Miracle-dz/UniFAR-FTUs huggingface dataset:
User Independent Human Stress Detection

Stress plays an integral role in influencing one’s decision making capability, attention span, learning, and problemsolving capacity. Stress detection and modeling has been an active area of research in the fields of psychology and computer science in recent times. Psychologists quantify stress using affective states, which is the experience of feeling the underlying emotional state. Most of the work in classifying human stress was achieved using user-dependent models, incapable of generalizing to a new user. This causes a new user to spend a significant amount of their time in training the model to predict their affective states. In this paper, the authors propose a User-Independent classification model for human stress detection where a new user requires no prerequisite calibration of their affective state. The classification of affective states were carried out on the publicly available dataset WESAD to confirm our hypotheses. The authors of this paper demonstrate User-Independent models for Bi-affective state classification (Stress vs Non-Stress) and Tri-affective state classification (Stress vs Amusement vs Baseline) cases in addition to providing a novel classification model for the Multi-affective state classification case (Stress vs Amusement vs Baseline vs Meditation). The models achieved a classification accuracy of 95% in the Bi-affective state, 85% in Tri-affective state and 83% in the Multi-affective state cases.
Fully Projection-Free Proximal Stochastic Gradient Method With Optimal Convergence Rates

Proximal stochastic gradient plays an important role in large-scale machine learning and big data analysis. It needs to iteratively update models within a feasible set until convergence. The computational cost is usually high due to the projection over the feasible set. To reduce complexity, many projection-free methods such as Frank-Wolfe methods have been proposed. However, those projection-free methods have to solve a linear programming problem for every update of models which still leads to high computational cost for a complex feasible set, and can be unbearable in practical scenarios. Motivated by this problem, we propose a fully projection-free proximal stochastic gradient method, which has two advantages over previous methods. First, it enjoys high efficiency. The proposed method does not conduct projection directly but finds an approximately correct projection point with a very low computational cost. Second, it achieves tight and optimal convergence rates. Our theoretical analysis shows that the proposed method achieves convergence rates of <inline-formula> <tex-math notation="LaTeX">${\mathcal {O}\left ({\frac {1}{\sqrt {T}} }\right)}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathcal {O}\left ({\frac {\log T}{T} }\right)}\vphantom {{ {\mathcal {O}\left ({\frac {\log T}{T} }\right)}}_{j}}$ </tex-math></inline-formula> for convex and strongly convex functions, respectively. These convergence rates successfully match with the known lower bounds. Therefore, in this paper, we provide a valuable insight that <italic>some loss of accuracy of projection can improve the efficiency significantly, but does not impair convergence rates</italic>. Finally, empirical studies show that the proposed method achieves more than <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> speedup than previous methods.
Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas

This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot’s position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.
Composición de sedimentos en las zonas costeras de Costa Rica utilizando Fluorescencia de Rayos-X (FRX)

Using an energy dispersive X-Ray fluorescence analysis, simultaneous evaluation of K, Ca, Ti, Cr, Mn, Ge, Ni, Cu, Zn, Br, Rb, Sr and Pb in 74 marine sediment samples from the Costa Rica intertidal zones was conducted. Samples were collected between June 1999 and December 2001, from Caribbean and Pacific beaches of Costa Rica. Calcium and iron showed the highest abundances and are indicators of the natural origin of the sediments. Calcium is associated with biogenic processes such as coral reefs near the sampling sites and iron indicates a terrigenous origin. In general, the beaches of the Caribbean and North Pacific regions showed the greatest concentration of calcium. This is indicative of the abundant reef structures near these beaches. The beaches of the Central and South Pacific show the greatest iron concentrations, indicating an important lithosphere contribution and/or little contribution of calcium carbonate due to the poor development of coralline structures near the sampling sites. Finally, the analyses did not show evidence of elements associated with anthropogenic pollution. Only a northern section of Puerto Viejo beach showed high concentrations of lead, zinc and titanium, perhaps associated with hydrothermal sources.
Pose-guided Auto-Encoder and Feature-Based Refinement for 6-DoF Object Pose Regression

Accurately estimating the 6-DoF object pose from a single RGB image is a challenging task in computer vision. Though pose regression approaches have achieved great progress, the performance is still limited. In this work, we propose Pose-guided Auto-Encoder (PAE), which can distill better pose-related features from the image by utilizing a suitable pose representation, 3D Location Field (3DLF), to guide the encoding process. The features from PAE show strong robustness to pose-irrelevant factors. Compared with traditional auto-encoder, PAE can not only improve the pose estimation performance but also handle the ambiguity viewpoints problem. Further, we propose Feature-based Pose Refiner (FPR), which refines the pose from the extracted features without rendering. Combining PAE with FPR, our approach achieved state-of-the-art performance on the widely used LINEMOD dataset. Our approach not only outperforms the direct regression-based approaches with a large margin but also thrillingly surpasses current state-of-the-art indirect PnP-based approach.
Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Through Convolutional Neural Network

Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this article, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat gland. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show a 2.47% EER of internal fingerprints that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.
On the Effects of Data Distribution on Small-error Approximate Adders

Approximate computing is a technique to tradeoff accuracy and hardware cost. It increases energy efficiency that leverages application-level tolerance to few errors in many applications including image processing, multimedia, machine learning and wireless communication. Truncated adders, as the most conventional approximate architectures, compute the addition of most significant bits, and produce small errors with high probabilities. In prior art, the adders have been analyzed considering uniformly distributed input data. However, in digital signal processing, the data has a distribution which can be considered as Gaussian distribution characterized by a mean value and standard deviation. This paper studies the effects of input data distribution on small-error approximate adders. We will show that the effects of Gaussian distribution can be modeled for the approximate adder architectures.
Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an ‘Agree’ and a ‘Disagree’ network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.
A Power Allocation Method for Multistack PEMFC System Considering Fuel Cell Performance Consistency

The purpose of this work is to design a power allocation method (PAM) for multistack fuel cell system (MFCS). Due to the limitations of technology, large-scale application of proton exchange membrane fuel cell (PEMFC) in the field of rail transportation has not been implemented. Therefore, this study presents an MFCS to promote the use of PEMFCs in the high-power energy market. During the operation of the MFCS, since the output power of the FC varies dynamically with different operation conditions, the performance of each stack is usually inconsistent. A method for evaluating the degree of performance degradation (DOPD) of stacks is presented in this study. In addition, a fuel cell (FC) semiempirical model is also adopted to simulate the effects of aging on the stacks’ performance. In order to maintain the uniform performance of stacks and enhance the lifespan of the MFCS, a PAM that considers the DOPD of each stack is presented. Besides, in order to facilitate system expansion, this article proposes a virtual droop control method to realize power splitting among the stacks. Also, a voltage control strategy is presented to compensate for the bus voltage drop caused by the droop control. The effectiveness and practicability of the presented PAM and MFCS are verified on the hardware-in-the-loop test bench constructed by RT-LAB.
Design and Control of an Index Finger Exoskeleton with Cable-Driven Translational Joints

This paper proposes a new cable-driven wearable index finger exoskeleton to exercise flexion and extension of the index finger. This exoskeleton adopts an opportune constraint planar four-bar mechanism with an actively driving translational joint to reduce vertical space occupation and to achieve joint axis alignment for human-robot kinematic compatibility. The mechanical structure of the proposed exoskeleton is further optimized to minimize the exoskeleton's volume. The proposed exoskeleton is fabricated by 3D printing, has two active degrees of freedom (DoFs) configured in the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints individually, and can be easily worn by buckling the Velcro straps. Moreover, to ensure patients move their fingers freely after powering down, a decoupling mechanism is designed to remove the coupling between the motor and the exoskeleton. Due to the fact that the human's hand main tremble unconsciously or pick up a load suddenly, such unexpected disturbances may influence the control performance of the proposed exoskeleton. To this end, the active disturbance rejection control (ADRC) algorithm is adopted to reduce the influence of external disturbances. Finally, the functionality of the proposed exoskeleton is verified by experiments, and the experiments also validate the performance of the ADRC algorithm by comparing to the traditional proportional control.
Metasurface aided Biophysical Differentiation of Radiated Cancer Cells: C band to THz perspective

A resonating plasmon like behavior is obtained when a metasurface composite is employed to sense and acquire the real-time carcinoma biophysical information. Miniaturization has been done with respect to frequency in order to combat the skin depth challenges of THz considering its non ionization capabilities. If cancerous cells are programmed to be ablated, absorbance property of metasurface is utilized for raising the temperature using thermal analysis. The threshold level of power density for the minimal energy that the cells can absorb without dielectric property changes is determined experimentally in the far field. Metamaterial trapped non radiative decay raises the temperature and the observed changes are applied for explicitly dissipating enough heat to initiate cell death. Thus apoptosis is analyzed with photonic manipulation of the spectroscopic response of cell complex permittivity on a metasurface design and related to the irradiated power density illuminating the sensor during measurement at 4GHz.
Instruction-level Reliability Improvement for Embedded Systems

