Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in home range of breeding and post-breeding male Pearly-eyed Thrashers in the Luquillo Mountains of Puerto Rico

Resumen. – Cambios en areas de dominio vital en reproduccion y post-reproduccion en machos de Zorzal Pardo en las montanas de Luquillo, Puerto Rico. – Abundancia de alimento, periodo del ano y estado reproductivo son factores importantes que afectan el area de dominio vital de aves. Entre el 23 de Enero y 28 de Noviembre de 2003, evaluamos las areas de dominio vital y las areas nucleares de actividad en 10 machos de zorzales pardos (Margarops fuscatus; Mimidae) radio-rastreados en el Bosque Experimental de Luquillo, noroeste de Puerto Rico. Encontramos diferencias significativas en el area de dominio vital entre los estados reproductivos. Los estados de polluelos y de huevos presentaron las mayores y menores areas de dominio vital (medianas = 2,0 y 0,5 ha, respectivamente) y las areas nucleares (medianas = 0,14 y 0,09 ha, respectivamente). La reducida area utilizada por machos durante el estado de polluelos podria estar relacionada con cambios de defensa territorial a alimentacion de polluelos, siendo esta ultima relacionada con un aumento de presa animal en la dieta adulta para elevar el nivel de proteina accesible a la cria. Las areas de dominio vital fueron mas grandes durante el periodo post-reproductivo (mediana = 17,9 ha, rango = 0,5–178,7 ha) que durante el periodo reproductivo (mediana = 2,1 ha, rango 1,3–7,7 ha), con diferencias significativas entre ambos periodos. Presumimos que el incremento de area de dominio vital durante post-reproduccion fue una respuesta a la disminucion en la abundancia de frutas de Prestoea acuminata, especialmente notoria al final del periodo reproductivo. No encontramos una correlacion significativa entre la abundancia de frutas de P. acuminata y area de dominio vital, ni entre areas nucleares de actividad y la abundancia de frutas. La carencia de correlaciones entre estas variables podria explicarse por la dieta omnivora y oportunista de los zorzales pardos.
Detecting and Mitigating Jamming Attacks in IoT Networks Using Self-Adaptation

Internet of Things (IoT) networks consist of small devices that use a wireless communication to monitor and possibly control the physical world. A common threat to such networks are jamming attacks, a particular type of denial of service attack. Current research highlights the need for the design of more effective and efficient anti-jamming techniques that can handle different types of attacks in IoT networks. In this paper, we propose DeMiJA, short for Detection and Mitigation of Jamming Attacks in IoT, a novel approach to deal with different jamming attacks in IoT networks. DeMiJA leverages architecture-based adaptation and the MAPE-K reference model (Monitor-Analyze-Plan-Execute that share Knowledge). We present the general architecture of DeMiJA and instantiate the architecture to deal with jamming attacks in the DeltaIoT exemplar. The evaluation shows that DeMiJA can handle different types of jamming attacks effectively and efficiently, with neglectable overhead.
A Notion of Real-Time Anomaly Detection for IoT Devices Based on Hardware-Level Performance

Internet of Things (IoT) is becoming a considerable topic due to its benefits in the modern world. IoT devices carry out simple routine duties, but they can be valuable. IoT devices or a group of devices are connected to the internet, anomaly detection is essential, considering securing the IoT devices within the isolated environments. The most known and typical attacking modes for IoT devices are denial-of-service (DoS) and password brute-force attacks. The most dangerous attack is the Zero-day attack. The best mechanism for finding those issues as a solution is the concept of anomaly detection. Considering IoT device hardware-level anomaly detection mechanism uses the heat and the power consumption for detections. The results of those concepts can be misleading due to environmental situations. Here, it discusses the distinct approach to merely overcoming those problems using CPU and RAM utilization and driving the solution efficiently and effectively up to 99.9%.
An Auto-Weighting Incremental Random Vector Functional Link Network for EEG-Based Driving Fatigue Detection

