Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Washing Solvents on the Properties of Air-Synthesized Perovskite CsPbBr3 Quantum Dots for Quantum Dot-Based Light-Emitting Devices

In order to improve the stability and cost of perovskite CsPbBr3 and establish a preparation process for quantum dot materials with excellent luminescence properties, CsPbBr3 quantum dots were synthesized by the hot injection method and the ice bath method in air ambient. Then, a proprietary purification system was used to improve the properties of the quantum dots and prepare high-quality perovskite quantum dot films. Optimal material and light characteristics were exhibited with ethyl acetate:hexane as the purification solvent. In the solution form, the photoluminescence wavelength could be maintained at 508 nm with almost no shift compared with other conditions, and the quantum yield could reach 99%; however, superior performance was obtained in the thin-film form, which exhibited little to no shift in the photoluminescence wavelength and, surprisingly, achieved a quantum yield of 91.6% with a narrow PL emission with a full width at half-maximum of 28 nm. In addition, exceptional thin-film uniformity was observed from scanning electron microscopy image analyses. The CsPbBr3 quantum dot light-emitting diode purified with ethyl acetate:hexane demonstrated an open-circuit voltage of 4 V, a maximum luminous brightness of 488 cd $\text{m}^{-2}$ , and a maximum external quantum efficiency of 0.14%.
HPBS: A Hybrid Proxy Based Authentication Scheme in VANETs

As a part of intelligent transportation, vehicle ad hoc networks (VANETs) have attracted the attention of industry and academia and have brought great convenience to drivers. As an open communication environment, any user can broadcast messages in the system. However, some of these users are malicious users and malicious users can broadcast false messages to interfere with the normal operation of the system. Therefore, we needed to authenticate the identity of the message sender. Currently, there are two main authentication methods in VANETs, one using public key infrastructure (PKI) to verify message integrity and sender identity, and the other using anonymous authentication schemes. Due to the high computational and transport overhead involved in validation, the certification efficiency of most existing schemes is not satisfactory. Therefore, these schemes are generally not applicable to real-world scenarios. In order to improve the efficiency of certification and satisfy the security requirements, in this paper, we proposed a hybrid proxy based authentication scheme (HPBS). In HPBS, by introducing the concept of agent vehicles and integrating identity-based and PKI-based hybrid authentication, we solved three problems in the VANETs environment: (1) improving the effectiveness of roadside units (RSUs) in terms of authenticating messages; (2) reducing the computational burden of RSUs; (3) protecting the privacy of users. The simulation results illustrate that the scheme not only ensures network security, but also greatly improves the efficiency of information verification.
A Compiler Comparison in the RISC-V Ecosystem

The GNU Compiler Collection (GCC) is the traditional compiler for most embedded systems, since it supports many different instruction set architectures (ISA) in its back-end. GCC has also been the first compiler that supported the RISC-V ISA. Since a while Clang/LLVM has gained more and more interest in the embedded software community. Recently, RISC-V is also supported in the LLVM back-end and maintained in the official LLVM release. In this paper we propose a benchmark environment for the comparison of compilers in the RISC-V ecosystem. We perform a comparison of GCC against LLVM for an embedded software benchmark considering compile time, size of the resulting binary, number of instructions and execution time. The results show that LLVM compiles faster in 88% of the experiments, while GCC and LLVM produce nearly the same binary size in 51% of the experiments. In 37% GCC wins and in 12% LLVM wins. In 94% of the experiments the difference between the resulting binary size in GCC and LLVM is +/-5%. The execution time analysis shows that in 42% of the experiments GCC and LLVM have nearly the same execution time clock cycles while in 40% GCC wins and in 18% LLVM wins.
Progress report on the LNE Thompson-Lampard Calculable Capacitor

This paper outlines a practical realization of the farad in the revised SI at LNE. To improve the traceability of the unit of capacitance in the SI with a target relative standard uncertainty of one part in 108, a new Thompson-Lampard calculable capacitor is currently developed. This paper presents the latest developments on the alignment of the electrodes and the positioning of the movable guard in the cavity.
A Privacy-Preserving Federated Learning System for Android Malware Detection Based on Edge Computing

