Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning application for placing points of plumage regions on avian specimen images

Ultraviolet (UV) colouration is thought to be an important signalling mechanism in many bird species, yet broad insights regarding the prevalence of UV plumage colouration and the factors promoting its evolution are currently lacking. Here, we develop a novel image segmentation pipeline based on deep learning that considerably outperforms classical (i.e. non-deep learning) segmentation methods, and use this to extract accurate information on whole-body plumage colouration from photographs of >24,000 museum specimens covering >4,500 species of passerine birds. Our results demonstrate that UV reflectance, particularly as a component of other colours, is widespread across the passerine radiation but is strongly phylogenetically conserved. We also find clear evidence in support of the role of light environment in promoting the evolution of UV plumage colouration, and a weak trend towards higher UV plumage reflectance among bird species with ultraviolet rather than violet-sensitive visual systems. Overall, our study provides important broad-scale insight into an enigmatic component of avian colouration, as well as demonstrating that deep learning has considerable promise for allowing new data to be bought to bear on long-standing questions in ecology and evolution.
Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transmitted over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free-space optical (FSO) communication system to enhance the reliability and efficiency of video communication. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a low bit error rate (BER). Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using a trial-and-error method. The simulation results imply that LDPC-coded VFs achieve the largest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% for the CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10 – 20%.
Effect of microbubbles in deep flow hydroponic culture on Spinach growth

ABSTRACT Microbubbled air may be efficiently supplied to the culture solution, since microbubbles are more efficiently dissolved in water than millibubbles. To establish an enhanced air supply method in the hydroponic culture of vegetables, microbubbles were applied using the deep flow technique to a hydroponic culture system of spinach. Spinach was cultivated in culture solution treated microbubbles for 21 days, then harvested and measured growth and concentrations of inorganic components and chlorophyll in the leaf blade. The results showed that microbubbles applied to the culture solution promoted the growth of spinach more than that by millibubbles. Using microbubbles, cultivars maintained a high concentration of dissolved oxygen at the middle growth stage, during which the dissolved oxygen concentration in the culture solution was reduced by the active root respiration. This result suggests that microbubbles are more effective than millibubbles in the hydroponic culture of vegetables.
Tracking Control of Overhead Crane Using Output Feedback With Adaptive Unscented Kalman Filter and Condition-Based Selective Scaling

Most of the advanced nonlinear control strategies reported in the literature for underactuated mechanisms, such as overhead cranes, require the knowledge of all state variables. For cranes, the state vector includes variables related to the load sway and its velocity. The flatness property of crane-like systems can be exploited to solve both motion planning and tracking problems, so that the load (whose coordinates are included in the set of the flat outputs) exponentially follows a rapid reference trajectory. However, unmodeled friction phenomena and limitations on the direct measurement of sway-related state variables usually impede the practical implementation of flatness-based control laws. This paper proposes the use of an adaptive unscented Kalman filter to estimate friction forces and unmeasured state variables. The convergence of the filter is improved using a novel technique, called condition-based selective scaling. The performance of the suggested scheme is verified through a set of computer simulations on a 2D overhead crane system.
Reducing Manual Effort to Label Stock Market Data by Applying a Metaheuristic Search: A Case Study From the Saudi Stock Market

Computational intelligence and machine learning techniques have been widely considered for a variety of domains, including financial and data analysis applications. Stock market trading, as a part of the financial domain, has benefited from these techniques in learning models to forecast the direction of stock prices. Traders typically rely on these models, trained using historical stock market data, for monitoring market events to make the right decisions, which can result in more profits while exchanging stocks. One observed limitation of the practices currently being utilized for learning stock market models is the use of manually labeled datasets. This reduces the effectiveness of the data labeling and can cause a lower model prediction accuracy. To address this limitation, this paper proposes an automatic labeling approach that exploits a metaheuristic search to perform the labeling task for stock market data. The results of empirical experiments demonstrate that this approach is very promising as it outperforms the current manual approaches for stock data labeling and achieves higher labeling effectiveness.
Understanding the Impact of Emotions on the Quality of Software Artifacts

