Miracle-dz/UniFAR-FTUs huggingface dataset:
6D Pose Estimation from Point Cloud Using an Improved Point Pair Features Method

The Point Pair Features (PPF) method has been shown to be effective for pose estimation under clutter and occlusion. Our improved method mainly includes: (1) an approach for solving normal orientations of closed geometries based on Odd-even Rule; (2) an efficient downsampling approach by dividing a voxel grid into equivalent angle cells; (3) a verification step based on fitting points. The method is evaluated on real datasets of clutter and shows better performance as well as efficiency for 6D pose estimation compared to original PPF method.
2-layer Parallel SVM Network Based on Aggregated Local Descriptors for Fingerprint Liveness Detection

Fingerprint liveness detection is an effective way to ensure the security and reliability of fingerprint recognition algorithms against spoof fingerprint attacks. Local descriptors are one of the most widely studied fingerprint liveness detection algorithms. However, the performance of simplex local descriptors or simple voting models among multiple descriptors still can-not achieve satisfactory accuracy, robustness, and applicability. This paper proposes a 2-layer parallel Support Vector Machine (SVM) network to improve the classification performance of local descriptors and achieve 95.32% accuracy on the LivDet datasets (2009, 2011, 2013, and 2015). The experimental results and theoretical analysis indicate that the proposed 2-layer parallel SVM network based on aggregated local descriptors shows better detection accuracy and model robustness against adversarial attacks compared with simplex descriptors and state-of-the-art neural network structures. Besides, the 2-layer parallel SVM network can save training time through parallel computing, and achieve extremely high accuracy and reliability through ultra-high-dimensional descriptor classification.
Efficient Skyline Computation Over an Incomplete Database With Changing States and Structures

Skyline query has been studied extensively and a significant number of skyline algorithms have been proposed, mostly attempt to resolve the optimisation problem that is mainly associated with reduction in the processing time of skyline computations. While databases change their states and/or structures throughout their lifetime to reflect the current and latest information of the applications, the skyline set derived before changes are made towards the initial state of a database is no longer valid in the new state/structure of the database. The domination relationships between objects identified in the initial state might no longer hold in the new state. Nonetheless, computing the skylines over the entire new state/structure of the database is inefficient, as not all pairwise comparisons between the objects are necessary to be performed. In tackling the above issue, this paper proposes a solution, named <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> <italic>Skyline</italic>, which aims at avoiding unnecessary skyline computations when a database changes its state and structure due to a data definition operation(s) (add or remove a dimension(s)). This is achieved by identifying and retaining the prominent dominance relationships when pairwise comparisons are performed; which are then utilised in the process of computing a new skyline set. <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> <italic>Skyline</italic> consists of two optimisation components, namely: <inline-formula> <tex-math notation="LaTeX">$\Delta ^{+}$ </tex-math></inline-formula> <italic>Skyline</italic> which derives a new skyline set when a new dimension(s) is added to a database and <inline-formula> <tex-math notation="LaTeX">$\Delta ^{-}$ </tex-math></inline-formula> <italic>Skyline</italic> which derives a new skyline set when an existing dimension(s) is removed from a database. To make our solution more useful, it is applied on a database with incomplete data. Extensive experiments have been conducted to evaluate the performance and prove the efficiency of our proposed solution.
Enhancement of Magnetic Dipole Spontaneous Emission with Silicon Hollow Nanocuboid Resonator in Visible Range

