Miracle-dz/UniFAR-FTUs huggingface dataset:
Out With the Old and in With the New: Repairing Damaged Cartilage

Imagine experiencing aching pain with every step and every bend of your knee. As time passes, the pain only worsens. Sounds terrible, does it not? Such pain could be caused by a disease called osteoarthritis. This disease involves the breakdown of joint tissue, called cartilage, which normally keeps the bones from rubbing together. Even small injuries to cartilage can lead to osteoarthritis because cartilage lacks the ability to heal itself. A surgical procedure called autologous chondrocyte implantation (ACI) can be performed after small cartilage injuries, to help prevent osteoarthritis from developing. In ACI, healthy cartilage cells are grown outside the body and then re-implanted into the damaged area. ACI is not perfect; healing is limited because of the artificial environment that the cells are grown in before implantation. This article discusses the changes these cartilage cells undergo when they are grown in an artificial environment, and how our research is addressing this issue to improve the success of ACI.
Patterns of Complementarity in the Social World of Children in Rural Madagascar

The U Research on childhood in anthropology and neighboring disciplines has continuously broadened the range of the social partners that are considered relevant for young children’s development—from parents to other caregivers, siblings, and peers. Yet most studies as well as interventions in early childhood still focus exclusively on parents, who are presumed to be the most significant socializing agents. Objecting to such a hierarchical understanding of the social world of children, I propose a complementarity view. Rather than being linearly ranked in a hierarchy of significance, children’s social partners may complement each other by providing different but equally significant experiences. My suggestions are based on an ethnographic study in a rural community in Madagascar. Focusing on children in the first 3 years of life, I explore the full range of their social partners and the respective experiences they provide. Caregivers focus on children’s physical needs and aim to keep them in a calm emotional state, while other young related children are themost crucial partners when it comes to play, face-to-face interaction, and the exchange of intense emotions. These complementary roles, I argue, lead to the parallel formation of two distinct socioemotional modes: a hierarchical one and an egalitarian one.
Cluster-Based Multipolling Sequencing Algorithm for Collecting RFID Data in Wireless LANs

Abstract With the growing use of RFID (Radio Frequency Identification), it is becoming important to devise ways to read RFID tags in real time. Access points (APs) of IEEE 802.11-based wireless Local Area Networks (LANs) are being integrated with RFID networks that can efficiently collect real-time RFID data. Several schemes, such as multipolling methods based on the dynamic search algorithm and random sequencing, have been proposed. However, as the number of RFID readers associated with an AP increases, it becomes difficult for the dynamic search algorithm to derive the multipolling sequence in real time. Though multipolling methods can eliminate the polling overhead, we still need to enhance the performance of the multipolling methods based on random sequencing. To that extent, we propose a real-time cluster-based multipolling sequencing algorithm that drastically eliminates more than 90% of the polling overhead, particularly so when the dynamic search algorithm fails to derive the multipolling sequence in real time.
Fast analysis of combinatorial netlists correctness rate based on binomial law and partitioning

This paper targets fault propagation evaluation in combinational circuits and focuses on netlist-based logic masking prediction. We propose an approach that combines clustering and analytical estimation methods to allow to control the trade-off between accuracy and computation time. The experimental results performed on netlists with large number of gates and complex reconvergent structures show that the proposed solution is better adapted to large combinatorial netlists than the analysis found in the literature.
Exploiting Physical Layer Vulnerabilities in LoRaWAN-based IoT Networks

Low Power Wide Area Networks (LPWAN) are used worldwide in several Internet of Things (IoT) applications that rely on large-scale deployments. Despite most of these networks include their own security mechanisms with built-in encryption, they are still vulnerable to a range of attacks that can be performed using low-cost Software Defined Radio (SDR) hardware and low-complexity techniques. This work provides an experimental setup implemented to exploit physical layer vul-nerabilities with SDR techniques. Several attack vectors typically related to LPWAN within the IoT ecosystem are implemented and tested such as Global Positioning (GPS) Spoofing, Replay Attacks, Denial-of-Service (DoS) and Jamming, in environments that rely specifically on LoRaWAN networks. The results show that, in LoRAWAN networks, a set of vulnerabilities can be exploited leading to the incorrect functioning of the executed applications, and possible damage to the systems in which they operate. It was possible to verify that, depending on the type of activation method used between the devices and the LoRaWAN server, the communications and the devices can be compromised.
Storm time impulsive enhancements of energetic oxygen due to adiabatic acceleration of preexisting warm oxygen in the inner magnetosphere

