Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for AoI Aware VNF Placement in Multiple Source Systems

Age of Information (AoI) is a newly emergent performance metric to quantify the freshness of data from destinations' perspectives. In this paper, we investigate and analyze AoI in the context of a multiple source updating system. In such a system, multiple loT devices continuously monitors physical environment and sends data to a remote destination for status updates through a Network Function Virtualization (NFV)-enabled network. Considering that the Virtual Network Function (VNF) placement can unnecessarily influence the AoI of the updates, we study the VNF placement problem in such a system. The problem is hence formulated as a mathematical optimization problem aiming to minimize the long-term average AoI of all updates received at the destination. To solve this prob-lem, we propose a Deep Reinforcement Learning (DRL)-based VNF placement approach called VNF-AoI, where a learning agent or decision-maker interacts with a system environment and consequently provides an optimal VNF placement policy according to the experience it has learned. Finally, we conduct extensive simulations to validate the effectiveness of our proposed approach. Numerical results clearly demonstrate that our VNF-AoI surpasses other two baseline algorithms by averagely 13.8 % higher acceptance ratio and 20.3 % lower average AoI at the destination.
Evaluating a Game-based and a Web-based User Interface Using Skin Conductivity Signals and Gamers and Non-Gamers Preferences

There is a growing research interest in how video games influence individuals, with many studies highlighting the positive aspects of gaming to players, with multiple benefits, including task switching, working memory, visual short-term memory, mental rotation, etc. In our work, we examine if the benefits found in previous research influence how users interact with computers and in particular user interfaces (UIs). To this end, we designed two interfaces for the same car renting application, one incorporating game UI elements -that are specifically used in video games- and the other as a conventional version of the same web application that follows the UI design of similar websites. A lab experiment was conducted where the participants (n=51) used the UIs to book a car. The experiment examined the differences between gamers and non-gamers concerning efficiency (measured by time to complete a task), stress events during the use of the interfaces (measured by a sensor indicating skin conductance response), users’ evaluation of the UIs, and user preference on which interface was more satisfying to use. Results indicated that there were no significant differences between gamers and non-gamers in efficiency, the users’ evaluation of the UIs, and the number of stress events for both UIs tested, but there was a significant difference in the user preference regarding which UI was more satisfying to use, with gamers selecting the web-based UI while non-gamers the game-based one.
A Machine Learning Approach to Minimization of the Sim-To-Real Gap via Precise Dynamics Modeling of a Fast Moving Robot

How well simulation results can be transferred to the real world depends to a large extent on the sim-to-real gap that therefore should be as small as possible. In this work, this gap is reduced exemplarily for a robot with an omni-directional drive, which is challenging to simulate, utilizing machine learning methods. For this purpose, a motion capture system is first used to record a suitable data set of the robot's movements. Then, a model based on physical principles and observations is designed manually, which includes some unknown parameters that are learned based on the training dataset. Since the model is not differentiable, the evolutionary algorithms NSGA-II and -III are applied. Finally, by the presented approach, a significant reduction of the sim-to-real gap can be observed even at higher velocities above 2 m/s. The ablation study also shows that the elements beyond normal simulations, such as the engine simulation, and the machine learning approach, are essential for success.
Determination of the electrochemically active surface area of Pt/C PEM fuel cell electrodes using different adsorbates

The characterization of fuel cell electrodes is paramount to the optimum design and performance of fuel cell systems. The specific electrochemical surface area and structural properties of two Pt/C fuel cell electrodes have been determined. Three different electrochemical adsorbates, namely, H2 (adsorption and desorption), CO and Ag (underpotentially deposited, UPD) have shown that the estimated surface areas and roughness factors vary. These variations were shown to be ca 100% between Ag and H2 adsorption for the high-loaded sample, whilst for the low-loaded sample the difference is ca 30%. The surface areas obtained show that the low-loaded Pt electrode possesses a higher specific surface area than the higher-loaded sample. The results illustrate the importance of using multiple adsorbates to accurately determine and understand the structure of complex, porous fuel cell electrodes.
System modeling process with SysML assuming the use of models