The downscaling of optical devices into nanometer scale has the potential to enable exciting technologies such as all-optical computing and ultra-fast optical communications. With the advancements in nanofabrication techniques, confinement and manipulation of light below the diffraction limit have been achieved. However, the realization of ultra-fast and efficient nano light-sources is still a challenge. Utilizing Purcell effect with all-dielectric nanophotonics can be a promising solution to address the limitations. In this context, we offer an all-dielectric silicon hollow nanocuboid structure that can be employed to obtain enhanced magnetic dipole emission. According to the simulation results, silicon hollow nanocuboid provides a strong magnetic resonance and enhances magnetic dipole emission of a source in visible range. In this design, the hollow in the middle of structure supports accessible magnetic hotspots which can interact with magnetic dipole sources to force them emit with a higher decay rate. According to the results, the hollow nanocuboid design has a higher potential to increase the magnetic dipole decay rate enhancement in comparison to the previously reported structures and it can be used as a nano light-source in photonic integrated circuits, all-optical processors, and wideband optical communication networks.
Pseudo Bidirectional Linear Discriminant Analysis for Multivariate Time Series Classification

Multivariate time series (MTS) is a kind of matrix data, typically consisting of multiple variables measured at multiple time points. Due to the high dimensionality of MTS data, many methods for MTS classification have been proposed within the literature to reduce the redundancy in time or variable mode, but there is relatively little work on exploring the redundancy in both modes concurrently. In this paper we propose a new method for MTS classification based on bidirectional linear discriminant analysis (BLDA). The advantage is that BLDA can utilize label information in reducing the redundancy in time and variable modes simultaneously. Moreover, the existing procedures for BLDA suffer from two problems: (i) BLDA cannot be performed when one of the within-class matrices is singular; (ii) the computational burden could be very heavy when one of the data dimensionality is high. A new procedure for BLDA based on pseudo-inverse (PBLDA) and an efficient algorithm for PBLDA are proposed in this paper to overcome the two problems. The performance of our proposed method is illustrated through the experiments on a number of real MTS datasets.
Radio Receivers based on Spin-Torque Diodes as Energy Detectors

Radiofrequency receivers based on energy detection usually require bulky and costly antenna filters. A promising alternative comes from spin-torque diodes that inherently provide both rectifying and filtering effects. This work proposes a behavioral electrical model, extracted from measurement results, that considers both the non-linearity of the magnetic junction and its parasitic. This model has been used at system-level to design a frequency-shift keying receiver.
Optimal Iterative Learning Control for Discrete Linear Time-Varying Systems with Varying Trial Lengths

In this study, an optimal iterative learning control scheme is designed for discrete linear time-varying systems with varying trial lengths. Since the trial lengths are different from iteration to iteration, the theoretical information is used to compensate the absent section at the current iteration. In order to obtain the fast convergence speed, an iteration performance index is to maximize the declining quantity of the tracking error of two adjacent iterations, and the argument is the iteration-time-varying learning gain vector. The bigger the difference value, the faster the convergence speed. Furthermore, the optimal iterative learning control scheme is adaptive to the tracking error, which can guarantee the convergence of the tracking error. Numerical simulations are shown to verify the effectiveness of the proposed scheme.
Study of magnetoelastic resonance for chemical sensors: Ribbons vs microwires

In this work, the magnetoelastic resonance behaviour has been studied in amorphous metallic ribbons and microwires using a custom-made setup. First, optimal setup conditions were determined for both devices, then the frequency shift dependence on polymer mass deposition was studied. Both devices show a predictable response to the mass deposition of the polymer tested, making them suitable for contactless chemical sensors.
Reinforcement-Learning-Enabled Massive Internet of Things for 6G Wireless Communications

