Miracle-dz/UniFAR-FTUs huggingface dataset:
An Isolated High-Power Bidirectional Five-Level NPC Dual Active Bridge DC-DC Converter with Anti-Windup PI Controller for Electric Vehicle-to-Home Application

This paper introduces the design and analysis of an isolated bidirectional five-level (5L) neutral point clamped (NPC) dual active bridge DC-DC converter with an improved anti-windup strategy using proportional-integral (PI) controller for electric vehicle-to-home applications. The proposed converter is more advantageous in that it provides a 5L output voltage at the high-frequency isolation transformer sides thanks to its NPC structure compared to the conventional full-bridge circuit. Reducing switching stresses, providing galvanic isolation, ensuring bidirectional power flow and simple control strategy are the highlights of the proposed structure. A single phase shift (SPS) modulation strategy has been employed to regulate the output voltage and to generate switching signals of the proposed DC-DC converters' switches. To improve the dc-link voltage and to reduce the output voltage tracking errors, the anti-windup PI (PIAW) controller has been utilized instead of the conventional PI controller. To demonstrate the verification of the proposed converter and its controller, a simulation model has been developed in MATLAB/Simulink software. The performance and effectiveness of the proposed converter have been evaluated under steady-state and dynamic variations by the simulation results.
Autoencoders and AutoML for intrusion detection

Industrial internet of things and operational technology (IIoT/OT) lead the edge use case implementations. 5G and multi-access edge computing (MEC) offer the means to implement IIoT scenarios, ensuring business growth and deployment protection against network attacks. A variation of MEC and IIoT security measures are studied in the literature, and intrusion detection solutions are consequently proposed - including machine learning based solutions for anomaly detection. Automated machine learning (autoML) frameworks aim to create high accuracy models for users with little expertise in machine learning. This paper suggests autoencoders to improve autoML best model performance on a learning task: binary classification of network traffic. The experiment was performed on a benchmark dataset with intrusion detection examples: Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD). In order to optimize the learning process, autoencoders are suggested for feature encoding. The approach presented in this paper achieves a 4% increase in model accuracy and lower training time, when compared to the AutoML baseline model.
On the Design and Development of a Tabletop Robot for Interaction with Children

This article presents a novel emotionally expressive robot platform targeting social engagement with children. This platform was implemented in accordance with UNICEF’s policy guidance on artificial intelligence (AI) for children, focusing on factors such as safety, transparency, reliability and explainability. The robot prototype is presented from a design and development perspective, outlining all utilized electromechanical components that enable its 11 degrees-of-freedom and sensing functions. Preliminary evaluation results are provided in terms of dependability and expressiveness of basic emotions, thus demonstrating the robot’s potential to facilitate trustworthy and secure interactions with children.
Research on Visual Detection Methods and Development Trends of Surface Defects of Urban Tunnels

The detection of surface defects in urban tunnels is a key focus of safety operations and maintenance. Structural surface defect detection has gone through three key phases: manual visual inspection phase, manual instrumental inspection phase, and image visual perception phase, with most current studies focusing on the third phase. This paper analyses the current situation and problems of existing surface defects detection technologies at two levels: traditional image processing and intelligent machine vision perception. Correspondingly, future trends in surface defect detection techniques for urban tunnels are discussed, which provide solutions for the development of intelligent perception of the structural safety status of urban tunnels.
Discovering Depressurization Events in Service Difficulty Reports using Machine Learning

Service Difficulty Reports (SDRs) are reports submitted by aircraft operators and certified repair stations after they discover or experience a failure, malfunction, or defect while operating, or performing maintenance on an aircraft. The SDR records are rich in information pertaining to aviation safety. However, most of that data is not easily accessible as the problems are described in free form text. The text records often describe critical safety events such as depressurization, onboard fire, and runway excursion. Extracting critical information like the safety events in millions of records is labor intensive and infeasible without automated methods. In this study, we describe a machine learning approach to automatically discover depressurization safety events in SDR records. We are able to achieve the F1 score up to 95% to discover the depressurization events.
Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task

