Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on the Efficiency of Intrusion Detection Systems in IoT Networks

Intrusion detection is one of the cornerstones of cyber defense. In order to prevent intruders from gaining unauthorized access to Internet of Things (IoT) infrastructure, efficient intrusion detection systems (IDS) must be implemented in IoT networks to detect attacks, protect information and secure communications. Nonetheless, IDS in IoT networks impose additional design challenges compared to traditional IT systems such as strict efficiency requirements. In this paper, we propose an approach for the classification of IDS-based detection models for detecting attacks in IoT networks. More specifically, we conduct an extensive review on current solutions related to IDS frameworks, methods, models, and detection mechanisms used in IoT networks. We investigate the advantages and limitations of these IDS in terms of their performance and efficiency and study their suitability to deployment in IoT systems.
Constant Impedance Transmission Line with Leaky-Wave Radiation

This paper proposes a leaky-wave transmission line (TL) based on n = -1 space harmonic. Its main structure is a modulated parallel line. However, different from all other space harmonic leaky wave TLs (to date), this modulation introduces nearly no disturbance to the impedance of its transmission line. Therefore, it naturally has no open-stopband (OSB). Different from the other leaky-wave antenna (LWA) designs, where the OSB is generated first, then eliminated with additional structures, the proposed design has no OSB from the starting point. It has a special manufacturing process based on 3D-printing and copper tape. It also features high radiation gain, and ultra-wideband. The simulated result shows a peak realized gain of 10.6 dBi for broadside radiation. Measured return loss is mostly better than 10 dB within 6-18 GHz.
DeltaBin: An Efficient Binary Data Format for Low Power IoT Devices

The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.
Physics-Based Spatio-Temporal Modeling With Machine Learning for the Prediction of Oceanic Internal Waves

Accurately predicting the occurrence of oceanic internal waves in the northeastern South China Sea is of great importance to marine ecosystems, and economy. The traditional physics-based models for monitoring the occurrence of internal waves require complex parameterization, and the partial differential equations (PDEs) are relatively difficult to solve. The emergence of integrating physical knowledge and data-driven models brings light to solving the problem, which improves interpretability and meets the physical consistency. It not only inherits the advantages of machine learning in massive data processing but also makes up for the “black box” characteristics. In this paper, we propose a physics-based spatio-temporal data analysis model based on the widely used LSTM framework to achieve oceanic internal wave prediction. The results show higher prediction accuracy compared with the traditional LSTM model, and the introduction of physical laws can improve data utilization while enhancing interpretability.
360-deg FOV Scanning LiDAR versus Non-Repetitive Scanning LiDAR: A Rover Navigation Experiment

This paper presents an experimental comparison of two types of LiDAR sensors for the navigation of rovers: a 360-deg FOV Scanning LiDAR, the Ouster OS1-32, and a non-repetitive scanning LiDAR, the Livox Horizon. The study aims to determine the performance differences between these two sensors in terms of accuracy, and reliability, which are crucial factors for rover navigation in complex environments. The data collected by the LiDAR were processed as follows: the trajectory was reconstructed using the LiDAR-SLAM algorithm FAST-LIO and the obstacle map was created using the VoxBlox library. The experiments were conducted in various scenarios, including terrain with obstacles and uneven surfaces, and the results were analyzed in terms of trajectory error.
Nitride Semiconductors Realizing Sustainable Society

The history, current status, and future of nitride semiconductors, which are famous for blue light-emitting diodes, are described, including the technology of their epitaxial growth. As game-changing topics in the realization of a sustainable society, laser diodes, solar cells, and high-power and high-frequency transistors are introduced.
Scheduling Delay-Sensitive Bag-of-Task Jobs with Imprecise Computations on Distributed Resources

The evolution of the distributed computing paradigm had as a result new computing models such as grid and cloud computing. Furthermore, in these environments it is common to run complex parallel applications thus making the employment of appropriate scheduling strategies of paramount importance. This research examines the scheduling of multiple-task applications of bag-of-tasks type that have different characteristics, utilizing imprecise computations. Two scheduling schemes are utilized in a distributed system. Their performance is evaluated and compared using simulation. The experimental results demonstrate that the proposed scheduling technique that considers the delay sensitivity of the applications and allows partial task executions, can provide significant performance improvement at the expense of a low decrease in the results precision.
Exploiting Federated Learning for EEG-based Brain-Computer Interface System

