Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Bags of Binary Words for Place Recognition in Challenging Scenarios

To perform autonomous tasks, robots in real-world environments must be able to navigate in dynamic and unknown spaces. To do so, they must recognize previously seen places to compensate for accumulated positional deviations. This task requires effective identification of recovered landmarks to produce a consistent map, and the use of binary descriptors is increasing, especially because of their compact representation. The visual Bag-of-Words (BoW) algorithm is one of the most commonly used techniques to perform appearance-based loop closure detection quickly and robustly. Therefore, this paper presents a behavioral evaluation of a conventional BoW scheme based on Oriented FAST and Rotated BRIEF (ORB) features for image similarity detection in challenging scenarios. For each scenario, full-indexing vocabularies are created to model the operating environment and evaluate the performance for recognizing previously seen places similar to online approaches. Experiments were conducted on multiple public datasets containing scene changes, perceptual aliasing conditions, or dynamic elements. The Bag of Binary Words technique shows a good balance to deal with such severe conditions at a low computational cost.
Comparison of Deep Reinforcement Learning Algorithms in Enhancing Energy Trading in Microgrids

This paper aims to introduce a solution to Sudan’s inadequate electricity supply; focusing on current unconnected rural areas and the high cost of connecting these areas to Sudan’s national grid. Microgrids were introduced as a viable option to create small scale distributed grids that depend solely on renewable energy to generate sufficient electricity to satisfy their loads. The paper also aims to enhance the usability of Microgrids by introducing a Machine learning technique to their secondary control that uses energy trading to ensure that all loads in islanded Microgrids are secured. The algorithm uses Reinforcement learning as control for the trading procedure. Data was extracted from a Matlab simulation and was then used to enhance the design of the Reinforcement learning environment. A generic environment for microgrids was designed and implemented which be further used in Reinforcement learning smart grids applications. A set of trading rules were implemented so that the Reinforcement Learning agents can use them across three Microgrids. The agent sees the three micro grids as one primary and two acting as trading game players. Two deep Reinforcement learning algorithms were explored as a solution; the first was an on-policy algorithm; Proximal Policy Optimization (PPO), and the second was an off-policy algorithm; Deep Deterministic Policy Gradients (DDPG). The results of applying both algorithms at three villages in Northern Kordufan State, Hamza ELsheikh, Tannah, and Um Bader were then compared. The algorithms achieved grid equilibrium without any grid loss and achieved profit from the trading process, reducing the time of return for the initial cost of the microgrid.
Impact of Tick Size Reduction on Liquidity Index in Tokyo Stock Exchange

Tick size is minimum price unit which investors buy or sell stock shares in stock market. Tick size is reduced with the aim of improving liquidity. Liquidity means how easily investors buy or sell stock shares. Tokyo Stock Exchange (TSE) introduced new tick size rules twice in 2014. In phase 1 (P1), it enforced on January 14th, 2014. In phase 2 (P2), it enforced on July 22nd, 2014. Actually, liquidity is measured liquidity measures. Danyliv, Bland, and Nicholass proposed new liquidity measure, Liquidity Index (LIX). This is the relationship between price change and trading volume. It shows how much the stock price is moved by trading. As for LIX, there is no study of the impact of tick size reduction. And in previous study, Purwoto and Tandelilin showed the relationship between stock price and liquidity measure when tick size was reduced. Bessembinder also showed the relationship between market value and liquidity measure when tick size was reduced. Additionally, Amihud showed the relationship between stock return and illiquidity. So, in this paper, we have following two research purposes. First, we investigate how much tick size reduction impacts on LIX. Second, we investigate how much stock price, market value, and stock return affect LIX when tick size is reduced. As a result, we got following three results. First, tick size reduction increase LIX in P1, and decrease in P2. Second, there is interaction effect of tick size reduction and stock price in P2. Third, there is interaction effect of tick size reduction and stock return.
Perceived Employability and Career Adaptability of Engineering Graduates

