Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact Sequence Planning for Hexapod Robots in Sparse Foothold Environment Based on Monte-Carlo Tree

Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Conventional methods plan gaits and footholds separately and treat them as a single-step optimal process. However, such approaches cause poor passability in sparse foothold environments. This letter proposes a novel coordinative planning method for hexapod robots. It treats gait and foothold planning as a sequence optimization problem while considering the harshness of the environment as the leg’s fault. The Monte Carlo tree search (MCTS) algorithm is used to optimize the entire traversing motion sequence. A slidingMCTS method is proposed to effectively strike a balance between optimization and search operations by introducing a moving root node and controlling the sampling time. The proposed planning algorithm takes advantage of the fault-tolerant mechanism, lifting legs without valid footholds and planning the contact sequence of the remained legs, to improve the passability of the hexapod robot in harsh terrains. The method has been compared with the RRT-based search method for terrains with different densities of foothold, and experiments on challenging terrains are carried out to verify the efficiency. The results have shown that the proposed method dramatically improves the hexapod robot’s ability to pass through sparse-foothold environments.
Adaptive algorithm for obtaining in-phase (i) and quadrature-phase (q) pseudo-noise (pn) sequences in cdma systems

In the CDMA wireless systems, the forward pilot channel is spread in quadrature spreading. The quadrature spreading is a spreading sequence of length 215 pseudo-noise (PN) chips. For spreading rate (SR) 1, each PN chip is about 0. 813μs. This sequence is called the pilot PN sequence and consist of a pair of PN sequences called the In-phase ( I) and quadrature-phase (Q) sequences generated by a Linear Feedback Shift Register (LFSR). This pair of PN sequences is used to spread the forward and the reverse CDMA Channel. During the forward Pilot and Sync channel processing, the mobile station acquires and synchronizes to the CDMA system while it is in the initialization state. In the current CDMA systems, to obtain I and Q Pilot PN sequences for time alignment, a zero is inserted in the LFSR sequences after 14 consecutive “0” outputs. When the mobile station power is turned on, the MS synchronizes with the base station timing using Global Positioning System (GPS) timing reference on the Sync channel and the pilot channel. In this thesis, we propose an algorithm that reduces the computation time to find the I and Q Pilot PN sequences. We begin by developing evolutionary computation models to find the initial values (IV) and polynomial feedback coefficients (PFC) for the maximal-length LFSR that is used to generate the CDMA PN sequences. Next, we propose an adaptive algorithm for computing the I and Q pilot PN sequences in the CDMA systems that can result in a shorter synchronization time between the base station and mobile station. To study the performance of the proposed algorithm, many simulation experiments are carried out under noisy and non-noisy channel conditions. Our simulation test results show that the proposed algorithms cuts the synchronization time by a minimum of 32753 PN chips without degrading the channel performance, and with less computational complexity when compared to the currently used methods.
A Probabilistic Batch Oriented Proactive Workflow Management

Workflow management is a widely studied research subject due to its criticality for the efficient execution of various processing activities towards concluding innovative applications. The ultimate goal is to eliminate the required time for delivering the final outcome considering the dependencies between workflow’s tasks. In this paper, we enhance the decision making of a scheduler with a batch oriented approach to deal with multiple workflows. A probabilistic data oriented approach combined with an infrastructure oriented scheme is provided to pay attention on dynamic environments where the underlying data are continuously updated trying to minimize the network overhead for migrating data. Workflows are mapped to the available datasets according to their data requirements, then, we combine the outcome with an optimization model upon the time and cost requirements of every placement. The performance of our model is revealed by a high number of experiments depicting the advantages in the network overhead.
Modeling of millimetre-wave and terahertz imaging systems

In order to improve the design and analyse the performance of efficient terahertz optical systems, novel quasi-optical components along with dedicated software tools are required. At sub-millimetre wavelengths, diffraction dominates the propagation of radiation within quasi-optical systems and conventional geometrical optics techniques are not adequate to accurately guide the beams or assess optical efficiency. In fact, in general Optical design in the terahertz waveband suffers from a lack of dedicated commercial software packages for modelling the range of electromagnetic propagation regimes that are important in such systems. In this paper we describe the physical basis for efficient CAD software tools we are developing to specifically model long wavelength systems. The goal is the creation of a user-friendly package for optical engineers allowing potential systems to be quickly simulated as well as also providing an analytical tool for verification of existing optical systems. The basic approach to modelling such optical trains is the application of modal analysis e.g. [1][2], which we have extended to include scattering at common off-axis conic reflectors. Other analytical techniques are also ncluded within the CAD software framework such as plane wave decomposition and full physical optics. We also present preliminary analytical methods for characterising standing waves that can occur in terahertz systems and report on novel binary optical components for this wavelength range. Much of this development work has been applied to space instrumentation but is relevant for all Terahertz Imaging systems.
Machine Learning Concepts for Dual-Arm Robots within Virtual Reality

