Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft Inflatable Robotic Sleeves for Actuation and Shock Absorption

As robots are becoming compact and autonomous, they are more frequently deployed to disaster sites for various activities, such as search, rescue, and transportation. However, hazardous environments in disaster areas (e.g. cluttered, unstructured and highly constrained spaces) bring harm not only to humans but also to robots. Without a mechanism of physical protection, robots can be vulnerable to ongoing and potential dangers, such as piles of debris and falling objects. In order to prevent the system failure from physical damages, we proposed a shock absorption (or reduction) mechanism using soft inflatable sleeves [1]. The proposed inflatable sleeve can be actively inflated and deflated depending on the surroundings, not degrading the mobility of the robot. For example, the robot can inflate itself when falling from height but deflate when passing through narrow and low pathways. Heat sealable materials are used for the proposed sleeves. Depending on the sealing patterns, the inflated shape of the sleeve can make different shapes. In order to make various patterns, a three-axis translation stage, equipped with a roller-tip with a heating element was built, shown in Fig. 1, which is similar to the system described in [2]. This system facilitates repetitive work, sealing complex patterns, and allows rapid fabrication. Also, it enables the tip to maintain uniform normal force to the materials during fabrication. Using this system, multi-chambered inflatable structures can be made with various heat sealable materials.
Identifying Gender Differences in Information Processing Style, Self-efficacy, and Tinkering for Robot Tele-operation

As robots become more ubiquitous it is important to understand how different groups of people respond to possible ways of interacting with the robot. In this study, we focused on gender differences while users were tele-operating a humanoid robot that was physically co-located with them. We investigated three factors during the human-robot interaction (1) information processing strategy (2) self-efficacy and (3) tinkering or exploratory behavior. Experimental result show that the information on how to use the robot was processed comprehensively by the female participants whereas males processed them selectively $(\pmb{p} < \mathbf{0.001})$. Males were more confident when using the robot than females $(\pmb{p}=\mathbf{0.0002})$. Males tinkered more with the robot than females $(\pmb{p} = \mathbf{0.0021})$. Tinkering might have resulted in greater task success and lower task completion time for males. Similar to existing work on software interface usability, our results show the importance of accounting for gender differences when developing interfaces for interacting with robots.
NECTAR: Knowledge-based Collaborative Active Learning for Activity Recognition

Due to the emerging popularity of pervasive healthcare applications, tools for monitoring activities in smart homes are gaining momentum. Existing methods mainly rely on supervised learning algorithms for recognizing activities based on sensor data. A key issue with those approaches is the acquisition of comprehensive training sets of activities. Indeed, that task incurs significant costs in terms of manual labeling effort; moreover, labeling by external observers violates the individual's privacy. For these reasons, there is an increasing interest in unsupervised activity recognition methods. A popular approach relies on knowledge-based models expressed by ontologies of activities, environment and sensors. Unfortunately, those models require significant knowledge engineering efforts, and are often limited to a specific application. In this paper, we address the issues of existing methods by proposing a novel hybrid approach. Our intuition is that a generic knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants. Specifically, we propose a collaborative active learning method to refine correlations among sensor events and activity types that are initially extracted from a high-level ontology. Generic correlations are personalized to each target smart-home considering the similarity between the feedback target and the feedback provider in terms of environment and inhabitant's profiles. Moreover, thanks to this method, new sensors installed in the home are seamlessly integrated in the recognition framework. In order to reduce the burden of providing feedback, we also propose a technique to carefully select the conditions that trigger a feedback request. We conducted experiments with a real-world dataset and a generic ontology of activities. Results show that our hybrid method outperforms state-of-the-art supervised and unsupervised activity recognition techniques while triggering an acceptable number of feedback queries.
An Extended Case Study about Securing Smart Home Hubs through N-version Programming

Given the proliferation of smart home devices and their intrinsic tendency to offload data storage and processing to cloud services, users’ privacy has never been more at stake than today. An obvious approach to mitigate this issue would be to contain that data within users’ control, leveraging already existing smart hub frameworks. However, moving the storage and computation indoors does not necessarily solve the problem completely, as the pieces of software handling that data should also be trusted. In this paper, we present a thorough study to assess whether N-version programming (NVP) is a valid approach in bootstrapping trust in these data handling modules. Because there are considerable complexity differences among the modules that process home environment data, our study addresses less complex modules that strictly follow exact specifications, as well as complex and looser modules which although not following an exact specification, compute the same high level function. Our results shed light on this complexity and show that NVP can be a viable option to securing these modules.
Data Clustering Based on Community Structure in Mutual k-Nearest Neighbor Graph

