Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Generation Scheme of Blended Trajectory for Rehabilitation Robots

The trajectory generation ensuring uniform velocity and reduced-jerk is desired for rehabilitation robots. However, this requirement is largely overlooked in the research of rehabilitation robotics. Most research prototypes of existing rehabilitation robots used a cubic polynomial approach in which sudden and large acceleration changes have occurred at the start and the end of the trajectory, which causes theoretically infinite jerk. Moreover, the cubic polynomial approach cannot maintain uniform velocity during the robot's maneuvering. To bridge this gap, in this research, a blended (i.e., hybrid) scheme using fifth-order polynomial at the start and the end of the trajectory and a linear segment in between the polynomials is proposed to generate trajectories for rehabilitation robots. The trajectory generated using the proposed scheme shows no sudden change in acceleration and reduced jerk compared to the cubic polynomial approach.
WeMoD: A Machine Learning Approach for Wearable and Mobile Physical Activity Prediction

It is indisputable that physical activity (PA) is vital for an individual’s health and well-being. However, globally, one in four adults do not meet the recommended levels of PA, with substantial personal and socioeconomic implications. In recent years, a significant amount of work has explored the potential of pervasive computing and self-tracking for increasing PA. Adaptive and personalized goal-setting has proven to be one of the most efficient methods in this direction. To this end, we propose a Machine Learning (ML) approach, WeMoD, which can be used to predict a user’s future daily step count for setting challenging yet achievable goals. To develop WeMoD, we utilize heterogeneous, multimodal human data collected unobtrusively in the wild. Additionally, we use a novel fusion of physiological, behavioral, and contextual features, which according to the experimental results, has a positive effect on the predictive ability of the models. Specifically, we can predict a user’s step count with a MAE of 1930 steps and further improve this performance through personalization with a MAE of 1908 steps, paving the way for future work in this field.
Spatiotemporal Nonreciprocal Filters: Theoretical Concepts and Literature Review

Nonreciprocal components are of paramount importance in many electronic systems. Traditionally, reliance on magnetic materials, such as ferrites, has been the basis of the functionality of such components. The internal magnetic moment under the influence of a strong external magnetic bias breaks the reciprocity of the materials <xref ref-type="bibr" rid="ref1">[1]</xref>. However, today’s state-of-the-art systems demand cost-effective, miniaturized, and integrated solutions. This can be achieved with magnetless nonreciprocal devices that are capable of breaking the Lorentz reciprocity principle <xref ref-type="bibr" rid="ref2">[2]</xref>. In the past decade, passive metastructures have also been devised to achieve Faraday rotation without magnetic bias. Spatiotemporal biasing of the metasurfaces initiates frequency translation and harmonic mixing properties that, in turn, generate nonreciprocity <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref4">[4]</xref>. Recently, with the advent of magnetless nonreciprocal devices, time-varying circuits have been viewed as a remarkable breakthrough. This has, in fact, paved the path for researchers to develop nonreciprocal devices such as circulators <xref ref-type="bibr" rid="ref5">[5]</xref>–<xref ref-type="bibr" rid="ref6"/><xref ref-type="bibr" rid="ref7"/><xref ref-type="bibr" rid="ref8"/><xref ref-type="bibr" rid="ref9"/><xref ref-type="bibr" rid="ref10"/><xref ref-type="bibr" rid="ref11"/><xref ref-type="bibr" rid="ref12"/><xref ref-type="bibr" rid="ref13"/><xref ref-type="bibr" rid="ref14">[14]</xref>, isolators <xref ref-type="bibr" rid="ref15">[15]</xref>–<xref ref-type="bibr" rid="ref16"/><xref ref-type="bibr" rid="ref17"/><xref ref-type="bibr" rid="ref18">[18]</xref>, gyrators <xref ref-type="bibr" rid="ref19">[19]</xref>, and even leaky-wave antennas <xref ref-type="bibr" rid="ref20">[20]</xref>, <xref ref-type="bibr" rid="ref21">[21]</xref>. Still, there remain some areas in the field of nonreciprocity that can be explored with the aid of magnetless time-varying circuits. One such area is the implementation of nonreciprocity in bandpass filters.
Emotional and cognitive dissonance revealed using VEMOS emotion analysis system

