Miracle-dz/UniFAR-FTUs huggingface dataset:
SBI Model for the Detection of Advanced Persistent Threat Based on Strange Behavior of Using Credential Dumping Technique

This study investigated the shift from the manual approach of processing data to the digitized method making organizational data prone to attack by cybercriminals. The latest threat Advanced Persistent Threats (APT) was originated by the United States Air Force in 2006 by Colonel Greg Rattray. APT is constantly ravaging industries and governments, which causes severe damages including data loss, espionage, sabotage, leak, or forceful pay of ransom money to the attackers. This study introduces a new model built on Adversarial Tactics Techniques and Common Knowledge (ATT&CK) matrix for detecting APT attack. This is to identify the APT on the first potential victim when the attackers use credential dumping technique. Strange Behavior Inspection Model incorporating several models investigates and monitors APT behavioral features in the CPU, RAM, windows registry, and file systems proposed to detect APT Attack at the first potential victim machine. The Strange Behavior Inspection (SBI) Model proposed in this paper is designed to detect the attack before being developed to more advanced phases. The results of this study are presented at four levels:1- random access memory, 2-central processing unit, 3- windows registry, and 4- file systems. This study proposes a unique model as evidence to detect APT attacks before any other techniques are used. The proposed model reduces the detection time from nine-months to 2.7 minutes.
A Review of IoT Systems Engineering: Application to the Smart traffic lights system

We are gradually witnessing a transformation of our technological environment; more and more physical objects are connected to the Internet. The so called “Internet of Things” (IoT) is a revolution that has been going on for more than 10 years. However, the rapid evolution of this field and the special features of its systems make their engineering very delicate and the application of conventional development methodologies unsuitable. Indeed, there exists a limited number of methodologies that can be used to effectively develop IoT systems and clearly represent their specific concerns and characteristics. The aim of our work is to establish a state of the art on the IoT technology to identify the different characteristics of IoT systems and then analyze the existing methodologies used to design them and see to which extent those methodologies respect the selected criteria. On the basis of this synthesis, the IDeA methodology meets well the established criteria and is adopted for the design and development of IoT systems. Besides, we contribute to the extension of its domain model so that it can support the security aspects and mechanisms to make safe IoT systems. We illustrate the methodology adopted through the design and implementation of a smart traffic lights system.
Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people’s consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.
Automotive Big Data Pipeline: Disaggregated Hyper-Converged Infrastructure vs Hyper-Converged Infrastructure

Big data disrupts everything it touches, but automotive is probably one of the top industries that enjoy and leverage the benefits. The Automotive Big Data Pipeline (ABDP) is a big data pipeline base on the automotive use case and is required to scale up agile and high performance in real-time or in batch. Nonetheless, there’re many alternative infrastructure designs but lack of knowledge, which fits the best for the automotive domain. It leads this paper into a question: What kinds of infrastructure design could provide better performance for the ABDP? In this paper, we introduce two well-known infrastructure designs called Hyper-Converged infrastructure (HCI) and Disaggregated Hyper-Converged infrastructure (DHCI). HCI combines standard data center hardware using locally attached storage resources to create fast, common building blocks. However, does single standard hardware fit all the requirements? DHCI scale independently from compute and storage provides an option. It provides a more cost-efficient and flexible solution; however, there is no comparison from the performance point of view. Therefore, to address it, our objective is to conduct an empirical performance comparison to see which one performs better.The experiment result shows that DHCI performs almost the same as HCI on CPU utilization, memory, and network consumption. However, regarding storage and running time metrics, DHCI performs slightly higher storage throughput, IOPs, and less running time than HCI.
Impact of NCFET on Neural Network Accelerators