Data clustering is a fundamental machine learning problem. Community structure is common in social and biological networks. In this article we propose a novel data clustering algorithm that uses this phenomenon in mutual k - nearest neighbor (MKNN) graph constructed from the input dataset. We use the authentic scores-a metric that measures the strength of an edge in a social network graph-to rank all the edges in the MKNN graph. By removing the edges gradually in the order of their authentic scores, we collapse the MKNN graph into components to find the clusters. The proposed method has two major advantages comparing to other popular data clustering algorithms. First, it is robust to the noise in the data. Second, it finds clusters of arbitrary shape. We evaluated our algorithm on synthetic noisy datasets, synthetic 2D datasets and real-world image datasets. Results on the noisy datasets show that the proposed algorithm clearly outperforms the competing algorithms in terms of Normalized Mutual Information (NMI) scores. The proposed algorithm is the only one that does not fail on any data in the the synthetic 2D dataset, which are specifically designed to show the limitations of the clustering algorithms. When testing on the real-world image datasets, the best NMI scores achieved by the proposed algorithm is more than any other competing algorithm. The proposed algorithm has computational complexity of O(k^3n+kn\log (kn)) and space complexity of O(kn), which is better than or equivalent to the most popular clustering algorithms.
Comparitive study of Single Phase Power Factor Correction based on Fixed and Variable PWM Techniques using Bridgeless Cuk Converter

This paper deals withthe comparative study of single phase Power Factor Correction (PFC)based on fixed Pulse Width Modulation (PWM) and variable frequency control techniquesusing Bridgeless Cuk converter. Design equations and state model of the BridglessCuk converter to aid the control loop are derived based on the modes of operation. The control strategy uses an outer Proportional Integral (PI) controller to normalize output voltage and inner PI controller to shape the input current. PI controller parameters are determined using Tyreus-Luben method. Simulation results are presented to validate the two approaches. Advantages of the proposed scheme are simple method of generating reference currents using the multiplier approach, power factor close to unity, low %THD, regulated output voltage for load variations and effective set point tracking.
Control of Wind Turbine System with PMSG for Low Voltage Ride Through

In the paper the advanced control scheme of the wind turbine with PMSG (Permanent Magnet Synchronous Generator) is described. The direct-driven mechanical system of wind turbine and generator is considered. The issues of Low-Voltage Ride-Through (LVRT) are in particular analyzed. During the grid voltage sags, the control schemes of Machine Side Converter (MSC) and Grid Side Converter (GSC) are forced to meet the LVRT requirements. In order to keep the constant DC-link voltage during voltage sag, the surplus active power is storing in the mechanical system inertia of the wind turbine. To validate the effectiveness of the proposed control, scheme the experimental studies have been performed in the test bench. On the basis of experimental research, good operation of the considered voltage sag control of system has been demonstrated.
A Lightweight Authentication Scheme for Wearable Medical Sensors

Development in wireless communication technology is paving the way for wireless sensors. By 2022, wireless sensors' market is expected to reach the estimation of about USD 15.05. Application of wireless sensors can improve the health of a patient by continuously monitoring patient's health and sending the data to hospitals. Data are communicated through the public network which has no security and is open to everyone. Before sending the data two tasks, i.e., ensuring the security of data and authentication of the party to which data are given, must be done. Also, the wireless sensors are resource constraint devices. So the security mechanism should use lightweight cryptography algorithms. In this paper, a lightweight authentication protocol based on elliptic curve cryptography (ECC) is proposed by us for the wearable wireless sensors. Our scheme provides security against many practical attacks such as man-in-the-middle attack, secret guessing, replay attack, etc. It also takes less computation cost than the other existing schemes.
On Tree-Based Neural Sentence Modeling

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.
pH Effect and Chemical Mechanisms of Antioxidant Higenamine

In this article, we determine the pH effect and chemical mechanism of antioxidant higenamine by using four spectrophotometric assays: (1) 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide radical (PTIO•)-scavenging assay (at pH 4.5, 6.0, and 7.4); (2) Fe3+-reducing power assay; (3) Cu2+-reducing power assay; and (4) 1,1-diphenyl-2-picryl-hydrazyl (DPPH•)-scavenging assay. The DPPH•-scavenging reaction product is further analyzed by ultra-performance liquid chromatography, coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) technology. In the four spectrophotometric assays, higenamine showed good dose-response curves; however, its IC50 values were always lower than those of Trolox. In UPLC-ESI-Q-TOF-MS/MS analysis, the higenamine reaction product with DPPH• displayed three chromatographic peaks (retention time = 0.969, 1.078, and 1.319 min). The first gave m/z 541.2324 and 542.2372 MS peaks; while the last two generated two similar MS peaks (m/z 663.1580 and 664.1885), and two MS/MS peaks (m/z 195.9997 and 225.9971). In the PTIO•-scavenging assays, higenamine greatly decreased its IC50 values with increasing pH. In conclusion, higenamine is a powerful antioxidant—it yields at least two types of final products (i.e., higenamine-radical adduct and higenamine-higenamine dimer). In aqueous media, higenamine may exert its antioxidant action via electron-transfer and proton-transfer pathways. However, its antioxidant action is markedly affected by pH. This is possibly because lower pH value weakens its proton-transfer pathway via ionization suppression by solution H+, and its electron-transfer pathway by withdrawing the inductive effect (-I) from protonated N-atom. These findings will aid the correct use of alkaloid antioxidants.
A Distribution-Free Analysis of Outage Probability Over Block Fading Channels