Artificial intelligence is used in different fields. This paper describes a new opportunity to connect artificial intelligence to cognitive social psychology. We touch two important psychology paradigms: emotional and cognitive dissonance. Our hypotheses are proved using the new video based emotions analysis system (VEMOS) by testing labeled videos samples filmed by an actor according to specific scenarios and situations proposed by an expert on cognitive psychology describing the two paradigms. First, our approach has shown relevant results that confirm the notion of emotional and cognitive dissonance and the reliability of the intelligent system VEMOS on recognizing emotions efficiently. Then, we successfully add a new method of measuring a score of the individual emotional dissonance as well as his degree of spontaneity and degree of control.
DoubleCheck: Detecting Single-Hand Cycling with Inertial Measurement Unit of Smartphone

Riding bikes with only one hand on the handlebar can severely undermine the steering capability of riders and risk road safety. In this study, we propose a first detection framework for monitoring single-hand cycling on bicycle travel, called DoubleCheck. It is based on the premise that riders adapt their body movement during single-hand cycling, which is distinguishable to the sensors even amid noise from the exasperate road surface. The system can detect handlebar-holding under different road conditions using motion signals from a built-in inertial measurement unit (IMU) in a handlebar-mounted smartphone. We implemented the system and invited 10 participants for our evaluation experiment. Our results show that DoubleCheck achieved an F1-score of 0.94 for hand detection, proving its efficacy for real-life implementation to improve road safety.
Performance Analysis of MIMO-NOMA Iterative Receivers for Massive Connectivity

The Fifth Generation (5G) of wireless networks introduced support to Machine-Type Communications (MTC), which is the wireless connectivity solution for Internet of Things (IoT) applications. MTC is split into two different categories: massive MTC (mMTC) and critical MTC (cMTC). Current 5G standards and technologies are not capable of fully satisfying the requirements of both mMTC and cMTC use cases, thus industry and academia have already started developing solutions for MTC in beyond-5G and 6G networks. In some mMTC use cases, receivers might not be equipped with a large number of antennas owing to cost, size or power limitations, thus the number of active devices in a time slot may surpass the number of antennas. Due to the limited spatial multiplexing capabilities, only multi-antenna techniques are not enough to provide connectivity to a massive number of devices in such scenarios. In this paper, we propose and evaluate the performance of iterative linear receivers that can address this issue. By combining Multiple-Input Multiple-Output (MIMO) techniques with Non-Orthogonal Multiple Access (NOMA) exploiting Successive Interference Cancellation (SIC) or Parallel Interference Cancellation (PIC) decoding, the proposed novel receivers are capable of performing dynamic ordering SIC/PIC decoding of multiple overlapping signals even when the number of active devices surpasses that of receive antennas. The performance of the receivers is studied in terms of outage probability and computational complexity. Simulation results show that, among all the receivers studied in this paper, the PIC-based Minimum Mean Square Error (MMSE) receiver presents the best performance while at the same time reducing the number of complex signal operations such as matrix inversions.
Capability Based Network Access Control for Smart Home Devices

Many smart home devices, such as smart speakers, are connected to a home network. However, conventional home networks do not control communications among devices including malicious devices. As a result, some malicious devices in a home network can steal private information from or manipulate devices that have no or weak access control mechanism. Some methods have been proposed to protect devices in home networks, but they require users to configure policies to do fine-grained control of device communications and it is a difficult task for ordinary people. In this paper, we propose an architecture to protect devices from malicious or unintended communications. We designed a capability based authorization architecture based on requirements for easy and user-centric access control in home networks. Also, we designed a method to control communications among devices relying on capabilities that are authorized in the proposed architecture. We have implemented a prototype system and have shown that it is possible to do fine-grained access control among devices in a home network with capability based authorization.
Inference System for Automatic Identification of Braille Blocks Using a Pressure Sensor Array

Visually impaired people usually use braille blocks as guidance marks when walking outside. Therefore, detecting the braille blocks accurately is very important for them to walk safely. In our previous research we proposed a novel system for recognizing braille blocks, mainly in an outdoor setting, using not only the information from a white cane but also a pressure sensor array to realize the braille block more accurately. Moreover, we showed our system could be constructed using a Neural Network with an extremely simple structure. In this research we developed an inference system for automatic identification of braille blocks based on the previous research results. The system consisted of a 16-channel foot pressure sensor array, a microcomputer and a cloud system, which respectively detected the difference in pressure pattern depending upon the type of braille block, made inferences about the kinds of braille blocks and implemented machine learning. We made a prototype system and confirmed that the system could infer the kinds of braille blocks with high accuracy in real-time.
Interworking between LoRaWAN and non-LoRa IoT Systems