With the increasing number of applications in embedded computing systems, it became indispensable for the system designers to consider multiple objectives including power, performance, and reliability. Among these, reliability is a bigger constraint for safety critical applications. For example, fault tolerance of transportation systems has become very critical with the use of many embedded on-board devices. There are many techniques proposed in the past decade to increase the fault tolerance of such systems. However, many of these techniques come with a significant overhead, which make them infeasible in most of the embedded execution scenarios. Motivated by this observation, our main contribution in this paper is to propose and evaluate an instruction criticality based reliable source code generation algorithm. Specifically, we propose an instruction ranking formula based on our detailed fault injection experiments. We use instruction rankings along with the overhead tolerance limits and generate a source code with increased fault tolerance. The primary goal behind this work is to improve reliability of an application while keeping the performance effects minimal. We apply state-of-the-art reliability techniques to evaluate our approach on a set of benchmarks. Our experimental results show that, the proposed approach achieves up to 8% decrease in error rates with only 10% performance overhead. The error rates further decrease with higher overhead tolerances.
A Secure and Fine-Grained Scheme for Data Security in Industrial IoT Platforms for Smart City

With the high popularity of IoT devices, industrial IoT platforms, such as smart factories and oilfield industrial control systems, have become a new trend in the development of smart city. Although various manufacturers pay wide attention to the different functional requirements of IoT platforms, they seldom consider security issues, especially in terms of data security, which has led to a large number of cases of privacy leakage. Some works have been made to provide secure and reliable communication solutions for industrial IoT platforms, unfortunately, as different communication protocols and interaction models are adopted in different scenarios, these solutions are mainly isolated and fragmented. Therefore, it is an urgent challenge to construct a universal cross-platform secure communication scheme for industrial IoT platforms. In this article, we analyze the logic and requirements of different industrial IoT scenarios to abstracts them into a universal model. We summarize the possible attacks on different industrial IoT platforms and design a security scheme to capture these attacks based on the conditional proxy re-encryption primitive. The proposed scheme ensures that data cannot be accessed by an unauthorized user. We also evaluate the security and performance of our scheme, and the experimental results show that our scheme can achieve the functionality and security requirements with low overhead.
Automated Diagnosis of Skin Lesions

Recent years have seen significant progress in the automatic diagnosis of pigmented skin lesions, including advances in self-surveillance technologies accessible to patients and computer-aided diagnosis (CAD) tools for dermatologists. Rapid advances in mobile technologies and applications are playing a central role in providing educational aids and self-surveillance tools for patient use. At the same time, machine learning, specifically, deep learning is a fast-growing field that is being used for multiple medical imaging related problems, such as skin lesions classification. Recent studies based on deep networks produced promising results which have the potential to change the landscape of skin lesion diagnosis. Systems created based on these new advancements aim to provide support for both dermatologists in the decision making process and for patients that do not have access to skin professionals. This paper focuses on the current state of automated skin lesion diagnosis, while also providing a comprehensive view into the challenges and opportunities in dermatology care.
Design of 3D-printed assembly mechanisms based on special wooden joinery techniques and its application to a robotic hand

Recently, it has become possible to easily design and fabricate robotic systems in the laboratory and at home due to the recent development of 3D printer technology. On the other hand, the strength of the plastic materials used in reasonably priced 3D printers and the accuracy of the printed parts are generally low. These problems affect the part-joining quality. Therefore, this paper describes a design method inspired by ancient Japanese wooden joinery techniques for assembling 3D-printed parts and presents the design of a robotic hand as its application. The joinery techniques use special shapes to assemble components and allow us to assemble the robotic hand without glue, screws or nails and to easily disassemble it.
A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this article, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.
Reinforcement Learning Based Manipulation Skill Transferring for Robot-assisted Minimally Invasive Surgery

The complexity of surgical operation can be released significantly if surgical robots can learn the manipulation skills by imitation from complex tasks demonstrations such as puncture, suturing, and knotting, etc. This paper proposes a reinforcement learning algorithm based manipulation skill transferring technique for robot-assisted Minimally Invasive Surgery by Teaching by Demonstration. It employed Gaussian mixture model and Gaussian mixture Regression based dynamic movement primitive to model the high-dimensional human-like manipulation skill after multiple demonstrations. Furthermore, this approach fascinates the learning and trial phase performed offline, which reduces the risks and cost for the practical surgical operation. Finally, it is demonstrated by transferring manipulation skills for reaching and puncture using a KUKA LWR4+ robot in a lab setup environment. The results show the effectiveness of the proposed approach for modelling and learning of human manipulation skill.