Miracle-dz/UniFAR-FTUs huggingface dataset:
RegionNet: Region-feature-enhanced 3D Scene Understanding Network with Dual Spatial-aware Discriminative Loss

Neural networks have recently achieved impressive success in semantic and instance segmentation on 2D images. However, their capabilities have not been fully explored to address semantic instance segmentation on unstructured 3D point cloud data. Digging into the regional feature representation to boost point cloud comprehension, we propose a region-feature-enhanced structure consisting of adaptive regional feature complementary (ARFC) module and affinity-based regional relational reasoning (AR3) module. The ARFC module aims to complement low-level features of sparse regions adaptively. The AR3 module emphasizes on mining the potential reasoning relationships between high-level features based on affinity. Both the ARFC and AR3 modules are plug-and-play. Besides, a novel dual spatial-aware discriminative loss is proposed to improve the discrimination of instance embedding. Our proposal-free point cloud instance segmentation network (RegionNet) equipped with the region-feature-enhanced structure and dual spatial-aware discriminative loss achieves state-of-the-art performance on S3DIS dataset and ScanNet-v2 dataset.
A Four-Feature Keyword Extraction Algorithm Based on Word Length Priority ratio

With the rapid development of Internet technology and the advent of the information age, it has become a research hotspot to obtain key information from numerous data. Due to the diversity and irregularity of network data, it is difficult for people to find the literature they want, especially knowledge scholars working in the frontier field of science and technology, who have higher requirements on the accuracy and efficiency of literature keyword extraction than ordinary people. The feature values selected by the current keyword extraction algorithm are usually limited to word frequency and word length, which is incomplete and affects the accuracy of the algorithm. Given this phenomenon, this paper, by comparing with TF-IDF and KEA algorithm, define the concept of word length priority ratio, and applies this concept to the calculation of word length-weight, proposes a four-feature keyword extraction algorithm (WPR-TOC algorithm) based on word frequency, word length, word position and the degree of association between words. Through experiments, compared with the KEA algorithm, KEA++ algorithm, and four features extraction algorithm, the precision of the WPR-TOC algorithm is improved by 40%, 30%, and 10% respectively, and the recall rate is also increased by 40%, 30%, and 10% respectively.
Fast Tennis Swing Motion by Ball Trajectory Prediction and Joint Trajectory Modification in Standalone Humanoid Robot Real-time System

In this work, we propose a system for humanoid robot fast motions. When a humanoid robot performs a motion such as a tennis forehand stroke motion, a whole-body fast motion in reaction to visual information is required. There are three problems to tackle. (1) Motion is desired to be quick. (2) Real-time visual processing considering visual noises is needed. (3) Real-time joint angle modification with balance keeping is needed. To solve the problem (1), we used an offline optimization system to enhance the motion speed. To solve the problem (2), we implement a ball trajectory prediction algorithm using the Extended Kalman Filter (EKF). To solve the trade-off between (1) and (3), we propose an offline optimization condition with an estimated balance margin. By using these methods, we achieved a non-step tennis forehand stroke motion with a humanoid robot by predicting a ball’s trajectory with stereo cameras on the robot’s head.
A Deep Learning Based End-to-End Locomotion Mode Detection Method for Lower Limb Wearable Robot Control

To function effectively in real-world environments, powered wearable robots such as exoskeletons and robotic prostheses must recognize the user’s motion intent by detecting the user’s locomotion modes such as walking, stair ascent and descent or ramp ascent and descent. Traditionally, intent detection is achieved using rule based methods such as state machines or fuzzy logic using data from wearable sensors. Due to the difficulty of manual rule design, these methods are limited to detect certain simple locomotion modes. Machine learning (ML) based methods can perform classification on a large number of classes without manual rule design and recent research has explored several ML methods for locomotion mode classification. However, current ML based methods for locomotion mode detection use classical methods that require use of feature engineering to achieve acceptable accuracies. Additionally, current ML strategies only classify when certain motion events are detected. This strategy, while computationally efficient could result in misclassifications affecting large sections of motion recognition. To overcome these limitations, this paper proposes an end-to-end deep learning based method for locomotion mode detection that eliminates the need for feature engineering and classifies at a fixed sample rate. This paper introduces a new metric called confidence index and proposes a strategy for tuning confidence index thresholds to achieve a stable intent recognition and overall accuracy of greater than 95% on a publicly available benchmark dataset.
Towards Dynamic Transparency: Robust Interaction Force Tracking Using Multi-Sensory Control on an Arm Exoskeleton

