Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Inductor Dual-Output DC-DC Converter with Multiple Flying Capacitors

This paper presents a novel single-inductor dual-output (SIDO) DC-DC converter topology with two flying capacitors. The two flying capacitors are located in the same current path, targeting an enhanced voltage drop reduction on the inductor, and a decrease in the inductor current slope, aiming for improved power efficiency and relaxed coil current constraints. Using specifications addressing battery-powered display driver applications, simulation results show up to 7.7% efficiency improvement compared to the conventional SIDO converter and up to 4.9% efficiency improvement compared to the SIDO converter with one flying capacitor topology. In addition, a reduction in inductor peak current and ripple can be achieved, along with a lower duty cycle operation, which all lead to relaxed coil selection criteria and relaxed converter stability constraints.
A new solution of measuring thermal response of prestressed concrete bridge girders for structural health monitoring

This study develops a novel buckling-based mechanism to measure the thermal response of prestressed concrete bridge girders under continuous temperature changes for structural health monitoring. The measuring device consists of a bilaterally constrained beam and a piezoelectric polyvinylidene fluoride transducer that is attached to the beam. Under thermally induced displacement, the slender beam is buckled. The post-buckling events are deployed to convert the low-rate and low-frequency excitations into localized high-rate motions and, therefore, the attached piezoelectric transducer is triggered to generate electrical signals. Attaching the measuring device to concrete bridge girders, the electrical signals are used to detect the thermal response of concrete bridges. Finite element simulations are conducted to obtain the displacement of prestressed concrete girders under thermal loads. Using the thermal-induced displacement as input, experiments are carried out on a 3D printed measuring device to investigate the buckling response and corresponding electrical signals. A theoretical model is developed based on the nonlinear Euler–Bernoulli beam theory and large deformation assumptions to predict the buckling mode transitions of the beam. Based on the presented theoretical model, the geometry properties of the measuring device can be designed such that its buckling response is effectively controlled. Consequently, the thermally induced displacement can be designed as limit states to detect excessive thermal loads on concrete bridge girders. The proposed solution sufficiently measures the thermal response of concrete bridges.
Unveiling Threats: Leveraging User Behavior Analysis for Enhanced Cybersecurity

The rapid evolution of cyber threats has made it imperative for organizations to develop robust cybersecurity strategies. While traditional defense mechanisms focus on network and system-level protection, recent research has highlighted the critical role of understanding user behavior in preventing and mitigating cyberattacks. This paper introduces a novel approach which utilizes advanced analytics techniques to analyze and interpret user actions, patterns, and anomalies to identify potential threats and enhance overall cybersecurity measures. The methodology employed in this research leverages user behavior analysis (UBA) as a proactive defense mechanism against emerging cyber threats. By collecting and analyzing data from various sources, including user interactions, login activities, system logs, and application usage patterns, the proposed approach aims to identify abnormal behaviors that could indicate the presence of malicious actors or compromised user accounts. Furthermore, by incorporating machine learning algorithms and anomaly detection techniques, the system can adapt and learn from evolving attack vectors, increasing its effectiveness over time.
Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency

A major challenge in deep reinforcement learning is that it requires more data to converge to an policy for complex problems. One way to improve sample efficiency is to use n-step updates to reduce the number of samples required to converge to a good policy. However n-step updates are known to be brittle and difficult to tune. Elastic Step DQN has shown that it is possible to automate the value of $n$ in DQN to solve problems involving discrete action spaces, however the efficacy of the technique when applied on more complex problems and against problems with continuous action spaces is yet to be shown. In this paper we adapt the innovations proposed by Elastic Step DQN onto the DDPG algorithm and show empirically that Elastic Step DDPG is able to achieve a much stronger final training policy and is more sample efficient than DDPG.
Reducing Peak Temperature by Redistributing Idle-Time in Modern MPSoCs

Reducing heat dissipation is critical for modern multi-core systems to meet increasing computational performance requirements. In this paper, we investigate the impact of idle-time distribution on the peak temperature of Multi-processor System-on-Chip (MPSoCs) for the constrained-deadline non-preemptive task scheduling problem that is common in safety-critical systems. It is assumed that the transient thermal behavior of the platform cannot be neglected and must be modeled and accounted for by the optimization algorithms. In this context, we derive a dual-node thermal model that can be well applied to a dual-cluster i.MX8 QuadMax from NXP. Based on this model, we implement two offline optimization-based strategies, including an iterative per-core approach based on the principles presented in the related literature and a novel holistic approach. The results show that the per-core approach and the holistic approach reduce the peak temperature by 7.1% and 14% on average compared to the traditional non-thermal approach. We perform the experiments on the i.MX8 QuadMax platform to validate the applicability of the results and observe a good match between the model-based simulations and the actual physical platform measurements.
Deep Learning Strategies For Rare Drug Mechanism of Action Prediction