The collaboration between humans and artificial intelligence (AI) driven robots lay the foundations for new approaches in industrial production. However, intensive research is required to develop machine learning behavior that is not only able to execute shared tasks but also acts following the expectations of the human partner. Rigid setups and restrictive safety measures deny the acquisition of adequate training samples to build general-purpose machine learning solutions for evaluation within experimental studies. Based on established research that trains AI systems within simulated environments, we present a machine learning implementation that enables the training of a dual-arm robot within a virtual reality (VR) application. Building upon preceding research, an activity diagram for a shared task for the machine learning model to learn, was conceptualized. A first approach, using vector distances, led to flawed results, whereas a revised solution based on collision boxes resulted in a stable outcome. While the implementation of the machine learning model is fixed on the activity diagram of the shared task, the presented approach is expandable as a universal platform for evaluating Human-Robot Collaboration (HRC) scenarios in VR. Future iterations of this VR sandbox application can be used to explore optimal workplace arrangements and procedures with autonomous industrial robots in a wide range of possible scenarios.
Lightning Talks of EduHPC 2021

The EduHPC lightning talks are an opportunity for educators to discuss early results. These talks are compiled into this paper. The EduHPC 2021 lightning talks cover diverse pedagogical topics in parallel and distributed computing: teaching instruction-level parallelism in the context of computer organization, parallelizing cipher algorithms using single-instruction multiple threads, teaching edge computing to undergraduates, and deploying education-as-a-service on HPC resources.
Does a motion base prevent simulator sickness

The use of high-fidelity motion cues to reduce the discrepancy between visually implied motion and actual motion is tested experimentally using the NASA Vertical Motion Simulator (VMS). Ten pilot subjects use the VMS to fly simulated S-turns and sawtooths which generate a high incidence of motion sickness. The subjects fly the maneuvers on separate days both with and without use of a motion base provided by the VMS, and data are collected regarding symptoms, dark focus, and postural equilibrium. The motion-base condition is shown to be practically irrelevant with respect to the incidence and severity of motion sickness. It is suggested that the data-collection procedure cannot detect differences in sickness levels, and the false cues of the motion condition are theorized to have an adverse impact approximately equivalent to the absence of cues in a fixed-base condition.
Analyse des performances d'un système de compression distribuée de séquences vidéo transmises sur un lien non fiable

In this paper, we consider a video transmission system based on Wyner-Ziv coding. Unlike previous works that considered error-free transmission of the encoded data, we study the influence of noise on the system performances. We also present a novel approach for estimating the theoretical compression bound of each video frame based on the amount of motion inside the frame and on the channel conditions. Furthermore, we show how the entropy limits can be used for estimating the optimal amount of information to be transmitted per frame, in case of a broadcasting application where feedback information from the receiver is not available. Simulation results show a small degradation in the system rate-distortion performance, compared to the case of a source- channel coding system with a feedback channel.
A comparative study of WHO and WHEN prediction approaches for early identification of university students at dropout risk

Reducing the students' dropout is one of the biggest challenges faced by educational institutions, especially in underdeveloped countries. Identification of the student with the highest risk of dropping out is generally used to apply corrective actions (WHO). Therefore, it is also important to determine WHEN a student will drop out, which is fundamental to planning preventive actions. In this work, we perform a study to quantitatively compare several approaches to address the early identification of dropout students in universities. We categorize our study into three main methods families, i.e., analytical methods, traditional classification methods, and probabilistic methods. The first is exploited at preprocessing step for selecting significant variables into the dropout identification task. The second uses machine learning models to classify students into dropout prone or non-dropout prone classes. The third family uses survival models to determine when the student would desert. To evaluate the predictive capacity of the classification models, the Kappa coefficient was incorporated into the usual machine learning metrics and shows that Kappa is handy for evaluating performance in unbalanced data. Similarly, in the survival models, the concordance index was applied to evaluate the predictive capacity. Our approach was applied over a real data set of Peruvian university graduate students to identify when and who will drop out.
MM-cat: A Tool for Modeling and Transformation of Multi-Model Data using Category Theory