LoRaWAN is a communication protocol and system architecture for connecting possibly very constrained devices through long-range wireless connectivity to application servers via LoRaWAN gateways. Currently there are several commercial and/or open-source platforms providing a complete LoRaWAN backend system that includes a LoRaWAN network server, join server and application server. Device data are exchanged between the end devices and the application servers and can be usually accessed by the user by proper application server interfaces (like REST API or MQTT). Unfortunately, these systems are heterogeneous in terms of external interfaces and do not provide inter-system communications. Moreover they provide a network solution for LoRaWAN-only devices, as no interoperability with non-LoRa devices is usually provided. Aiming to solve these issues, in this paper we introduce some interworking entities for letting a LoRaWAN system to communicate with other non-LoRa systems (either single devices or IoT networks). Then, leveraging this solutions, we propose an overall architecture capable of creating a larger heterogeneous data-centric IoT system. A complete implementation has been carried out and tested in order to prove the feasibility and simplicity of the proposed systems.
Reconfigurable Intelligent Surface-Assisted Massive MIMO: Favorable propagation, channel hardening, and rank deficiency [Lecture Notes]

Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for 5G-and-beyond wireless networks, capable of providing large array gain and multiuser spatial multiplexing. Without requiring additional frequency bands, those technologies offer significant improvements in both spectral and energy efficiency by simultaneously serving many users. The performance analysis of an RIS-assisted massive MIMO system as a function of channel statistics relies heavily on fundamental properties, including favorable propagation, channel hardening, and rank deficiency. The coexistence of both direct and indirect links results in aggregated channels, whose properties are the main concerns of this “Lecture Notes” article. For practical systems with a finite number of antennas and engineered scattering elements of the RIS, we evaluate the corresponding deterministic metrics, with Rayleigh fading channels as a typical example.
Texture Classification by Audio- Tactile Crossmodal Congruence

This work presents a large-scale texture classification method using a novel texture feature Projected Spectral Mapping (PSM) based on audio-tactile crossmodal congruence in unconstrained tool-surface interactions. We describe a quick-computable extraction process for PSM from the proposed crossmodal inter-band spectral mapping (IBSM) that relates the frequency components in different bands between the modalities. We conducted a texture classification on the LMT Haptic Texture Dataset with 69 textures in 9 categories to evaluate the PSM feature by both random sampling train-test split and participant-specific cross-validation. Compared to a variety of texture features from previous work, the results showed that our PSM feature reached >74 % classification accuracy by 3 out of 4 classifiers and outperformed all other features with significant improvement.
Retrieval-based Annotation of Multi-channel Time-Series Data for HAR

This contribution proposes a semi-automated annotation procedure for multi-channel time-series data for human activities. This procedure leverages the human annotators’ decision by employing ranked hypotheses from a deep architecture, decreasing annotation time and maintaining consistency. In general, our approach performs retrieval by querying activity classes, Query-by-Activity. First, retrieved segments are ranked w.r. t a query using a similarity metric. Then, a human annotator can accept the ranked segments instead of selecting from a set of classes and attributes for each segment. We evaluate the approach in two parts, a retrieval and an annotation task. The retrieval performance is examined via the average precision of retrieved activities on a testing set. The effort is measured in terms of annotation time and consistency when annotating a small dataset for human activities.
Pivotal B+tree for Byte-Addressable Persistent Memory

Over the past few years, various indexes have been redesigned for byte-addressable persistent memory. In this work, we design and implement PB+tree (Pivotal B+tree) that resolves the limitations of state-of-the-art fully persistent B+trees. First, PB+tree reduces the number of expensive shift operations by up to half by managing two sub-arrays separated by a pivot key. Second, PB+tree reads cachelines in ascending order, which makes PB+tree benefit from hardware prefetchers and run faster than state-of-the-art persistent B+trees that access cachelines in non-contiguous or descending order. Third, PB+tree employs an optimistic lock-free search algorithm to avoid repeatedly visiting the same tree node. Although the optimistic lock-free search algorithm involves a risk of visiting incorrect child nodes, PB+tree guarantees correct search results using the lazy correction algorithm using doubly linked sibling pointers. Our performance study shows that PB+tree outperforms the state-of-the-art fully persistent indexes by a large margin. A search algorithm without optimistic locking risks visiting the wrong child node, but PB+tree uses a lazy correction algorithm with doubly linked sibling pointers to ensure correct search results. Our performance studies show that PB+trees outperform state-of-the-art fully persistent indexes.
Adaptive L p Reconstruction Method for Synthetic Aperture Interferometric Radiometer

