Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Based Access Class Barring for Massive Machine Type Communication Random Access Congestion Control in LTE-A Networks

Massive Machine Type Communication (mMTC) is an enabling technology for cellular network, like LTE-A, scalability and densification. However, LTE-A network is mainly designed for Human Type Communications (HTCs) and hence the inclusion of large number of MTC devises (MTCDs) results in several challenges including Random Access (RA) congestion. With the availability of limited RA resources per LTE-A cell, a massive bursty access request from MTCDs results in RA network overload. This is a severe problem in that it degrades the RA throughput at large which results in access delay, increased energy consumption (by the MTCDs), and hence degraded Quality of Service (QoS). To this end, 3GPP has included Access Class Barring (ACB) scheme in the LTE-A specifications to control the congestion. The scheme controls the congestion by spreading the devices access requests in time using its parameters namely, barring factor and barring time. Thus, literatures show that the scheme can effectively manage Human Type Communications (HTCs) and MTCs' sporadic access requests. However, its performance (in terms of access success probability, access delay, and energy efficiency) degrades when the RA network gets continuously congested. Therefore, learning techniques are used by different researchers to adaptively vary one or both parameters of the scheme along with the access request intensity. In this paper we investigate learning based ACB solutions. For ease of study, we grouped the contributions on learning based ACB solutions into two classes namely; adaptive barring factor ACB solutions and adaptive barring factor and barring time ACB solutions. The achieved performance improvements and the limitations of each class of schemes are identified in the paper.
Room Layout Estimation Using a Machine Learning Technique

The purpose of this paper is to propose a room layout estimation pipeline from indoor point cloud data using a machine learning technique. During data pre-processing, each point cloud was aligned along the X, Y, and Z axes, and then projected along the XZ dimensions to a 2D image called a point density image; the number of points along the Y dimension (room height) that fell into the same pixel coordinate was also counted. The pixels with a high accumulation of points were typically wall areas and were colored in a higher color range index. We trained pairs of point density images and ground truth images using the U-Net model. The trained model could predict rough wall areas of unseen images. Post-processing was applied to the predicted result in order to estimate connected walls, generate a vector image, and draw the length of the walls. Because the results from the automated process always contained some errors, we created a user interface to complete the final layout with just a few modifications.
An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.
Dual Modal Meta Metric Learning for Attribute-Image Person Re-identification

Attribute-image person re-identification (AIPR) aiming to retrieve persons from massive images via an attribute query is a meaningful but challenging cross-modal retrieval task. Because there is a huge modal difference between person images and attributes, e.g., on the image modal one subject usually contains of varying instances, but on the attribute modal, one subject only contains an explicit instance. Unlike most existing AIPR methods focusing on shrinking feature differences crossing modals, we propose a dual modal meta metric learning (DM3L) method for AIPR in this paper. Specifically, in each episode, we sample a subset as a new task and split the training data into a single-modal support set of person images and a dual modal query set consisting of both person images and attributes. Based on the single-modal support set and the dual modal query set, our DM3L learns not only attribute-image cross-modal metrics but also learns image-image intra-modal metrics. Therefore, our DM3L method encourages data on both attribute and image modalities are discriminate to improve AIPR. Experiments show that our DM3L outperforms state-of-the-art approaches on Market-1501 Attribute and PETA datasets.
An analytical method for modeling parasitic capacitances of planar components for EMI filters

This paper presents an analytical method for calculating the parasitic capacitances of planar components with 3 dimensions. The calculation is based on the Electric Field Decomposition (EFD) between multi-conductors. 2D formulations used in micro-electronics have been extended to enable the applications on the cases of planar magnetic components. The 3D calculation is then based on an energy approach with the consideration of the magnetic core effects. Prototypes of inductors and coupled inductors allow the validation of this fully analytical approach.
Distributed formation flight control of multi-UAV system with nonuniform time-delays and jointly connected topologies

This paper aims to investigate formation control problem of multi-UAV system with nonuniform time-delays and jointly-connected topologies. No explicit leader exists in the formation team, and therefore a consensus-based distributed formation control protocol which requires only the local neighbor-to-neighbor information between UAVs is proposed for the system. The stability analysis of the proposed formation control protocol is also performed. The research suggests that when the time-delay, communication topology and control protocol satisfy the stability condition, the formation control protocol will guide the multi-UAV system asymptotically converge to the desired velocity and shape the expected formation team, respectively. Numerical simulations verify the effectiveness of the formation control system.
A Staged Path Planner for an Unmanned Air System Performing Surveillance