In Model-Based System Engineering with SysML, describing a valuable system model for a system engineer is important. The system model which is a descriptive model is intended to support the system engineers in decision making. Therefore, the system model is required to present information and utilization that supports system engineers in the decision making that consider the characteristics of the system. However, SysML does not provide information when and which diagrams should be selected and how to describe relationships between model elements. Therefore, if too many relationships are described, the system model loses its value to the system engineers because inability to extract information needed for decision making. Space crafts are operated in harsh environments, so it is essential to analyze physical constraints (e.g., limited communication, power, attitude etc.) at the system level of high architectural hierarchy. Therefore, system engineers need to not only model the behavior that satisfies the needs of stakeholders, but also model to ensure that the architecture of the system satisfies the physical constraints. Due to the high complexity and multiple architectural layers of spacecraft, the SysML model also describes a lot of relationships exponentially. The result is a model that is not informative for the system engineer’s decision making. To solve that problem, we proposed a modeling process that extends No Magic’s grid approach based on a Zachman-style matrix. It is useful to describe constraints between structural elements and pre/post conditions of functions in SysML for system engineers to confirm trade-off analysis and impact of changes. This paper proposes a method to describe the relationship of model elements from the purpose of model utilization (e.g., architectural change), and how to modeling a usable system model that is valuable to system engineers.
Optimal consumption, investment and life insurance with surrender option guarantee

We consider an investor, with an uncertain lifetime, endowed with deterministic laboor income, who has the possibility to continuously invest in a Black–Scholes market and to buy life insurance or annuities. We solve the optimal consumption, investment and life insurance problem when the investor is restricted to fulfil an American capital guarantee. By allowing the guarantee to depend, in a very general way, on the past we include, among other possibilities, the interesting case of a minimum rate of return guarantee, commonly offered by pension companies. The optimal strategies turn out to be on option-based portfolio insurance form, but since the capital guarantee is valid at every intermediate point in time, re-calibration is needed whenever the constraint is active.
Lightweight XOR based FEC Algorithm for Fragment Forwarding in 6LoWPAN Networks

More and more devices connect to the network leading to the emergence of Internet of Things (IoT). One of the most popular use cases is the Industrial IoT (IIoT), in which ultra-high end-to-end network reliability and low latency are the main requirements. In order to fulfill these features, the Institute of Electrical and Electronics Engineers (IEEE) has published the 802.15. 4-2015 standard and in particular, its Time Slotted Channel Hopping (TSCH) mode more specifically. Furthermore, to enable transmission of Internet Protocol version 6 (IPv6) packets, the IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) adaptation layer was introduced in RFC 4944 standard, which defines three functions, fragmentation, reassembly and fragment forwarding. However, RFC 4944 does not cope well with lossy links since it requires reception of all fragments in order to reassemble the original IPv6 packet. In order to improve network reliability, Forward Error Correction (FEC) techniques were proposed that enable recovery of lost fragments using redundancy. In this paper, we thoroughly present the state of the art approaches that use FEC techniques. We then introduce our proposal, namely E-XORFEC, which deploys the ability of recovering different fragments based on redundancy factors. Finally, we implement the considered methods in the 6TiSCH simulator, evaluate their performance and finally show that our proposed method outperforms all of them.
A PAM-4 Link for High Data Rate PMF Communication

In this work a high speed PAM-4 link for polymer microwave fiber (PMF) communication at D-band (110-170GHz) is presented using a commercial 130 nm SiGe BiCMOS process. Link measurements are performed over a one meter long foam-cladded PMF which verifies that the link can support data rates up to 30 Gbps with a bit error rate (BER) of 3 * 10−8. The transmitter is RF-DAC based including and LO multiplier and a six stage amplifier. The receiver consist of an LNA and a power detector (PD). The DC power consumption is 143 mW for the transmitter and 126 mW for the receiver.
Layered Modeling of Porous Structures with Voronoi Diagrams

AbstractExisting computer-aided geometric design methods are inadequate to model and design 3D porous structures. A layer-based approach with 2D Voronoi tessellation is proposed to reduce the 3D challenge to a 2D problem. We define each Voronoi generating point to be either solid or void point and organize the points into concentric clusters for designing small 2D shape regions. Dynamic 2D shape regions are achieved by specifying the motion of each generating point in the 2D plane. When expanding the time-dimension of these regions in the third dimension of the 2D plane, small pieces of 3D structures are defined. Moreover, when these regions undergo topological variations, small pieces of 3D branching structures are obtained. A technique on symmetry in 2D plane is utilized as a simple method to expand these structures to variations of large 3D branching and porous structures. The proposed method is a layer-based model as each horizontal cross-section of a final structure is a layer of region pattern speci.
Price Chain Analysis from Primary Energy Prices to Electricity Market Clearing Prices