Recently, extensive research efforts have been devoted to developing beyond fifth generation (B5G), also referred to as sixth generation (6G) wireless networks aimed at bringing ultra-reli-able low-latency communication services. 6G is expected to extend 5G capabilities to higher communication levels where numerous connected devices and sensors can operate seamlessly. One of the major research focuses of 6G is to enable massive Internet of Things (mIoT) applications. Like Wi-Fi 6 (IEEE 802.11ax), forthcoming wireless communication networks are likely to meet massively deployed devices and extremely new smart applications such as smart cities for mIoT. However, channel scarcity is still present due to a massive number of connected devices accessing the common spectrum resources. With this expectation, next-generation Wi-Fi 6 and beyond for mIoT are anticipated to have inherent machine intelligence capabilities to access the optimum channel resources for their performance optimization. Unfortunately, current wireless communication network standards do not support the ensuing needs of machine learning (ML)-aware frameworks in terms of resource allocation optimization. Keeping such an issue in mind, we propose a reinforcement-learning-based, one of the ML techniques, a framework for a wireless channel access mechanism for IEEE 802.11 standards (i.e., Wi-Fi) in mIoT. The proposed mechanism suggests exploiting a practically measured channel collision probability as a collected dataset from the wireless environment to select optimal resource allocation in mIoT for upcoming 6G wireless communications.
Trajectory Planning Optimization for the Autonomous Three-Wheeled Omnidirectional Mobile Robot

Trajectory planning optimization in the robotics field identified as the series of actions that must be taken in order to generate the optimal path such that it improves the overall performance of the robot properties or reduces the consumption of the resources where the restriction system remains maintained. In this study, a new method is developed which hybridizes the Constrained Dynamic Inversion Based (CDIB) method with the Simulated Annealing (SA) algorithm in order to achieve improvements in the trajectory planning optimization (TPO) for the omnidirectional wheeled mobile robot (OWMR) with three omnidirectional orthogonal wheels. Numerical tests and simulations are presented aiming to ensure the efficiency and the competence of the hybridized method.
A Dictionary-based Oversampling Approach to Clinical Document Classification on Small and Imbalanced Dataset

Medical document classification is one of the prominent research problems in document classification domain. As medical discharge notes are collected from real patients, they are often imbalanced. Moreover, these datasets are usually too small for data-hungry models (specially in rare disease cases). Both of these issues can lead to poor classification performance. In this work a new probabilistic dictionary-based data augmentation approach is proposed to address these issues by oversampling on the minority class. This method works by creating new documents with high variety by using the extracted synonyms from WordNet with awareness of synonyms’ similarities with the original word. To verify the effectiveness of the proposed oversampling approach, three different machine learning methods are used to learn classifiers from the augmented clinical text datasets generated by the oversampling approach. The experimental results show that the proposed method not only provides better classification accuracy than the imbalanced dataset case, but also can outperform some existing augmentation methods on the dataset of 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge.
aTLAS: a Testbed to Examine Trust for a Redecentralized Web

The redecentralization of the web introduces new challenges on trusting data from other sources due to many unknown or even hidden parties. An application working trustworthy in a decentralized web must evaluate trust and take trustaware decisions autonomously without relying on a centralized infrastructure. This autonomy and the huge amount of available applications necessitates the web to be modelled as an open dynamic Multi-Agent System (MAS). To evaluate the trust of web agents, the most suitable trust models need to be identified and used. Despite the various trust models proposed in the literature for evaluating a web agent’s trust, the examination of them with different scenarios and configurations is not trivial. To address these challenges, we showcase aTLAS, a Trust Laboratory of Multi-Agent Systems which is a web-based wizard testbed for researchers and web engineers to evaluate trust models systematically. aTLAS will enable future research regarding trust evaluations in a decentralized web. We show the different features provided by aTLAS through a set of experiments.
Edge-Based Meta-ICP Algorithm for Reliable Camera Pose Estimation