This paper presents a privacy-preserving federated learning (PPFL) system for the detection of android malware. The proposed PPFL allows mobile devices to collaborate together for training a classifier without exposing the sensitive information, such as the application programming interface (API) calls and permission configuration, and the learned local model by each mobile device. This work implements the privacy-preserving federated learning system based on support vector machine (SVM) and secure multi-party computation techniques. It also demonstrates the feasibility using the Android malware dataset by National Institute of Information and Communication Technology (NICT), Japan. The presented experiments evaluate the performance of the trained classifier by the proposed PPFL system. The evaluation also compares the performance of the classifier of PPFL and that of centralized training system for the use cases of i) different data set and ii) different features on distinct mobile device. The results show that the performance of the PPFL classifier outperforms that of centralized training system. Moreover, the privacy of app information (i.e., API and permission information) and trained local models is guaranteed. To the best of our knowledge, this work is the first Android malware detection system based on privacy-preserving federated learning system.
Single Image Face Morphing Attack Detection Using Ensemble of Features

Face morphing attacks have demonstrated a severe threat in the passport issuance protocol that weakens the border control operations. A morphed face images if used after printing and scanning (re-digitizing) to obtain a passport is very challenging to be detected as attack. In this paper, we present a novel method to detect such morphing attacks using an ensemble of features computed on the scale-space representation derived from the color space for a given image. Given the limited availability of datasets representing realistic morphing attacks, we introduce and present a new print-scan image dataset of morphed face images. Experiments are carried out on the two different datasets and compared with sixteen existing state-of-art Morphing Attack Detection (MAD) mechanism based on single image MAD (S-MAD). The proposed approach indicates a superior MAD performance on both datasets suggesting the applicability in operational scenarios.
Functional Variability in Team-Handball Players during Balance Is Revealed by Non-Linear Measures and Is Related to Age and Expertise Level

Postural control is considered a key variable in team sports, such as handball, which require abilities strongly related to balance. However, postural control and its relationship to the performance of handball skills according to the players’ skill level and age has not been evaluated to date. This study analyzes the relationship between balance ability and team-handball performance according to age and expertise, applying a non-linear approach to balance assessment. Postural control from 114 male team-handball players was analyzed through the center of pressure (COP) during a balance task. Sport performance was measured by the accuracy and speed in throwing. Expert players threw faster, but not more accurately than recreational players. Balance performance was better for 18+ players (older than 18 years old) than those U12 (under 12 years old), but no differences were found according to their skill level. Players who threw with less accuracy showed slower COP velocity during the balance task and their moves were less irregular. Players who threw faster displayed more irregular and less auto-correlated COP movements. In conclusion, experienced team-handball players exhibited better balance performance, and this seems to be related to the maturation of the motor system more than to sport performance level. Nevertheless, non-linear measures of COP excursion revealed an exploratory behavior during balance in expert players, exhibiting more motion adjustments to reduce motor output error. Traditional variables measuring balance performance did not show sensitivity to this motor control process. A non-linear approach to balance assessment revealed functional variability during balance as an intrinsic characteristic of individuals’ motor control according to age and skill level.
Multisize plate detection algorithm based on improved Mask RCNN

The sorting of plates is an indispensable part of the plate processing production line. In order to achieve plate detection in complex detection scenarios, a multisize plate detection algorithm based on improved Mask RCNN is proposed. The model fusion method is used to introduce the DenseNet network structure to optimize the feature transfer path to make feature extraction more efficient. At the same time, the boundary distance constraint is added to the segmentation loss function, which makes the model more precise for the target with high stacking complexity and fuzzy boundary information. The experimental results show that the improved Mask RCNN performance is significantly improved, compared with other models, it achieves an optimization effect with an average accuracy of more than 98%.
Distributed Linear Parameter Varying Model Predictive Controller with Event-Triggered Mechanism for Nonholonomic Mobile Robot

This paper deals with the design of distributed linear parameter varying model predictive controller (LPVMPC) to solve consensus problem on nonholomonic model with event-triggered mechanism. The nonlinear dynamic of nonholomonic model is simplified into linear time variant model by discretizing with Euler method. A quadratic cost function is determined by including terminal state and varying weight matrices, in order to reduce offset due to modeling simplification. The control signal for very agents are calculated by solving quadratic programming problem. A local optimal state controller is integrated with LPV-MPC during eventtriggered mechanism. In order to reduce computational load, the predictive controller only performs optimization only when the trigger conditions are met. The proposed controller is also verified in case of numerical simulation test, and shown its capability to provide good response of nonholomonic mobile robot’s consensus protocol.
The BIPM Kibble Balance: improvements and measurements