The recent boost of primary energy prices (PEP) exerts considerable influence on the electricity market clearing prices (EMCP), which in turn affects the energy production and consumption. This paper proposes a novel price chain analysis model to investigate the correlation between PEP and EMCP. In particular, we first establish a correlation model to quantitatively analyze the impact of PEP on power generation cost. Then, a market clearing model is developed incorporating the variation of PEP in the objective function. Furthermore, various network, generation, and flexible load operating constraints are considered and relaxed to construct the Lagrangian function. Consequently, the corresponding KKT conditions are derived, and the EMCP is further expressed as a function of PEP and congestion cost. Finally, the proposed model is tested on an IEEE 30-bus transmission network on the platform GAMS, and the numerical results verify the effectiveness of the proposed model.
Adaptive Decision Method of Directional Drilling Tool Face Based on Model Free Online Learning

Tool face adjustment in oil and gas drilling are vital issues that affect the efficiency and safety of directional drilling. The existing tool face adjustment methods mainly rely on manual real-time intervention for continuous adjustment. Affected by manual experience, the effect is unstable and the labor cost is high. With rising energy consumption, the need for intelligent directional drilling is becoming more pressing. However, establishing an autonomous adjustment approach for the tool face remains challenging because of the variety of complex downhole environments encountered during actual drilling operations. This paper proposes a model-free online learning adaptive decision strategy for cross well intelligent adjustment and stability of tool face. A reward function embedded with expert operating experience is designed to learn the directional policy from the driller’s corrective actions. Further, to improve the efficiency of online learning, a priority-based experience playback algorithm is developed. A data-driven directional drilling simulation environment is proposed to realize the accurate simulation of the directional drilling process and pre-training of directional strategy. Simulations are carried out to validate the efficacy of the proposed method. The outcomes of field application suggest that the proposed strategy can achieve decision-making goals in a short period.
Two-Stage Optimization Framework for Battery Energy Storage System Co-Optimization in Regulation Market

We propose a two-stage model predictive control (MPC) based optimization framework to enable regulation market participation for battery energy storage systems (BESSs). Specifically, the first stage of the optimization, named “Stage-A”, aims to compute optimal charging and discharging power references for the BESS. The second stage, named “Stage-B”, aims to track the regulation signal closely, while working in conjunction with Stage-A. The proposed framework can be readily extended to multiple number of BESSs and can be also configured to support different regulation market models. We consider Singapore regulation market as a case study and show that the model can strictly abide by market rules of state of charge (SOC) based basepoint setting and prevention of reverse behavior, while following the regulation signal closely.
Constructive Analysis and Design of Interconnected Krasovskii Passive and Quadratic Dissipative Systems

This paper investigates the quadratic dissipativity properties required when interconnecting subsystems. A constructive method for analysing the results of interconnected quadratic dissipative subsystems is repurposed for the design of subsystems without predefined dissipativity properties. The proposed constructive design method uses optimisation to find the least restrictive dissipativity requirements for the unknown subsystems while ensuring interconnected stability or dissipativity. Furthermore, methods for calculating the passivity indices for Krasovskii passive systems are provided, allowing their inclusion in the interconnection framework. A numerical example demonstrates the design of an unknown subsystem using the proposed methods. The design and analysis methods provide sufficient criteria and hold for any fixed, linear interconnection scheme.
Robust Stabilization for a Class of Descriptor Systems under Input Delays: The Attractive Invariant Ellipsoid Approach

The paper deals with the robust control design for descriptor (implicit) linear systems governed by a semi-explicit Differential Algebraic Equation (DAE) under two conditions: the unknown time-varying input delay and the unbounded exogenous perturbation presence. In order to provide numeric stabilizing conditions of the descriptor system solution, a linear feedback control and a Luenberguer-like observer are designed by the application of the conventional Attractive Invariant Ellipsoid Method (AIEM). The AIEM ensures both practical stability and robustness properties of the descriptor system solution. Practical stability is associated with the implicit system solution convergence into a specific geometrical region delimited by an Ellipsoid. Robustness properties are described in the sense of the exogenous perturbation rejection. The AIEM is an effective and well-known approach to ensure stability and robustness of dynamical systems governed by ordinary differential equations (ODE) but not for implicit systems governed by DAE. The main contribution on this work is the use of AIEM under unknown time-varying input delay presence. From the principles of AIEM for time-delay systems we propose an adequate linear feedback control and a Lyapunov-Krasovskii functional associated to a convergence zone in form of an ellipsoid. Minimization of the attractive ellipsoid is numerically described by the solution of a Bilinear Matrix Inequiality (BMI). Finally, an academic example supports the theoretical results.
Importance-Aware Message Exchange and Prediction for Multi-Agent Reinforcement Learning

