Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change

Designing a profitable trading strategy plays a critical role in algorithmic trading, where the algorithm can manage and execute automated trading decisions. Determining a specific trading rule for trading at a particular time is a critical research problem in financial market trading. However, an intelligent, and a dynamic algorithmic trading driven by the current patterns of a given price time-series may help deal with this issue. Thus, Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment. A comprehensive representation of the environment states is indeed vital for proposing a dynamic algorithmic trading using RL. Therefore, we propose a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold. We refer to the proposed algorithmic trading approach as the DCRL trading strategy. In addition, the proposed DCRL trading strategy was trained using the Q-learning algorithm to find an optimal trading rule. We evaluated the DCRL trading strategy on real stock market data (S&P500, NASDAQ, and Dow Jones, for five years period from 2015-2020), and the results demonstrate that the DCRL state representation policies obtained more substantial trading returns and improved the Sharpe Ratios in a volatile stock market. In addition, a series of performance analyses demonstrate the robust performance and extensive applicability of the proposed DCRL trading strategy.
Segmentation of Cervical Cell Images Based on Generative Adversarial Networks

The segmentation of cervical cell in liquid-based smear image plays an important role in cervical cancer detection. Despite of research for many years, it is still a challenge for the complexity of cell images such as poor contrast, cell irregularity, and overlapping. To solve this problem, a novel method is proposed based on Cell-GAN - a generative adversarial network. Firstly, the Cell-GAN is trained to learn a probability distribution of cell morphology by comparing the difference between the generated single-cell images and annotated single-cell images. Thus, the Cell-GAN has the ability to judge the integrity of a cell and treat other cellular information of a cell image, except for overlapping parts, as the background. Then, a complete single-cell image is generated by the trained Cell-GAN for each cell, which is located by a guide factor. The guide factor is constructed by a part of the cell to be segmented, such as the nucleus, to help Cell-GAN locate the cell and avoid generating a multi-cell image in the presence of overlapping, which means the contours of cells still cannot be distinguished. Finally, the segmentation line is defined by the contour of the generated cell, and the input image is cropped using the cell size information. The cropped image is reused for image generation until the area of generated cell varies within a small range.The proposed method is evaluated on the segmentation of single-cell images and overlapping cell images and obtained significant values of 94.3% DC, 7.9% FNRo for single-cell images and 89.9% DC, 6.4% FNRo for overlapping cell images respectively. The experimental results indicate that the proposed method can adaptively approach the boundary lines of cells to handle with different cases of overlapping in cervical cell images through what learned by the Cell-GAN. The proposed method outperforms most current methods in both segmentation accuracy and robustness.
Towards More Effective Data Visualization Methods Using Haptics

Cognitive overload in complex multi-dimensional data visualization can cause challenges for users to comprehend, navigate, and determine statistical measures. To address this problem, we tested the feasibility of integrating vibrotactile and force-feedback haptics with bubble charts to test several haptic rendering techniques for visualizing data. The preliminary results suggest that force-feedback was effective in quickly navigating to points of interest in the data, and vibrotactile feedback was effective in representing higher dimensions and locating statistical measures of data.
Workspace Evaluation of Long-Distance Midair Haptic Display Using Curved Reflector*

In mid-air haptic presentation, an ultrasound phased array can control the sound pressure distribution to provide the desired pressure pattern by electronically controlling the amplitude and phase of the transducers. However, the distance to the presentation plane from the array is limited due to the diffraction-limit. In this paper, we evaluate the workspace in long-distance midair haptic display that is enabled by a large-aperture concave reflector. By experiment, we confirmed that a 20cm-square size of array can create a workspace of 20cm-cube at a distance of 1.6m by synchronizing the phases of the reflected waves. It was also confirmed the feasibility of a phase calculation method producing a focus based on sound field interpolation using the experimentally obtained phase data for discrete focal point positions.
Branched AND/OR Graphs: Toward Flexible and Adaptable Human-Robot Collaboration

In this work, we present a framework for human-robot collaboration allowing the human operator to alter the robot plan execution online. To achieve this goal, we introduce Branched AND/OR graphs, an extension to AND/OR graphs, to manage flexible and adaptable human-robot collaboration. In our study, the operator can alter the plan execution using two implementations of Branched AND/OR graphs for learning by demonstration, using kinesthetic teaching, and task repetition. Finally, we demonstrated the effectiveness of our framework in a defect spotting scenario where the operator supervises robot operations and modifies online the plan when necessary.
Design and Experimental Evaluation of Foldable Robot Arms for a Holding and Installation Work: FRAHI