The application of machine learning algorithms to predict the mechanism of action (MoA) of drugs can be highly valuable and enable the discovery of new uses for known molecules. The developed methods are usually evaluated with small subsets of MoAs with large support, leading to deceptively good generalization. However, these datasets may not accurately represent a practical use, due to the limited number of target MoAs. Accurate predictions for these rare drugs are important for drug discovery and should be a point of focus. In this work, we explore different training strategies to improve the performance of a well established deep learning model for rare drug MoA prediction. We explored transfer learning by first learning a model for common MoAs, and then using it to initialize the learning of another model for rarer MoAs. We also investigated the use of a cascaded methodology, in which results from an initial model are used as additional inputs to the model for rare MoAs. Finally, we proposed and tested an extension of Mixup data augmentation for multilabel classification. The baseline model showed an AUC of 73.2% for common MoAs and 62.4% for rarer classes. From the investigated methods, Mixup alone failed to improve the performance of a baseline classifier. Nonetheless, the other proposed methods outperformed the baseline for rare classes. Transfer Learning was preferred in predicting classes with less than 10 training samples, while the cascaded classifiers (with Mixup) showed better predictions for MoAs with more than 10 samples. However, the performance for rarer MoAs still lags behind the performance for frequent MoAs and is not sufficient for the reliable prediction of rare MoAs.
EVONChain: A Public Blockchain Architecture with Bi-tiered Network for Edge Computing

This paper introduces EVONChain, a nature-inspired evolvable blockchain that addresses scalability issues in large-scale blockchain applications. EVONChain decouples the functions of a full blockchain node into two consensus phases. A small number of high-performance cloud nodes in the inner network handle transaction validation, block packaging, and consistent replication using the “longest chain” rule. Meanwhile, a large number of low-performance user nodes connected by Mobile Edge Computing (MEC) providers in the outer network handle the Proof of Intelligent (PoI), a novel Proof of Work (PoW) mechanism that resists Application-Specific Integrated Circuit (ASIC) computing and enables “one-CPU-one-vote” principle. This architecture enables EVONChain to achieve perfect decentralization, high scalability and security. To evaluate EVONChain’s performance, we developed a simulator. Simulation results show that EVONChain can achieve a throughput of over 10,000 transactions per second (TPS) with an inner network bandwidth of 4Gbps, a block interval of 10 seconds, and an orphaned block rate of less than 7%. This indicates that EVONChain significantly improves performance scalability for high-frequency applications, reduces confirmation time to the minute level, while maintaining a comparable level of security to the Ethereum network.
Wideband 3D-Printed Cylindrical DRAs Exploiting Customizable Permittivity Variation in Radial Direction

One of the main constraints in designing RF devices that leverage on the use of multiple materials with different permittivity is related to the physical properties of the materials available for the realization. Often, the optimal design is sacrificed in the name of feasibility. In this work, a new method for tailoring the effective permittivity of a substrate for realizing DRA is presented. The method is tested with the designing and realization of an ultra-wideband Cylindrical DRA (CDRA) which exploits the well-known technique of applying stacked layers of materials with different permittivity to achieve a bandwidth improvement. Specifically, a CDRA, fed with a coaxial probe and operating between 3.45 GHz and 6.45 GHz, has been designed, realized, and tested exhibiting very good agreement with the simulations. If compared with the traditional one, the new design is more feasible, cost-effective, and customizable to tailor virtually any value of electrical permittivity smaller than that of the bulk material.
Honey Infiltrator: Injecting Honeytoken Using Netfilter

Deception based cyber security is already well-established in form of honeypots, honeytoken and moving target defense. With these techniques, attacks can be detected, slowed down or prevented. Many techniques to deploy such deception measures have been researched. In this paper, a novel technique is proposed, where honeytoken are deployed in application traffic through a layer 2 network bridge. This way its functions similarly to a reverse-proxy, but is ’invisible’ in the sense that it does not need its own network address. This makes the installation and integration easier, and does not require any alteration of existing systems in the network. This functionality is made possible by the use of various modifications to the iptables firewall on the network bridge and libnetfilter queue and Scapy for capturing packets and passing them to the user space for processing. In this work a proof of concept implementation for injecting decoy web pages into TCP traffic is presented. Thereby it is shown that both simple and complex modifications or inventions of TCP packets on the network bridge are possible. Existing packets can be modified, for example by adding a HTML comment to the response of a requested HTML webpage, and decoy HTML pages can be created.
A Named-Entity-based TTP-free Authentication and Authorization Architecture for IoT Systems