This is the first work to investigate the impact that Negative Capacitance Field-Effect Transistor (NCFET) brings on the efficiency and accuracy of future Neural Networks (NN). NCFET is at the forefront of emerging technologies, especially after it has become compatible with the existing fabrication process of CMOS. Neural Network inference accelerators are becoming ubiquitous in modern SoCs and there is an ever-increasing demand for tighter and tighter throughput constraints and lower energy consumption. To explore the benefits that NCFET brings to NN inference regarding frequency, energy, and accuracy, we investigate different configurations of the multiply-add (MADD) circuit, which is the core computational unit in any NN accelerator. We demonstrate that, compared to the baseline 7nm FinFET technology, its negative capacitance counterpart reduces the energy by 55%, without any frequency reduction. In addition, it enables leveraging higher computational precision, which results to a considerable improvement in the inference accuracy. Importantly, the achieved accuracy improvement comes also together with a significant energy reduction and without any loss in frequency.
Research on fidelity technology evaluation index of virtual simulation experiment

In recent years, universities have paid more and more attention to virtual simulation experiments. Tens of thousands of virtual simulation teaching products have been developed and widely used in the market. However, there is a lack of effective evaluation indicators for virtual experimental teaching products. How to use the fast-developing virtual simulation technology to improve the fidelity of virtual experimental images is an urgent problem that needs to be solved in virtual experimental teaching. This paper proposes specific indicators for the fidelity of virtual simulation experiments, including first-level fidelity evaluation indicators such as experimental environment, experimental objects, experimental operations, and experimental phenomena. After corresponding first-level indicators, second-level evaluation indicators are proposed. By selecting 474 national virtual simulation experiment teaching declaration projects issued by the Ministry of Education in 2019 for testing, there are no controversial views, and a series of evaluation data are obtained and analyzed, and then the fidelity technical evaluation index of the virtual simulation experiment teaching project is proposed. The plan can be used as a reference for relevant researchers when evaluating virtual simulation experiment teaching projects.
How to Control Coronavirus Conspiracy Theories in Twitter? A Systems Thinking and Social Networks Modeling Approach

Complexity and dynamicity of the social networks are categorized as NP-hard problems to solve and analyze. These variables on social networks such as actions and interrelationships between the network’s users, different behaviors, users’ feedbacks and the networks’ dynamics make them intractable. Systems thinking and modeling methods orient the relationships between all parts in online social networks. Complexity theories, system dynamics, and game theoretic approaches implemented by system thinkers help investigate the system’s local parties’ relationships. These methods also present a useful tool to interpret the social networks’ complex interactions, dynamic activities in online networks and communities between users and their online dynamic interactions. In this paper, systems thinking concepts and organizational cybernetics are employed to analyze the armed protest demonstration against COVID-19 lockdown at Michigan capitol on May 12th through May 15th on Twitter. Utilizing these methods, we also present a systematic analysis to control, analyze, and comprehend the actions, tweets, and retweets exchanged between users’ supporting and opposing the campaign in a complex and dynamic environment.
A Grid Interactive Sensorless Synchronous Reluctance Motor Drive for Solar Powered Water Pump for Agriculture and Residential Applications

This paper presents a grid interactive photovoltaic (PV) water pumping system (PVWPS) driven by a synchronous reluctance (SyR) motor. This system is a multipurpose solar PV grid interactive system which assists the utility power grid while maintaining the rated water discharge continuity. In this work, the PV array power supplied to SyR motor through a voltage source inverter (VSI). A front-end converter (FEC) is used at the grid side for facilitating the bidirectional power flow between PV array and the grid. This PVWPS provides the uninterrupted water supply throughout the day for the irrigation as well as residential application, irrespective of change in solar insolation. This control provides unity power factor (UPF) operation and inherent power quality improvement at the grid. This PVWPS rotates at rated speed with rated discharge throughout the day irrespective of variation in climatic condition and injects the excess power to the utility grid. Moreover, during fault in any of the source, the performance of the water pump remains unaltered. The suitability of the PVWPS is validated on a help of laboratory prototype under various climatic conditions.
An Efficient Energy Utilization Approach in SWIPT Dual-Relay Cooperative System