The new demands of the labor market have led to the emergence of more comprehensive and complexes employability models, including not only a set of skills determining employability but also a subjective dimension that integrates personal beliefs and attitudes. Besides that, there is also evidence that individuals' psychological resources, namely in terms of career adaptability, are significantly associated with further employability. In this paper, we present data on the perceptions of employability (four dimensions: my university, my study field, the labor market, and self-efficacy) and on the adaptability resources (concern, control, curiosity and confidence) of master's final-year students (n=362), comparing a group of engineering students with students from other study fields (Social Sciences and Humanities and Economics). Regarding the perceptions of employability, the obtained results demonstrate that engineering graduates present more positive perceptions in the dimensions of study field and of external labor market, comparing with their colleagues from other fields. Concerning career adaptability resources, the differences are reversed, with engineering students with scoring lower in the dimensions of concern and confidence.
Ergowear: an ambulatory, non-intrusive, and interoperable system towards a Human-aware Human-robot Collaborative framework

Motivated by industry 5.0 paradigm and Human-robot collaboration (HRC) technology, this paper presents the first steps of the design and development of Ergowear, a wearable, non-intrusive, interoperable upper body inertial Motion Capture system. This system was developed aiming to be used as a sensing technology to make the robot aware of the human’s intentions and states and, therefore, achieving human-awareness. Consequently, it was developed to run on ROS2, to ease its integration in a HRC framework. This work depicts the design process and development of the prototype. Firstly, the system requirements are presented along with the system components and architecture. To verify interoperability, a protocol was designed to test the Ergowear’s hardware, namely its autonomy, storing capacity, wireless communication’s performance, and overall mechanical robustness. Overall, the achieved results are within the specified technical requirements, presenting the Ergowear as a promising sensing technology to be integrated within a Human-aware HRC framework.
Designing Contracts for Trading Private and Heterogeneous Data Using a Biased Differentially Private Algorithm

Personal information and other types of private data are valuable for both data owners and institutions interested in providing targeted and customized services that require analyzing such data. In this context, privacy is sometimes seen as a commodity: institutions (data buyers) pay individuals (or data sellers) in exchange for private data. In this study, we examine the problem of designing such data contracts, through which a buyer aims to minimize his payment to the sellers for a desired level of data quality, while the latter aim to obtain adequate compensation for giving up a certain amount of privacy. Specifically, we use the concept of differential privacy and examine a model of linear and nonlinear queries on private data. We show that conventional algorithms that introduce differential privacy via zero-mean noise fall short for the purpose of such transactions as they do not provide a sufficient degree of freedom for the contract designer to negotiate between the competing interests of the buyer and the sellers. Instead, we propose a biased randomized algorithm to generate differentially private output and show that this algorithm allows us to customize the privacy-accuracy tradeoff for each individual. We use a contract design approach to find the optimal contracts when using this biased algorithm to provide privacy and show that under this combination the buyer can achieve the same level of accuracy with a lower payment as compared to using the conventional, unbiased algorithms, while at the same time incurring lower privacy loss for the sellers.
E-D-Net: Automatic Building Extraction From High-Resolution Aerial Images With Boundary Information

The automatic extraction of buildings from high-resolution aerial imagery plays a significant role in many urban applications. Recently, the convolution neural network (CNN) has gained much attention in remote sensing field and achieved a remarkable performance in building segmentation from visible aerial images. However, most of the existing CNN-based methods still have the problem of tending to produce predictions with poor boundaries. To address this problem, in this article, a novel semantic segmentation neural network named edge-detail-network (E-D-Net) is proposed for building segmentation from visible aerial images. The proposed E-D-Net consists of two subnetworks E-Net and D-Net. On the one hand, E-Net is designed to capture and preserve the edge information of the images. On the other hand, D-Net is designed to refine the results of E-Net and get a prediction with higher detail quality. Furthermore, a novel fusion strategy, which combines the outputs of the two subnetworks is proposed to integrate edge information with fine details. Experimental results on the INRIA aerial image labeling dataset and the ISPRS Vaihingen 2-D semantic labeling dataset demonstrate that, compared with the existing CNN-based model, the proposed E-D-Net provides noticeably more robust and higher building extraction performance, thus making it a useful tool for practical application scenarios.
Terahertz Wireless Communications With Flexible Index Modulation Aided Pilot Design