This paper summarizes the various improvements to and measurements carried out with the BIPM Kibble balance for realizing the definition of the mass unit in terms of the Planck constant. Measurement uncertainty has been significantly reduced by a factor of four. Mass measurement of a 1 kg Pt/Ir artefact was carried out during three periods in 2019. The mass of the artefact was measured in vacuum with a standard uncertainty of about $50\ \mu\mathrm{g}$. Efforts are underway to further reduce measurement uncertainty which is dominated by the misalignment and the electrical measurements.
A New Link Prediction in Directed Networks Based on Attributes Fusion

Link prediction, which utilizes the information of endpoint and network structure to predict the unknown links between two nodes, has attracted much attention in recent years. The network topological attributes contain the structure attributes and node attributes. However, some existing methods focus on the node attributes, while others focus on the structure attributes. To solve this problem, we propose a prediction method based on attributes fusion which combines node attributes and structure attributes. In our proposed method, we first analyze the structural attributes based on common neighbors in directed networks and define the structural attribute similarity. Then the similarity contribution of the influence of the common neighbors to the predicted nonadjacent nodes is analyzed. Experimental results on 9 directed networks show that our proposed index achieves higher performance than existing mainstream baselines under the precision evaluation.
3D Wafer-to-Wafer Bonding Thermal Resistance Comparison: Hybrid Cu/dielectric Bonding versus Dielectric via-last Bonding

3D wafer-to-wafer bonding is a promising fabrication method to create 3D systems with a very high interconnect density. The thermal resistance of the 3D bonding interface can represent a significant contribution of the overall thermal resistance in the 3D chip stack and should therefore be accurately characterized. In this paper, we present the experimental characterization of two different types of 3D wafer-to-wafer bonding: Cu/dielectric hybrid bonding and via-last dielectric bonding. First, we introduce the wafer-level test vehicles and the characterization methodology for the analysis of both bonding interfaces. Then, we estimate the thermal resistance of the bonding interface based on a combination of temperature measurements and finite element thermal simulations. Benchmarking of the measurement results with available literature data on thermal resistance values of 3D interfaces shows that wafer-to-wafer bonding results in a reduction of the bonding layer thermal resistance of 5x and 20x for the hybrid bonding and dielectric bonding respectively, compared to the standard die-to-wafer bonding approach with micro-bumps and underfill.
RNN-VED for Reducing False Positive Alerts in Host-based Anomaly Detection Systems

Host-based Intrusion Detection Systems HIDS are often based on anomaly detection. Several studies deal with anomaly detection by analyzing the system-call traces and get good detection rates but also a high rate off alse positives. In this paper, we propose a new anomaly detection approach applied on the system-call traces. The normal behavior learning is done using a Sequence to sequence model based on a Variational Encoder-Decoder (VED) architecture that integrates Recurrent Neural Networks (RNN) cells. We exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to structure and optimize the model input-data representation. After the learning step, a one-class classification is run to categorize the sequences as normal or abnormal. The architecture may be used for predicting abnormal behaviors. The tests are achieved on the ADFA-LD dataset.
Feature Selection Optimization in Software Product Lines

Feature modeling is a common approach for configuring and capturing commonalities and variations among different Software Product Lines (SPL) products. This process is carried out by a set of SPL design teams, each working on a different configuration of the desired product. The integration of these configurations leads to inconsistencies in the final product design. The typical solution involves extensive deliberation and unnecessary resource usage, which makes SPL inconsistency resolution an expensive and unoptimized process. We present the first comprehensive evaluation of swarm intelligence (using Particle Swarm Optimization) to the problem of resolving inconsistencies in a configured integrated SPL product. We call it <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT (<inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>ptimization-based Software Product LIne Tool) and validate <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT with standard ERP, SPLOT (Software Product Lines Online Tools), and BeTTy (BEnchmarking and TesTing on the analYsis) product configurations along with diverse feature set sizes. The results show that Particle Swarm Optimization can successfully optimize SPL product configurations. Finally, we implement <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT as a decision-support tool in a real, local SPL setting and acquire subjective feedback from SPL designers which shows that the teams are convinced of the usability and high-level decision support provided by <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT.
Fabrication, Mechanical Modeling, and Experiments of a 3D-Motion Soft Actuator for Flexible Sensing

