Miracle-dz/UniFAR-FTUs huggingface dataset:
An Augmented Reality Spatial Referencing System for Mobile Robots

The deployment of a mobile service robot in domestic settings is a challenging task due to the dynamic and unstructured nature of such environments. Successful operation of the robot requires continuous human supervision to update its spatial knowledge about the dynamic environment. Thus, it is essential to develop a human-robot interaction (HRI) strategy that is suitable for novice end users to effortlessly provide task-specific spatial information to the robot. Although several approaches have been developed for this purpose, most of them are not feasible or convenient for use in domestic environments. In response, we have developed an augmented reality (AR) spatial referencing system (SRS), which allows a non-expert user to tag any specific locations on a physical surface to allocate tasks to be performed by the robot at those locations. Specifically, in the AR-SRS, the user provides a spatial reference by creating an AR virtual object with a semantic label. The real-world location of the user-created virtual object is estimated and stored as spatial data along with the user-specified semantic label. We present three different approaches to establish the correspondence between the user-created virtual object locations and the real-world coordinates on an a priori static map of the service area available to the robot. The performance of each approach is evaluated and reported. We also present use-case scenarios to demonstrate potential applications of the AR-SRS for mobile service robots.
Machine Learning Techniques for 5G and Beyond

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.
Enhancement of room temperature electrical conductivity of polypyrrole by chemical modification

Polypyrrole and polypyrrole−tetracyanoquinodinodimethane (TCNQ) have been prepared to see the energy band structure modification and associated change in the electrical and optoelectrical properties. The temperature-dependent electrical conductivity of the samples show interesting behavior, namely, higher room-temperature conductivity of polypyrrole−TCNQ and a crossover phenomenon which may be considered as a competition between two opposing factors, charge-transfer interaction and disorderness. CHN microanalysis, UV−vis absorption spectroscopy, and positron annihilation lifetime spectroscopy corroborate the findings.
Branching Initial Streamers to Inhibit the Streamer Propagation in Natural Ester-based Nanofluid

A multi-branch pre-breakdown streamer model for natural ester-based nanofluid is established in this paper to study the influence of electrons density fluctuation on the streamer branching and propagation during the pulsed discharge. The mechanism of electron density fluctuation on the streamer branching and propagation characteristics is further studied using the finite element software. Results show that the initial streamer branched more likely near the needle tip in the nanofluids than that in the pure oils, and the branching of the initial streamers in nanofluid significantly inhibit the streamer propagation. The fluctuation of carrier density and the low charge density regions caused by adsorption of electrons with nanoparticles near the needle tip are the leading causes for the initial streamer branching in the nanofluid. The repulsion of the charge with the same polarity between the streamer branches can change the propagation pattern of the streamer, resulting in the inhibition of the streamer propagation. Results provide a novel strategy for the inhibition of streamer propagation by electron density fluctuations-induced branched initial streamer in nanofluids.
LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction

The task of link prediction for knowledge graphs is to predict missing relationships between entities. Knowledge graph embedding, which aims to represent entities and relations of a knowledge graph as low dimensional vectors in a continuous vector space, has achieved promising predictive performance. If an embedding model can cover different types of connectivity patterns and mapping properties of relations as many as possible, it will potentially bring more benefits for link prediction tasks. In this paper, we propose a novel embedding model, namely LineaRE, which is capable of modeling four connectivity patterns (i.e., symmetry, antisymmetry, inversion, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many) of relations. Specifically, we regard knowledge graph embedding as a simple linear regression task, where a relation is modeled as a linear function of two low-dimensional vector-presented entities with two weight vectors and a bias vector. Since the vectors are defined in a real number space and the scoring function of the model is linear, our model is simple and scalable to large knowledge graphs. Experimental results on multiple widely used real-world datasets show that the proposed LineaRE model significantly outperforms existing state-of-the-art models for link prediction tasks.
No-Regret Shannon Entropy Regularized Neural Contextual Bandit Online Learning for Robotic Grasping