Recently, electroencephalogram (EEG) has been receiving increasing attention in driving fatigue attention because it is generated by the neural activities of central nervous system and has been regarded as the gold standard to measure fatigue. However, most existing studies for the EEG-based driving fatigue detection have some common limitations such as: 1) using the batch learning mode and no incremental updating ability; 2) converting continuous fatigue indices into discrete levels which deviates far from the essence of fatigue detection; and 3) neglecting considering the different contributions of EEG feature dimensions in fatigue expression. To handle these problems, we propose an auto-weighting incremental random vector functional link (AWIRVFL) network model for EEG-based driving fatigue detection, which simultaneously implements online regression prediction and incremental learning. Moreover, an auto-weighting variable is introduced to adaptively and quantitatively explore the importance of different feature dimensions. A novel optimization algorithm is proposed to solve the AWIRVFL objective function. Experiments were conducted on the SEED-VIG and sustained-attention driving task (SADT) datasets to validate the performance of AWIRVFL, and the results demonstrated that AWIRVFL greatly outperforms the state-of-the-arts in terms of the two regression evaluation metrics, root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the quantitative feature importance values are obtained.
Immediate Biomechanical Effects of Providing Adaptive Assistance with an Ankle Exoskeleton in Individuals After Stroke

Recent studies on ankle exoskeletons have shown the feasibility of this technology for post-stroke gait rehabilitation. The main contribution of the present work is a comprehensive experimental analysis and protocol that focused on evaluating a wide range of biomechanical, usability and users' perception metrics under three different walking conditions: without exoskeleton, with an ankle exoskeleton unpowered, and with an ankle exoskeleton powered. To carry out this study, we developed the ABLE-S exoskeleton that can provide time-adapted ankle plantarflexion and dorsiflexion assistance. Tests with five participants with chronic stroke showed that walking with the ABLE-S exoskeleton significantly corrected foot drop by 25 % while reducing hip compensatory movements by 21 %. Furthermore, asymmetrical spatial gait patterns were significantly reduced by 51 % together with a significant increase in the average foot tilting angle at heel strike by 349 %. The total time to don, doff and set-up the device was of 7.86 ± 2.90 minutes. Finally, 80 % of the participants indicated that they were satisfied with their walking performance while wearing the exoskeleton, and 60 % would use the device for community ambulation. The results of this study add to the existing body of evidence supporting that ankle exoskeletons can improve gait biomechanics for post-stroke individuals.
A Design of Thin-Layer Frequency Selective Surface with Wideband Tunability and Incidence Stability

In this paper, a thin-layer frequency selective surface (FSS) with wideband tunability and incidence stability is presented. The proposed design is composed of square metallic patches array and grid separated from a thin dielectric substrate where the varactor diodes are placed symmetrically. Adjacent metallic patches are connected with opposite electrodes, so that all varactor diodes are reverse biased in parallel. The results show that continuous modulation in the range of 2.798 GHz to 6.896 GHz (84.5%) can be realized by tuning the external bias voltage. The unit cell dimension is only 0. 065λ0, which ensures that it has excellent angular stability within 75 degrees incident angles. Besides, it has stable response on both vertical and parallel polarized wave owing to its symmetry.
BLOC: Balancing Load with Overload Control In the Microservices Architecture

The microservices architecture has become ubiquitous in the cloud environment. It simplifies application development by breaking monolithic applications into manageable micro services that can be developed and deployed independently of the whole. However, the move from a monolithic or simple multi-tier architecture to a distributed microservice "service mesh" leads to new challenges due to the more complex application topology.A particular problem when automatically managing the performance of microservices is that since each service component scales up and down independently, it can easily create load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms were designed for centralized load balancers sitting between a group of clients and a server farm. These algorithms, however, do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this paper we propose a self managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes.We show that different service layers scaling independently can create unacceptably wide response time distributions and long tails, hurting client experience. This is because popular microservice load balancing algorithms, like Least Connection, only use a single component’s view of the backend load to guide decisions. This limited perspective leads to an unevenly balanced system and the potential for incast problems where a large number of frontend components can easily overload a shared backend. BLOC uses overload control approaches like rate limiting, active queue management and backpressure to provide feedback to the load balancers. The load balancers react to this feedback with techniques like backoff and retries. We show that this performs significantly better in solving the incast problem in microservice architectures.Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 to 4 times and the tail, 99th percentile, latency by two times.
Integration of Magnetic Particle Imaging in Anisotropic Navigation Platform for Tracking of Magnetic Nanoparticles