This paper introduces the modular design and manufacturing method, mechanical model, and test verification of a new type of soft actuator driven by fluid. First, the modular design scheme and driving principle of omnidirectional bending and elongation of the soft actuator are described. Three print-based elastic air cavities constrained by fire lines are distributed radially inside the actuator. The actuator can complete the 3 DOF motions of omnidirectional bending and elongation. From the basic principle of material mechanics, a novel mechanical model of the soft elastomer actuator is established. By numerically solving the nonlinear model, the relationship between actuator elongation/bending angle and driving pressure is obtained. The theoretical prediction and test results show that the deformation of the actuator exhibits a linear relationship with pressure when the chambers are charged. Additionally, the maximum allowable load force on the actuator terminal also exhibits good linearity when the driving pressure increases. Furthermore, the established mechanical model, which considers gravity effects can more accurately describe the features of bend and elongation of the actuator. The results shows that the proposed model is more convenient than the FEM models. This study provides theoretical support for accurate control of a soft actuator.
Graph Optimization Methods for Large-Scale Crowdsourced Mapping

Automotive players have recently shown an increasing interest in high-precision mapping, with the aim of enhancing vehicles safety and autonomy. Nevertheless, the acquisition, processing, and updates of accurate maps remains an economic challenge. Collaborative mapping through vehicles crowdsourcing represents a promising solution to tackle this problem. However, the potential scalability and accuracy provided by such an approach have yet to be studied and assessed. In this paper, we study the use of graph optimization in the scope of collaborative mapping. We build a map of geo-localized landmarks by crowdsourcing observations from multiple vehicles, and applying several successive map updates. We present different strategies to adapt graph optimization to the crowdsourced approach, and compare their performances in terms of map quality and scalability on simulation data. We show the critical requirement, in a long-term context, to ensure consistency of the map updates, and we propose a scalable solution which is able to build an accurate map of geolocalized landmarks.
Standalone Rate-Distortion FME Architecture

The high computing complexity of video encoders hinders real-time throughput using a software implementation. This drawback can be amended by implementing hardware accelerators for the most computing-intensive encoder tools to achieve real-time processing while maintaining energy efficiency. In this work, we investigate how approximate and precise versions of the Fractional Motion Estimation (FME), one of the most demanding steps of modern encoders, compare in terms of hardware resources and coding efficiency. To that purpose, we designed a hardware architecture for FME targeting real-time throughput for High Definition (HD) sequences and beyond. This architecture searches over 48 candidates and, unlike most related work, uses 16-bit wide horizontal interpolated samples, which is in compliance with the High Efficiency Video Coding (HEVC) reference software. Also, it integrates all FME required computations, including the rate cost of each candidate block, what makes it a standalone FME accelerator. We analyze the impacts of our design choices in terms of coding efficiency, using software simulations, and in terms of power/energy consumption, using results obtained from standard cell synthesis.
Polarimetric Covariance Gridmaps for Automotive Self-Localization

Automotive radars are becoming increasingly popular sensors for vehicle self-localization tasks because of their robustness and affordability. Newly available polarimetric sensors provide rich information about the occurring scattering mechanisms and thus enhance the uniqueness of landmarks leading to increased reliability. This paper presents a novel approach to incorporate the polarimetric information into a gridmap based on covariance matrices. Thereby, knowledge about up to three different scattering mechanisms per cell can be preserved. Such a discrimination is essential to account for the changing scattering information caused by varying viewing angles with respect to the landmark, which result from the vehicle passing by on the one hand and different mounting positions in a multi-sensor fusion setup on the other. To demonstrate suitability of the covariance representation, an evaluation of the proposed approach based on real-world experiments is presented. A substantial improvement in landmark recognition performance in difficult situations is achieved by taking advantage of the polarimetric covariance information.
KARNA for a Trustable Hardware

Due to globalization, hardware threats are emerging challenges for the design engineers in the integrated chip (IC)manufacturing unit. Nowadays application-specific integrated circuits (ASICs) are mostly replaced with reconfigurable field-programmable gate array (FPGAs), to provide a flexible system to support many applications. The secured reconfigurable hardware is the need for end-users to use the hardware in several trustable applications. The proposed method delivers secured hardware, such that whenever a malicious activity occurs, KARNA an embedded logic will detect the threat with the proof modeled in the design. To minimize computational complexity, specific nodes are selected to model the proof. So that KARNA will check only those nodes and this increases the detection accuracy as well. The proposed framework is validated on ISCAS'85 benchmark circuits, implemented on Xilinx Spartan 6 field-programmable gate array (FPGA), and evaluated to provide a trustable platform with PCH with minimal design overhead and accurate detection coverage.