Authentication and authorization are essential functionalities in Internet of Things (IoT) systems in the areas of device monitoring, access control, data sharing, and privacy preservation. The current authentication and authorization technologies are mainly based on ID/Password (credential), public key cryptography collaborating with multi-factor approaches, and manual basis static access control for data resources. However, they have disadvantages in terms of identity/credential management, privacy preservation, and the mandatory requirement of Trusted Third Party (TTP) Certificate Authorities (which are not cryptographically secure) for certificate verifications. The objective of this fast abstract is to present a novel idea for named-entity-based TTP-free authentication and authorization architecture in IoT systems. The proposed architecture is based on the collaboration of blockchain technology and DNS-based Authentication of Named Entity (DANE) protocol to provide secure and privacy-preserved authentication and authorization functionalities with considering effective device identity management and secure data access control in IoT systems.
Flexible Connection Hand-off: A Software-Defined Networks Load Balancing Algorithm

This paper proposes a new load-balancing algorithm for Software Defined Networking (SDN) framework. The algorithm emphasizes the Flexible Connection Hand-off (FCH) that considers the maximum load and the difference of loads between the Access Points. The load represents the number of connected stations to a particular Access Point. The proposed algorithm optimizes the load between Access Points by considering the fairness index.
Plain Source Code Obfuscation as an Effective Attack Method on IoT Malware Image Classification

IoT malware is rapidly increasing due to variants easily generated from publicly available source codes. Malware image classification capable of fast and accurate malware identification attracts attention. Since the classification by imaging is affected by malware binary changes, a binary modification without behavioral changes can be a potential attacking method to the classification by imaging. There are concerns that by combining the publicly available malware source code with readily available source code obfuscation tools, it is possible to construct an effective attack that bypasses image classifiers relatively simply. In this study, we show the effectiveness of the attack by source code obfuscation and the possibility of defense against the attack. The contribution of this research is twofold. 1) We showed that Obfuscator-LLVM (oLLVM) code obfuscation could be used as an attack method on malware image classification. The obfuscated malware binaries made by oLLVM were misclassified by VGG16-based image classifier for all the attacked malware families including Mirai, Lightaidra, and Bashlite. 2) We showed that classifier training with obfuscated samples could address this attack method. We confirmed that the malware image classifier trained with obfuscated malware binaries made by oLLVM could classify with an accuracy of 100% the malware family with obfuscation as the obfuscated original malware family.
Optimization of a Hybrid Renewable Energy System for power generation on Greek Non-Interconnected Islands: The case of Amorgos

Areas isolated from the central grid, where power generation relies on fossil fuels, will have to deal with keeping their energy autonomy while increasing the RES penetration to high levels. In such areas this can only be achieved by the development of hybrid electricity production systems along with appropriate energy storage. Representative examples of such areas are undoubtedly the Greek islands. Specifically, the Greek island of Amorgos can become a model of an autonomous island with the installation of a hybrid renewable energy system at an affordable investment cost which makes it feasible. The study proposes a hybrid system that consists of renewable energy sources (wind turbines, photovoltaic panels) and storage technologies (batteries). The optimal dimensioning of each subsystem came from solving a linear programing problem that has the objective of cost minimization for the proposed system, while managing to maintain lost load at the lower possible level. The optimization took place in GAMS environment and Matlab was utilized to simulate the case study. The system resulting from the optimization process consists of 4.9 MW wind power capacity, 3.2 MW PV power capacity and batteries with a total power capacity of 2.1 MW and energy capacity of 15.9 MWh. The system manages to achieve annual load curtailment as low as 1% of total load and the LCOE is estimated to be 0.21 €/kWh.
A Robust Two-Dimensional DOA Estimation Approach Based on Convolutional Attention Network

The direction of arrival (DOA) estimation of the signal is an important task in radio signal positioning. Various methods have been investigated to cope with the DOA task. However, since the imperfect interference factors are often present in practical antenna arrays, the performance of DOA estimation is often significantly degraded. Besides, few methods deal with the DOA estimation for signals of multiple frequencies. In this paper, we consider the problem of two-dimensional DOA estimation in the presence of imperfect factors, and propose a novel approach where the convolutional attention network is used for DOA estimation. The frequency information is introduced as a token added to the network, which improves the network robustness while taking into account the case of the signal of multiple frequencies. Besides, we extend the mean square error (MSE) to the design of a new loss function for training to improve the accuracy of the model. The advantages of the proposed DOA estimation scheme are demonstrated through numerical experiments.
Interstitial Lung Disease Patterns Classification using Hybrid Features Set and Multi Level Segmentation Implemented by Machine Learning Algorithm