The support for multi-model data has become a standard for most of the existing DBMSs. However, the step from a conceptual (e.g., ER or UML) schema to a logical multi-model schema of a particular database, as well as its further correct maintenance, is not simple and straightforward.In this paper, we introduce MM-cat, a modular and extensible framework that enables to perform all the necessary steps to design a multi-model schema. An initial ER schema is transformed to a unified categorical representation which can then be easily mapped to any combination of models in a particular DBMS, or their set. In addition, MM-cat also provides a categorical representation of multi-model data instances to be processed uniformly. Both the schema and instance categories serve as the core for further management of multi-model data. The whole process of transformation to/from the categorical representation is demonstrated using MongoDB and PostgreSQL.
A Review on Various Facial Expression Recognition Techniques

In present days, Facial Expression Acknowledgment (FER) has now become an unbelievable issue in contemporary science study. Facial expression, a vital mode of communication of human emotions, has been studied throughout the world in the fields of Driver Protection, human computer interaction (HCI), deception detection, health care, monitoring etc. Generative Adverse Networks (GANs), with various gestures, can create more one-to-one faces that can be used to improve data base. Facial expression recognition is one of the most effective naturalizing and instant means for people to convey their feelings and intentions. FER is a common research subject which has brought a range of computational vision tasks, such as image generation, video generation, super resolution reconstruction and image translation. Happiness, sorrow, surprise, disgust, fear and rage all constitute six universal facial expressions. Other things such as eye change, breathing and combing the variance in expression will underline all the emotion above. A face recognition device is a behavior request for the identification or authentication of an individual automatically from a digital picture or from a video source. This analysis paper helps to explain methods, strategies and challenges in the real time FER climate, which discuss and examine problems and challenges. This paper finally concludes the latest developments and addresses the problems facing the FER process and the potential of future growth.
Impact of Controller Clock Error on Emission in Supraharmonic Caused by Converters

With the use of more and more power electronic devices, the issue of supraharmonics has attracted attention. This paper introduces the impact of the controller clock error on the supraharmonic emission characteristics. There are different clock errors in the controllers of different converters unavoidably, which makes the converters become harmonic sources with different but similar frequencies. Similar frequencies are difficult to distinguish on the frequency spectrum so that beating phenomenon can be observed. It has an impact on the measurement and analysis of supraharmonic emission characteristics. The correctness of analysis was verified by experiments in the laboratory.
Mesh segmentation guided by seed points

Segmenting 3D models into meaningful parts is a fundamental problem in computer graphics. In this paper, we present an algorithm which is guided by the mesh vertices for segmenting a mesh into meaningful sub-meshes. First, a candidate set of feature points is selected to highlight the most significant features of the model. After that, a diversity measure is calculated by using Hausdorff distance. The collection of seed points we defined is a subset of the candidate set. The collection of seed points consists of two parts, namely, the basic point and lucky points. By maximizing the diversity measure between the seed set and candidate set, an appropriate seed point is selected. Based on the collection of seed points, the segmentation process can be guided by these seeds. Because humans generally perceive desirable segmentations at concave regions, and the geodesic distance and curvature are well-known noise sensitive. Considering the above factors, in order to partition the target into meaningful parts, we define a distance function between each pair of mesh vertices. This function is formed by arc length, angular distance and curvature-related correction term. We have tested the method developed in this paper with 3D meshes from the Princeton segmentation benchmark. Moreover, our segmentation method also has been compared with other methods. The experimental results have demonstrated the effectiveness of the proposed segmentation method.
Network pharmacology-based research on the action mechanism of Caulis Sinomenii in treating rheumatoid arthritis

Caulis sinomenii (CS) is one of the main herbs for the treatment of rheumatoid arthritis (RA) in the southwestern minority areas of China. However, there are multiple components in CS, and their synergy in treating RA is still not clear. In this study, we aimed to explore action mechanism of CS in treating RA. The CS component was obtained by TCMSP and ETCM, and the active component of CS was screened by reference to oral bioavailability and drug-like properties. We obtained the target of RA through DisGeNET and CTD, and mapped the target of CS active component with disease target by BATMAN-TCM to obtain the potential target of CS treatment of RA. Next, we performed GO and KEGG pathway enrichment analysis on the potential targets of CS treatment of RA, and constructed a network of interactions between targets, CS-active component-target-critical pathway networks. Finally, we referred to the network's topological parameters, KEGG pathway annotation information, etc. to screen and analyze target points, GO terms, and pathways, and used molecular docking technology to verify the selected key targets. We obtained 9 active components of CS (Beta-sitosterol, Stigmasterol, Stepholidine, etc.) and 30 potential targets for CS treatment of RA (AKT1, NFKB1, JUN, etc.). In the results of enrichment of these targets, 20 key pathways (Osteoclast differentiation, Toll-like receptor signaling pathway, Leukocyte transendothelial migration, etc.) and more than 160 GO entries (lysosome, cell surface, NADPH oxidase complex, etc.) were screened. We constructed CS-active component-target-key pathway network to visually demonstrate the relationships between the various levels. We screened 7 hub targets in the network, and the molecular docking results of the 7 Hub target-corresponding protein and CS active components showed strong binding ability. In this study, we predicted the multi-component and multi-target synergy of CS in treating RA, and provided a reference for further experimental verification and clinical application.
A Systematic Literature Review of Empirical Methods and Risk Representation in Usable Privacy and Security Research