This paper proposes a framework for investigating the impact of emotions on the quality of software artifacts and portrays some observations captured during the development of several software engineering projects. The paper uses eight primary emotions and their intensity levels defined in Plutchik’s Wheel of Emotions. This study utilizes two major artifacts in the development environment; the software artifacts and the communication artifacts. The findings show that most developers experienced optimism in their work, felt joy, and found interest in the project. It also reveals that the most three influencing negative emotions are distraction, anger and boredom. Almost 20% of the developers were distracted during the project activities. Some developers also experienced anger with other team members. Nearly one-fifth of the participants were bored or lost interest in the project. These results confirm the findings of other similar research. However, we have also found that this claim does not always hold in some cases. Positive emotions do not always contribute to good quality artifacts. Similarly, negative emotions sometimes result in a positive impact on the quality of artifacts. The obtained results show how various kinds of emotions impact differently on the quality of software artifacts.
Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter

This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.
Advances in Photosensitive Polymer Based Damascene RDL Processes: Toward Submicrometer Pitches With More Metal Layers

In this study, we demonstrate the scaling potential of our damascene redistribution layer (RDL) approach by manufacturing an RDL process using a photosensitive polymer and copper metal lines with a target pitch of $\mathrm{1}\ \mu\mathrm{m}$. Among the various advantages of using a photosensitive dielectric and a damascene approach, the presence of a chemical-mechanical polishing (CMP) step after the copper growth process is key to guarantee the flatness of the stack. Ultimately, it allows for more RDL layer to be patterned at resolution limits. This approach is demonstrated by processing a 4 metal layers RDL with a minimal pitch of $\mathrm{3.2}\ \mu\mathrm{m}$. Collected electrical data as well as FIB cross-sections and SEM imaging performed after various process steps confirm the excellent patterning of the metal wires thus demonstrating the superiority of the damascene approach compared to the semi-additive process.
A 14nm 100Kb 2T1R Transpose RRAM with >150X resistance ratio enhancement and 27.95% reduction on energy-latency product using low-power near threshold read operation and fast data-line current stabling scheme

This study proposes an 2T1R Transpose RRAM (T-RRAM) macro supports highly efficient transpose accessibility featuring (1) a 2T1R cell with low-power near-threshold-voltage (NTV) read operations for resistance ratio (R-ratio) enhancement (>150X) and read disturb suppression, and (2) a customized macro structure with fast data-line current stabling scheme (FDCS) to reduce the energy-latency product (27.95%). A 100Kb 2T1R T-RRAM macro is silicon verified using 14nm CMOS process with TaOx-based RRAM. This paper firstly demonstrates a 14nm T-RRAM with large R-ratio, small area overhead and improved energy-latency product.
Modeling of Effect of Underfill Properties on Flip Chip Bumps and Solder Balls of FCBGA Package in Automotive Underhood Applications

Thermo-mechanical reliability of Flip Chip Ball Grid Arrays in automotive underhood environments has received a lot of attention owing the emergence of new application in advanced driver assist systems including guidance, navigation and control. The reliability and the extended time survivability of the materials and interfaces in the advanced package architectures is not well understood. In this paper, the effect of four different developmental underfill encapsulant properties on flip chip bumps and solder balls were investigated using lidded FCBGA model. The material properties of the developmental underfills which have been measured in a prior study by the authors, have been used for the purpose of modeling. The effect of the underfill properties on the plastic work in the package-level solder joints and chip-level solder joints has been modeled under the automotive thermal cycle of −40 to +125°C. Each cycle is of 2 hours and consist of 40 and 20 minutes of ramp and dwell times, respectively. Interaction of the material properties on the plastic work on the flip-chip solder joints and the package-level joints has been examined. The results have been computed for packages with and without a compressive heat sink. Non-linear interconnect material models have been used for both chip-level and package-level interconnects. Anand viscoplasticity has been used for modeling the interconnects and the hysteresis loops studied as a function of the underfill properties.
Influencing Factors in the Scalability of Distributed Stream Processing Jobs

