Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Rate DoS Attacks, Detection, Defense, and Challenges: A Survey

Low-rate Denial of service (LDoS) attacks has become one of the biggest threats to the Internet, cloud computing platforms, and big data centers. As an evolutionary species of DDoS attack, LDoS attack is essentially different from the DDoS attack. DDoS attacks are the behavior of malicious blocking legitimate network traffic by destroying the targets and the infrastructure around it with huge network traffic. While, LDoS attacks are the behavior of intentional degrading the quality of TCP links by throttling TCP flows to a small fraction of its ideal rate with periodic small pulse sequence. Hence, LDoS attack has a very small flow (around 10%–20% of the background traffic), it is easy to eluding the detection of routers and counter-DoS mechanisms. We try to reveal the mechanism of the LDoS attack and attempt to figure out the generation principle of LDoS attack in this paper. We classify the LDoS attacks and existing defense methods according to time domain and frequency domain in which detection and defense are performed. Furthermore, we highlight the filter approach to defense against LDoS attack. The initial purpose of our work is to encourage researchers to study effective ways to detect and defend against LDoS attacks with innovation and aggressiveness.
A Cellular Automata-based Healthcare Data Encryption Technique for IoT Networks

The IoT devices used for healthcare data monitoring have very limited memory and power. In general conventional encryption and decryption algorithms are not suitable for IoT applications because of their complexities and power requirements. In this paper, we propose a cellular automata (CA)-based encryption technique for ECG healthcare data. Lightweight CA is suitable for IoT devices because it does not use redundant data for encryption.
Nonlinear system identification with model structure selection via distributed computation

The problem of identifying a model of a system from input/output observations is typically formulated as an optimization problem over all available data that are collected by a central unit, in the same operating conditions. However, the massive diffusion of networked systems is changing this paradigm: data are collected separately by multiple agents and cannot be made available to some central unit due to, e.g., privacy constraints. In this paper, we address this novel set-up and consider the case in which multiple agents are cooperatively aiming at identifying a model for a nonlinear system, by performing local computations on their private data sets. The problem of identifying the structure and parameters of the system has a mixed discrete and continuous nature, which hampers the application of classical distributed schemes. Here, we propose a method that overcomes this limit by adopting a probabilistic reformulation of the model structure selection problem.
Singular regimes for the maximization of metabolite production ∗

In nature, microorganisms are continuously facing nutrient availability changes in the environment, and thus they have evolved to dynamically adapt their physiology to cope with this phenomenon, by dynamically allocating resources to different cellular functions. In order to study their behaviours, the fitness of such microorganisms can be represented as a dynamical growth maximization strategy, which is formulated as an OCP (Optimal Control Problem) in coarse-grained selfreplicator systems. This study inspired the use of biotechnological engineering to maximize the production of a metabolite of interest in E. coli by means of both analytical and computational techniques. Motivated by this approach, we incorporate the metabolite production scheme in a CSTR (Continuous StirredTank Reactor) Bioreactor, which can be interpreted as a general case of the preceding models. We then derive two particular cases and study the associated OCP, so as to stress the importance of singular regimes and chattering arcs in optimal solutions. From a biological point of view, our results show that the natural allocation of resources of bacteria has to be modified in order to achieve optimal metabolite production. Finally, we go over the computations of the second order singular arc, and provide a numerical check of the Legendre-Clebsch condition.
Fluid-structure interaction modelling of a patient-specific arteriovenous access fistula

This research forms part of an interdisciplinary project that aims to improve the detailed understanding of the haemodynamics and vascular mechanics in arteriovenous shunts that are required for haemodialysis treatments. A combination of new PCMRA imaging and computational modelling of in vivo blood flow aims to determine the haemodynamic conditions that may lead to the high failure rate of vascular access in these circumstances. This thesis focuses on developing a patient-specific fluid-structure interaction (FSI) model of a PC-MRA imaged arteriovenous fistula. The numerical FSI model is developed and simulated within the commercial multiphysics simulation package ANSYS® Academic Research, Release 16. The blood flow is modelled as a Newtonian fluid with the finite-volume method solver ANSYS® Fluent®. A pulsatile mass-flow boundary condition is applied at the artery inlet and a three-element Windkessel model at the artery and vein outlets. ANSYS® MechanicalTM, a finite element method solver, is used to model the nonlinear behaviour of the vessel walls. The artery and vein walls are assumed to follow a third-order Yeoh model, and are differentiated by thickness and by material strength characteristics. The staggered FSI model is configured and executed in ANSYS® WorkbenchTM, forming a semi-implicit coupling of the blood flow and vessel wall models. This work shows the effectiveness of combining a number of stabilisation techniques to simultaneously overcome the added-mass effect and optimise the efficiency of the overall model. The PC-MRA data, fluid model, and FSI model show almost identical flow features in the fistula; this applies in particular to a flow recirculation region in the vein that could potentially lead to fistula failure.
A controlled Murali-Lakshmanan-Chua memristor circuit to mimic neuron dynamics