We examine enhancements of energetic (>50 keV) oxygen ions observed by the Radiation Belt Storm Probes Ion Composition Experiment (RBSPICE) instrument on board the Van Allen Probes spacecraft in the inner magnetosphere (L ~ 6) at 22–23 h magnetic local time (MLT) during an injection event of the 6 June 2013 storm. Simultaneous observations by two Van Allen Probes spacecraft located close together (~0.5 RE) indicate that particle injections occurred in the premidnight sector (< ~24 h MLT). We also examine the evolution of the proton and oxygen energy spectra at L ~ 6 during the injection event. The spectral slope did not significantly change during the storm. The oxygen phase space density (PSD) was shifted toward higher PSD in a wide range of the first adiabatic invariant. The spectral evolution manifests the characteristics of adiabatic acceleration and density increase of oxygen ions. Warm (0.1–10 keV) oxygen measured by the Helium, Oxygen, Proton, and Electron (HOPE) instrument was enhanced prior to the storm mostly in magnetic field‐aligned directions. The most reasonable scenario of this event is that warm oxygen ions that preexisted in the inner magnetosphere were picked up and adiabatically transported and accelerated by spatially localized, temporarily impulsive electric fields.
Unknown Signal Detection in Switching Linear Dynamical System Noise

A machine learning approach is presented for detecting unknown or anomalous signals in a complicated background of interfering signals and noise. The approach can be employed in RF spectrum monitoring applications to efficiently detect transmissions that deviate from a typical signal environment. For example, in the cognitive radio domain, the technique may be applied to learn the typical behavior of spectrum sharing secondary users and efficiently detect noncompliant transmissions. A switching linear dynamical system (SLDS) is trained to represent the interference and noise environment via a Bayesian nonparametric hierarchical Dirichlet process (HDP)-SLDS technique. An unknown signal is detected if the Viterbi hidden switching state path of the test data is sufficiently unlikely under the learned background SLDS. The detection scheme is derived as a generalized likelihood ratio test (GLRT) for an unknown deterministic signal in SLDS noise. The distribution of the Viterbi likelihood test statistic under the null hypothesis (signal absent) is analyzed and an asymptotic upper bound on the false alarm probability is derived as a function of the detection threshold. Numerical simulation and experimental results on a software-defined radio (SDR) testbed demonstrate that the empirical false alarm rate obeys the upper bound and that the SLDS detection approach substantially outperforms an earlier HMM-based scheme as well as a standard energy detector in a challenging interference and noise background.
Bandwidth Enhancement of Low-Profile Metasurface Antenna Using Nonuniform Geometries

Artificial magnetic conductor metasurface antennas are investigated using a robust, surrogate-assisted, differential evolution optimization technique. Using a uniform metasurface array configuration as a starting point, multiple array configurations are parameterized and the differential evolution optimizer yields nonuniform array geometries exhibiting wideband performance. The bandwidth improvement is attributed to the structure’s ability to support additional higher-order modes with good impedance matching. A comparison between the uniform metasurface antenna cases and their ‘evolved’ nonuniform counterparts is presented to demonstrate the advantages of the proposed technique. Two prototypes are fabricated and characterized to experimentally confirm the advantages of proposed designs. The first prototype, a 3x3 case, measured a fractional bandwidth of 36.8% with peak gain of 8.3 dBi. The second prototype, a 4x4 case, measured a fractional bandwidth of 51.8% with peak gain of 8.8 dBi. When compared to standard uniform cases, the proposed nonuniform geometries exhibit substantial impedance bandwidth enhancement.
Impact of pre-amorphization implantation schemes using beam line or plasma ion implantation on Ni(Pt)Si/Si specific contact resistivities

The specific contact resistivity of Ni(Pt)Si silicide layers on Si active areas has been assessed using the transfer length method (TLM) for a broad range of pre-amorphization implantation conditions, including various implanted species and implantation energies as well as two different implantation processes (beam line ion implantation (BLII) and plasma immersion ion implantation (PIII)). Using BLII, the interest of dopant segregation for Schottky barrier height engineering seems to be limited by the difficulty to localize an important quantity of dopants at the Si surface. Using PIII, this difficulty is overcome and a reduction of the specific contact resistivity is obtained when using CF4-based implantation processes.
Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern

Plant diseases are one of the primary causes of decreased agricultural production quality and quantity. With ongoing changes in plant structure and cultivation techniques, new diseases are constantly arising on plant leaves. Thus, accurate classification and detection of plant leaf diseases in their early stages will limit the spread of the infection and support the healthy development of plant production. This work proposes a novel lightweight deep convolutional neural network (CNN) model for obtaining high-level hidden feature representations. The deep features are then fused with traditional handcrafted local binary pattern (LBP) features to capture local texture information in plant leaf images. The proposed model is trained and tested on three publicly available datasets (Apple Leaf, Tomato Leaf, and Grape Leaf). On the three datasets, the proposed approach achieves 99%, 96.6%, and 98.5% validation accuracies, respectively, and 98.8%, 96.5%, and 98.3% test accuracies, respectively. The results of the experiments show that the proposed approach can provide a better control solution for plant diseases.
Ethical Biases in Machine Learning-based Filtering of Internet Communications

