Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing Zero Trust in a Cybersecurity Course

Zero trust (ZT) is a conceptual and architectural model for cybersecurity teams to design networks into secure micro-perimeters and strengthen data security by systematically integrating state-of-the-art technology, risk management, and threat intelligence. ZT has recently gained momentum in the industry to defend against lateral movement of malicious actors in today’s borderless networks. The United States 2021 President Executive Order requires the federal government must adopt security best practice and advance toward a zero trust architecture (ZTA). However, it is not a trivial task to implement a ZTA due to its novelty and complexity. We need to understand what ZTA is to take the advantage of it. Therefore, there is a need to introduce the fundamental concepts, principles, and architectures of ZT in cybersecurity courses at a college to better prepare our new cybersecurity professionals for their careers. We have introduced ZT in a cybersecurity course for senior undergraduates and another course for graduate students. This article provides an overview of the materials we have used to introduce ZT in both courses, including the problems in a traditional perimeter-based security model and how these problems can be either resolved or mitigated with a ZT security model. We expect our work will serve as a good reference for educators to introduce ZT security model in a cybersecurity course.
A Comprehensive Review on Vision-Based Violence Detection in Surveillance Videos

Recent advancements in intelligent surveillance systems for video analysis have been a topic of great interest in the research community due to the vast number of applications to monitor humans’ activities. The growing demand for these systems aims towards automatic violence detection (VD) systems enhancing and comforting human lives through artificial neural networks (ANN) and machine intelligence. Extremely overcrowded regions such as subways, public streets, banks, and the industries need such automatic VD system to ensure safety and security in the smart city. For this purpose, researchers have published extensive VD literature in the form of surveys, proposals, and extensive reviews. Existing VD surveys are limited to a single domain of study, i.e., coverage of VD for non-surveillance or for person-to-person data only. To deeply examine and contribute to the VD arena, we survey and analyze the VD literature into a single platform that highlights the working flow of VD in terms of machine learning strategies, neural networks (NNs)-based patterns analysis, limitations in existing VD articles, and their source details. Further, we investigate VD in terms of surveillance datasets and VD applications and debate on the challenges faced by researchers using these datasets. We comprehensively discuss the evaluation strategies and metrics for VD methods. Finally, we emphasize the recommendations in future research guidelines of VD that aid this arena with respect to trending research endeavors.
Distributed Localization with Grid-based Representations on Digital Elevation Models

It has been demonstrated that grid cells in the brain are encoding physical locations using hexagonally spaced, periodic phase-space representations. We explore how such a representation may be computationally advantageous for related engineering applications. Theories of how the brain decodes from a phase-space representation have been developed based on neuroscience data. However, theories of how sensory information is encoded into this phase space are less certain. Here we show a method for how a navigation-relevant input space such as elevation trajectories may be mapped into a phase-space coordinate system that can be decoded using previously developed theories. We also consider how such an algorithm may then also be mapped onto neuromrophic systems. Just as animals can tell where they are in a local region based on where they have been, our encoding algorithm enables the localization to a position in space by integrating measurements from a trajectory over a map. In this paper, we walk through our approach with simulations using a digital elevation model.
Towards Ubiquitous Personalized Music Recommendation with Smart Bracelets

Nowadays, recommender systems play an increasingly important role in the music scenario. Generally, music preferences are related to internal and external conditions. For example, mood state and ongoing activity will affect users' music preferences. However, conventional music recommenders cannot capture these conditions since they only utilize the online data but ignore the impact of physical-world information. In this paper, we leverage the contexts from low-cost smart bracelets for ubiquitous personalized recommendation to meet users' music preference. We first conduct a large-scale questionnaire survey, which illustrates moods, activities, and environments will affect music preferences. Then we perform a one-week field study among 30 participants, where they receive personalized music recommendation and record preferences and mood. Meanwhile, participants' context information is collected with bracelets. Analyses on the data demonstrate significant relationships between music preference, mood, and bracelet contexts. Furthermore, we propose a novel Multi-task Ubiquitous Music Recommendation model (MUMR) to predict personalized music preference with bracelet contexts as input and mood prediction as an auxiliary task. Experiments show significant improvement in music recommendation performances with MUMR. Our work demonstrates the possibility of ubiquitous personalized music recommendations with smart bracelets data, which is an encouraging step towards building recommender systems aware of physical-world contexts.
Plug-and-play Physical Computing with Jacdac