Terahertz (THz) wireless communication is envisioned as a promising technology, which is capable of providing ultra-high-rate transmission up to Terabit per second. However, some hardware imperfections, which are generally neglected in the existing literature concerning lower data rates and traditional operating frequencies, cannot be overlooked in the THz systems. Hardware imperfections usually consist of phase noise, in-phase/quadrature imbalance, and nonlinearity of power amplifier. Due to the time-variant characteristic of phase noise, frequent pilot insertion is required, leading to decreased spectral efficiency. In this paper, to address this issue, a novel pilot design strategy is proposed based on index modulation (IM), where the positions of pilots are flexibly changed in the data frame, and additional information bits can be conveyed by indices of pilots. Furthermore, a turbo receiving algorithm is developed, which jointly performs the detection of pilot indices and channel estimation in an iterative manner. It is shown that the proposed turbo receiver works well even under the situation where the prior knowledge of channel state information is outdated. Analytical and simulation results validate that the proposed schemes achieve significant enhancement of bit-error rate performance and channel estimation accuracy, whilst attaining higher spectral efficiency in comparison with its classical counterpart.
Internal Task-Aware Command Scheduling to Improve Read Performance of Embedded Flash Storage Systems

Many NAND flash storage systems access flash memories by generating flash commands to process input/output (I/O) requests from a host system. Because the order in which flash commands are processed affects the I/O performance, command scheduling has been performed in previous studies to prioritize flash read commands for improving the read performance. However, in addition to the flash commands for accessing user data requested by the host, flash commands for internal tasks that improve the I/O performance and operation efficiency of the flash storage are issued. Particularly in embedded flash storage, the flash commands by the map cache management have a significant influence on the latency of I/O requests. The processing time of the I/O request depends on the execution time of the flash commands for mapping information and the flash commands for user data. In this paper, we propose command scheduling to improve read performance to address the occurrence of map flash commands. Priority is given to the flash read and program command from the read request, and among these commands, the flash command originating from the read request, which has a shorter processing time, is executed first. Consequently, the waiting time of the flash command is reduced, thereby improving the read latency. Experiments conducted with real workloads show that the proposed scheduling scheme reduces the average read latency by up to 51% compared with the existing scheduling scheme and demonstrates an effective performance improvement in a small map cache.
Galvanically Isolated, Power and Electromagnetic Side-Channel Attack Resilient Secure AES Core with Integrated Charge Pump based Power Management

A galvanic isolation (GI) technique for cryptographic cores is proposed to mitigate power and electromagnetic (EM) sidechannel analysis (SCA) attacks. The design uses deep N-well technology and an integrated charge pump-based power delivery and management to completely isolate VCC, VSS, and substrate nodes from the external supply and ground pins, improving the SCA resilience due to supply as well as ground bounce. Measured results from a 128-bit Advanced Encryption Standard (AES) core implemented in a 40nm CMOS show $\gt600\mathrm{x}$ and $\gt220\mathrm{x}$ improvement against a correlation power analysis (CPA) and coarse-grained EM SCA attack, respectively, while operating at 20% lower frequency, consuming 2.3x more power, and occupying 0.0136 mm2 larger area.
Developing a Topic Analysis Instant Feedback System to Facilitate Asynchronous Online Discussion Performance

This research presents a Topic Analysis Instant Feedback System (TAIFS) that can explore and visualize the topics according to the contents of the asynchronous online discussion from the learners of different groups. The TAIFS was developed based on Latent Dirichlet Allocation (LDA) to help learners grasp the change of overall discussion topics in real-time and see the similarities and differences of the discussion among different groups, thereby effectively improving their asynchronous online discussion performance. A quasi- experimental research method was adopted to examine the effects of the proposed TAIFS-assisted online discussion on learners’ socio-scientific reasoning performance including the complexity and multiple perspectives. A total of 61 students from two classes in a high school in Taipei city were recruited as the research participants. One class of 31 students was assigned to the experimental group using the TAIFS-assisted discussion board for discussing the socio-scientific issue (SSI) entitled "Utilization of the Coastal Area", while the remaining 30 students from another class were assigned to the control group using a general Moodle discussion board. Analytical results show that the experimental group was significantly better than the control group in the overall discussion performance and in the complexity and multiple perspectives. The contribution of this study is to propose an innovative and effective learning tool that can promote learners’ asynchronous online discussion performance to discuss socio-scientific issues considering more factors and from different perspectives.
Fully Inkjet-Printed Carbon Nanotube-PDMS-Based Strain Sensor: Temperature Response, Compressive and Tensile Bending Properties, and Fatigue Investigations