This paper presents a new concept of supplementary arms for the collaborative task with a worker during a thin panel installation on the wall. Foldable robot arms for holding and installation (FRAHI) using scissor structure have been proposed as a new concept of the human-robot collaboration. The foldable and wearable FRAHI arm has been designed to influence cooperativeness and worker safety. FRAHI arms are developed and integrated with a worker's safety suit, and a soft and flexible waist belt is introduced for the arms and worker interface, comfortable to the user while working. The design principle of FRAHI is based on folding capability, gravity compensation for the cooperative thin-panel installation with workers. The experimental demonstration of a thin panel installation on the wall has been performed for the feasibility of the supplementary arm for worker assistance and cooperativeness in construction works.
Calibration of tactile/force sensors for grasping with the PRISMA Hand II

The PRISMA Hand II is a mechanically robust anthropomorphic hand developed at PRISMA Lab, University of Naples Federico II. The hand is highly underactuated, three motors drive 19 joints via elastic tendons. Thanks to its particular mechanical design, the hand can perform not only adaptive grasps but also in-hand manipulation. Each fingertip integrates a tactile/force sensor, based on optoelectronic technology, to provide tactile/force feedback during grasping and manipulation, particularly useful with deformable objects. The paper briefly describes the mechanical design and sensor technology of the hand and proposes a calibration procedure for tactile/force sensors. A comparison between different models of Neural Networks architectures, suitable for sensors calibration, is shown. Experimental tests are provided to choose the optimal tactile sensing suite. Finally, experiments for the regulation of the forces are made to show the effectiveness of calibrated sensors.
Preferred Oil and Ceramics Options for EHA Drive Systems and Computed Torque Control of an EHA-Driven Robot Manipulator

6-DOF robot manipulator Hydracer was developed to gain high output torque and high backdrivability by adopting electro-hydrostatic actuators, however control of overall system of Hydracer is not yet conducted. To achieve flexible force control of Hydracer, we worked on the system improvements: enhancement of reliability of ceramics components, reduction of internal leakage by considering the property of hydraulic oil, and the identification of inertial parameters to improve its controllability. By using identified parameters, flexible force control of Hydracer by zero-torque control with gravity compensation was realized which reveals the potential of safe human-robot interaction.
Does a wearing change perception toward a robot?

Wearable robots are traditionally used as tools for assisting muscular strength and decrease the physical loads of users. In this study, we are interested in the social aspect of wearable robots and investigate the effects of wearing them toward perceived impressions of them. For this purpose, we newly developed a wearable robot, Fylgear, with minimum capabilities to interact with a user. We experimentally investigated whether wearing a robot changes people’s perception of the robot by using different questionnaire items: a set of positive impressions and kawaii (a Japanese word that means "cute" that has positive connotations) feeling. The experiment results showed that participants who wear the robot positively evaluated the robot in the context of likeability and total impression, but there are no significant differences in perceived kawaii feelings.
Automatic Learning of Cognitive Exercises for Socially Assistive Robotics

In this paper, we present a learning approach to facilitate the teaching of new board exercises to assistive robotic systems. We formulate the problem as the learning of action models using Boolean predicates, disjunctive preconditions, and existential quantifiers from demonstrations of successful exercise executions. To be able to cope with exercises whose rules depend on a set of features that are initialized at the beginning of each play-out, we introduce the concept of dynamic context. Furthermore, we show how the learnt knowledge can be represented intuitively in a graphical interface that helps the caregiver understand what the system has learnt. As validation, we conducted a user study in which we evaluated whether and to which extent different types of feedback can affect the subjects’ performance while teaching three types of exercises: (1) sorting numbers; (2) arranging letters; and (3) reproducing shapes sequences in reversed order. The results suggest that textual and graphical feedback are beneficial.
Effects of erring behavior in a human-robot joint musical task on adopting Intentional Stance toward the iCub robot

In this study, we examined whether the likelihood of attributing intentionality to robots is influenced by the human-likeness of errors during HRI. To this end, we designed an experimental protocol in which users performed a melody in a joint task with the iCub robot. We programmed the iCub robot to make an error in 60% of the repetitions. For half of the users, in the erroneous trials, the robot displayed a human-like error, i.e. switched one element of the melody by pressing the wrong key. For the other half of users, the robot erred mechanically, i.e., it interrupted to play the melody and moved back and forth between two keys in an "endless" loop. Before and after the joint musical task, we administered the InStance Test to evaluate the likelihood of treating the robot as an intentional agent. Results showed that mechanical errors during HRI reduced intentionality attribution toward the robot.
Distributed Large-scale Time-series Data Processing and Analysis System Based on Spark Platform