In this paper, we propose a novel contextual bandit algorithm that employs a neural network as a reward estimator and utilizes Shannon entropy regularization to encourage exploration, which is called Shannon entropy regularized neural contextual bandits (SERN). In many learning-based algorithms for robotic grasping, the lack of the real-world data hampers the generalization performance of a model and makes it difficult to apply a trained model to real-world problems. To handle this issue, the proposed method utilizes the benefit of an online learning. The proposed method trains a neural network to predict the success probability of a given grasp pose based on a depth image, which is called a grasp quality. We theoretically show that the SERN has a no regret property. We empirically demonstrate that the SERN outperforms ϵ-greedy in terms of sample efficiency.
NeuroSky Mindwave Mobile Headset 2 as an Intervention for Reduction of Stress and Anxiety Measured With Pulse Rate Variability

NeuroSky Mindwave Mobile headset 2 (MMH2) is an EEG-based biofeedback device that can be used to assist in relaxation by providing users feedback on their level of relaxation. In this study, we aimed to assess the effect of using MMH2 on pulse rate variability (PRV) as a measure of relaxation in addition to the in-App real-time data. Six participants were recruited and provided information about the study once they contacted the university. Participants were required to use the Brainwave Visualizer application as part of the NeuroSky suite for relaxation for 10 minutes. PRV of each participant was determined before and after use of the MMH2. Biosignals were initially preprocessed to remove artifacts and resampled at 8Hz for time and frequency domain analysis using purpose written Matlab software. We obtained multiple parameters including the average value of the inter-pulse intervals, standard deviation, root mean square of the successive differences, and stress index. The stress score from the MMH2 screen indicated a decrease in overall stress by the participants. RMSSD decreased from pre-MMH2 to Post-MMH2 (4.6±5.9; 2.9±0.9; p=) whilst the Kubios Stress Index decreased as well (0.74±1.5; 0.44±1.1, p =) MMH2 can be used to help reduce stress and anxiety levels, making it a potentially useful tool for home use.
Do My Emotions Influence What I Share? Analysing the Effects of Emotions on Privacy Leakage in Twitter

Social media has become an integral part of modernday society. With increasingly digital societies, individuals have become more familiar and comfortable in using Online Social Networks (OSNs) for just about every aspect of their lives. This higher level of comfort leads to users spilling their emotions on OSNs and eventually their private information. In this work, we aim to investigate the relationship between users' emotions and private information in their tweets. Our research question is whether users' emotions, expressed in their tweets, affect their likelihood to reveal their own private information (privacy leakage) in subsequent tweets. In contrast to existing survey-based approaches, we use an inductive, data-driven approach to answer our research question. We use state-of-the-art techniques to classify users' emotions, and privacy scoring and employ a new technique involving BERT for binary detection of sensitive data. We use two parallel classification frameworks: one that takes the user's emotional state into account and the other for the detection of sensitive data in tweets. Consecutively, we identify individual cases of correlation between the two. We bring the two classifiers together to interpret the changes in both factors over time during a conversation between individuals. Variations were found with respect to the kinds of private information revealed in different states. Our results show that being in negative emotional states, such as sadness, anger or fear, leads to higher privacy leakage than otherwise.
A Novel Safety-Oriented Control Strategy for Manipulators Based on the Observation and Adjustment of the External Momentum

Human safety assurance in physical human-robot interactions (pHRIs) has become a challenge for current robotic applications. Considering the safety assurance of a pHRI validation experiment, the viscoelastic properties of a human body part were investigated in a previous study and were added in a pHRI simulation. This study focuses on a threshold momentum value as an injury criterion for momentum control strategy design. A novel safety-oriented control strategy is proposed, which consists of a conventional proportional-derivative (PD) controller, an external momentum observer-based compensator for monitoring the external momentum, and an adjustment loop for shaping the trajectory motion command to bind the external momentum within the injury criterion. The proposed control strategy can be designed without needing to obtain the mechanical properties of the environment. The strategy can also be combined with the related injury criterion. In addition, it shows the effectiveness of ensuring safety even in the worst pHRI clamping situation, where a human body part can escape from such a situation. Simulation results show that the proposed control strategy performs well in the trajectory tracking task, and the adjustment loop successfully shapes the predetermined trajectory motion command to bind the external momentum within the injury criterion.
Leaf Loss and Drying Rate of Alfalfa as Affected by Conditioning Roll Type