More and more use cases require fast, accurate, and reliable processing of large volumes of data. To do this, a distributed stream processing framework is needed which can distribute the load over several machines. In this work, we study and benchmark the scalability of stream processing jobs in four popular frameworks: Flink, Kafka Streams, Spark Streaming, and Structured Streaming. Besides that, we determine the factors that influence the performance and efficiency of scaling processing jobs with distinct characteristics. We evaluate horizontal, as well as vertical scalability. Our results show how the scaling efficiency is impacted by many factors including the initial cluster layout and direction of scaling, the pipeline design, the framework design, resource allocation, and data characteristics. Finally, we give some recommendations on how practitioners should undertake to scale their clusters.
Multi-Objective Bayesian Optimisation Using an Exploitative Attainment Front Acquisition Function

Efficient methods for optimising expensive black-box problems with multiple objectives can often themselves become prohibitively expensive as the number of objectives is increased. We propose an infill criterion based on the distance to the summary attainment front which does not rely on the expensive hypervolume or expected improvement computations, which are the principal causes of poor dimensional scaling in current state-of-the-art approaches. By evaluating performance on the well-known Walking Fish Group problem set, we show that our method delivers similar performance to the current state-of-the-art. We further show that methods based on surrogate mean predictions are more often than not superior to the widely used expected improvement, suggesting that the additional exploration produced by accounting for the uncertainty in the surrogate’s prediction of the optimisation landscape is often unnecessary and does not aid convergence towards the Pareto front.
A Priority and Guarantee-based Resource Allocation with Reuse Mechanism in LTE-V Mode 3

With the uprising demand for cooperative connected and autonomous driving, vehicle-to-vehicle (V2V) communications became a key technology enabler for future intelligent transportation systems. For years, IEEE 802.11p has been the technology of choice for V2V. Recently, LTE-V, a standard, developed under the umbrella of 3GPP, is considered a key step of cellular technology in addressing safety-critical requirements of vehicular networks. In this paper, a Priority and guarantee-bAsed Resource aLlocation approach with Reuse Mechanism (PEARL-RM) is proposed for infrastructure-managed LTE-V networks (mode 3). The main goal is to meet the strict QoS requirements of safety applications in terms of reliability and latency. Simulation results show significant improvements achieved by PEARL-RM in terms of system capacity utilization, packet reception ratio, and latency when dealing with safety and non-safety traffic.
Characterization of Fatigue and Its Recovery Behavior in Ferroelectric HfZrO