The cooperation among intelligent agents is the key to build smarter real-world intelligent systems. Recent researches have shown that wireless communication plays a vital role in multi-agent cooperation. However, limited wireless resources become the bottleneck of large-scale multi-agent cooperation. Therefore, we focus on how to improve the performance of multi-agent reinforcement learning under the constraint of communication resources. To share more valuable information among agents under resource constraint, we formulate the message importance and design a decentralized scheduling policy with query mechanism, so that agents can effectively exchange messages according to their message importance. We further design a message prediction mechanism to compensate for those messages that are not scheduled for transmission in the current round. Finally, we evaluate the performance of the proposed schemes in a traffic junction environment, where only a fraction of agents can broadcast their messages in each round due to limited wireless resources. Results show that the importance-aware message exchange can extract valuable information to guarantee the system performance even when less than half of agents can share their states. By exploiting message prediction, the system can further save 40% of communication resources while guaranteeing the system performance.
Low-Profile Wideband High-Gain Open Resonator Antenna Based on Multi-Resonant Modes Cavity and Metasurface

A high-gain wideband low-profile open resonator antenna (ORA) is proposed, which consists of a spherical geometry ground and a single layer partially reflective surface (PRS). A PRS with a suitable positive reflection coefficient phase gradient can correct the problem of multiple quasi-Laguerre-Gaussian beam modes that cannot be excited in a first-order height cavity. Combining these two techniques, we dramatically improve the ORA's 3-dB gain bandwidth at a low profile and reduce the antenna sidelobe level. We designed and fabricated this ORA prototype, and the measured results show a 3-dB gain bandwidth of 40% with a peak gain of 18.8 dBi. Our proposed method shows its effectiveness in improving the gain bandwidth of an ORA with a low profile and multiple resonant modes.
Calculation of the focusators into a longitudinal line-segment and study of a focal area

Abstract Using an iterative method modernized with the adaptive subadjustment, we calculated phase functions of the focusators into a longitudinal axial line-segment, which is characterized by the constant light intensity along the line. Numerical studies were performed of a focal area: a character of variations of both the transverse diameter and the focusing efficiency along a line are reported.
Driver Modeling Using Continuous Reasoning Levels: A Game Theoretical Approach

The focus of this paper is designing a game theoretical method using a continuous policy space for modeling human driver interactions on highway traffic. The proposed method is based on Gaussian Processes and developed as an enrichment to the hierarchical decision-making concept called "level-k reasoning". This concept conventionally assigns discrete levels of behaviors to agents. Although shown to be an effective modeling tool, the level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it provides. The proposed approach is put forward to fill this gap in the literature by introducing a continuous domain framework that enables an infinite policy space. By using the approach presented in this paper, more accurate driver models are obtained, which can be employed for creating high-fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a traffic dataset and compared with the conventional level-k approach to demonstrate its contributions and implications.
Nano Design of a Symmetrical Junction Non-Aligned Double Gate FET Device with Improved Analog Figure of Merit

The present work gives an overview of a new device model named, NADGFETs (Non-Aligned Double Gate Field Effect Transistors) structure. The simulations were performed using the 2D-Sentaurus TCAD. The parameters of NADGNFET and NADGPFET namely threshold voltage, Drain Induced Barrier Lowering (DIBL), subthreshold swing (SS), ON current (ION), ION/IOFF ratio, Intrinsic gain (Avo), Transconductance $(\mathrm{g}_{\mathrm{m}})$, and output conductance $(\mathrm{g}_{\mathrm{ds}})$, are being investigated. An inverter is simulated using the NADGFET devices giving a propagation delay of 37.5 $\mu\sec$ and a total power consumption of 3.5 nW.
Cyberbullying Detection in Urdu Language Using Machine Learning

Cyberbullying has become a significant problem with the surge in the use of social media. The most basic way to prevent cyberbullying on these social media platforms is to identify and remove offensive comments. However, it is hard for humans to read and remove all the comments manually. Current research work focuses on using machine learning to detect and eliminate cyberbullying. Although most of the work has been conducted on English texts to detect cyberbullying, limited to no work can be found in Urdu. This paper aims to detect cyberbullying from the users' comments posted in Urdu on Twitter using machine learning and Natural Language Processing (NLP) techniques. To the best of our knowledge, cyberbullying detection on Urdu text comments has not been performed due to the lack of a publicly available standard Urdu dataset. In this paper, we created a dataset of offensive user-generated Urdu comments from Twitter. The comments in the dataset are classified into five categories. n-gram techniques are used to extract features at character and word levels. Various supervised machine-learning techniques are applied to the dataset to detect cyberbullying. Evaluation metrics such as precision, recall, accuracy and F1 scores are used to analyse the performance of machine learning techniques.