This paper deals with the problem of modelling the classical dynamics of cortical neurons by using memristor circuits. A simple model, based on a controlled Murali-Lakshmanan-Chua memristor circuit, is proposed. The control law exploits the foliation property of circuits with ideal memristors in the uncontrolled case, which is at the basis of their dynamical richness, together with a mechanism able to mimic the typical neuron responses, such as regular and fast spiking, intrinsic bursting and chattering. To facilitate the electronic implementation of the circuit, the control input employs a sequence of sawtooth impulsive signals for the feedforward term and comparators and hysteresis blocks for the feedback terms. It is also shown that the designed control law is robust with respect the possible lack of ideality of memristors, due, e.g., to imperfections in their practical realization.
Secure Multiparty Computation via Homomorphic Encryption Library

Secure multiparty computation (MPC) is required when individuals want to privately evaluate a function over their inputs. While evaluating a common function, the participants do not reveal their inputs to each other. A homomorphic encryption (HE) scheme allows the evaluation of arbitrary computations on encrypted data without decrypting it. In theory, realizing MPC through a HE scheme is a simple and efficient approach. However, despite its promising theoretical power, the practical side of the approach remains underdeveloped. In this work, motivated by the rising MPC applications, e.g. cloud computation, a HE library is extended to provide the necessary methods for MPC. In particular HElib that implements Brakerski-Gentry-Vaikuntanathan (BGV), a HE scheme, is extended to support MPC protocols. This extension provides a broadcast protocol for the generation of a global public key by $N$ parties, where each party maintains a share of the corresponding private key. In addition, the homomorphic evaluation of functions on ciphertexts encrypted by the public key is extended. Furthermore, a decryption broadcast protocol is provided where ciphertexts are decrypted using the individual shares of the private key. The proposed extension can be adapted to other HE libraries. A second contribution of this work, is a $2^{n}$ factorial experimental design and analysis to study the memory, computation, and communication costs of HElib and the proposed extension. Four main factors are identified: the security parameter, the plaintext space, the number of levels of the evaluation function, and the number of parties. The proposed extensions are shown to be effective and efficient. On the experimented setup, it takes about 0.2 sec for multiparty key generation and 0.06 sec for multiparty decryption.
Study on Transient Thermal Characteristics of Epoxy Resin in Saturable Reactor

Under normal working conditions, the epoxy resin material in the saturable reactor of the converter valve withstand pulsed thermal stress continuously. Repetitive pulsed thermal stress is a potential threat to the durability of the insulation material. In order to study the transient temperature rise of epoxy resin in the saturation reactor under pulsed power, a simplified heat generation model is established and simulated, and the pulsed power heating and measurement platform is built to measure the temperature on the surface of the epoxy resin with different thickness under different pulsed currents. Finally, by comparing the simulation and experimental results, we find that the surface temperature of the epoxy resin with different thicknesses rises in microsecond level and shows the same trend corresponding to the pulsed current, and the peak temperature decreased linearly with the thickness of epoxy resin sample.
Design, Fabrication and characterisation of TE Mode MZI Device for Silicon Photonics Integrated Circuit

Designing the photonics integrated circuit on silicon platform is a challenging task. For most of the photonic devices interferometers acts as major foundational block. One of the famous interferometer in photonics design is called as Mach Zehender Interferometer(MZI). This paper explains design simulation, fabraction and measurement of fabricated MZI device. The wavelength range has been chosen to be TE polarization 1500–1600 nm with waveguide being strip waveguide. Such five MZI devices have been designed with variation in waveguide length and simulation was performed over larger number of data points. The design have been simulated in software Lumerical mode and Lumerical Interconnect. Based on the simulation the mask layout was prepared for fabrication using tool called KLayout. The complete layout was fabricated over a footprint of 605μm × 410μm at Applied Nanotools Inc. Canada and Washington Nanofabrication Facility USA. The measurement of the fabricated devices have been performed and compared with simulated device.
Gradient Based Pre-filter Design for Data-driven Parameter Updating for Regulatory Controller Based on Variance Evaluation