The development of appropriate models for dynamic functional connectivity is imperative to gain a better understanding of the brain both during rest and while performing a task. Leading eigenvector dynamics analysis is among the favored methods for assessing frame-wise connectivity, but eigenvectors are distributed on the sign-symmetric unit hypersphere, which is typically disregarded during modeling. Here we develop both mixture model and Hidden Markov model formulations for two sign-symmetric spherical statistical distributions and display their performance on synthetic data and task-fMRI data involving a finger-tapping task.
MaGnn: Binary-Source Code Matching by Modality-Sharing Graph Convolution for Binary Provenance Analysis

The number and variety of binaries running on electrical devices, public clouds, and on-premise infrastructure have been increasing rapidly. Recent successful supply chain attacks indicate that even for binaries known to be developed by trustful developers, they can still contain malicious functionalities and copy-and-pasted vulnerabilities that pose security risks to operational systems and end users. By analyzing the origin of a target code, code provenance analysis helps to relieve such problem by revealing information about the origin of a binary sample such as the author or the included software bill-of-materials. Since in most cases source symbol information is removed during the compilation process, given a binary code sample, matching it to its corresponding source code could improve the accuracy and efficiency of the provenance analysis. Existing binary-source code matching methods focus on comparing manually selected code literals (e.g. the number of if/else statements). However, these methods suffer from the issue of generalizability and require significant manual efforts.Different from the previous methods, we propose a machine learning-based binary-source code matching system, MaGnn, which measures the consistency of an input binary-source code pair by automatically extracting high-dimensional feature representations of the input and calculating the functionality similarity. With the Siamese architecture that shares a unified encoder across two modalities, McGnn is able to calculate the similarity of the input binary-source code pair with the automatically-extracted functionality representations. With the graph convolution neural network as the representation encoder, MaGnn is able to learn and encode the functionality information of the input pairs from their graph features into high-dimensional representation vectors. We benchmark MaGnn with a state-of-the-art binary-source code matching method and two machine-learning models on six out-of-sample datasets collected from five real-world libraries. Our experiment results show that MaGnn outperforms the baselines on most out-of-sample datasets.
A Broad Survey on Detection of Depression in Societal Platforms using Machine Learning Model for the Public Health Care System

Anxiety and depression are on the rise, particularly since the COVID-19 epidemic, yet detection rates have not kept pace. There has been a lot of talk about people showing signs of mental health problems on social media sites like Facebook, Twitter etc. The social media anxiety and sadness detected using machine learning algorithms is considered and reviewed in this research. Soon after depression was recognized as a major public health problem around the world, efforts were made to improve its detection. The speed with which technology is developing is changing the way people talk to one another. Standardized scales that rely on patients’ subjective reactions or clinical diagnoses from attending clinicians are typically used to detect depression, despite their limitations. First, the replies patients give on conventional standardized measures may be influenced by factors such as the patient’s current mental state, the nature of the clinician-patient relationship, the patient’s current mood, and the patient’s previous experiences and memory bias. Social media platforms like Twitter, Facebook, Telegram, and Instagram have exploded in popularity as places for people to open up about their innermost thoughts, psyche, and feelings with the proliferation of the Internet. Text is analyzed using psychological analysis to pull out relevant aspects, characteristics, and information from the perspectives of users. Psychological analysts rely on social media for the early identification of depressive symptoms and patterns of behavior. A person’s social network may tell us a lot about the thoughts and actions that precede the start of depression, such as the person’s isolation, the importance they place on themselves, and the hours they spend awake. This research presents a brief review that attempts to synthesize the literature on the use of Machine Learning (ML) techniques on social media text data for the purpose of detecting depressive symptoms and to point the way toward future research in this field.
Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges

A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction of the recorded EEG signal, using dynamical component analysis (DyCA) or principal component analysis (PCA), a persistent homology analysis of the resulting phase space trajectories is performed. Features are extracted from the persistent homology analysis and used to train and evaluate a support vector machine (SVM). Classification results based on these persistent features are compared with statistical features of the dimension-reduced signals and combinations of all of these features. Combining the persistent and statistical features improves the results (accuracy 94.7 %) compared to using only statistical feature extraction, whereas applying only persistent features does not achieve sufficient performance. For this classification example the choice of the dimension reduction technique does not significantly influence the classification performance of the algorithm.
2D Characterization Based on MSGMD And Its Application in Gearbox Fault Diagnosis

In recent years, the deep learning-based fault diagnosis method has made remarkable achievements, but it is still challenging in the small sample problem. The image texture features of the vibration signal can effectively represent different gearbox states, which is expected to alleviate the dependence on the number of training samples. Therefore, a new time-frequency diagram characterization method based on multi-symplectic geometric modal decomposition (MSGMD) is proposed. Based on the characterization analysis of multi-component simulation signals, it is proved that the MSGMD time-frequency diagram is feasible to characterize signals, and its advantages over other signal decomposition methods. On this basis, a gearbox fault diagnosis method based on MSGMD and convolutional neural network (CNN) is proposed and applied to solve the small sample problem. The experiment results show that the method can achieve more than 95% recognition accuracy even in dealing with small samples (the average number of training samples for each gearbox state is only 22). Compared with other intelligent diagnosis methods, it gets higher recognition accuracy. The above analysis shows that the proposed method is expected to be used in practical engineering gearbox fault diagnosis.
SGED-Net: A Self-organizing Graph Embedding Deep Network for Travel Time Estimation

In recent years, the travel time prediction has been receiving sustained attention because of the prevalence of location based applications, smart city engineering and online car-hailing. In this paper, a Self-organizing Graph Embedding Deep Network (SGED-Net) model is proposed to address the challenging Origin-Destination(OD) based travel time estimation (TTE). Specifically, SGED-Net comprises four modules, involving travel feature extraction, spatial association graph generation, graph embedding, and travel time deep learning prediction modules. First, we comprehensively extract three travel characteristics and feed them into a modified LightGBM component for learning the importance sort of features for different trips. Second, considering the lack of intermediate trajectories in the OD-based TTE, we employ a self-organizing feature mapping (SOM) approach to obtain the prominent nodes among pick-up and drop-off locations. Meanwhile clustering algorithm is used to divide a city into n-clusters districts of variant sizes. The topology learned by SOM is combined with the districts divided by clustering algorithm to obtain a district-based spatial association graph. Moreover, an improved SDNE algorithm is leveraged to gain a low-dimension spatial association representation while preserving the global and local structure of the graph. Then, we design a deep neural network for learning the captured spatial association representation. Finally, a series of experiments in two real-world large-scale datasets demonstrate the SGED-Net's excellent performance.
WIP: Two Packet Collision Model Parameter Sets

Some core advances in the early research on wireless networks have been made with simple analytical models for collisions. While complex simulations are nowadays often used to evaluate such networks, the more simple analytical models are still helpful for gaining fundamental understanding of effects. One challenge in using such models is finding appropriate parameters for them. To facilitate quick plausibility checks on these models, ready-to-use parameter sets for realistic example networks are desirable. In this paper, we provide such parameter sets for two networks, one in an office building and one in an industrial facility.
Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably.
Multi-view Clustering using Barycentric Coordinate Representation

We consider clustering issues where the available attributes can be divided into various independent groups that frequently offer complimentary information. We concentrate on real-world applications in this paper where a single instance can be represented by a number of heterogeneous features. As was performed successfully in the prior work of clustering on a single view dataset by using barycentric coordinate(BC) representation, and also a recent KMeans-based multi-view clustering RMKMC which proposed that the weights of views can be auto-updated by introducing a hyperparameter γ, we further propose a novel approach of multi-view clustering BCmvlearn by combining these two approaches to reduce complexity without sacrificing clustering quality. In addition, the vector form of the original dataset not being absolutely necessary due to the distance-based property of the BC representation, a variant application of multi-modal clustering is also achievable.
SATNet: Upgraded LSTM Network For Mining Time Series Correlation Utilizing The Self-Attention Mechanism