Field tests of seven mower-conditioners which utilized four different conditioning roll types were conducted. Resulting leaf loss and drying rate of alfalfa were determined. Average leaf loss for different roll types ranged from 5.2 to 5.9% of total dry matter. Intermeshing rubber rolls had the lowest average leaf loss, although loss was more dependent upon crop maturity or machine adjustment than roll type. Drying rate was not affected by roll type or machine.
Building Footprint Extraction From Unmanned Aerial Vehicle Images Via PRU-Net: Application to Change Detection

As the manual detection of building footprint is inefficient and labor-intensive, this study proposed a method of building footprint extraction and change detection based on deep convolutional neural networks. The study modified the existing U-Net model to develop the “PRU-Net” model. PRU-Net incorporates pyramid scene parsing (PSP) to allow multiscale scene parsing, a residual block (RB) in ResNet for feature extraction, and focal loss to address sample imbalance. Within the proposed method, building footprint extraction is conducted as follows: 1) unmanned aerial vehicle images are cropped, denoised, and semantically marked, and datasets are created (including training/validation and prediction datasets); 2) the training/validation and prediction datasets are input into the full convolutional neural network PRU-Net for model training/validation and prediction. Compared with the U-Net, PSP+U-Net (PU-Net), and U-Net++ models, PRU-Net offers improved footprint extraction of buildings with a range of sizes and shapes. The large-scale experimental results demonstrated the effectiveness of the PSP module for multiscale scene analysis and the RB module for feature extraction. After demonstrating the improvements in building extraction offered by PRU-Net, the building footprint results were further processed to generate a building change map.
Enhancing Challenge-based Collaborative Intrusion Detection Against Insider Attacks using Spatial Correlation

With cyber-attacks becoming more complicated and the networks increasingly interconnected, there has been a move towards using collaborative intrusion detection networks (CIDNs) to identify cyber-threats more effectively. However, insider attacks may remain challenging to mitigate in CIDNs, as the intruders are able to control one or more internal nodes. Challenge- based trust mechanism is one promising solution to help safeguard CIDNs against common insider attacks, but not necessarily against advanced attacks such as passive message fingerprint attacks. In this work, we focus on challenge-based trust mechanism and advocate that considering additional level of trust can enhance the robustness of CIDNs. Specifically, we design an enhanced trust management scheme by checking spatial correlation among nodes' behavior, regarding forwarding delay, packet dropping and sending rate. Then, we evaluate our approach in a simulated environment, as well as a realworld environment in collaboration with an IT organization. Experimental results demonstrate that our approach can help enhance the robustness of challenge-based trust mechanism by detecting malicious nodes faster than similar approaches (i.e., reducing time consumption by two to three days).
Multiobjective model-based optimization of diesel injection rate profile by machine learning methods

The contribution of this article is to present a model-based machine learning methodology for automatic and simultaneous optimization of the power output and exhaust emissions of diesel internal combustion (IC) engines. We carry out parametric optimization of the rate profile at which fuel is injected into the cylinder for producing minimal nitrogen oxide (NOx) emissions and maximal cylinder power (nIMEP) output, on a computational simulation model of an Agco Power 44 AWI engine calibrated by measurements. Our results display the tradeoffs in reaching these two contradictory optimization objectives on the Pareto frontiers. We show that the so-called boot injection profile, which is commonly used in practice, also emerges through mathematical optimization as a reasonable compromise of the objectives.
Compact Dual-Band Dual-Mode SIW Balanced BPF With Intrinsic Common-Mode Suppression