This paper presents data-driven parameter updating for discrete-time, linear controllers from closed-loop regulatory control data. The controller has a preset linearly parameterized structure and the controller parameters are updated directly from process input and output measurements so that updated controllers suppress the variance of the process output. The proposed approach firstly estimates a disturbance model from time-series analysis of the closed-loop process output, and then constructs a cost criterion from both the estimated disturbance model and collected data. The updated controller parameters are finally obtained by optimizing the data-driven cost criterion. The proposed approach introduces a pre-filter that makes a gradient vector of the data-driven cost criterion have the same direction as the original cost criterion representing control objective. Thus, the parameter updating from initial parameters toward minimizing parameters of the pre-filtered data-driven cost criterion definitely descends the original cost criterion. The effectiveness of the pre-filter design is shown through a numerical example.
Enhancement of Power Flow and Frequency Response Using Droop-Controlled Inverters in Microgrid Operation

Power distribution to remote locations is not an easy task, and we cannot always rely on centralized sources to do the job. If more than one sources of energy are present in any remote location which can be tapped in for power generation then Microgrid concept comes into picture. Microgrid is a combination of localized loads and power generation sources, mostly non-conventional, which behaves as a single controllable entity as seen from the grid side. For controlling the power generation from each distributed generation unit, various control techniques have been proposed. This paper uses droop control for regulating the power flow and to meet the desired frequency response in a microgrid. Case studies were conducted in a microgrid consisting of two distributed generation sources, one among them being a PV system. Both conventional PQ control and droop control were implemented, along with MPPT for PV system. The microgrid was operated in grid-connected mode as well as islanding mode to see the robustness of these control techniques. It was shown that in comparison to conventional PQ control of inverters, this method is more efficient.
Distributed Secure State Estimation Using Diffusion Kalman Filters and Reachability Analysis

The tight coupling of information technology with physical sensing and actuation in cyber-physical systems (CPS) has given rise to new security vulnerabilities and attacks with potentially life-threatening consequences. These attacks are designed to transfer the physical system into unstable and insecure states by providing corrupted sensor readings. In this work, we present an approach for distributed secure linear state estimation in the presence of modeling and measurement noise between a network of nodes with pairwise measurements. We provide security against measurement attacks and simplify the traditional distributed secure state estimation problem. Reachability analysis is utilized to establish a security layer providing secure estimate shares for the distributed diffusion Kalman filter. Furthermore, we consider not only attacks on the link level but also on the sensor level. The proposed combined filter protects against measurement and diffusion attacks without requiring specialized hardware or cryptographic techniques. The effectiveness of the approach is demonstrated by a localization example of a rotating target.
Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing

Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as “digital psychiatry. ” This article focuses on the increasing use of digital psychiatry outside of clinical settings, in the following sectors: education, employment, financial services, social media, and the digital wellbeing industry. We analyze the ethical challenges of deploying digital psychiatry in these sectors, emphasizing key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and wellbeing in information societies.
Low-cost instrumentation of high-tunnels for a small co-op farm

This paper describes the design and construction of a low-cost instrumentation of a high-tunnel on the Hmong American Farmers Association (HAFA) farm. As summer temperatures rise, it is important for farmers to be aware when the internal temperature and humidity of the high-tunnel exceeds a particular threshold. This partnership with the local Hmong immigrant farmers provides a low-cost means of environmental instrumentation (temperature, humidity) inside the high-tunnel, early prediction of impeding adverse temperatures and real-time warning to the farmers.
Kinematic Modeling and Trajectory Tracking Control of an Octopus-Inspired Hyper-Redundant Robot

This letter addresses the kinematic modeling and control of hyper-redundant robots inspired by the octopus arm. We propose a discrete multi-segment model in which each segment is a 6-DoF Gough-Stewart parallel platform. Our model is novel in that it can reproduce all generic motions of an octopus arm, including elongation, shortening, bending, and particularly twisting, which is usually not included in such models, while enforcing the constant-volume property of the octopus arm. We use an approach that is inspired by the unique decentralized nervous system of the octopus arm to overcome challenges in solving the Inverse Kinematics (IK) problem, including the large number of solutions for this problem and the impracticality of numerical methods for real-time applications. We apply the pseudo-inverse Jacobian method to design a kinematic controller that drives the tip of the hyper-redundant robot to track a reference trajectory. We evaluate our proposed model and controller in simulation for a variety of 3D reference trajectories: a straight line, an ellipse, a sinusoidal path, and trajectories that emulate octopus-like reaching and fetching movements. The tip of the simulated hyper-redundant robot tracks the reference trajectories with average root-mean-square errors that are less than 0.3% of the robot's initial length, demonstrating the effectiveness of our modeling and control approaches.
The SYNERGY project: Emotional Intelligence to Improve Motivation and Learning