Physical computing is becoming mainstream. More people than ever---from artists, makers and entrepreneurs to educators and students---are connecting microcontrollers with sensors and actuators to create new interactive devices. However, physical computing still presents many challenges and demands many skills, spanning electronics, low-level protocols, and software---road blocks that reduce participation. While USB has made connecting peripherals to a personal computing device (PC) trivial, USB components are expensive and require a PC to operate. This makes USB impractical for many physical computing scenarios where cost, size and low power operation are often important.
Higher Order Continuum Modelling for Predicting the Mechanical Behaviour of Solid Foams

A higher order continuum model is presented, which is able to capture size effects and regularize softening in rotational, shearing and especially compressive settings. It is shown that with a minimal setting of 3 additional constitutive parameters the model has all desired properties. For parameter identification an example system of a honeycomb structure is investigated. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Deep learning approach for classification microscopic images of fungi

Diagnosis of fungal infections can rely on microscopic examination, however, in many cases it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in implementation of targeted treatment is grave in consequences as the mortality rate for immunosuppressed patients is high. In this paper, we apply machine learning approach based on deep learning and bag of words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days and reducing the cost of diagnostic examination.
INVERSE PROBLEMS IN PARTIAL DIFFERENTIAL EQUATIONS.

Abstract : A procedure for identification in partial differential equations is described and illustrated by the Laplace equation and the unsteady heat conduction equation. The procedure for solution involves the substitution of difference operators for the partial derivatives with respect to all but one of the independent variables. The linear boundary value problem is solved by superposition of particular solutions. For non-linear boundary value problems which arise from the original form of the equation or from the identification procedure, a Newton-Raphson-Kantorovich expansion in function space is used to reduce the solution to an iterative procedure of solving linear boundary value problems. For the problems considered, this procedure has proven to be effective and results in a reasonable approximation to the solution of the boundary value problem in partial differential equations. For the identification problem, it is shown that the constant parameters are identified to the same accuracy as the supplementary data used in the identification procedure. (Author)
GROUPTHINK

Live performers often describe "playing to the audience" as shifts in emphasis, timing and even content according to perceived audience reactions. Traditional staging allows the transmission of physiological signals through the audience's eyes, skin, odor, breathing, vocalizations and motions such as dancing, stamping and clapping, some of which are audible. The Internet and other mass media broaden access to live performance, but they efface traditional channels for "liveness," which we specify as physiological feedback loops that bind performers and audience through shared agency. During online events, contemporary performers enjoy text and icon-based feedback, but current technology limits expression of physiological reactions by remote audiences. Looking to a future Internet of Neurons where humans and AI co-create via neurophysiological interfaces, this paper examines the possibility of reestablishing audience agency during live performance by using hemodynamic sensors while exploring the potential of AI as a creative collaborator.
AccMyrinx

The built-in loudspeakers of mobile devices (e.g., smartphones, smartwatches, and tablets) play significant roles in human-machine interaction, such as playing music, making phone calls, and enabling voice-based interaction. Prior studies have pointed out that it is feasible to eavesdrop on the speaker via motion sensors, but whether it is possible to synthesize speech from non-acoustic signals with sub-Nyquist sampling frequency has not been studied. In this paper, we present an end-to-end model to reconstruct the acoustic waveforms that are playing on the loudspeaker through the vibration captured by the built-in accelerometer. Specifically, we present an end-to-end speech synthesis framework dubbed AccMyrinx to eavesdrop on the speaker using the built-in low-resolution accelerometer of mobile devices. AccMyrinx takes advantage of the coexistence of an accelerometer with the loudspeaker on the same motherboard and compromises the loudspeaker by the solid-borne vibrations captured by the accelerometer. Low-resolution vibration signals are fed to a wavelet-based MelGAN to generate intelligible acoustic waveforms. We conducted extensive experiments on a large-scale dataset created based on audio clips downloaded from Voice of America (VOA). The experimental results show that AccMyrinx is capable of reconstructing intelligible acoustic signals that are playing on the loudspeaker with a smoothed word error rate (SWER) of 42.67%. The quality of synthesized speeches could be severely affected by several factors including gender, speech rate, and volume.
Solving the data imbalance problem in network intrusion detection : A MP-CVAE based method