The respiratory disease causing the death by wrong analysis might have an impressive effect on the patient’s health. In the pandemic asset limit has fundamentally postponed the standard conclusion of lung diseases patients including Interstitial Lung Disease (ILD). Early detection may improve the lives and health of patients. This paper proposed a methodology based on the Classifier using hybrid feature (statistical and structural) set extracted by superpixel image processing and fusion of K-means clusters on ILD images. Segmented super pixel images overcome the limitations of multiclass belongings. Semi-adaptive waveletbased fusion is applied over selected K-means clusters. Segmentation outputs then classified by Support Vector Machines (SVM).
RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution

Although syntactic information is beneficial for many NLP tasks, combining it with contextual information between words to solve the coreference resolution problem needs to be further explored. In this paper, we propose an end-to-end parser that combines pre-trained BERT [1] with a Syntactic Relation Graph Attention Network (RGAT) to take a deeper look into the role of syntactic dependency information for the coreference resolution task. In particular, the RGAT model is first proposed, then used to understand the syntactic dependency graph and learn better task-specific syntactic embeddings. An integrated architecture incorporating BERT embeddings and syntactic embeddings is constructed to generate blending representations for the downstream task. Our experiments on a public Gendered Ambiguous Pronouns (GAP) dataset show that with the supervision learning of the syntactic dependency graph and without fine-tuning the entire BERT, we increased the F1-score of the previous best model (RGCN-with-BERT) [2] from 80.3% to 82.5%, compared to the F1-score by single BERT embeddings from 78.5% to 82.5%. Experimental results on another public dataset - OntoNotes 5.0 demonstrate that the performance of the model is also improved by incorporating syntactic dependency information learned from RGAT.
Robust Reflective Beamforming For Non-Terrestrial Networks Under Thermal Deformations

In this paper, we present a beamforming method that is robust against thermal deformations for non-terrestrial reconfigurable intelligent surfaces (RIS). We analytically derive the expressions for the worst-case bound on perturbations of the covariance matrix and the corresponding steering vectors as functions of possible displacements of RIS elements. We apply these bounds during the optimization procedure to find the beamforming coefficients that are robust to thermal deformations. Moreover, we present a simple heuristic to obtain the constant modulus beamforming coefficients from the optimal beamforming via an array thinning operation. The simulation results confirm the robustness of the proposed solution against random but bounded perturbations caused by thermal deformations of the reflective surface.
Chaotic Behavior of Duffing Energy Harvester

Abstract A wide bandwidth energy harvester is designed with the purpose of operation under multiple excitations, where the excitation frequencies are generally incommensurate and probably spectrally close to each other. Owing this wideness of bandwidth to the nonlinearity of the circuit escalates the jeopardy of chaotic behavior while the circuit is exposed to multiple energy sources. In this study, the recently introduced Duffing based energy harvester is analyzed under multiple excitations and finally, a safe margin is calculated to avoid tumultuous behaviors which may affect adjacent sensitive electrical systems. The mathematical analyses given in this paper can be generalized to other types of nonlinear resonators.
Parameter identification related to vertical dynamic of a self-stabilizing monorail vehicle

Currently, numerous single-track railway lines are disused due to economic reasons. However, one way they could be reactivated for a bidirectional on-demand service traffic by small vehicles that use only one rail. MONOCABs are such small cabin-like vehicles, stabilized by a system of control moment gyroscopes and a trim mass. They could make an important contribution to improve the mobility offer especially in rural areas. Regarding the MONOCAB, there is currently no reference in comparison with other vehicles. It is mandatory to gain experience before transferring such a new vehicle concept into commercial operation. Especially the safe and robust commissioning of the stabilization control system is crucial and therefore requires an elaborated procedure. At this step, parameters related to the vertical dynamics have to be determined beforehand. This paper presents a comparative investigation of methods to estimate the moment of inertia and gravitational torque constant. Multiple methods in time-domain and frequency-domain are experimentally evaluated and compared with each other. Experimental tests are carried out with a full-scale monorail vehicle.
A novel hardware-efficient ergodic sequential logic spiking neural network and reproductions of biologically plausible spatio-temporal phenomena towards development of neural prosthetic device

An ergodic sequential logic (SL) neuron model is designed and it is shown that the model can reproduce various nonlinear responses of neurons. Using the neuron model, a novel ergodic SL spiking neural network is presented. It is shown that the network can reproduce biologically plausible spatio-temporal phenomena, (e.g., fundamental synchronization phenomenon and complicated chimera phenomenon) which are typically observed in the brain. In addition, the network is implemented by a field programmable gate array and it is shown that the presented network is more hardware-efficient compared to a commonly used digital processor spiking neural network. Furthermore, it is discussed that the presented network will contribute to develop a small and low power brain prosthetic device.