The brightness temperature reconstruction of synthetic aperture interferometric radiometers (SAIRs) is an underdetermined and ill-posed inverse problem. The classical Hilbert-space regularization methods have often the disadvantages of the oversmoothness effects and the oscillations. In this article, an adaptive Lp reconstruction method in Lebesgue spaces is introduced to obtain the accurate brightness temperature map in SAIRs. The proposed approach can adaptively adjust the exponent p between 1.2 and 2 according to the region of the map to be reconstructed. The experimental results show that the Lp reconstruction method can effectively reduce oversmoothness and Gibbs oscillations, and better improve the accuracy of the inverse results, compared to the traditional minimum-norm and band-limited regularization methods.
Brain-Computer Interface controlled Functional Electrical Stimulation: Evaluation with healthy subjects and spinal cord injury patients

This work presents the design, implementation, and feasibility evaluation of a Brain-Computer Interface (BCI) based in Motor Imagery (MI) developed to control a Functional Electrical Stimulation (FES) device. The aim of this system is to assist the upper limb motor recovery of patients with spinal cord injury (SCI). With this BCI-controlled FES system, the user performs open and close MI with either the left or right hand, which if detected is used to provide visual feedback and electroestimulation to muscles in the forearm to perform the corresponding grasping movement. The system was evaluated with seven healthy subjects (HS group) and two SCI patients (SC group) in several experimental sessions across different days. Each experimental session consisted of a training routine devoted to collect calibration EEG data to train the BCI machine learning model, and of a validation routine devoted to validate system in online operation. The online system validation showed an accuracy of the recognition of the MI task that ranged between 78% and 81% for HS participants and between 63% and 93% for SCI participants. Additionally, the time taken by the BCI system to trigger the activation of the FES device ranged between 7.05 and 7.29 s for HS participants and between 8.43 s and 13.91 s for SCI participants. Finally, significant negative correlations were observed (r = -0.418, p = 0.024 and r = -0.437, p = 0.018 for left and right hand MI conditions, respectively) between the online BCI performance with a quantitative EEG parameter based on event-related desynchronization/synchronization analysis. The results of this work indicate the feasibility of the proposed BCI coupled to a FES device to be used for SCI patients with a severe level of disability and provides evidence of the functionality of the proposed BCI system in a motor rehabilitation context.
Shape-Based Conditional Neural Field for Wrist-Worn Change-Point Detection

We consider the problem of online change-point detection in a time-series, with application to detecting switching of human activities through accelerometer readings on a wrist-worn device. Traditional approaches for change-point detection do not work well on human activities in the wild and are too computationally expensive for implementation on wrist-worn devices. To address these shortcomings, we propose ShapeCNF, a simple and fast online change-point detection method that uses shape-based features to model the activity patterns and a conditional neural field to model the temporal correlations among the time series segments. Extensive experiments conducted on a highly dynamic time-series dataset demonstrate the effectiveness and superiority of ShapeCNF over the state-of-the-art methods.
A Decision Support Framework based on FCM for Selecting Key Performance Indicators

One of the main issues that firms encounter in building an effective performance measurement system (PMS) is selecting the most relevant performance indicators, often known as key performance indicators (KPI). These indicators should be chosen according to the company’s objectives, strategies, and key success factors. On the other hand, in a production system, KPIs are not independent and may have inherent interactions with each other. These interactions should be investigated and taken into account in the KPI selection process. The goal of this research is to present a decision support framework based on a Fuzzy Cognitive Map (FCM) for guiding decision-makers in the selection of KPI. The model is based on the idea that KPI may be assessed and chosen based on their impacts on the company’s strategic objectives. In addition, the model allows for the consideration of different causal interdependence between the KPI.
A Novel Efficient Method for Tracking Evolution of Communities in Dynamic Networks

Tracking community evolution can provide insights into significant changes in community interaction patterns, promote the understanding of structural changes, and predict the evolutionary behavior of networks. Therefore, it is a fundamental component of decision-making mechanisms in many fields such as marketing, public health, criminology, etc. However, in this problem domain, it is an open challenge to capture all possible events with high accuracy, memory efficiency, and reasonable execution times under a single solution. To address this gap, we propose a novel method for tracking the evolution of communities (TREC). TREC efficiently detects similar communities through a combination of Locality Sensitive Hashing and Minhashing. We provide experimental evidence on four benchmark datasets and real dynamic datasets such as AS, DBLP, Yelp, and Digg and compare them with the baseline work. The results show that TREC achieves an accuracy of about 98%, has a minimal space requirement, and is very close to the best performing work in terms of time complexity. Moreover, it can track all event types in a single solution.
Continuous-time generative graph neural network for attributed dynamic graphs: student research abstract

The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques1. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s2 and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuro-science, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL [8, 29], deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.