In search and rescue, target tracking, and other missions, Unmanned Air Systems (UASs) assist in accomplishing mission goals while minimizing risk to human personnel. By automating the planning process we can reduce the workload for UAS operators. This paper presents a novel staged path planner that autonomously computes the ight path of the UAS, the path of the sensor optical axis along the terrain, and the sensor zoom level by maximizing the probability of detection over a nite time horizon. These algorithms are used to precompute UAS and sensor paths for a surveillance mission over a speci ed region. Simulated and actual ight test results are included.
An Unsupervised Ensemble Learning Approach for Novelty-based Botnet Detectors

Botnets are categorized as one of the top 15 security threats by the European Union Agency for Cybersecurity (ENISA) in 2020. Thus, various Machine Learning (ML) algorithms have been proposed to fight against bot activities. Meanwhile, attackers constantly update their malware, making it more undetectable. Consequently, many proposed methods fail in detecting Cyber attacks from novel botnets. This paper presents a novel ensemble learning framework in which diverse standalone novelty-based learners are trained with legitimate traffic to detect previously unseen benign and anomalous traffic flows. Then, the predictions of these base learners are fed into an unsupervised neural network-based as an ensemble model to be combined. The results show that our proposed unsupervised ensemble learning framework yields an improved botnet detection performance (F1 Score=(0.9957 ± 0.0000) %) while minimizing false alarms. Also, our framework outperforms any contributing base learner and the baseline ensemble method, i.e., majority voting.
Real-Time Sensor Fault Detection, Isolation and Accommodation for Industrial Digital Twins

The development of Digital Twins (DTs) has bloomed significantly in last years and related use cases are now pervading several application domains. DTs are built upon Internet of Things (IoT) and Industrial IoT platforms and critically rely on the availability of reliable sensor data. To this aim, in this article, we propose a sensor fault detection, isolation and accommodation (SFDIA) architecture based on machine-learning methodologies. Specifically, our architecture exploits the available spatio-temporal correlation in the sensory data in order to detect, isolate and accommodate faulty data via a bank of estimators, a bank of predictors and one classifier, all implemented via multi-layer perceptrons (MLPs). Faulty data are detected and isolated using the classifier, while isolated sensors are accommodated using the estimators. Performance evaluation confirms the effectiveness of the proposed SFDIA architecture to detect, isolate and accommodate faulty data injected into a (real) wireless sensor network (WSN) dataset.
Rectenna Designs for Energy Harvesting IoT Devices: Overview of Current Trends and Future Directions

The number of active Internet of Things (IoT) devices has continued to grow, and today, there are billions of interconnected devices in the world and this large number of devices need energy to be constantly powered, like sensor nodes. This puts a strain on energy requirements for all devices to be powered uninterruptedly. As IoT devices become more and more miniaturized, battery technology has been slower in terms of size and weight, making them inappropriate, or very difficult to replace, for most IoT applications. Radio Frequency energy harvesting has provided a technique for supplementing batteries or eliminating them altogether, especially in low power systems, using a device called Rectenna. In this paper, an overview of the more recent rectenna design trends in the literature are reviewed with respect to their configurations, size, and relevance to contemporary IoT applications. The paper concludes with a brief discussion of possible future directions of rectennas for IoT devices.
Peeking into the Gray Area of Mobile World: An Empirical Study of Unlabeled Android Apps

