requirements have generated interest in a computer-aided and fully automatic diagnosis of DR. Deep neural networks, on the other hand, have brought many breakthroughs in various tasks in the recent years. To automate the diagnosis of DR and provide appropriate suggestions to DR patients, we have built a dataset of DR fundus images that have been labeled by the proper treatment method that is required. Using this dataset, we trained deep convolutional neural network models to grade the severities of DR fundus images. We were able to achieve an accuracy of 88.72% for a four-degree classification task in the experiments. We deployed our models on a cloud computing platform and provided pilot DR diagnostic services for several hospitals; in the clinical evaluation, the system achieved a consistency rate of 91.8% with ophthalmologists, demonstrating the effectiveness of our work.
Response particles: The mysteries of yes and no

Response particles like English yes and no are expressions that allow for agreeing or disagreeing reactions to polar questions and assertions. Despite their apparently simple function, there is considerable variation between and within languages when they are used with antecedents that contain a negation. The article gives an overview of recent accounts and experimental insights. It discusses two major approaches: One that considers response particles as grammatical reflexes of clauses, and one for which they are anaphoric expressions.
Motor Unit Identification From High-Density Surface Electromyograms in Repeated Dynamic Muscle Contractions

We describe the method for identification of motor unit (MU) firings from high-density surface electromyograms (hdEMG), recorded during repeated dynamic muscle contractions. A new convolutive data model for dynamic hdEMG is presented, along with the pulse-to-noise ratio (PNR) metric for assessment of MU identification accuracy and analysis of the impact of MU action potential (MUAP) changes in dynamic muscle contractions on MU identification. We tested the presented methodology on signals from biceps brachii, vastus lateralis, and rectus famoris muscles, all during different speeds of dynamic contractions. In synthetic signals with excitation levels of 10%, 30% and 50%, and MUAPs experimentally recorded from biceps brachii muscle, the presented method identified 15 ± 1, 18 ± 1, and 20 ± 1 MUs per contraction, respectively, all with average sensitivity and precision >90% and PNR >30dB. In experimental signals acquired during low force contractions of vastus lateralis and rectus femoris muscle, the method identified 9.4±1.9 and 7.8±1.4 MUs with PNR values of 35.4±3.6 and 34.1±2.7 dB. In comparison with the previously introduced Convolution Kernel Compensation method, the capability of the new method to follow dynamic MUAP changes is confirmed, also in relatively fast muscle contractions.
A Magnetohydrodynamic Generator for Marine Energy Harvesting

In this article we present an approach to the description of Magneto-hydrodynamic Marine Energy Harvesting (MHMEH) system. Preliminarly, a general discussion on the principle of operation is presented. Successively, in order to move beyond the analytical model, a 3-D MHD modeling tool and a Runge Kutta method based solver are presented and they are used to investigate an alternative MHD solutions. Some numerical analyses are given
Efficient Object-Oriented Semantic Mapping With Object Detector

Incrementally, building a 3D map in which object instances are semantically annotated has a wide range of applications, including scene understanding, human–robot interactions, and simultaneous localization and mapping extensions. Although researchers are developing efficient and accurate systems, these methods still face a critical issue: real-time processing, because the task requires a series of heavy processing components, e.g., camera pose estimation, 3D map reconstruction, and especially recognition. In this paper, we propose a novel object-oriented semantic mapping approach aiming at overcoming such issues by introducing highly accurate object-oriented semantic scene reconstruction in real time. For high efficiency, the proposed method employs a fast and scalable object detection algorithm for exploiting semantic information from the incoming frames. These outputs are integrated into geometric regions of the 3D map, which are carried by the geometric-based incremental segmentation method. The strategy of assigning class probabilities to each segmented region, not each element (e.g., surfels and voxels), notably reduces the computational cost, as well as the memory footprint. In addition to efficiency, by geometrically segmenting the 3D map first, clear boundaries between objects appear. We complementarily improve the geometric-based segmentation results beyond the geometric only to the semantic-aware representation. We validate the proposed method’s accuracy and computational efficiency through experiments in a common office scene.
The First-Priniple Simulation Study on the Specific Grain Boundary Resistivity in Copper Interconnects

In this work, we present a systematic simulation study of numerous copper (Cu) grain boundaries with the nonequilibrium Green's function (NEGF) framework based on the Density Functional Theory (DFT). In order to evaluate the effect of specific resistivity of various grain boundary profiles we developed the required methodology and we proposed an analytical equation for predicting the specific resistivity at each GB configuration. Moreover, in this work we also considered different crystal transport orientations and coincidence site lattices. Based on our simulations, we found that the specific grain boundary resistivity strongly depends on the transport orientations of the grains but not on the coincidence site lattice (CSL) density.
Two-Input Power-Activation Neural Network Weights-Direct-Determination and Structure Optimized by Particle Swarm optimization