A high-quality free-motion rendering is one of the most vital traits to achieve an immersive human-robot interaction. Rendering free-motion is notably challenging for rehabilitation exoskeletons due to their relatively high weight and powerful actuators required for strength training and support. In the presence of dynamic human movements, accurate feedback linearization of the robot’s dynamics is necessary to allow for a linear synthesis of interaction wrench controllers. Hence, we introduce a virtual model controller that uses two 6-DoF force sensors to control the interaction wrenches of a multi-DoF torque-controlled exoskeleton over the joint accelerations and inverse dynamics. Furthermore, we propose a disturbance observer for controlling the joint acceleration to diminish the influence of modeling errors on the inverse dynamics. To provide a high-bandwidth, low-bias estimation of the system’s acceleration, we introduce a bias-observer which fuses the information from joint encoders and seven low priced IMUs. We have validated the performance of our proposed control structure on the shoulder and arm exoskeleton ANYexo. The experimental comparison of the controllers shows a reduction of the felt inertia and maximum reflected joint torque by a factor of more than three compared to state of the art. The controllers’ robustness w.r. t. a model mismatch is validated. The experiments show that the closed-loop acceleration control improves the tracking, particularly at joints with low inertia. The proposed controllers’ performance sets a new benchmark in haptic transparency for comparable devices and should be transferable to other applications.
Semantics-assisted Wasserstein Learning for Topic and Word Embeddings

Wasserstein distance, defined as the cost (measured by word embeddings) of optimal transport plan for moving between two histograms, has been proven effective in tasks of natural language processing. In this paper, we extend Nonnegative Matrix Factorization (NMF) to a novel Wasserstein topic model, namely Semantics-Assisted Wasserstein Learning (SAWL), with simultaneous learning of topics and word embeddings. In Sawl, we formulate an NMF-like unified objective that integrates the regularized Wasserstein distance loss with a context factorization of word context information. Therefore, Sawl can refine the word embeddings for capturing corpus-specific semantics, enabling to boost topics and word embeddings each other. We analyze Sawl, and provide its dimensionality-dependent generalization bounds of reconstruction errors. Experimental results indicate that Sawl outperforms the state-of-the-art baseline models.
A Novel Front-haul Bandwidth Compression Method for RAN Systems

Recently, there has been a significant increase in users as well as user data requirements in mobile communications. This is attributed to advances in mobile communication systems and networking, along with the advent of fifth generation (5G) mobile systems. As a result, front haul data compression techniques have become necessary to meet QoS requirements. In this paper, we resort to contemporary machine learning techniques and provide algorithms to, respectively, dynamically predict and compress the front haul data. The proposed scheme involves evaluating the Error Vector Magnitude (EVM) metric and comparing the performance with existing schemes. Furthermore, these algorithms can be deployed on contemporary C-RAN as well as O-RAN architectures. From simulations, we are able to demonstrate a compression of about 65%.
Time-Resolved Raman Spectrometer With High Fluorescence Rejection Based on a CMOS SPAD Line Sensor and a 573-nm Pulsed Laser