Usable privacy and security researchers have developed a variety of approaches to represent risk to research participants. To understand how these approaches are used and when each might be most appropriate, we conducted a systematic literature review of methods used in security and privacy studies with human participants. From a sample of 633 papers published at five top conferences between 2014 and 2018 that included keywords related to both security/privacy and usability, we systematically selected and analyzed 284 full-length papers that included human subjects studies. Our analysis focused on study methods; risk representation; the use of prototypes, scenarios, and educational intervention; the use of deception to simulate risk; and types of participants. We discuss benefits and shortcomings of the methods, and identify key methodological, ethical, and research challenges when representing and assessing security and privacy risk. We also provide guidelines for the reporting of user studies in security and privacy.
Progress on Hybrid EEG-fNIRs system and its application

Monitoring brain activity is of great significance in biomedical, neuroscience, and even computer science. Though many technologies can detect brain activity, such as EEG, MEG, fMRI and fNIRS, they all have limitations on spatial resolution or time resolution. Combining EEG and fNIRS systems can detect brain activity at both high time and spatial resolution. In this review article, basic principles and current application of EEG-fNIRS technology would be introduced, advantages and disadvantages of the technology will also be included. Moreover, we will emphasize its wide applicability in cognitive neuroscience with its great potential in further understanding human brain cognitive function and future directions to improve the hybrid system.
A dual‐band Gysel power divider with the even‐mode input extension/stub lines

A new dual‐band structure is proposed for the Gysel power divider. The proposed divider features an extension line at the input and a stub line at the opposite end, both located along the symmetry plane and operating at the even‐mode excitation of the divider. The divider is analyzed rigorously to derive the exact design equations in closed‐form formula. The design, simulation and experiments based on the proposed method confirm the dual‐band capability of the divider. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1213–1216, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25979
Instability of the attachment line boundary layer in a supersonic swept flow

Abstract Theoretical analysis of attachment-line instabilities is performed for supersonic swept flows using the compressible Hiemenz approximation for the mean flow and the successive approximation procedures for disturbances. The theoretical model captures the dominant attachment-line modes in wide ranges of the sweep Mach number ${M_e}$ and the wall temperature ratio. It is shown that these modes behave similar to the first and second Mack modes in the boundary layer flow. This similarity allows us to extrapolate the knowledge gained for Mack modes to the attachment-line instabilities. In particular, we find that at sufficiently large ${M_e}$, the dominant attachment-line instability is associated with the synchronisation of slow and fast modes of acoustic nature. Point-by-point comparisons of the theoretical predictions with the experiments of Gaillard et al. (Exp. Fluids, vol. 26, 1999, pp. 169–176) demonstrate that at ${M_e} > 4$, the theory captures a significant drop of the transition onset Reynolds number, which is below the contamination criterion of Poll $({R_\mathrm{\ast }} = 250)$ at ${M_e} > 6$. This contradicts the generally accepted assumption that the attachment-line flow is stable for ${R_\mathrm{\ast }} \le 250$. The theoretical critical Reynolds numbers lie well below the experimental transition-onset Reynolds numbers. Stability computations using the Navier–Stokes mean flow and accounting for the leading-edge curvature effect do not eliminate this discrepancy. Most likely, in the experiments of Gaillard et al., we face with an unknown effect that does not fit to the concept of transition arising from linear instability.
Using LFtext-TextCNN to classify short text of TCM symptoms

TCM (Traditional Chinese Medicine) symptoms are divided into six categories, and these symptom categories contain a lot of clinical diagnostic information. At present, the classification of TCM symptoms is mainly based on the experience of TCM physicians with there are many errors. The average length of the text description information of TCM symptoms is about 4, and labeling these symptom data is a very expensive task. Therefore, TCM symptom classification is a short text classification task with few samples. The current short text classification methods are difficult to effectively extract professional semantics and unique expressions in TCM symptom texts. Our method is based on LFtext-TextCNN to extract the general semantic information of TCM symptoms and the special semantic information of TCM, which are fused to linear network to classify TCM texts. Compared with the other 7 baseline models, our method is the best in TCM symptom classification task.