Multivariable time series prediction is a crucial machine learning application that spans many domains, such as the forecast of users' electricity consumption and exchange rate adjustment of various currencies. In these real-world datasets, there are remarkable similarities between the data of the time series itself, and these similarities can be used for prediction. Still, the traditional methods do not take full advantage of this feature. In this paper, we continue to tap the potential of the Self-Attention Mechanism and propose a new deep learning framework, Self-Attention Time Series Network (SATNet), to improve prediction accuracy. Time series data is preprocessed by SATNet using CNN and RNN, and the Self-Attention Mechanism is used to calculate the correlation between data to improve the prediction accuracy. Then, the time features and data correlation extracted from different structures are fused through the Learnable Addition Mechanism. Finally, the traditional linear model is added to extract the mutation factors in the time series to correct the prediction results. In comparison to several of the most sophisticated baseline approaches, SATNet significantly outperformed them in our examination of real-world data with complex time series, with an average of 7 % higher. Especially in the dataset with an obvious regular pattern, it has significant advantages compared with other advanced models. Online resources for data and experimental codes are accessible.
A multi-standard Universal Datalogger for industrial applications

Industrial applications rely on the remote data acquisition for their control systems. The possibility to implement devices that allow multi-protocol communication represents a relevant opportunity to enable more simple data exchange. This paper presents a multi-standard wired/wireless Universal Datalogger (UD) for data acquisition and control of industrial processes. In order to allow direct device deployment, the device implements data exchange mediums in the form of analog and digital inputs and outputs. First results in respect to its power consumption are presented.
Toward a Labeled Dataset of IoT Malware Features

IoT malware has accompanied the rapid growth of embedded devices over the last decade. Previous work has proposed static and dynamic detection and classification techniques for IoT malware. However, this work requires a diverse and fine-grained set of malware-specific characteristics. This paper presents a longitudinal, diverse, and open-source IoT malware dataset. To demonstrate the depth of the dataset, we propose an approach for recovering symbol tables and detecting the intent of stripped IoT malware binaries using function signature libraries and 14 defining Linux malware features with corresponding regular expressions. We publish a dataset with 65,956 IoT malware binaries detected over 14 years, containing 1006 unique malware threat labels designed for 15 different architectures. Our results indicate that our feature-specific regular expressions can detect the intent of an IoT malware binary. However, further work on function signature matching is needed to recover a feature-revealing symbol table in stripped IoT malware binaries.
Preserving Privacy of Neuromorphic Hardware From PCIe Congestion Side-Channel Attack

Neuromorphic systems are equipped with software-managed scratchpad to cache intermediate results and synaptic weights of a machine learning model. PCIe (Peripheral Component Interconnect Express) is the de facto protocol to interface between scratchpad and main memory. Congestion happens when PCIe traffic overwhelms the PCIe link capacity. This introduces transmission delay, which not only impacts model performance but also leaks sensitive information about a user (the victim). In this paper, we show that inefficient data placement in scratchpad using state-of-the-art compilers may trigger significant data movement over PCIe. An attacker can measure the PCIe congestion to indirectly infer the victim’s model. Therefore, the delay from PCIe congestion can be exploited as a side-channel.We propose a compiler extension to intelligently manage scratchpad in order to improve model privacy. First, we formulate a design metric to assess the vulnerability of a model to PCIe congestion side-channel attack. Next, we propose an optimization strategy integrated within the compiler to identify contents that should be retained inside scratchpad to minimize this design metric. Finally, we propose a Hill Climbing heuristic to allocate model operations to neuromorphic tiles and improve privacy by efficiently utilizing their on-chip scratchpad capacity.We evaluate our privacy-preserving model execution (PrivacyX) to mitigate PCIe congestion side-channel attack using one attack scenario and 16 image, object, and language-based machine learning models. We show that PrivacyX significantly reduces the vulnerability of a model to PCIe congestion side-channel attack compared to baseline compilers. We also show that PrivacyX, which is managed entirely in software, is complementary to several hardware-based privacy preserving solutions.