For the real-world dataset collected by our industrial partner, Pwnzen Infotech Inc., one of the leading industrial security companies, there are a large number of unlabeled Android applications (called unlabeled apps in this paper) that are unlikely to belong to known Android malware families nor ordinary benign apps according to the industrial black-list (i.e., signatures) and white-list (i.e., certificates). However, such apps have rarely been studied previously, but are important to peek into the gray area of mobile world. It is a time-consuming task for software analysts to understand the negative characteristics of these samples, which would lead to potential security or privacy threats for app users, significantly negative impacts on mobile system performance, and bad user experience, etc. To investigate the characteristics of these industrial unlabeled apps in a large-scale in practice, and provide insights to industrial software analysts as well as research communities, we collect a large-scale dataset of unlabeled apps (i.e., 22,886 in total) from our industrial partners. Given the common industrial perception of software analysts that a high percentage of these unlabeled apps could have some similar behaviors, we leverage the popular community-detection techniques based on widely-used app features in mal ware detection to cluster these unlabeled apps. After that, we investigate the common behaviors for different clusters with substantial human efforts and also conduct cross-validation across co-authors to check the results. Our manual analysis unveils the characteristics of these unlabeled apps by sampling data from different clusters, and discovers 11 categories, some of which have never been discovered by previous grayware research. Besides, from our exploration, we find that the community-based techniques are not effective enough in clustering unlabeled apps, so that manual analysis is encouraged. Manual analysis is an important first step towards studying unlabeled apps and understanding their characteristics. Finally, we highlight the lessons learned through real case studies, comparison study with existing malware/grayware research, in-depth discussion with industrial partners, and feedback from industrial partners.
Improved MDL method for estimation of source number at subarray level

The eigenvalue-based minimum description length (MDL) method for source number estimation is widely applied in practical scenarios. However, the performance of the MDL method degrades significantly when it is applied to an asymmetrical subarray situation due to the inequality of the noise power of each channel. Therefore, an improved MDL method for subarray level is proposed by normalising noise power to reconstruct the received signal covariance matrix. Simulation results show that the proposed method outperforms the conventional source number estimation methods for a subarray situation.
Randomized Pulse Position PWM and Its Realization on Matrix Converter

The matrix converter is a new cycle converter with matrix switch sequences, which has a series of advantages. A novel randomized pulse position PWM based on traditional voltage space vectors is proposed, which is worked on the model of matrix converter. In the strategy, the positions of the zero voltage vectors are randomly changed, and then two PWM modes with low switching losses are selected randomly, which make harmonic that comes from matrix converter distribute in a wider frequency range evenly. This paper illustrates a fast algorithm of SVPWM in the less switching loss mode. S-function models are programmed by means of MATLB, which are combined with the models of power system block in SIMULINK to set up the simulation model. And it produces the better waveform and data by the simulation using the two strategies separately. The simulation results verify the feasibility and the practicability of the novel randomized pulse position PWM.
Grad der standardisierten Zertifizierung des Berufs: ein Indikator zur Messung institutioneller Eigenschaften von Berufen (KldB 2010, KldB 1988)

"Occupations can be considered as an institution of the labour market, which has varying power to connect the educational system with the labour market. In order to make these institutional characteristics of occupations accessible for empirical analyses, I developed an indicator to measure the degree of standardized credentials of an occupation. The indicator provides the proportion of individual occupations within an occupational aggregate (e.g. 3-digit code of an occupational classification) whose educational qualifications are regulated by federal or state law uniform or equivalent credentials from higher education. This indicator reaches its highest value if an occupational unit contains only licensed individual occupations where the access to an occupation, the practicing and the use of a professional title are limited by legislative, regulatory or administrative provisions through required credentials (like physicians). Standardized credentials attest the holder of a professional title a minimum level of competencies and skills which are required for practicing occupational tasks. The lowest value of the indicator is reached if no access control exists for any of the individual occupations summed up to one unit (like helping professions). This methodological report describes the data base and the way of calculating and aggregating the degree of standardized certification. The indicator was created for analyzes with the three-digit and two-digit level of the German Classification of Occupations 2010 (KldB 2010) and the German Classification of Occupations 1988 (KldB 1988). " (Author's abstract, IAB-Doku) ((en)) Additional Information Anhang
Improving the Accuracy of Load Forecasting for Campus Buildings Based on Federated Learning

Load forecasting is important to the efficiency and reliability of the energy management systems in buildings. In general, the more data users have, the greater performance of load forecasting will be. However, collecting sufficient data for load forecasting takes a lot of time which can hardly be tolerated by users. To solve this problem, in this paper, we propose to derive the load forecasting model based on the Federated Learning for the building which has small and private data. The data are collected from the campus energy conservation supervision platform in Fuzhou University. Then the linear regression is used to study the best set of features for each building. The experimental results show that federated learning can improve the accuracy of load forecasting, while the privacy of each building is guaranteed.
Peatland Data Fusion for Forest Fire Susceptibility Prediction Using Machine Learning