In this paper, we derive a non-asymptotic upperbound on the outage probability for a multiple transmit diversity technique (with a low diversity gain), which is distribution-free. The derived bound can be used to determine key parameters for the initial stage of reliable connection (with a certain target outage probability) when a system operates under an unknown channel environment.
Ground Extraction from 3D Lidar Point Clouds with the Classification Learner App

Ground extraction from three-dimensional (3D) range data is a relevant problem for outdoor navigation of unmanned ground vehicles. Even if this problem has received attention with specific heuristics and segmentation approaches, identification of ground and non-ground points can benefit from state-of-the-art classification methods, such as those included in the Matlab Classification Learner App. This paper proposes a comparative study of the machine learning methods included in this tool in terms of training times as well as in their predictive performance. With this purpose, we have combined three suitable features for ground detection, which has been applied to an urban dataset with several labeled 3D point clouds. Most of the analyzed techniques achieve good classification results, but only a few offer low training and prediction times.
Lower Limb Exoskeleton Hybrid Phase Control Based on Fuzzy Gain Sliding Mode Controller

Exoskeleton robots have been developed to enhance human mobility and reduce the muscle fatigue caused by heavy loads. Now that the exoskeleton should reach transparency with the wearer, the human-machine interaction based control strategy will significantly enhance collaboration between the exoskeleton and the user. This paper proposes a novel method called Hybrid Phase Control (HPC) based on different dynamic models in stance and swing phase of a gait cycle. The dynamic models are derived by using Euler-Lagrange Equation. The proposed method is applied in a lower extremity exoskeleton to track a desired trajectory. In order to improve the adaptive capacity of the controller, we introduce the Sliding Mode Control (SMC) and further eliminate the chattering phenomenon of traditional SMC by using fuzzy logic system (FLS) to adjust SMC switch-gain. The proposed control strategy was proved stable with Lyapunov analysis and used for trajectory tracking in comparison with a conventional sliding mode control. Results with Matlab simulation shows that the proposed controller can achieve better tracking capability and the chattering effect is greatly reduced.
Topic Model Based Opinion Mining and Sentiment Analysis

This paper discusses a new topic model based approach for opinion mining and sentiment analysis of text reviews posted in web forums or social media site which are mostly in unstructured in nature. In recent years, opinions are exchanged in clouds about any product, person, event or any interested topic. These opinions help in decision making for choosing a product or getting feedback about any topic. Opinion mining and sentiment analysis are related in a sense that opining mining deals with analyzing and summarizing expressed opinions whereas sentiment analysis classifies opinionated text into positive and negative. Aspect extraction is a crucial problem in sentiment analysis. Model proposed in the paper utilizes topic model for aspect extraction and support vector machine learning technique for sentiment classification of textual reviews. The goal is to automate the process of mining attitudes, opinions and hidden emotions from text.
Sensing Characteristics of Helical Long-Period Gratings Written in the Double-Clad Fiber by CO2 Laser

We demonstrate the fabrication of helical long-period gratings (HLPGs) with the periodic helical index modulation by twisting the double-clad fiber (DCF) while the fiber was exposed by CO2 laser. Torsion, bending, surrounding refractive index, temperature and axial strain characteristics of the fabricated DCF-HLPG were investigated experimentally. The DCF-HLPG was found to have a high torsion sensitivity of −0.384 nm/(rad/m) and bending sensitivity of 18.112 nm/m−1 due to the asymmetric helical index modulation across the fiber section induced by the CO2 laser heating.
A Tool for Digital Circuits Synthesis Based on Notification Oriented Paradigm

FPGAs (Field Programmable Gate Arrays) are proving to be an alternative for applications that require great processing power because they allow parallelism. Still, the high-level synthesis tools allow professionals without hardware knowledge to use FPGAs. However, this high-level tools do not allow to properly explore the parallelism because they are either based on sequential techniques or rely on languages with an insufficient level of abstraction. In this paper it is proposed the use of the Notification Oriented Paradigm (NOP) for Digital Hardware as a high-level synthesis tool. The NOP is a development technique that presents a new concept for designing and executing applications, based on rules and collaborative entities, which communicate through notifications in a parallel way. In order to allow the use of the NOP in the creation of digital hardware as a new high-level synthesis tool, a new programming language was proposed, and both a compiler and an emulator where implemented. With these tools it is possible to generate VHDL code directly from a high level NOP program. In order to evaluate the performance and stability of circuits generated with these tools, some experiments were performed. These experiments demonstrated that the NOP for Digital Hardware allows quickly and easily creating reliable digital circuits with suitable performance and parallelism, directly from a high-level development environment.
Xenogeneic Graft-Versus-Host Disease in Humanized NSG and NSG-HLA-A2/HHD Mice