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients.
Certificate-based Enterprise Privacy Analysis and Anomaly Discovery

With the development of globalization, business collaboration and exchanges between companies become increasingly frequent. Data communication between two or more parties using certificate authentication is a frequently employed security assurance approach that creates a substantial quantity of certificate data traffic. In the process of utilizing conventional certificates, it is possible to leak sensitive information of the corporation. For example, certificate data traffic in the smart grid may expose important nodes and current security management level of a certain grid-related network. Therefore, a solution is required to evaluate the data of the grid-related network at the certificate level in a secure and efficient manner. This study provides a certificate-based enterprise privacy analysis and anomaly detection scheme. Using SSL/TLS and X.509 as standards, the scheme extracts valid certificate information by collecting certificate data traffic between enterprise networks and analyzing the certificate data. The scheme monitors and analyzes the flow of certificate data for the grid network, identifies suspicious anomalous behaviors and invalid certificates, and warns of risky behavior. By monitoring certificate data flow, it is feasible to improve the enterprise authentication framework and business communication method, prevent the leaking of enterprise privacy data.
A Brain Disease Classification Framework with Temporal Attribute and Feature Fusion

The fMRI functional imaging technology has become an important means to detect brain diseases. It mainly diagnoses whether there are lesions in the patient’s brain region by monitoring the changes of the blood oxygen level (BOLD) in the brain region. Existing brain disease classification techniques are mainly achieved through network construction, feature extraction and classification model construction. But in the existing methods, almost no one uses the time attribute. In this paper, we propose a brain disease diagnosis framework with temporal attribute and feature fusion, use temporal and BOLD signal’s changes in different time periods to construct a brain connectivity network. We extract frequent sequences and sequences’ connection rules in the subjects’ brain connection network, and finally use pseudo-binary matrix to fuse the two features and finish classification model construction. Experiment turns out that we achieve better accuracy than other schemes.
An Intelligent Scoring Method for Sketch Portrait Based on Attention Convolution Neural Network

It is very important for art students to get timely feedback on their paintings. Currently, this work is done by professional teachers. However, it is problematic for the scoring method since the subjectivity of manual scoring and the scarcity of teacher resources. It is time-consuming and expensive to carry out this work in practice. In this paper, we propose a depthwise separable convolutional network with multi-head self-attention module (DCMnet) for developing an intelligent scoring mechanism for sketch portraits. Specifically, to build a lightweight network, we first utilize the depthwise separable convolutional block as the backbone of the model for mining the local features of sketch portraits. Then the attention module is employed to notice global dependencies within internal representations of portraits. Finally, we use DCMnet to build a scoring framework, which first divides the works into four score levels, and then subdivides them into eight grades: below 60, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, and above 90. Each grade of work is given a basic score, and the final score of works is composed of the basic score and the mood factor. In the training process, a pretraining strategy is introduced for fast convergence. For verifying our method, we collect a sketch portrait dataset in the Guangdong Fine Arts Joint Examination to train the DCMnet. The experimental results demonstrate that the proposed method achieves excellent accuracy at each grade and the efficiency of scoring is improved.
GOAT: Gradient Scheduling with Collaborative In-Network Aggregation for Distributed Training

The surging scale of distributed training (DT) incurs significant communication overhead in datacenters, while a promising solution is in-network aggregation (INA). It leverages programmable switches (e.g., Intel Tofino switches) for gradient aggregation to accelerate the DT. Due to switches' limited on-chip memory size, existing solutions try to design the memory sharing mechanism for INA. This mechanism requires gradients to arrive at switches synchronously, while network dynamics make it common for the asynchronous arrival of gradients, resulting in existing solutions being inefficient (e.g., massive communication overhead). To address this issue, we propose GOAT, the first-of-its-kind work on gradient scheduling with collaborative in-network aggregation, so that switches can efficiently aggregate asynchronously arriving gradients. Specifically, GOAT first partitions the model into a set of sub-models, then decides which sub-model gradients each switch is responsible for aggregating exclusively and to which switch each worker should send its sub-model gradients. To this end, we design an efficient knapsack-based randomized rounding algorithm and formally analyze the approximation performance. We implement GOAT and evaluate its performance on a testbed consisting of 3 Intel Tofino switches and 9 servers. Experimental results show that GOAT can speed up the DT by 1.5× compared to the state-of-the-art solutions.
RoadSign at the SBFT 2023 Tool Competition Cyber-Physical Systems Track