A time-resolved Raman spectrometer is demonstrated based on a $256\times 8$ single-photon avalanche diodes fabricated in CMOS technology (CMOS SPAD) line sensor and a 573-nm fiber-coupled diamond Raman laser delivering pulses with duration below 100-ps full-width at half-maximum (FWHM). The collected backscattered light from the sample is dispersed on the line sensor using a custom volume holographic grating having 1800 lines/mm. Efficient fluorescence rejection in the Raman measurements is achieved due to a combination of time gating on sub-100-ps time scale and a 573-nm excitation wavelength. To demonstrate the performance of the spectrometer, fluorescent oil samples were measured. For organic sesame seed oil having a continuous wave (CW) mode fluorescence-to-Raman ratio of 10.5 and a fluorescence lifetime of 2.7 ns, a signal-to-distortion value of 76.2 was achieved. For roasted sesame seed oil having a CW mode fluorescence-to-Raman ratio of 82 and a fluorescence lifetime of 2.2 ns, a signal-to-distortion value of 28.2 was achieved. In both cases, the fluorescence-to-Raman ratio was reduced by a factor of 24–25 owing to time gating. For organic oil, spectral distortion was dominated by dark counts, while for the more fluorescent roasted oil, the main source of spectral distortion was timing skew of the sensor. With the presented postprocessing techniques, the level of distortion could be reduced by 88%–89% for both samples. Compared with common 532-nm excitation, approximately 73% lower fluorescence-to-Raman ratio was observed for 573-nm excitation when analyzing the organic sesame seed oil.
Sarcasm Detection in Newspaper Headlines

Sarcasm is an important part of communication, and detecting sarcasm is difficult for humans, let alone computers. Newspapers often seem to employ sarcasm in their headlines to grab the readers’ attention. However, more often than not, the readers find it difficult to detect the irony in the headlines, thus getting a wrong idea about that particular news and further passing on their understanding to their friends, colleagues, etc. Thus, a system which can automatically and reliably detect sarcasm is more important now than ever. We build sarcasm detectors using neural networks and attempt to understand how a computer learns the patterns of sarcasm. The input to our project consists of sequences that are labeled sarcastic or non-sarcastic. These sequences come from two different datasets containing news headlines and social media commentary. Our classifiers are evaluated on their accuracies. Our model performs highly and is capable of reliably classifying sarcastic or non-sarcastic phrases.
Improvement of Ultrashort Pulses by Serial Connection of Step Recovery Diodes

The application of serial connection of step recovery diodes (SRDs) in picosecond pulse generators was investigated in the letter. Parameters of 80 SRDs were measured, and their standard deviation was estimated. It was shown that the deviation of the SRD parameters significantly reduces the efficiency of their serial connection in picosecond pulse generators. It is proposed to select diodes whose dependencies of the stored charge on the direct current has a minor difference between each other to improve generated pulse parameters. It was shown experimentally that using this approach allows to increase the pulse amplitude by a factor of 1.4 and decrease the duration by 90 ps.
OceanVoy: A Hybrid Energy Planning System for Autonomous Sailboat

Towards long range and high endurance sailing, energy is of utmost importance. Moreover, benefiting from the dominance of the sailboat itself, it is energy-saving and environment-friendly. Thus, the sailboat with energy planning problem is meaningful. However, until now, the sailboat energy optimization problem has rarely been considered. In this paper, we focus on the energy consumption optimization of an autonomous sailboat. It has been formulated as a Nonlinear Programming problem (NLP). We deal with it with a hybrid control scheme, in which pseudo-spectral (PS) optimal control method is used in heading control, and a model-free framework guided by Extreme Seeking Control (ESC) is used in sail control. The optimal path is generated with the optimal input motor torques in time series. As a result, both simulation and experiments have validated motion planning and energy planning performance. Notably, about 7% of energy is saved on average. Our proposed method can make sailboats sailing longer and sustainable.
Scalable and Accurate Binary Search Method based on Simhash and Partial Trace

Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, compiler options and malware family. In this paper, we propose a scalable and accurate binary search engine which performs syntactic matching by combining a set of key techniques to address the challenges above. The key contribution is binary code searching technique which combined function filtering and partial trace method to match the function code relatively quick and accurate. In addition, a simhash and basic information based function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce a partial trace method for matching the shortlisted function accurately. The experimental results show that our method can find similar functions, even with the presence of program structure distortion, in a scalable manner.
A Unified Host-based Intrusion Detection Framework using Spark in Cloud

The host-based intrusion detection system (HIDS) is an essential research domain of cybersecurity. HIDS examines log data of hosts to identify intrusive behaviors. The detection efficiency is a significant factor of HIDS. Traditionally, HIDS is often installed with a standalone mode. Training detection engines with a large amount of data on a single physical computer with limited computing resources may be time-consuming. Therefore, this paper offers a unified HIDS framework based on Spark and deployed in the Google cloud. The framework includes a unified machine learning pipeline to implement scalable and efficient HIDS.
Targeted Vaccination for COVID-19 Using Mobile Communication Networks

Vaccination is an effective method for prevention of infectious diseases, but when the number of available vaccines is limited, it is not possible to vaccinate everyone in a society. In this paper, a two-step model is proposed to distribute a limited number of vaccines among the people of a society, in a way that would disrupt the transmission chain of the infectious disease most efficiently. In the first step, the vaccines are allocated to different communities in the society (e.g. cities in a country), and in the second step, the individuals whose vaccination removes the greatest number of transmission routes for the infection are identified in concordance with the regulations of international health organizations. In the second step, contact data is obtained from cellular networks and Bluetooth signals, and a graph-based modeling scheme is utilized in conjunction with a combined susceptibility metric specifically designed for selection of these individuals. The simulations indicate that a 30 % drop in infection rate compared to random vaccination could be achieved.
Routing Algorithm in Networks on the Globe

Packet switching of data in networks is done by either the distance-vector or the link-state routing protocols. These protocols use the Bellman-Ford and the Dijkstra's algorithms respectively for the least cost path from a source base station to a destination station. For inter-network transmission, the path-vector routing protocol is in use. With progress of time, the network topologies are becoming huge in size, requiring large demand on book keeping of routing tables and transmission of the data packets dynamically to several other stations of the net-work by broadcast, increasing the load on the network. Here, assuming the router stations to be terrestrially located with links along the ground, a large network is assumed to lie on a spherical surface, and so the shortest geodesic path from source to destination becomes a great circular arc. For fast transmission, the cost of a link to a node is multicast to its neighboring nodes only for selection of the path lying as close as possible to the geodesic line between the source and the destination. As the arrival and dispatch of data packets at a nodal station occurs randomly, the cost of a link is estimated in this paper by the waiting time of a queueing process. This process at a router station is thus modeled by the Markovian M/M/c model, where c is the number of servers at the router station. If other commercial fixed charge is involved for the use of a link, then that can be included in the total cost of a link. Finally, a method of search of a mobile destination is also presented using sphericity of the network. Algorithms for the near geodesic path, costs of links as waiting times and destination search in mobile environment are clearly presented.
A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images

In this work, we describe an unsupervised deep learning framework featuring a Laplacian-based operator as smoothing loss for deformable registration of 3D cine cardiac magnetic resonance (CMR) images. Before registration, the input 3D images are corrected for slice misalignment by segmenting the left ventricle (LV) blood-pool, LV myocardium and right ventricle (RV) blood-pool using a U-Net model and aligning the 2D slices along the center of the LV blood-pool. We conducted experiments using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We used the registration deformation field to warp the manually segmented LV blood-pool, LV myocardium and RV blood-pool labels from end-diastole (ED) frame to the other frames in the cardiac cycle. We achieved a mean Dice score of 94.84%, 85.22% and 84.36%, and Hausdorff distance (HD) of 2.74 mm, 5.88 mm and 9.04 mm, for the LV blood-pool, LV myocardium and RV blood-pool, respectively. We also introduce a pipeline to estimate patient tractography using the proposed CNN-based cardiac motion estimation.
Anti-collision Trajectory Planning and Tracking Control based on MPC and Fuzzy PID Algorithm