With the diversification of applications and the diversity of users, the era of big data is always producing various new types of data, such as graph data, time series data, and spatial data. When predicting the future trend of the system, we always need to learn from the historical working state, and time series data plays a huge value in this aspect. Hardware resources are becoming cheaper, computing power has made major breakthroughs, and multi-machine and multi-core scenarios have become commonplace. At present, the focus of large-scale time series data processing is either focused on data collection and storage, or on single-machine processing, which has been unable to meet the existing needs. In response to this problem, this paper designs and implements a distributed large-scale time series processing and analysis system based on the Spark platform. The system framework is mainly divided into storage layer, operator layer and algorithm layer. At the storage layer, the system completes the organization and indexing of large-scale time series data based on HDFS and Hive. At the operator layer, the system provides users with basic operations commonly used for time series data on the Spark platform, and allows users to directly use these operators to implement custom time series related processing algorithms. At the algorithm level, the system implements some commonly used time series analysis algorithms in the Spark platform, including time series similarity query, clustering and prediction, so that users can directly use these algorithms to analyze time series. Finally, the feasibility and practicability of this system are verified by testing the performance and function of the system.
Human or Robot University Tutor? Future Teachers’ Attitudes and Learning Outcomes

In the coming years, social robots will be widely used in the educational field. Therefore, it is important to identify the future educators’ attitudes towards the use of robots and technological tools. In this paper, we investigate how the exposure of students/pre-service teachers in a robot-tutor can affect their attitudes towards the use of robots in education. We compare a university professor with a robot-tutor in a university class and we demonstrate how each one changes the students’ attitudes towards the use of robots and technology in education as well as the students learning outcome from the course. Furthermore, based on the results of the four experiments we conducted, we also demonstrate the changes in students’ attitudes when they have just a single lesson with a robot-tutor and when they have two courses. Our results demonstrate all students were more positive about the use of robots (and technology) in education after having a course with the robot-tutor than when they had a lecture with a human. Moreover, they had a similar knowledge acquisition level, as indicated by the given knowledge test in both human and robot-tutor conditions. Especially in the case that they had a second lecture with the robot-tutor, they got statistically significantly higher scores in the test in comparison with those who had only one lecture. Our results suggest that a robot-tutor can be an effective tutor, capable of changing students’ attitudes and improving knowledge. Moreover, since the university students in our experiments were pre-service teachers, our paper can act as a reference for teaching, the next generation of teachers, that a robot-tutor can be a useful colleague.
A Predictive Torque Control Method for Dual Three-Phase Permanent Magnet Synchronous Motor Without Weighting Factor

In order to solve the problem of the difficult design of weighting factor for model predictive torque control (MPTC) of dual three-phase permanent magnet synchronous motor (DT-PMSM), this paper proposes a MPTC strategy without weighting factor. Firstly, the virtual voltage vector (V3) is introduced to reduce the harmonic currents. Secondly, an approximate alternative principle for predicted current is proposed, which simplifies the dual-vector prediction model and decreases the complexity of the prediction model. The control set at ( $k+1$ )th is selected by judging the change in torque and the position of the stator flux, which reduces the number of candidate voltage vectors. Thirdly, the cost function is selected according to the system state at two adjacent moments. The cost function separates the electromagnetic torque and the stator flux, and eliminates the weighting factor. Decreasing the complexity of the prediction model, reducing the number of candidate voltage vectors, the combination of the two methods in this paper can significantly reduce the computational burden of the CPU. Finally, experiments verify the effectiveness of the proposed method. The results show that the proposed method greatly reduces the calculation burden of the system under the performance of electromagnetic torque does not change much.
Augmented Fractional-order Reset Control: Application in Precision Mechatronics