In this paper, dual-relay simultaneous wireless information and power transfer(SWIPT) cooperative system is considered. Power splitting(PS) is adopted by the off-the-grid relay for energy harvesting(EH) and information processing in the first phase. Next, the harvested energy at the relay is used to forward the signals of the sorce node in the second phase. To enhance the reliability of the system and utilize the energy harvested more efficiently, an energy aware retransmission (EAR) scheme is adopted in the first phase to guarantee that a certain minimum amount of energy is harvested by the relay before launching in the second phase. Then, we analyze the ergodic capacity of the system. Simulation results demonstrate that the EAR scheme is superior than the traditional single transmission scheme and improves the reliability of SWIPT-based cooperative systems and makes energy utilization more efficient.
Scannable Leaky-Wave Antenna Based on Ferrite-Blade Waveguide Operated Below the Cutoff Frequency

This article proposes a scannable leaky-wave antenna (LWA). The antenna consists of a ferrite-blade waveguide with a longitudinal slot on the upper wall, which has a tunable composite right-/left-handed (CRLH) property. The left-handed (LH) behavior of this structure is based on the negative effective permeability of the ferrite material and the negative effective permittivity of the waveguide excited below its cutoff frequency. First, based on the modal analysis and calculated dispersion relation, the unbalanced CRLH response of the ferrite-blade waveguide is investigated. Second, an LWA is presented by using a longitudinal slot on the upper wall of the proposed ferrite-blade waveguide, where the tunable balance CRLH response is achieved and analyzed by the transverse resonance method (TRM). The proposed LWA can provide backward, broadside, and forward radiations by a change in the operation frequency or the magnetic bias, independently. The given results show a +59° to −57° sweep in the radiation beam when the magnetic bias of the ferrite changed from 1400 to 1600 Oe at the sampling frequency of 6.4 GHz. Moreover, the results show that the frequency tuning range is more than 600 MHz (6.2–6.8 GHz). The peak gain and efficiency of the proposed antenna are 7.1 dBi and 78.5% at 6.6 GHz, respectively.
Adaptive Rate Control for Live streaming using SRT protocol

Media delivery represents one of the main challenges for future networks which aim to converge Broadcast and Broadband video traffic into a common telecommunication network architecture. Nowadays, contents streamed over Internet are delivered in two different manners depending on the application: Video on Demand and Live Streaming. For the former, HTTP-based streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH), are widely employed for unicast and broadcast communications. It also enables Adaptive Rate Control on the client device allowing players to select a representation and bitrate matching the capabilities of the network at any moment. For the latter, MPEGDASH does not provide low latency for Live streaming when compared to a Broadcast service. Secure Reliable Transport (SRT) is proposed by SRT Alliance to overcome such limitations of unicast and broadcast communications. Nevertheless, it misses the adaptation of the content to the available network resources. In this paper, we show an implementation of Adaptive Rate Control for SRT protocol which exploits periodical network reports in order to adapt the content encoding process. The evaluation includes a real deployment of the solution and a comparison with a legacy SRT stream.
Application of Reinforcement Learning to a Mining System

Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.
A Hybrid Nearest Level Combined With PWM Control Strategy: Analysis and Implementation on Cascaded H-Bridge Multilevel Inverter and its Fault Tolerant Topology

This paper presents a hybrid control strategy which combines the nearest level control technique with PWM control technique on a cascaded H-Bridge Multilevel Inverters (MLI). MLIs have become very popular due to its several advantages in the application areas like industrial drives and grid-connected renewable energy generation systems. With the reduced number of switches, driver circuits, conduction losses, switching losses, voltage stresses, size, and cost of the system, MLI has outperformed its two-level counterpart in terms of power quality issues. Reduction in the Harmonics and consequently the Total Harmonic Distortion (THD) in the output voltage has added to the benefits of MLI. Topological advancements in MLI require a satisfactory output voltage control strategy. In this work, a hybrid control strategy for a smooth and wider range of control of output voltage is proposed. The hybridized scheme enhances the control range of the voltage considerably. Moreover, the THD is also reduced compared to the conventional sinusoidal PWM scheme. A thorough analysis of the scheme has also been presented in the paper. SIMULINK/ MATLAB environment is used for testing the simulation model of the proposed control scheme. This hybridized controlling technique is also simulated for thermal modelling on PLECS software and power loss analysis is performed. The mathematical and simulation analyses are validated on an experimental prototype using a TMS320F28335 DSP controller card. The hybrid scheme has also been tested for a fault tolerant model of the cascaded H-bridge inverter. The performance of the hybrid scheme under different fault condition has also been shown to work satisfactorily.
Automating the Evaluation of Education Apps With App Store Data