The active anti-collision technology can ensure vehicles to avoid car accidents in a short time, since vehicle collision accounts for a large proportion of most traffic accidents. It has become an important research content of active vehicle safety. In this paper a double-layer controller of trajectory planning and tracking is designed to improve driving safety for intelligent vehicles. Firstly, 2-DOF(degree-of-freedom) vehicle dynamics model is established based on the intelligent vehicle platform with front wheel steering. Secondly, the point mass vehicle model is applied to design the collision avoidance function and the trajectory re-planning function. The problem of anti-collision trajectory planning is transformed into a nonlinear quadratic planning problem with the constraints of speed, angle and angle increment. Based on MPC algorithm, the anti-collision trajectory in dynamic environment is re-planned, and the trajectory planner of active collision avoidance model for intelligent vehicle is established. Thirdly, a fuzzy adaptive PID trajectory tracking controller is employed as the trajectory tracking layer of this anti-collision model. Finally, the collision avoidance effect is simulated in Matlab/Simulink environment. The results show that the model has better collision avoidance effect and helps to track the original driving trajectory at 36km/h speed. The vehicle tests show that the dynamic trajectory planning algorithm based on MPC can ensure the vehicle to plan the anti-collision trajectory in real time when sensing obstacles, and the fuzzy PID algorithm as the trajectory tracking layer can ensure the tracking accuracy of the original trajectory after collision avoidance.
Robust Force Tracking Impedance Control of an Ultrasonic Motor-actuated End-effector in a Soft Environment

Robotic systems are increasingly required not only to generate precise motions to complete their tasks but also to handle the interactions with the environment or human. Significantly, soft interaction brings great challenges on the force control due to the nonlinear, viscoelastic and inhomogeneous properties of the soft environment. In this paper, a robust impedance control scheme utilizing integral backstepping technology and integral terminal sliding mode control is proposed to achieve force tracking for an ultrasonic motor-actuated end-effector in a soft environment. In particular, the steady-state performance of the target impedance while in contact with soft environment is derived and analyzed with the nonlinear Hunt-Crossley model. Finally, the dynamic force tracking performance of the proposed control scheme is verified via several experiments.
From Human to Robot Everyday Activity

The Everyday Activities Science and Engineering (EASE) Collaborative Research Consortium’s mission to enhance the performance of cognition-enabled robots establishes its foundation in the EASE Human Activities Data Analysis Pipeline. Through collection of diverse human activity information resources, enrichment with contextually relevant annotations, and subsequent multimodal analysis of the combined data sources, the pipeline described will provide a rich resource for robot planning researchers, through incorporation in the OpenEASE cloud platform.
Geometrical Interpretation and Detection of Multiple Task Conflicts using a Coordinate Invariant Index

Modern robots act in dynamic and partially unknown environments where path replanning can be mandatory if changes in the environment are observed. Task-prioritized control strategies are well known and effective solutions to ensure local adaptation of robot behaviour. The highest priority in a stack of tasks is typically given to the management of correct robot operation or safe interaction with the environment such as obstacles or joint limits avoidance, that we can consider as constraints. If a constraint makes impossible achieving a certain task, such as tracking a Cartesian trajectory, a local control algorithm partially sacrifices the latter which is only accomplished to the best of the robot’s ability to generate internal motions. In this control framework, problems may occur in some applications, like in the surgical domain, where it is not safe that some tasks are simply sacrificed without prior notice. The contribution of this work is to introduce a coordinate invariant index, that is used to provide a geometrical interpretation of task conflicts in a task-priority control framework and to develop a method for on-line detection of algorithmic singularities, with the goal of increasing safety and performances during robot operations.