Linear control such as PID possesses fundamental limitations, seen through the Waterbed effect. Reset control has been found to be able to overcome these limitations, while still maintaining the simplicity and ease of use of PID control due to its compatibility with the loop shaping method. However, the resetting action also gives rise to higher order harmonics that hinders consistent realization of the aforementioned expected improvement. In this paper, a fractional-order augmented state analogue of the reset integrator is investigated. This analogue is composed of a series of augmented states that each possesses unique reset values, providing the same first order harmonic behavior but reduced higher order harmonics magnitude compared to the reset integrator. The optimal number of augmented states along with the corresponding tuning values are investigated. To validate the improvement, the reset integrator and the optimal fractional order analogue are tuned to equally improve disturbance rejection of a high precision 1 DOF positioning stage while maintaining the stability level, with both designed to overcome linear control. From simulation and experimental results, it was found that the novel fractional-order augmented state analogue gives rise to disturbance rejection performance that is closer to the desired and expected improvement, compared to using the traditional reset integrator.
The Director Task: a Psychology-Inspired Task to Assess Cognitive and Interactive Robot Architectures

Assessing robotic architecture for Human-Robot Interaction can be challenging due to the number of features a robot has to endow to perform an acceptable interaction. While everyday-inspired tasks are interesting as reflecting a realistic use of such robots, they often contain a lot of unknown and uncontrolled conditions and specific robot behavior can be hard to test. In this paper, we propose a new psychology-inspired task, gathering perspective-taking, planning, knowledge representation with theory of mind, manipulation, and communication. Along with a precise description of the task allowing its replication, we present a cognitive robot architecture able to perform it in its nominal cases. We finally suggest some challenges and evaluations for the Human-Robot Interaction research community, all derived from this easy-to-replicate task.
Optimization of non-binary LDPC coded massive MIMO systems with partial mapping and EP detection

In this work, a non-binary low density parity check (LDPC) coded high dimensional multiple input multiple output (MIMO) scheme with partial mapping for high order modulation is proposed. For the proposed scheme, when $M$ -ary quadrature amplitude modulation (QAM) is employed, then non-binary LDPC code constructed over Galois field with order $\sqrt{M}$ is used for partial mapping, where $\sqrt{M}$ is an integer. At the receiver side, a real-valued expectation propagation (REP) based detection algorithm is used. Furthermore, symbol-wise extrinsic information transfer (SEXIT) chart based iterative optimization algorithm is used to optimize the concatenated non-binary LDPC code. A simplified method is proposed to calculate the component EXIT chart of the massive MIMO detector, which can avoid a large amount of simulations. Numerical simulation results demonstrate the validity of the above idea.
Progress in Q-carbon and related materials with extraordinary properties

ABSTRACT This paper summarizes our research related to Q-carbon and Q-BN and direct conversion of carbon into diamond and h-BN into c-BN. Synthesis and processing of these materials are accomplished by nanosecond laser melting and subsequent quenching of amorphous carbon and nanocrystalline h-BN. Depending upon the degree of undercooling, molten carbon (or h-BN) can be converted into Q-carbon (or Q-BN) or diamond (or c-BN). The primary focus here is on the outstanding properties of these materials, including hardness greater than diamond, ferromagnetism, p- and n-type doping, NV nanodiamonds, high-temperature superconductivity in B-doped Q-carbon, enhanced field emission, superhard composite coatings, and future applications. IMPACT STATEMENT This research represents a fundamental breakthrough in the direct conversion of carbon into diamond at ambient temperatures and pressures in the air and their extraordinary properties. GRAPHICAL ABSTRACT
Urban traffic detectors – comparison between inductive loop and magnetic sensors

Vehicle detection is a key element in traffic management systems. An accurate count will provide the system with proper information so it can make the best decisions. Inductive loops have been used for many years (as physical equipment, or virtual detection in video cameras), and they proved to be a very good choice for road traffic detection. There are also additional solutions, some of them being less accurate, but improved over time, and others being used in specific applications, but can also be considered for vehicle detection in the road network. The latter solution may be represented by the magnetic sensors, typically used in parking systems. In this paper, the authors compared the two solutions, trying to determine if this is a proper alternative for inductive loops in usual traffic conditions.
Investigation of Torque Controlled Robots with Flexible Links Using a Flexible Multibody Simulation

The past two decades have brought forward a new class of robotic arms designed for close human-robot cooperation. Their lighter design has greater structural flexibilities compared to classical industrial robots, which leads to undesired vibrations at the tool center point. Joint torque feedback controllers have been designed to counteract effects of robot joint flexibility. The goal of this project is to study the impact of link flexibilities on the accuracy of robots with such torque controllers. The study is performed using a flexible multibody simulation that is experimentally validated with the torque controlled robot Franka Panda. It is shown that, for both a single link and a 6-axis robot model, the torque controllers can dampen structural vibrations within the joint space if the torque feedback loop has high enough bandwidth. However, modes orthogonal to the joint space cannot be dampened.