For the last decade, our university has experienced a fairly clear decrease in first-year license completion. So, the wish is to be a strong proponent to go towards the experimentation of original devices and students training policy in order to foster success, reduce the failure rate, significantly increase the graduation rate, and ensure a good transition from high school to university. The aim of SYNERGY project is to experiment whether Emotional Intelligence coaching helps to improve students’ motivation and learning. In its first stage, the project considers an experiment with twenty students which followed 3 sessions of Emotional Intelligence. The evaluation has been performed through EVA methodology and the results are very conclusive. Indeed, not only does the coaching increase students’ motivation and involvement in the work, but students also testify that learning seems more affordable.
Optimising Deep Learning Split Deployment for IoT Edge Networks

The Internet of Things (IoT) often generates large volumes of messy data which are difficult to process efficiently. While deep learning models have demonstrated their suitability in processing this data, the memory and processing requirements makes it difficult to deploy on edge nodes while achieving viable throughput results. Current solutions involve deploying the model in the cloud, but this leads to increased network costs due to the transfer of raw data. However, the layer based design of deep learning models allows for a model to be split into sub-models and deployed separately across IoT nodes. By deploying parts of the model on the edge node and in the cloud, the edge node is able to transmit an intermediate layer's feature output to the following sub-model instead of the raw input data. This reduces the size of the data being transmitted and results in a lower cost to the network. However, selecting the best layer to split the model becomes a multi-objective optimisation problem. In this paper, we propose an optimisation method that considers the network cost, input rate and processing overhead in selecting the best layer for splitting a model across an IoT network. We profile several popular model architectures to highlight their performance using this split deployment. Results from simulated and physical tests of the optimal layers are provided to demonstrate the method's effectiveness in real-world applications.
Optimal Parameter Tuning of Feedback Controllers with Application to Biomolecular Antithetic Integral Control

We consider the deterministic setting of a general nonlinear plant in feedback with a nonlinear controller that is parameterized by a finite number of unknown constants to be tuned. This setting is particularly useful in applications where the architecture of the feedback controller is constrained to have a specific structure, but the controller parameters can be tuned to optimize a given performance measure (e.g. biomolecular controllers, PID controllers, etc.). We first cast the tuning problem as a dynamically constrained optimization problem, then we convert the latter to an unconstrained one by introducing a suitable nonlinear operator. It is shown that the necessary conditions of optimality can be written as a parameter-dependent Two-Point Boundary Value Problem (TPBVP) that is difficult to solve analytically. Hence, we derive and compare two (first order) numerical methods to solve the optimization problem based on the Gradient Descent (GD) and the Conjugate Gradient Descent (CGD) algorithms. Finally, we apply the developed algorithms to tune a biomolecular antithetic integral controller. Tuning this controller has the advantages of shaping the dynamic response of the plant and minimizing the effect of dilution of the controller species.
A Simplified Decoding Method of Polar Codes Based on Hypothesis Testing

In this letter, a simplified successive cancellation decoding algorithm of polar codes is proposed, where a hard decision rule is adopted by exploiting the prior information in frozen bits for better decoding performance. Considering the difference of the node reliability, a hypothesis-testing-based strategy is designed to select reliable unstructured nodes for hard decision. Then the reliable nodes are decoded by the proposed hard decision method without serial recursion, which reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the decoding latency and error-correction ability compared with the existing methods.
Overlapping Community Detection of Complex Network: A Survey

It is well established that the network is ubiquitous. Social platforms, academic system, and other systems all exist in the form of networks, which often reflect the connections between different individuals in the real world. Effective community detection algorithm can explore the hidden community structure in the network, which has a great positive impact on people's daily life. At present, it has been widely applied in online public opinion monitoring, personalized recommendation, advertising and other fields. As the network structure tends to be complicated, the detection of community structure of complex networks has become a hot topic of current research. This paper reviews the state-of-the-art in the overlapping community detection of complex networks, and briefly summarizes the advantages and applications of each algorithm. Furthermore, the current challenges in overlapping community detection of complex networks are illustrated and some suggestions on future research are proposed.