In the area of network intrusion detection, harmful cyber-attacks are usually rare when compared to normal samples, which makes feature extraction and intrusion detection difficult. Increasing the amount of the anomalous samples through data generation can effectively relieve class imbalance, and thus significantly improve the classifier's performance. Variational Autoencoder (VAE) is an effective data generation model that assumes that the latent variables satisfy one prior distribution and creates new data by decoding the latent variables sampled from the distribution. This assumption is reasonable for unsu-pervised learning. However, when multiple types of attack data have obvious distribution differences, using the traditional VAE may cause various types of generated data to be mixed together. To solve this problem, we propose an improved generation model based on the conditional variational autoencoder, which encodes different types of samples into Gaussian distributions with significant difference to strengthen the discrimination between different classes in the data generation process. A series of experiments based on fitted data and real data, such as NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018, demonstrate the effectiveness of this method.
A Random Forest-Assisted Decomposition-Based Evolutionary Algorithm for Multi-Objective Combinatorial Optimization Problems

Many real-world optimization problems involve time-consuming fitness evaluation. To reduce the computational cost of expensive evaluations, researchers have been developing surrogate models to approximate the objective function values of unevaluated candidate solutions. However, most of the research has been developed for continuous optimization problems, while only a few of them address surrogate modeling for expensive multi-objective Combinatorial Optimization Problems (COPs). COPs have inherently different challenges than continuous optimization. For example, (i) many COPs have categorical and nominal decision variables; (ii) they often require the combination of both global and local search mechanisms; and (iii) some of them have constraints that make them NP-hard problems, which makes them even more difficult to solve with a reasonable number of fitness evaluations. To address these issues, this paper proposes a surrogate-assisted evolutionary algorithm that combines the decomposition-based algorithm MOEA/D, Tabu Local Search, and Random Forest as a surrogate model to approximate the objective function of unevaluated individuals on multi-objective COPs. Experiments were conducted on constrained and unconstrained well-known multi-objective combinatorial optimization benchmark problems. The experimental results demonstrate that the proposed design outperforms state-of-the-art algorithms without violating the restrictions in the number of objective function evaluations, which indicates that it may be suitable for real-world expensive multi-objective COPs.
Yolov5-based defect detection for wafer surface micropipe

Micropipe defects on the surface of silicon carbide wafers can have a significant impact on the quality of the wafers. Therefore, it is necessary to identify and locate them during the production process. Due to micropipe defects being small and dense, which are difficult to detect completely, we propose a real-time defect detection network model based on the Yolov5. The model adds a detection branch in the neck and head block of Yolov5 to obtain high-resolution features. To get the spatial and channel attention, we apply a CBAM attention module in each neck branch, and DA attention module in each head branch. The experiments show that our model improves AP by 1.89% and increases precision and recall by 10.12% and 2.95%, respectively, compared with the Yolov5 model. The results show that our model has a better ability to detect small and dense defects.
Real-Time Optimization by Extremum-Seeking Control

From the Publisher: Extremum seeking is a method of adaptive control outside the classical paradigm or model reference and related schemes. The unique feature of extremum seeking as an optimization tool is that is is an on-line methodology, performed by feedback, and with the ability to achieve rapid convergence. A second distinction between classical adaptive control and extremum seeking is that the latter is not model based. Its non-model based character explains the remarkable popularity of extremum seeking in the last half decade: the recent applications in fluid flow combustion, and biomedical systems are all characterized by complex, unreliable models. Written by authorities in the field and pioneers in adaptive nonlinear control systems, this book presents both significant theoretic value and important practical potential.
Structure Peeling Based on Link Time Optimization in the GCC compiler

The huge gap between the speed of processors and their memory has become one major bottleneck in modern computer systems. In order to achieve higher performance, sophisticated techniques are increasingly needed to improve the data locality of the programs. In this paper, the structure peeling optimization based on LTO is implemented in the GCC compiler for that purpose. The structure types suitable for peeling are selected through adequate escape analysis and then split into multiple pieces, each containing one field corresponding to that in the original structure. This optimization is placed in the stage after whole program analysis of LTO so that can handle functions' parameters and global variables which cannot be handled from a local perspective. The experimental result shows that by adopting our optimization the geometric mean performance acceleration ratio of five SPEC CPU benchmarks can be achieved by 1.23, with individual benchmark performance increasing by up to 59.29%.
Variational Autoencoders and Evolutionary Algorithms for Targeted Novel Enzyme Design

Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful representations, the design of proteins with optimised properties remains a challenge. We combined deep learning architectures with evolutionary computation to steer the protein generative process towards specific sets of properties to address this problem. The latent space of a Variational Autoencoder is explored by evolutionary algorithms to find the best candidates. A set of single-objective and multi-objective problems were conceived to evaluate the algorithms' capacity to optimise proteins. The optimisation tasks consider the average proteins' hydrophobicity, their solubility and the probability of being generated by a defined functional Hidden Markov Model profile. The results show that Evolutionary Algorithms can achieve good results while allowing for more variability in the design of the experiment, thus resulting in a much greater set of possibly functional novel proteins.
Visualization, Clustering, and Graph Generation of Optimization Search Trajectories for Evolutionary Computation Through Topological Data Analysis: Application of the Mapper

Topological Data Analysis (TDA) is an analytical technique that can reveal the skeletal structure inherent in complex or high-dimensional data. In this study, we considered the optimization search trajectories obtained from multiple trials of evolutionary computation as a single data set and challenged to represent the similarities and differences of each search trajectory as a topological network. Mapper is one of TDA tools and it includes the dimensionality reduction of data and clustering during graph generation. We modified Mapper to apply into this problem. The proposed framework is Mapper for evolutionary computation (EvoMapper). In the numerical experiments, multiple searches were conducted at different initial points to provide a basic review of the effectiveness of EvoMapper. The test functions were the One-max and Rastrigin function. A graph providing intuitive insights on the analysis results was constructed and visualized. In addition, the trials that reached the optimal solution and those that did not were clustered and found to have similar topology.
Identifying Key Components in Power Grid based on Multi-metric Fusion

The safe and stable operation of the power system is a critical support to maintain the security of the national science and technology industry and the steady operation of the social economy. Complex network theory abstracts power grid components and transmission lines into nodes and links in the network, respectively, providing a new perspective to explore the structure and operation characteristics of the power grid. Therefore, it is of great significance to apply complex network theory to the prevention and control of large-scale blackouts. In this paper, we propose an approach, IDEC, to Identifying key Components in a power grid based on multi-metric fusion. IDEC considers the network's topological characteristics and the electrical operating characteristics of the power system and introduces multiple metrics, such as degree centrality, one-order structural entropy, electrical betweenness centrality, and node contribution degree, to characterize the importance of components in the power grid, which are further combined together by AHP (Analytic Hierarchy Process) to characterize the importance of components overly. In the experiments, the IEEE-39 bus system is taken as a case study, and the results are compared with those in the literature. The results show that IDEC is superior to the approaches in the literature.
Relative Attitude Estimation Approach Based on Correlated Frames Using Planar Target

A three-dimensional (3D) attitude measurement approach for planar target based on correlated frames is proposed to solve the problem of low off-plane angle measurement accuracy for 3D relative attitude estimation using planar target as cooperative target. First, the target attitude of each frame of image is calculated by attitude estimation approach by a single-frame image. Second, the high-precision angle information provided by the fiber-optic gyro unit is used to realize the correlation of adjacent frames of target images. Finally, the 3D attitude of planar target is estimated by superimposing multiple frames of correlated images. This approach makes full use of the high-accuracy of short-time attitude of the fiber-optic gyro unit, and reduces the influence of image random noise on attitude measurement by correlating sequence image. Simulation and experimental results show that when the relative attitude angle varies from −2° to 2°, the proposed approach improves the measurement accuracy of the off-plane angle by a factor of 6 compared with the traditional ones.
Some results on disturbance attenuation for Hamiltonian systems via direct discrete‐time design

The disturbance attenuation and robust disturbance attenuation problems for Hamiltonian systems in the discrete‐time setting are considered and some new results are presented. The new results are derived utilizing the recently presented dissipativity equality obtained by adding the dissipation rate function to the classical dissipativity inequality. A selection of the dissipation rate function yields new results. These results include a condition on the dissipation structure of the system to achieve the desired disturbance attenuation level and gives direct construction of optimal control laws for any desired disturbance attenuation level. The results remove the need to solve Hamilton–Jacobi–Isaacs inequalities. Copyright © 2014 John Wiley & Sons, Ltd.
Application of Neural Network Based Cascade-able Transceiver Model in Serial Link Simulation

This paper describes the work-flow of developing black-box transceiver models for channel cascading using feedforward neural network (FNN). Compared to transistor level models, the FNN model is independent of the circuit simulator, whereas the former suffers from slow Newton-Raphson iterations. Furthermore, the generation of FNN model requires no substantial knowledge in circuit design, making it more feasible to the end-users who work in another field.