Magnetic particle imaging (MPI)-based navigation is an attractive method to improve the performance of magnetic drug targeting. Almost all existing navigation systems have isotropic/symmetric structures with multiple orthogonal pairs of coils, where the field-free point (FFP) can be easily calculated and controlled. However, the electromagnetic actuation (EMA) function of the existing navigation system is not precise enough and does not have a sufficient number of degree-of-freedoms (DOFs) for three-dimensional (3D) manipulation. The difficulties of FFP estimation in other system structures are limited to the integration of MPI function. In this paper, we present a method for FFP generation using an anisotropic system with different numbers of coils (4, 6, 9). A selected EMA system is then integrated with the MPI module to perform the particle tracking application in both in vitro and in vivo. Our methodology will allow the current existing EMA systems to improve their functionalities which are capable of simultaneous manipulation and localization of magnetic nanoparticles with resolution of around 1mm.
The PrHand: Functional Assessment of an Underactuated Soft-Robotic Prosthetic Hand

Functional tests aim to compare the functionality of a prosthesis with a human hand. The main objective of this work is to present and evaluate an affordable prosthesis (PrHand) built with soft robotic technologies and novel joints based on compliant mechanisms. Two functional tests have been selected in this work. The first is the AHAP protocol, which evaluates how the prosthesis performs eight different grips; three variables are considered: grasping, maintaining, and grasping ability score (GAS). The results were 69.03% with 57.77% in grasping and 80.28% in maintaining. The second test is the AM-ULA, which evaluates the prosthesis by performing 23 Activities of Daily Living. PrHand prosthesis had a score of 2.5 over 4.0. The functionality of the PrHand prosthesis has similar results to other prostheses evaluated in the literature. The comparison with the human hand was 69%. PrHand presents a promising solution for amputees in developing countries regarding cost and functionality.
One-side Virtual Histological Staining Model for Complex Human Samples

Virtual histological staining technique with a label-free auto-fluorescence image as an input is a challenging scientific pursuit to visualize complicated biological structures with distinct features. Recently, most of the related methods follow the two-side image translation architecture to get rid of paired data restriction, which is necessary for unprocessed and thick tissue virtual histological staining style transformation. However, the associated cycle consistency loss will inevitably lead to huge calculation consumption and cannot address the problem of incorrect translation among intracellular and extracellular components, which we termed as incorrect staining. In this paper, we propose a novel and computational-efficient one-side image translation framework to transfer unstained auto-fluorescence images into virtual hematoxylin- and eosin-stained counterparts for both thin and thick human samples. To address the incorrect nuclear staining issue, we design a region-classification loss to incorporate partial supervision information. Experimental data on both thin and thick human lung samples are used to demonstrate that our method is computationally efficient while achieving a comparable transformation performance over the two-side framework.
Towards Antifragility in Contested Environments: Using Adversarial Search to Learn, Predict, and Counter Open-Ended Threats

Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-* properties to be of key importance: self-exploration, self-learning and self-training. Systems should be able to efficiently self-explore – using adversarial search – the potential impact of the adversary’s attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary’s capabilities and attack strategies, which can be self-learned – using opponent modelling – from previous attacks and interactions. The system can self-train – using reinforcement learning – such that it evolves and improves itself as a result of being attacked. This paper discusses those visions and outlines their realisation in AWaRE, a cyber-resilient and self-adaptive multi-agent system.
Enhancing Continuous Glucose Monitoring-based Eating Detection with Wearable Biomarkers

Proper diet monitoring is a cornerstone of preventing and treating Type 2 Diabetes. However, this usually relies on burdensome manual meal logging. Continuous glucose monitors (CGMs), which have recently gained popularity as a tool to help Type 2 Diabetics with their treatments, may allow for a burden-free, sensor-based approach to logging periods of eating through monitoring the glucose dynamics and attempting to identify periods of post-prandial glucose response. However, CGMs-alone may not be sufficient in properly detecting periods; periods such as those present in gastric emptying may result in false positives for eating detection, given the sharp rise in glucose response. This work seeks to augment CGM-captured signals with that of other wearable biomarkers, captured from smartwatches, to aid in the detection of eating periods. These signals have been shown to detect eating motions. We explore a hierarchical model approach to augmenting CGM-based eating detection with additional sensing modalities. We test our model data collected from healthy participants eating in free-living conditions. We find that CGM-based eating detection can be improved by retrospectively reviewing wearable sensing data for confirmation, improving our model performance of eating detection, as measured by the area under the receiver operating characteristic curve, by 0.15 (from 0.64 to 0.79), and similarly across additional performance metrics.
Unobtrusive In-Home Respiration Monitoring Using a Toilet Seat