The use of automated systems based on artificial intelligence and machine learning for filtering and moderating online communications has become commonplace. While this allows for high levels of efficiency and fine-grained control of malicious behaviors, it could also produce unintended disparities in treatment of legitimate users. In this paper, we aim at identifying some possible field-related biases in the wellknown Google Perspective API machine learning-based engine for controlling Internet communications. For this purpose, we consider communications in the fields of health, trade, finance, and defense and build a data set collecting Twitter-based online communications of the World Health Organization (WHO), World Trade Organization (WTO), International Monetary Fund (IMF) and North Atlantic Treaty Organization (NATO). Collected data are then analyzed through Perspective API to assign them an alleged likelihood of being abusive for specific emotional concepts, referred to as attributes. Upon analysis, discrimination between the considered users is identified for all attributes. This result, although preliminary, apparently indicates that Perspective API creates discrimination for field-related content as a result of semantic biases in the data, thus highlighting the need for an ethically sound design of these systems, following an ethics by design approach.
Continuous-Emission Markov Models for Real-Time Applications: Bounding Deadline Miss Probabilities

Probabilistic approaches have gained attention over the past decade, providing a modeling framework that enables less pessimistic analysis of real-time systems. Among the different proposed approaches, Markov chains have been shown effective for analyzing real-time systems, particularly in estimating the pending workload distribution and deadline miss probability. However, the state-of-the-art mainly considered discrete emission distributions without investigating the benefits of continuous ones. In this paper, we propose a method for analyzing the workload probability distribution and bounding the deadline miss probability for a task executing in a reservation-based server, where execution times are described by a Markov model with Gaussian emission distributions. The evaluation is performed for the timing behavior of a Kalman filter for Furuta pendulum control. Deadline miss probability bounds are derived with a workload accumulation scheme. The bounds are compared to 1) measured deadline miss ratios of tasks running under the Linux Constant Bandwidth Server with SCHED-DEADLINE, 2) estimates derived from a Markov Model with discrete-emission distributions (PROSIT), 3) simulation-based estimates, and 4) an estimate assuming independent execution times. The results suggest that the proposed method successfully upper bounds actual deadline miss probabilities. Compared to the discrete-emission counterpart, the computation time is independent of the range of the execution times under analysis, and resampling is not required.
Dynamic Link Prediction Using Graph Representation Learning with Enhanced Structure and Temporal Information

The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future time. How to effectively learn the temporal and structural pattern of the network dynamics is the key. In this paper, we propose a graph representation learning model based on enhanced structure and temporal information (GRL_EnSAT). For structural information, we exploit a combination of a graph attention network (GAT) and a self-attention network to capture structural neighborhood. For temporal dynamics, we use a masked self-attention network to capture the dynamics in the link evolution. In this way, GRL_EnSAT not only learns low-dimensional embedding vectors but also preserves the nonlinear dynamic feature of the evolving network. GRL_EnSAT is evaluated on four real datasets, in which GRL_EnSAT outperforms most advanced baselines. Benefiting from the dynamic self-attention mechanism, GRL_EnSAT yields better performance than approaches based on recursive graph evolution modeling.
Systematic Control of Electro-hydraulic Velocity Servomechanism using Fractional Order Technique

Electro-Hydraulic ServoMechanism (EHSM) is a nonlinear system with tentative dynamics, whose control is a difficult task. In this research, an alternative to the existing technique, a fractional order PID control-based approach, is presented. Initially, a SIMULINK based nonlinear model is constructed. The linear model is obtained by approximating the non-linearity using the bilinear transformation. Pole placement is blended with the Ziegler-Nichols tuning method to carry out the controller design and controller parameter tuning. The results are compared with conventional PID, and stability analysis is also analyzed. The results of the fractional order controller show satisfactory improvement than the results of conventional PID.
Machine Learning Vulnerability Assessment of Ring Oscillator Physical Unclonable Functions

Devices authentication and cryptographic keys production techniques are implemented in various security applications. Because of its ability to generate unique, unclonable, and unpredictable cryptographic keys, physically unclonable functions (PUFs) have been utilized in various hardware security applications. However, PUFs can be vulnerable to adversary attacks like machine learning (ML)-based modeling attacks. Input-output combinations of PUFs are called challenge-response pairs (CRPs). This article provides a vulnerability assessment of three recent Ring Oscillator (RO) PUFs approaches against ML modeling attacks. Assuming that adversary obtains part of the RO-PUF CRPs, and they can be used to predict the unrevealed CRPs. The article has a secondary objective to assess the impact of aging on the tested RO-PUF approaches in regenerating the same response.
Secure IoT Deployment Checklist for Building Management System