RoadSign is a search-based test generation tool which aims to generate failure-revealing roads for self-driving cars. RoadSign combines a seeding approach which ensures a diverse initial population of roads with a multi-objective optimization process which aims to maximize road features which may be likely to reveal driving failures. This tool has been evaluated in the SBFT 2023 CPS tool competition, where various road-generation tools have been evaluated in a simulated environment with two different driving agents.
STACE-GCN: A Spatio-Temporal-Aware Channel Excited Graph Convolutional Network for Skeleton-based Action Recognition

Spatio-temporal graph convolutional network methods have achieved impressive results in skeleton-based action recognition. However, existing methods treat spatiotemporal features about joint correlation equally, ignoring the difference in contribution from different spatio-temporal patterns along the channel dimension, thus losing the ability to distinguish confusing actions with subtle differences. In this paper, a spatio-temporal-aware channel excitation (STACE) module is proposed to explore channel-wise discriminative features of actions. More specifically, a spatio-aware channel excitation (SACE) sub-module is incorporated to capture the global body structure patterns to excite the spatial-sensitive channels, which helps the network to focus on vital channels of crucial joints. Similarly, a temporal-aware channel excitation (TACE) submodule is designed to learn inter-frame dynamics and to excite the temporal-sensitive channels, which aims to explore the more informative dynamic patterns along the channel dimension. Finally, equipped with STACE, a spatio-temporal-aware channel excited graph convolutional network (STACE-GCN) is proposed and evaluated on two large datasets NTU RGB +D and NTU RGB +D 120, showing that our method outperforms SOTAs.
Atmospheric effects on rotary LiDAR-based systems for UAS missions

Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) are new air transportation concepts in urban areas, both based on Unmanned Aircraft Systems (UAS). The last decade has witnessed a constant growth of UAS missions in urban areas, for various military operations, civil and scientific tasks. During UAS missions, identification and tracking of the surrounding environment is fundamental (e.g., obstacles or other vehicles, stationary or moving). It is therefore crucial to test atmospheric effects like temperature, pressure, and humidity variation on the on-board or ground sensors. This paper investigates the effects of temperature and relative humidity variation on rotary LiDAR (Light Detection and Ranging)-based distance measurements, to characterize its functionality by simulating several weather conditions. The objective this work is characterizing the sensor and validating a mathematical model of distance measurements in the presence of temperature and humidity variations. The rotary LiDAR is part of a ground control station system prototype for UAS operations, recently developed by the authors, to date tested and validated only in indoor scenarios. The core of the system is a real-time mechanically rotating LiDAR sensor (RPLIDAR Model A1M8), linked to a Raspberry Pi 3 board for data management and interfaced to a PC-based ground station.
Multi-Horizon and Multi-Rate Model Predictive Control for Integrated Longitudinal and Lateral Vehicle Control

Model predictive control (MPC) has been widely used for controlling multi-input multi-output (MIMO) systems. MIMO systems might consist of dynamics with different response speeds. Therefore, different horizons and prediction rates should be applied according to the response speed of each dynamic. However, multi-horizon and multi-rate prediction leads to mismatches of prediction points and results in prediction difficulties. In addition, multiple control rates should also be considered due to hardware constraints. This paper presents a multi-horizon and multi-rate MPC (MM-MPC) with zero-order hold interpolation for dealing with the mismatches of prediction points. Furthermore, by running MM-MPCs at different rates, a multi-control-rate system is constructed without ignoring the dynamic interaction between the dynamics. The presented methods were demonstrated through simulations. The results show that the presented MM-MPC can reach a better overall performance compared to conventional unified MPCs. In addition, the multi-control-rate system consisting of MM-MPCs with multiple execution rates reduces the average computation time without deteriorating the performance.
Kōan-like ambiguities in John Cage's Electronic Music for Piano: a novel application of formal concept analysis