Printed and flexible sensors are in the focus of recent efforts to establish the advantages of low-cost manufacturing techniques such as screen printing or inkjet printing for printed electronical applications. Devices based on conductive carbon nanotube (CNT) networks within polymeric matrices such as polydimethylsiloxane (PDMS) are already exceeding mere technological demonstrations. Therefore, we investigate the application-oriented behaviour of fully inkjet-printed CNT/PDMS strain sensors under different conditions such as short- and long-term performance. The sensors exhibit a quasi-linear piezoresistive behaviour with vanishing hysteresis to tensile strain. Significant differences in the resistive response between compressive and tensile strain suggest complex re-orientation mechanisms of CNTs inside the matrix. No clear indication for this phenomenon could be observed in the evolution of the CNT network resistance during fatigue measurements within an uncured or cured PDMS matrix, where both scenarios exhibit no visual degradation. However, these measurements over thousands of cycles show different permanent changes in the overall device resistance exhibiting damages but also recovery in the network. Considering these findings facilitates the development of printed sensor devices.
Deep Learning-Based Smart IoT Health System for Blindness Detection Using Retina Images

Deep Learning-based Smart Healthcare is getting so much attention due to real-time applicability in everyone life’s, and It has obtained more attention with the convergence of IoT. Diabetic eye disease is the primary cause of blindness between working aged peoples. The major populated Asian countries such as India and China presently account for millions of people and at the verge of an eruption of diabetic inhabitants. These growing number of diabetic patients posed a major challenge among trained doctors to provide medical screening and diagnosis. Our goal is to leverage the deep learning techniques to automate the detection of blind spot in an eye and identify how severe the stage may be. In this paper, we propose an optimized technique on top of recently released pre-trained EfficientNet models for blindness identification in retinal images along with a comparative analysis among various other neural network models. Our fine-tuned EfficientNet-B5 based model evaluation follows the benchmark dataset of retina images captured using fundus photography during varied imaging stages and outperforms CNN and ResNet50 models.
Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-of-the-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.
A Generalized Reverse-Electrodialysis Model Incorporating Both Continuous and Recycle Modes for Energy Harvesting From Salinity Gradient Power

Salinity gradient power (SGP) derived from sea and fresh water through reverse electrodialysis (RED) is an emerging discipline with huge potential for carbon-free energy harvesting. SGP technology is still in an infant stage and there is a need for accurate mathematical tools to study its energy harvesting process. Previous models assume a constant salinity gradient with a continuous flow of sea water with constant salinity. In the case of recycling used sea water, such assumption is no longer valid because the salinity gradient reduces with operating time. This paper presents a generalized RED model that covers both of the continuous and recycle modes. It combines an improved kinetic battery module (KiBaM) with an electrical circuit module (ECM), for capturing the behaviors of both RED stacks operating in continuous mode (C-mode) and those in recycle mode (R-mode). To intuitively describe the compound effects of salinity variation and concentration polarization on electrical performance of the R-mode RED stack, nonlinear capacity effects (i.e., recovery effect and rate capacity effect) and self-consumed effect are introduced into the proposed model. The derivation and extraction procedures of the proposed model are included. An RED stack prototype with 50 pairs of alternating membranes is constructed for model validation. Various pulsed and constant current discharge experimental tests are performed to validate the accuracy of the proposed model.
A Dual-Domain CNN-Based Network for CT Reconstruction

Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.
TypoSwype: An Imaging Approach to Detect Typo-Squatting

Typo-squatting domains are a common cyber-attack technique. It involves utilising domain names, that exploit possible typographical errors of commonly visited domains, to carry out malicious activities such as phishing, malware installation, etc. Current approaches typically revolve around string comparison algorithms like the Demaru-Levenschtein Distance (DLD) algorithm. Such techniques do not take into account keyboard distance, which researchers find to have a strong correlation with typical typographical errors and are trying to take account of. In this paper, we present the TypoSwype framework which converts strings to images that take into account keyboard location innately. We also show how modern state of the art image recognition techniques involving Convolutional Neural Networks, trained via either Triplet Loss or NT-Xent Loss, can be applied to learn a mapping to a lower dimensional space where distances correspond to image, and equivalently, textual similarity. Finally, we also demonstrate our method’s ability to improve typo-squatting detection over the widely used DLD algorithm, while maintaining the classification accuracy as to which domain the input domain was attempting to typo-squat.