A Building Management System (BMS) enables the capability to control the infrastructure within a building. BMS can be considered a miniature industrial control system, which is more conventional and prevalent. The legacy architecture of the BMS assumes every device has to be connected physically. Due to this reason, installing Internet of Things (IoT) devices may introduce new security loopholes in the existing BMS. Moreover, an adversary may utilize the communication channel between the BMS and IoT devices to launch cyber-attacks. In this paper, we created a prototype setup of a BMS with IoT devices to study the deployment and how the system can be installed. We introduced a comprehensive IoT security deployment checklist to assess IoT solutions' security posture, specifically focusing on the BMS system. This checklist is novel to the market as the existing works are mostly targeted at IoT but are not fully applicable to BMS.
Network Embedding with Enhanced Feature Representations for Link Prediction

Link prediction is a hot research topic in graph data analytics, which aims to identify the likelihood of a future connection between two nodes in a network. Among the approaches to link prediction, network embedding has gained tremendous popularity in tackling link prediction tasks. Basically, there are several network embedding methods, such as matrix factorization, random walk, and deep learning. However, most of the existing methods for network embedding solely rely on structure information or fail to effectively combine network structure and feature data. To this end, this paper proposes a novel method that learns network embedding with enhanced feature representations for link prediction (EFRLP). Specifically, structure graphs, bipartite graphs, and co-feature graphs are generated from the attributed network, weighted bipartite graphs are then built over bipartite graphs and correlation weights between features. We further adopt a biased random walk to obtain more representative node sequences. With the help of enriched feature representations, not only can the higher-order semantic relationships be effectively captured but also network sparsity can be alleviated. Then, node sequences are selected into the Skip-Gram model for learning low-dimensional embeddings. Finally, the probability of the existence of edges between nodes is predicted by deep fusion. The effectiveness of the EFRLP method is demonstrated by extensive comparison experiments with several baselines on two real-world datasets.
A Survey on Fault Tolerant and Diagnostic Techniques of Multilevel Inverter

Multilevel inverters are used in a number of high-power applications, including industrial motor drives, flexible power transmission, and, renewable energy systems. Since the MLI design incorporates a large number of devices, the system’s reliability suffers. This has caught the interest of academics all over the world, who are searching for methods to increase the dependability of the MLI design and include fault-tolerant qualities into it, either through software or hardware. Redundancy is a method that has recently gained popularity in solid-state transformer applications where fault tolerance is a highly desired quality. This is because the method provides for greater fault tolerance. The goal of this research is to offer a complete knowledge of the fault tolerance capabilities of MLIs created by the research community over the last few years
LiDAR Point Cloud Classification with Coordinate Attention Blueprint Separation Involution Neural Network

With the advent of the era of Industry 4.0 and the continuous development of point cloud data acquisition technology, point cloud data has been widely used in unmanned distribution of intelligent logistics. This paper designs a 3D point cloud classification model with coordinate attention, blueprint separation involution neural network (BICANet). Firstly, the combination of 2D features and 3D features is adopted to maintain the spatial structure of the point cloud. Secondly, the Involution network is introduced to reduce the amount of redundant data for neural network computation and improve the whole network computation efficiency. At the same time, to further enhance the network feature learning capability, the blueprint separation convolution is combined with coordinate attention. The experimental results prove that the overall accuracy of BICANet in Vaihingen and GML B datasets reaches 86.0% and 98.8%, respectively. It is highly competitive with the currently available methods.
Adaptive Federated Learning-based Joint Pilot Design and Active User Detection in Scalable Cell-free Massive MIMO Systems

Massive ultra-high reliability ultra-low latency communication (mURLLC), a promising core service of 6G, is likely to adopt grant-free random access (GFRA) as its access scheme for low access latency. However, massive user access brings many challenges to the process, especially the huge computing overhead and delay will hinder the scalability of the system. Based on a distributed cell-free massive multiple input multiple output (CF-mMIMO) edge computing system, we propose a scalable active user detection (AUD) scheme in the first step of GFRA. First, we divide the remote radio units (RRUs) into several clusters and allocate pilots and power to users with deep deterministic policy gradient (DDPG) algorithm. Then, within each RRU cluster, a noise-learning-based convolutional structure is applied on the edge distributed unit (EDU) to train local detection network. Furthermore, by introducing an adaptive federated learning algorithm, the globally optimal AUD performance is achieved under the delay constraint. The simulation results show that the proposed scalable AUD scheme is suitable for massive users scenario.