In this article I present a novel application of Formal Concept Analysis (FCA) to the problem of interpreting and analysing the non-standard graphical music score Electronic Music for Piano (1965) by American composer John Cage. The avant-garde nature of Cage's score, presented on a single page as a series of anecdotal texts written in natural language, resists traditional approaches of music theory analysis. I utilise FCA for its suitability for delving into the work's taxonomy as well as its ability to visualise and analyse semiotic patterns and relationships within the natural language text fragments of the work. In this regard, FCA is also used as a means in which to frame what many writers have previously identified as kōan-like qualities in many of Cage's works. A kōan is generally understood as a type of unsolvable Zen Buddhist riddle filled with contradictions and ambiguities. I subsequently theorise Electronic Music for Piano as a type of musical kōan, and by applying FCA, demonstrate how various derived formal concepts, object extensions, and subset groupings of the work support this theory.
The Danube River Levels Through Two-Dimensional Predictors

Recently, two-dimensional predictors have been introduced and their properties have been analyzed. Later, it was shown how two-dimensional forward predictors can be obtained by cascading one-dimensional predictors in different directions. The aim of the paper is to apply certain simple forward two-dimensional predictors to estimate the future levels of the Danube River in Romanian ports, based on measurements from previous days and upstream ports. Experimental results are also presented.
Overview of Electron Emission Laws from 2D Materials

In this paper, we present an overview of the novel scaling laws of thermionic emission (TE), field emission (FE) and photoemission (PE) for two-dimensional (2D) materials. For these emerging materials, we express the emission models in the form of $\ln(J/K^{\beta})\propto 1/K$, where J is the emission current density, $K$ is temperature (T) for TE and $K$ is dc or optically field for FE and PE. Here, the scaling is $\beta = 3/2$ and $\beta = 1$ for electron emission in lateral (or edge) and vertical (or surface) direction, respectively, which is different from the traditional scaling of $\beta = 2$ for bulk 3D materials.
A general computational framework for designing morphing structures

Structural morphing is a bio-inspired design strategy that aims to conform structures to different operating environments, typically by adapting the geometrical shape in a compliant manner. Composites play an enabling role for larger-scale morphing structures as multi-functionality is readily incorporated in the manufacturing process. To date there is no predominantly accepted technique of analysing and designing these structures, especially in a manner that integrates easily with established tools used in industry. One potential candidate in this respect is generalised path-following , which combines a numerical continuation algorithm with the geometrical versatility of the ﬁnite element method. Morphing composites often take the form of multistable laminates which, as the name suggests, are driven by instabilities. The two (or more) stable states that are exploited in a multistable laminate are typically a product of postbuckling or other forms of prestress, and the ensuing snap-through phenomenon is a catastrophic event (in a mathematical sense) in itself. In this respect, a generalised path-following algorithm is ideally suited for analysing these structures as it is capable of detecting critical instability points, switching to secondary bifurcated branches, and evaluating the parametric response of critical points with respect to any other parameter. We show that the full complexity of multi-snap events of bistable lam-inates is robustly captured by this algorithm, and that the ability to determine loci of instability points with respect to additional parameters is especially useful for rapid parametric studies.
SmartCircles: A Benefit-Evaluation-Based Privacy Policy Recommender for Customized Photo Sharing

Online Social Networks(OSNs) have grown in popularity along with the use of mobile devices and the advancement of network technologies. Every time we experience a wonderful moment in our daily lives, we feel the impulse to capture it on camera and share it on OSNs. Users are frequently hesitated or struggled while sharing photos because photos often contain some privacy. To address this problem, we propose the design, implementation, and evaluation of SmartCircles, a personalized policy recommendation scheme based on attribute analysis. SmartCircles acts as a plugin to existing photo sharing OSNs, and it enables the following: a)extract various factors when users upload their photos, b)calculate the intimacy of photos and users according to extracted factors, c)calculate profit and loss, and recommend optimal policy by trade-off them. We have implemented a prototype of SmartCircles, and evaluation results have demonstrated its effectiveness and efficiency, the accuracy rate of strategy recommendations can reach about 90%.