Camera pose estimation is crucial for 3D surface reconstruction and augmented reality applications. For systems equipped with RGB-D sensors, the corresponding transformation between frames can be effectively estimated using the iterative closest point (ICP) algorithms. Edge points, which cover most of the geometric structures in a frame, are good candidates for control points in ICP. However, the depth of object contour points is hard to accurately measure using commercial RGB-D sensors. Inspired by the model-agnostic meta-learning (MAML) algorithm, this work proposes a meta-ICP algorithm to jointly estimate the optimal transformation for multiple tasks, which are constructed by sampled datapoints. To increase task sampling efficiency, an edge-based task set partition algorithm is introduced for constructing complementary task sets. Moreover, to prevent ICP from being trapped in local minima, a dynamic model adaptation scheme is adopted to disturb the trapped tasks. Experimental results reveal that the probability of unstable estimations can be effectively reduced, indicating a much narrower error distribution of repeated experiments when adopting re-sampled points. With the proposed scheme, the overall absolute trajectory error can be improved by more than 30% as compared to the related edge-based methods using frame-to-frame pose estimation.
Fractional-Order New Generation of $6k+1$-Order Repetitive Control for Three-phase Grid-connected Converters

This paper proposed a fractional-order new generation of <tex>$6k\pm 1$</tex>-order repetitive control scheme (FO-NG-<tex>$6k\pm 1$</tex> RC). FO-NG-<tex>$6k\pm 1$</tex> RC is composed of NG-<tex>$6k\pm 1$</tex> RC and a Farrow structure fractional delay filter based on Taylor series expansion. Since <tex>$6 k\pm 1$</tex> RC/FO-NG <tex>$6k\pm 1$</tex> RC occupies less digital memory space, it has better dynamic performance than CRC/FO-CRC, which is more suitable for the control of three-phase power systems dominated by the <tex>$6k\pm 1$</tex>-order harmonics. By updating filter coefficients online in real time, FO-NG-<tex>$6k\pm 1$</tex> RC can quickly and effectively deal with the frequency variations of grid-connected converters, and has good frequency adaptability. Through a simulation example applied to a three-phase grid-connected inverter, the effectiveness and superiority of the proposed FO-NG-<tex>$6k\pm 1$</tex> RC control scheme are fully verified.
On the Interplay Between Static and Dynamic Analysis for Mining Sandboxes

Due to the popularization of Android and the full range of applications (apps) targeting this platform, many security issues have emerged, attracting researchers and practitioners’ attention. As such, many techniques for addressing security Android issues appeared, including approaches for mining sandboxes. Previous research studies have compared Android test case generation tools for this specific goal. Our research aims to explore new techniques for mining sandboxes, especially we are interested in understanding the limits of both static and dynamic analysis in this process. Although the use of tests for mining sandboxes has been explored before, the potential to combine static analysis and dynamic analysis has not been sufficiently investigated yet. That is, in this thesis we will investigate the hypothesis that combining static and dynamic analysis techniques increases the process of mining Android sandboxes.
Clustering Nico Nico Douga Videos by Using the Distribution of Time-Synchronyzed Comments

Nico Nico Douga (NND) is one of the most famous social media platforms for sharing videos in Japan. There are 17 million videos posted to NND, for which users search by using keyword search and related video recommendation. However, it is difficult for the users to find interesting videos because videos generally are associated with only short texts and a few tags. In this paper, we present a method for clustering videos in NND by using the distribution of time-synchronized comments. Our method regards a video as the set of the users commenting on it and enables the clustering of videos based on users’ shared interests. In our experiment, we applied the proposed method to videos posted to NND, and evaluated our method by quantitatively comparing it with existing text-based methods and by qualitatively performing the subjective evaluation of clustering results. In the result of the quantitative evaluation, the proposed method showed a higher score of normalized mutual information than the existing methods when results of text-based methods and category metadata were used as correct results. The experimental results of the qualitative evaluation showed that the proposed method was as good as or better than the existing text-based methods.
Quarenteners vs. Chloroquiners: A framework to analyze how political polarization affects the behavior of groups