Forest fires have been a severe hydrometeorological hazard during the dry season in Indonesia. Pulang Pisau Regency in Central Kalimantan has become one of the most forest fires affected areas during the 2015 El Nino event. Based on MODIS data, more than 120.000 hotspots have been recorded between 2014 and 2019. Previous studies concluded that peatlands act as contributing factor to forest fires in this country. This study proposed the peat-effect on the development of machine learning models for forest fire susceptibility (FFS), which can be alternative tool to support forest fire disaster management. In addition to the peat effect, such as elevation, slope, Normalized Difference Vegetation Index (NDVI), rainfall, distance from the road network, and distance from the residents also analyzed. Those variables were divided into training (2014 – 2018) and testing (2019). Random Forest (RF), Support Vector Classifications (SVC), and Gradient Boosting Classification (GBC) models were used to build the FFS map. The experiment results showed an increase in Area Under Curve (AUC) from 0.84 – 0.87 to 0.87 – 0.88 with the addition of the peat depth variable. The complete test resulted in the highest accuracy of 0.80 in the RF and SVC.
Context-Aware IoT Device Functionality Extraction from Specifications for Ensuring Consumer Security

Internet of Thing (IoT) devices are being widely used in smart homes and organizations. An IoT device has some intended purposes, but may also have hidden functionalities. Typically, the device is installed in a home or an organization and the network traffic associated with the device is captured and analyzed to infer high-level functionality to the extent possible. However, such analysis is dynamic in nature, and requires the installation of the device and access to network data which is often hard to get for privacy and confidentiality reasons. We propose an alternative static approach which can infer the functionality of a device from vendor materials using Natural Language Processing (NLP) techniques. Information about IoT device functionality can be used in various applications, one of which is ensuring security in a smart home. We demonstrate how security policies associated with device functionality in a smart home can be formally represented using the NIST Next Generation Access Control (NGAC) model and automatically analyzed using Alloy, which is a formal verification tool. This will provide assurance to the consumer that these devices will be compliant to the home or organizational policy even before they have been purchased.
A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case

We are witnessing an emergence of a new era of applications delivered via a paradigm of flexible and softwarized communication networks. This has opened the market to a wider movement towards virtualized applications and services in key verticals such as automated vehicles, smart grid, virtual reality (VR), Internet of Things (IoT), industry 4.0, telecommunications, etc. With an increasing emergence of verticals driven by the vision of low latency and high reliability, there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient in the long run. In this work, we present a Residual Long Short-Term Memory (LSTM) based multi-label classification framework for proactive SLA management in a latency-critical Network Function Virtualization (NFV) application use case. We compose a multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, and associate a multi-label classifier for a granular prediction of individual Service Level Objective (SLO) violations for each step in the forecast horizon. The Residual LSTM approach achieves an improvement of 31.1% over the baseline on the forecast classification accuracy, and a 2.65% improvement on the interpolated average precision over the standard LSTM methodology.
Coupling Effects of Crosstalk and Parasitic Loss on Capacitive Micromachined Ultrasonic Transducers

Capacitive micromachined ultrasonic transducers (CMUTs) have been widely utilized in applications of biomolecule monitoring, ultrasound medical diagnosis, and ultrasound therapy because of their advantages in high-level integration with integrated circuits, good impedance matching for acoustic transmission, and flexible element and array configuration compared with conventional piezoelectric ceramic transducers. Crosstalk effects on dynamics of a CMUT element, i.e. electrical, mechanical, and acoustic performance, would lead to unexpected spurious resonances. In this study, the mutual interaction from fluid and nearby cells in a CMUT element is newly investigated specially aiming to characterize their coupling effects with series and parallel parasitic effects based on a nonlinear Equivalent Circuit Model (ECM). The mutual interaction is evaluated with the electromechanical and acoustic characteristics by comparison between ECM and 3D Finite Element Analysis (FEA). Two representative CMUT element configurations, that are the rectangular with nine cells and the hexagonal with seven cells, are analyzed with different center-to-center distance. The radii of cells are set to be $\lambda $ /8 with different edge-to-edge distances. The parasitic effects are modeled by a series parasitic factor and a parallel-capacitance parasitic ratio. The coupling influence of crosstalk and parasitic effects on the performance of a CMUT element is evaluated, such as the impedance, phase, conductance, velocity, and ultrasound pressure field. The results of ECM-FEA-based methods show good agreements, and the improved ECM can be used for co-parasitic extraction with front-end interface.