In this letter, a compact dual-band dual-mode substrate integrated waveguide (SIW) balanced band-pass filter (BPF) with intrinsic common-mode (CM) suppression is designed by using a single perturbed SIW circular cavity. The vias and slot perturbations are introduced to tune the resonant modes TE010, TE110 (a pair of degenerate modes), and TE210 of a circular cavity. Thus, they can be used to form two differential-mode (DM) passbands with independently adjustable center frequencies and bandwidths. The SIW can be seen as a natural balun with intrinsic CM suppression. Furthermore, the planar differential excitation structure is designed to excite the perturbed SIW cavity. The CM noise cannot excite SIW cavity due to the characteristics of hybrid electromagnetic boundary. Finally, a compact dual-band dual-mode SIW balanced BPF at 8 and 10 GHz is designed and fabricated, and the measured results show good agreement with the simulated ones.
Secure and Privacy Preserving Method for Biometric Template Protection using Fully Homomorphic Encryption

The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multishot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by ~ 3% when compared to state-of-the-art, while providing high template security.
A tnGAN-Based Leak Detection Method for Pipeline Network Considering Incomplete Sensor Data

Due to the widely deployed sensors in the pipeline network, the data-driven detection method is a natural choice with multiple sensor measurements. However, the incomplete data problem caused by device failure or network interruption seriously hinders the implementation of pipeline status monitoring. Aiming at this difficulty, this article proposes a generative adversarial network based on trinetworks form (tnGAN) to handle leak detection problems with incomplete sensor data. First, the generative model is proposed to recover incomplete data through fully exploiting the same-level nature similarity of data features. Therein, the same type of sensor data, obtained from the pipeline network, is used as the input. Next, to further boost the temporal evolvement characteristics and the spatial similarity, a multiview awareness strategy is incorporated in the established model to facilitate the integration of inherent information. Then, a dual-discriminative network architecture is proposed to detect pipeline status through computing the similarity of the latent features of samples. With the abovementioned structure, the proposed method can achieve different incomplete data recovery situations, such as individual lost and random missing. In addition, it can also aggregate the output and features of the discriminative networks to obtain the pipeline leak detection result. Finally, the experiment results on a pipeline network demonstrate the capability and effectiveness of the proposed method in both data recovery and leak detection.
Modeling Personalized Out-of-Town Distances in Location Recommendation

Location recommendation becomes increasingly important in the mobile era. Particularly, how to exploit personalized geographical preferences determines the quality of recommended results. A number of efforts have been made on this task, however, there exists a common limitation called the out-of-town recommending problem, i.e., those far places can hardly be recommended. In this paper, we first reveal why modeling the geographical patterns is difficult with the help of the extreme value theory. We find that out-of-town distances are heavy-tailed variables with few observations and extreme values, making it difficult to use common distributions to describe them. To address this issue, we propose a new function called volcano function to model out-of-town distances and personalize it for different users. Empirical results show that we can learn effective patterns from limited observations. Finally we extend the volcano function to a ranking-based collaborative filtering framework, naming it as volcano network (VolNet). Experimental results show the superior performance of VolNet, especially the recall is improved from 0.2 to 0.35 in recommending remote venues compared with the state-of-the-art method GeoMF++.
Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery*

Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be performed in simulation and the learned behavior can be then deployed in real environments. In this work, we introduce an open-source simulation environment providing support for position based dynamics soft bodies simulation and state-of-the-art RL methods. We demonstrate the capabilities of the proposed framework by training an RL agent based on Proximal Policy Optimization in fat tissue manipulation for tumor exposure during a nephrectomy procedure. Leveraging on a preliminary optimization of the simulation parameters, we show that our agent is able to learn the task on a virtual replica of the anatomical environment. The learned behavior is robust to changes in the initial end-effector position. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the RL agent. The proposed simulation environment represents an essential component for the development of next-generation robotic systems, where the interaction with the deformable anatomical environment is involved.