With the vast number of apps and the complexity of their features, it is becoming challenging for teachers to select a suitable learning app for their courses. Several evaluation frameworks have been proposed in the literature to assist teachers with this selection. The iPAC framework is a well-established mobile learning framework highlighting the learners’ experience of personalization, authenticity, and collaboration (iPAC). In this article, we introduce an approach to automate the identification and comparison of iPAC relevant apps. We experiment with natural language processing and machine learning techniques, using data from the app description and app reviews publicly available in app stores. We further empirically validate the keyword base of the iPAC framework based on the app users’ language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results (<inline-formula><tex-math notation="LaTeX">$F$</tex-math></inline-formula>1 score <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>72%) and evaluates them similarly as domain experts (Spearman's rank correlation 0.54). We discuss how our findings can be useful for teachers, students, and app vendors.
A detailed spectroscopic study of an Italian fresco

In the present work we characterized samples of plasters and pictorial layers taken from a fresco in the Acireale Cathedral. The fresco represents the Coronation of Saint Venera, patron saint of this Ionian town. By performing a detailed spectroscopic analysis of the plaster preparation layer by Fourier-transform infrared (FTIR) spectroscopy and x-ray diffraction (XRD), and of the painting layer by FTIR and confocal Raman microspectroscopy, scanning electron microscopy+energy dispersive x-ray spectroscopy, and XRD, we were able to identify the pigments and the binders present. In particular, Raman investigation was crucial to the characterization of the pigments thanks to the high resolution of the confocal apparatus used. It is worth stressing that the simultaneous use of complementary techniques was able to provide more complete information for the conservation of the artifact we studied.
Displacement Sensor Integrated Into a Remote Center Compliance Device for a Robotic Assembly

Compliance devices facilitate robotic assemblies by preventing excessive contact force on automated devices. However, the unavailability of displacement information regarding compliance devices makes it difficult to protect robots and objects during robotic assembly. It also limits repeatability and efficiency, especially regarding peg-in-hole and similar operations. We propose a six-axis displacement sensor installed on a remote center compliance device to detect the three translation and three rotation components generated by external forces and torques applied to the device, respectively. Triangular prism-shaped structures are used with capacitive sensing technology to measure the vertical and horizontal displacements in mm scale generated by the external normal and shear forces applied to the sensor. We designed and fabricated the sensor that could be integrated into a remote center compliance device, demonstrating it has a simple structure and is easy to install. To obtain six-axis force/torque information and six-axis displacement, calibration experiments were conducted using a specific setup and considering the device’s geometry. The high sensing performance was verified through complementary experiments.
Impulse Life Evaluation Method of MOV Based on Weibull Distribution

The working life evaluation of metal oxide varistor (MOV) is always expected by MOV manufacturers and users. In Oct. 2019, the International Electrotechnical Commission (IEC) SC37B established a project team PT 61643–333 led by the author of this paper to draft a technical report about MOV life assessment. As an early researching achievement of PT 61643-333, this paper provides an evaluation method for the impulse life of MOV with the median rank regression (MRR) method based on the Weibull distribution, which provides an effective means for evaluating the impulse life of MOV. Firstly, the theory of Weibull distribution and impulse life characteristics of MOV are introduced. Then the evaluation method and procedure of MOV impulse life is derived from the combination of the MRR method and the impulse characteristics of MOV. Finally, an application example of the proposed evaluation method is given with the test results from real MOV samples. By establishing three basic impulse life characteristics equations, the life evaluation of MOV under arbitrary impulse current can be achieved. In addition, the fitting validity of six groups of samples is checked to prove the effectiveness of the proposed parameter model. And further sensitivity analysis of the variables has been done to examine the effect of changes in the values of the parameters on the propose model.