The worldwide COVID-19 pandemic has struck people’s lives over-night. With an alarming contagious rate and no effective treatments or vaccines, it has evoked all sorts of reactions. In this paper, we propose a framework to analyze and characterize groups’ behavior with opposed stances and the relation with the political polarization, using the Brazilian COVID polarized scenario as a case study. The pro/against social distance stances are represented by two Twitter groups referred to as Chloroquiners and Quarenteners. The framework encompasses: a) techniques to automatically infer from users their demographics and political orientation, b) topic modeling to discover the major concerns expressed by each group; c) network analysis and community detection to characterize their behavior as a social network group; d) analysis of linguistic characteristics to identify psychological aspects, and e) the characterization of information sources and external influences to evaluate the ”echo chamber effect”. These properties aim to explain the group’s behavior using the Identity Protective Cognitive theory, in which culturally grouped individuals give biased credit and dismiss evidence according to the predominant group beliefs. Our main findings confirm that the political polarization of Chloroquiners and Quarenteners influence the arguments of economy and life, and support/opposition to the president. As a group, the network of Chloroquiners is more closed and connected, and highly influenced by information passed through social networks. Quarenteners have a more diverse political engagement and are more influenced by formal media. In terms of psychological aspects, the use of Identity Protection Cognition provides more robust arguments to explain these behaviors.
Iterative Learning enhanced Integral Terminal Sliding Mode Control for Precision Motion Systems

The rapid development and applications of precision motion systems pose a great challenge on tracking performance improvement to complete various industrial or scientific tasks. In this paper, an iterative learning enhanced integral terminal sliding mode control (IL- ITSMC) is developed to further enhance the performance of such systems under repetitive trajectory and disturbance. For the generally used second-order model in precision motion systems, an integral terminal sliding surface is utilized to improve the steady-state performance and robustness to unexpected disturbance. A novel reaching law is also designed to realize the finite-time convergence of the sliding surface. In addition, an iterative learning law is proposed based on the sliding surface to compensate the repetitive term through updating the feedforward control input iteratively. The stability in time domain and convergence in iterative domain are proven theoretically based on the well-known Lyapunov theory, respectively. The simulation results on a pizeo-actuated stage with hysteresis nonlinearity demonstrate that the proposed IL-ITSMC achieves the best tracking performance through comparisons, and the convergence speed is improved significantly in comparison with ITSMC with traditional P-type ILC (PIL- ITSMC) for a 10Hz sinusoidal repetitive trajectory.
Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

Dynamic pricing is a beneficial strategy for firms seeking to achieve high revenues. It has been widely applied to various domains such as the airline industry, the hotel industry, and e-services. Dynamic pricing is basically the problem of setting time-varying prices for a certain product or service for the purpose of optimizing revenue. However, a major challenge encountered when applying dynamic pricing is the lack of knowledge of the demand-price curve. The demand-price curve defines the customer’s response towards changing the price. In this work, we address the dynamic pricing problem in case of unknown demand-price relation. This work introduces a less myopic pricing approach based on looking ahead for one or several future steps in our quest to optimize revenue. Specifically, the proposed formulation maximizes the summation of the immediate revenue and the expected future revenues of one or multiple look-ahead steps. A key benefit of the proposed approach is that it automatically strikes a balance between the conflicting goals of revenue maximization and demand learning, by producing less myopic and profitable prices. We provide a formulation for the presented look-ahead pricing approach, and we implement two variants of it: one-step and two-step look-ahead methods. Experiments are conducted on synthetic and real datasets to compare the proposed pricing methods to other pricing strategies in literature. The experimental results indicate that the proposed look-ahead methods outperform their counterparts in terms of the achieved revenue gain.
Novel Decimation Topology with Improved Jitter Performance for Clock and Data Recovery Systems

This paper discusses the effect of the decimation topology on the jitter performance of Bang-Bang phase detector-based digital Clock and Data Recovery (CDR) systems. It compares the jitter performance of the most common decimation topologies for a fixed input jitter power. A new decimation topology is also proposed to help decouple the transferred jitter (JTRAN) and the generated jitter (JGEN) from the CDR jitter tolerance (JTOL). The jitter performance of the newly proposed decimator has been verified with a MATLAB/simulink model of the CDR.