Artificial network, with back-propagation (BP) training algorithms, has shown inherent weaknesses in determining weights and optimal network structure. In order to overcome those weaknesses, in this paper, a two-input single-output (TISO) power-activation feed-forward neural network model is investigated and constructed, based on theory of multivariate approximation and power series expansion. Then, the weights-direct-determination (WDD) method for our feed-forward network is studied and proposed. Further, in order to optimize the structure of network, particle swarm optimization (PSO) is applied to search for global optimal solution to the use of neurons in hidden layer. Then, these lead us finally to propose a weights-structure-determination (WSD) methodology by combining WDD and PSO novelly. Finally, computer numerical experiments based on various objective functions substantiate the superiority of our network in terms of approximation and denoising. Further comparison experiments with stochastic gradient-descent (SGD) and previous work substantiate the efficacy of the WSD methodology proposed.
Electronic Transaction Device Based on Contact Smart Card using Programmable System-on-Chip

The technology advancement brings new innovation on how a secure transaction will be done. In this work, we designed an electronic transaction device using a programmable system-on-chip (SoC). The system development starts by following tasks: 1) designing the hardware part of the device, 2) designing the operating system (OS), and 3) designing the main application as a GUI that uses to check the balance, top up, and make a payment. We also developed the application in the server that serves as a logger. This device used a contact smart to make a transaction.
Wind Turbine Power System for Hydrogen Production and Storage: Techno-economic Analysis

This paper is concerned with the use of renewable energy sources (RESs) such as wind energy (WE) for the cost effective hydrogen production. The effect of electrolyser nominal power on the cost of hydrogen (COH) produced by an off-grid wind-hydrogen production system (WHPS) is addressed. Furthermore, optimal configuration of WHPS leading to the minimum COH is identified. WHPS incorporating wind turbine (WT), battery bank, power converters, water electrolyser, and hydrogen tank. Battery bank is used as an energy storage system (ESS). The economic analysis is conducted based on the net present cost (NPC) method that provides an estimation of the cost of energy (COE) and hydrogen. This method uses the NPC value and total annual amount of energy and hydrogen produced by WHPS. For the wind potential and hydrogen demand profile conditions considered in this paper, requirement of high ESS capacity due to the necessity to store both extra wind power output compared to the nominal power of electrolyser and wind power output lower than the minimum power input of electrolyser, keep the COH from WHPS uncompetitive with other system design.
A Model-Driven Approach to Automate the Deployment and Management of Cloud Services

Although many provisioning tools are available for deployment and management of composite cloud services to overcome the manual efforts that are tedious and error-prone, users are often required to specify Infrastructure-as-Code (IAC) solutions via low-level scripting. IAC demands domain knowledge for provisioning the services across heterogeneous cloud platforms and incurs a steep learning curve. To address these challenges, we present a technology-and platform-agnostic self-service framework called CloudCAMP. It incorporates domain-specific modeling so that the specifications and dependencies imposed by the cloud platform and application architecture can be specified at an intuitive, higher level of abstraction without the need for domain expertise. CloudCAMP transforms the partial specifications into deployable Infrastructure-as-Code (IAC) using the Transformational-Generative paradigm and by leveraging an extensible and reusable knowledge base. The auto-generated IAC can be handled by existing tools to provision the services components automatically. We validate our approach quantitatively by showing a comparative study of savings in manual and scripting efforts versus using CloudCAMP.
The Effect of Horizontal Rotation Intervention on Brainwaves and Sleeping Quality

The study focuses on the effect of Horizontal Rotation (HR) intervention on the brainwaves and sleeping quality. HR system is a custom-engineered rotating bed, designed to relax the physical body and to synchronize the electrical activity of the brain. It is known to enhance sleeping quality which resulted in a more relax and focus condition leading to healthier lifestyle. Electroencephalogram (EEG) is used to measure the brainwave electrical activity after being picked up by the metal electrodes connected at the scalp surface of a human. The brainwave signal is recorded before and after the intervention of HR undergoing for three sessions. The result shows that most participants experienced an improvement in sleeping pattern and sleeping quality. In addition, participants achieved higher Beta and Alpha brainwaves showing better focus and relaxation.
Effects of Environmental Factors on the Stability of Silver Nanowire Transparent Electrodes

Silver nanowires (AgNWs) are currently one of the commercialized materials competing with indium-tin oxide for the application in transparent electrodes. Due to nanoscale instability effect, AgNWs degradation caused by environmental stresses is inevitably an obstacle to the wide acceptance of AgNWs. In this study, effects of irradiance of UV, UV photon energy, and heat on the degradation behaviors of pristine AgNWs was investigated under dry conditions of 15 % relative humidity. A minimal influence from substrate and encapsulant materials degradation was designed for discovering unambiguous degradation behavior of AgNWs. The synergistic aging effects between UV and temperature were demonstrated, and the results can be used for further understanding the degradation of encapsulated or module-level AgNWs transparent electrodes.