Despite the increasing use of humanized mouse models to study new approaches of graft-versus-host disease (GVHD) prevention, the pathogenesis of xenogeneic GVHD (xGVHD) in these models remains misunderstood. The aim of this study is to describe this pathogenesis in NOD/LtSz-PrkdcscidIL2rγtm1Wjl (NSG) mice infused with human PBMCs and to assess the impact of the expression of HLA-A0201 by NSG mice cells (NSG-HLA-A2/HHD mice) on xGVHD and graft-versus-leukemia (GvL) effects, by taking advantage of next-generation technologies. We found that T cells recovered from NSG mice after transplantation had upregulated expression of genes involved in cell proliferation, as well as in TCR, co-stimulatory, IL-2/STAT5, mTOR and Aurora kinase A pathways. T cells had mainly an effector memory or an effector phenotype and exhibited a Th1/Tc1-skewed differentiation. TCRβ repertoire diversity was markedly lower both in the spleen and lungs (a xGVHD target organ) than at infusion. There was no correlation between the frequencies of specific clonotypes at baseline and in transplanted mice. Finally, expression of HLA-A0201 by NSG mice led to more severe xGVHD and enhanced GvL effects toward HLA-A2+ leukemic cells. Altogether our data demonstrate that the pathogenesis of xGVHD shares important features with human GVHD and that NSG-HLA-A2/HHD mice could serve as better model to study GVHD and GvL effects.
Design of Robot Hand With Pneumatic Dual-Mode Actuation Mechanism Powered by Chemical Gas Generation Method

In this letter, we introduce an anthropomorphic robot hand by proposing a pneumatic dual-mode actuation mechanism powered by a compact pneumatic power generator based on the decomposition of hydrogen peroxide. Since the pneumatic power generator has limited gas supply ability for each fuel charge, a pneumatic dual-mode actuation mechanism is proposed to reduce the amount of gas consumed for each grasp action while maintaining the robot hand's fast grasping speed and large grasping force. Driven by the proposed pneumatic dual-mode actuation mechanism, the designed robot finger provides maximum 468°/s flexion speed and 29.1 N fingertip force. Based on the proposed pneumatic dual-mode actuation mechanism, a robot hand with 198 × 79 × 31 mm3 size and 420 g weight is designed. A differential mechanism and passive thumb abduction–adduction mechanism are applied for compact design and efficient gas consumption and the performance is verified with experiments. Finally, the robot hand was connected to the pneumatic power generator loaded with 80 ml of 70% hydrogen peroxide solution and provided more than 1250 repetitions of the grasping motion for the maximum gas consumption case, which is suitable to perform daily work when applied as a prosthetic hand.
Effects of phloroglucinol on the active phase of labour (EPAL trial): a single blinded randomised controlled trial in a tertiary hospital in sub-Sahara Africa

Introduction One of the most recognized factors of maternal and neonatal outcome pertaining to the peripartum period is the duration of labour. Finding a drug that will decrease the duration of labour with no effects on mother and foetus will be welcomed. Thereby in this study we aimed to evaluate the effects of phloroglucinol on the duration of the active phase of labour. Methods We did a single blinded placebo controlled randomised 1:1 parallel designed superiority trial between January and June 2017 in Douala general hospital. Participants greater than 18 years with singleton uncomplicated pregnancy who consented following randomisation, were administered either 80mg/8ml intravenous phloroglucinol or 8ml of sterile water when in active labour. The primary outcome was the duration of labour. Modified intention to treat analysis was done with the level of significance set at a p value of 0.05. Results 122 participants received the intervention. The mean total duration labour in the treatment and placebo group were 216.8 ± 38.7 and 358.5 ± 65.8 respectively (p value = 0.243). The mean duration of the active phase of labour in the treatment and placebo group were 183.0±35.6 and 316.0±52.2 respectively (p value = 0.046). The mean rate of cervical dilatation in the treatment and placebo group were 2.1 ± 0.4 and 1.3 ± 0.4 respectively (p value = 0.322). There was no difference in maternal and foetal outcomes between the two groups. Conclusion Phloroglucinol shortens the duration of active phase of labour by about 2 hours (42%). It is safe to mother and baby and does not cause adverse foetal or maternal outcomes.