Non-invasive monitoring of pulmonary health could revolutionize the care of health conditions ranging from COVID-19 to asthma to heart failure, but current technologies face challenges that limit their feasibility and adoption. Here, we introduce a novel approach to monitor respiration by measuring changes in impedance from the back of the thigh. The integration of electrodes into a toilet seat ensures patient compliance with unobtrusive daily respiration monitoring benefitting from repeatable electrode placement on the skin. In this work, the feasibility of the thigh and the sensitivity of impedance to respiration have been investigated empirically by comparing thorax and thigh-thigh bioimpedance measurements to spirometer measurements, and computationally, using finite element modeling. Empirical results show a measurable peak-peak impedance (0.022 ohm to 0.290 ohm for normal breathing across 8 subjects) with respiration across thigh-thigh and a high correlation (0.85) between lung tidal volume and impedance change due to respiration. Thigh-thigh bioimpedance measurements were found to be able to distinguish between shallow, normal, and deep breathing. Further, day-to-day variability in the relationship between impedance and tidal volume was investigated. The results suggest that the novel approach can be used to detect respiration rate and tidal volume and could provide valuable insight into disease state for conditions ranging from COVID-19 to heart failure.
A portable passive clutch system for selective upper extremity movements

A portable passive clutch system was implemented to provide selective exercise movements. The clutch system, which acts parallel to the muscle responsible for the movement, was designed to provide resistance force when a spring was stretched by the movement. The device had two exercise modes, clutch and resistance modes. The clutch mode provided resistance when the target muscle was concentrically activated and a force was not applied when the joint angle exceeded a predefined value. Conversely, in resistance mode, an elastic force was applied to the target muscle when it was stretched beyond its initial state. The changes in muscle activations were measured for both the modes through surface electromyography of the four muscles on the forearm during wrist and elbow movements, respectively. The amount of target muscle activations increased proportionally with an increase in the spring stiffness coefficient for the four movements. This device can be used for upper extremity movements for rehabilitation and exercise.
Programming Autonomous Machines : Special Session Paper

One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.
Self-Integration and Self-Organisation of Autonomous Surface Vessels in Traffic Busy Waterways

The modern industrial system relies heavily on maritime transport to secure the elements required for the production process. Therefore, surface vessel navigation is becoming increasingly complex especially in narrow and busy waterways. Autonomous surface vessels are becoming an attractive solution to govern safe maritime operations in such waterways. Due to the many possible ways of encountering a ship, it is impossible to employ a rule-based navigation developed at design time. Instead, the manoeuvre decision for safe collision avoidance must be shifted to operation time with added requirement of being able to adapt with new scenarios that are not exploited at training time. In such highly dynamic interwoven systems the demand of mastering the ever-changing system integration becomes essential. In this article, I describe for the challenges that appear with the goal of training autonomous surface vessels for the purpose of public transport, and I summarise the proposed approaches to realise a collision-free navigation system for such surface vessels.
Towards Continuous Acute Pain Detection using Deep Learning and Electrodermal Activity

Measuring pain objectively, namely, based on physiological signals instead of self-reported measures, would be highly valuable for better treating people with chronic pain. The subjectivity of the gold standard to quantify pain, which is based upon subjects' self-reported assessment using numerical or visual scales, makes pain management extremely complicated and, in many cases, has led to abuse of pain medication. Electrodermal activity (EDA) is a highly sensitive measure of sympathetic activity and has been increasingly used to objectively assess pain. In this study, we evaluated convolutional neural networks (CNN) and long short-term memory (LSTM) architectures for the task of detecting pain continuously. Additionally, we tested the use of the time-frequency spectrum of the phasic component of the electrodermal activity, as feature for this task. We used a merged database composed of thirty-six healthy subjects that underwent heat pain stimuli by means of a thermal grill. The LSTM models obtained better performance than the CNN ones by more of 3% in the F1-Score. Moreover, the best performance was achieved by a stacked bi- and uni-directional LSTM architecture, with 75.3% F1-Score, being able to accurately detect the onset and end of the pain response on EDA. Continuous objective pain detection using deep learning can contribute to continuous monitoring pain sensation and to reduce the consequences of subjectiveness of current pain assessment methods.