The discovery of ferroelectric properties in HfO2-based materials has attracted immense interest in both logic and memory applications. However, the degradation in remnant polarization upon repeated cycles is a major concern for reliability, but the mechanism remains unclear. In this study, polarization fatigue of HfZrO ferroelectric is investigated with SILC (stress-induced-leakage-current) measurement under different E-field stresses. Under high-field, we observed strong correlation between polarization wake-up and SILC increase. This is attributed to oxygen vacancy redistribution and percolation path formation, especially at high frequency cycling. However, polarization fatigue at low field is found to occur without SILC increase. P-E loop measurements and material analysis by TEM/PED (precession electron diffraction) revealed that charge trapping is the main contributor under the low-bias. We demonstrated that the fatigue caused by low-field stress could be effectively recovered through an interspersed periodical, short-term cycles at high-field to manage charge trapping and oxygen vacancy redistribution, thus resulting in prolonged endurance to >1E12 cycles without SILC degradation at room temperature. We also validated that a negligible fatigue switching in HfZrO can be achieved at -40°C as low-temperature operation further reduces charge trapping.
SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks targeting conventional and new generation networks, such as the Internet of Things (IoT), cloud computing, and fifth-generation (5G) communication networks. In recent years, DDoS attacks have become not only massive but also sophisticated. Software-Defined Networking (SDN) technology has demonstrated effectiveness in counter-measuring complex attacks since it provides flexibility on global network monitoring and inline network configuration. Although several works have proposed to detect DDoS attacks, most of them did not use up-to-date datasets that contain the newest threats. Furthermore, only a few previous works assessed their solutions using simulated scenarios, easing the migration to production networks. This document presents the implementation of a modular and flexible SDN-based architecture to detect transport and application layer DDoS attacks using multiple Machine Learning (ML) and Deep Learning (DL) models. Exploring diverse ML/DL methods allowed us to resolve which methods perform better under different attack types and conditions. We tested the ML/DL models using two up-to-date security datasets, namely CICDoS2017 and CICDDoS2019 datasets, and they showed accuracy above 99% on classifying unseen traffic (testing set). We also deployed a simulated environment using the network emulator Mininet and the Open Network Operating System (ONOS) SDN controller. In this experimental setup, we demonstrated high detection rates, above 98% for transport DDoS attacks and up to 95% for application-layer DDoS attacks.
Reproduction and Sexual Maturity of European Sardine (Sardina pilchardus) in the Central Mediterranean Sea

Understanding drivers of fish maturity is essential to predict the productivity, stability, and resiliency of exploited populations. Size at maturity for European sardine (Sardina pilchardus) in the Central Mediterranean Sea has never been published within rigorous peer review process. In the past, stock assessment working groups in the Mediterranean requested such information; however, the size at which 50% of the fish population reaches the sexual maturity (L50) often received weak estimates based on a single or few years of observation, which do not necessarily reflect the whole stock. To address this data gap for the Central Mediterranean Sea, the present research estimated size at maturity of European sardine in the Strait of Sicily. In the study period (2009–2017), sampled individuals exhibited significant differences in size range as well as in body condition and reproductive effort. Data also permitted a clear identification of the spawning season, starting in September–October and ending in March–April the following year. Along the analyzed period, the size at first maturity ranged from 108 to 124 mm (total length) for females and from 102 to 122 mm for males. Significant differences were recorded among genders and years. Moreover, a goodness-of-fit measure was proposed to assess the robustness and reliability of L50 estimates, and thus selecting those that minimize the fitting-associated errors. Finally, results suggested that the low proportion of immature individuals in the samples represents the main source of bias in L50 estimation and possible solution was also proposed.
The Impact of Artificial Aging on the Color Stability and Hardness of Nanocomposite Resin

Objective: This study aimed to investigate the impact of artificial aging on the color stability and hardness of nanocomposite resin. Methods: Four nanocomposite resin materials were used: Filtek Z350 XT (FZ), Synergy D6 (SD), Grandio (GD), and Clearfil Majesty Esthetic (CM). Thirty specimens were created from each material, which were divided into three (A,B,C) groups of 10 specimens each. For each material, the values of visual lightness (L*), degree of redness and greenness (a*), and degree of yellowness and blueness (b*) of the specimens in group A before and after aging, as well as the hardness of the specimens in group B before aging and in group C after aging, were measured. The American Society for Testing and Materials (ASTM) G155 Cycle 1 standard was adopted to test the aging of the specimens. The color difference (ΔE00) value and hardness difference (ΔH) value of the specimens before and after aging were calculated. Results: Aging was found to have an impact on the ΔE00 values of the resin materials in each group (H = 17.6, p = 0.001), and the hardness of the specimens in each group after artificial aging was significantly higher than before aging (p < 0.05). The difference in ΔE00 between the FZ group and the SD, GD, and CM groups was statistically significant (p < 0.05). The FZ group had the highest ΔE00 values. There was no correlation between ΔE00 and the hardness change percentage after aging (r = 0.114). Conclusion: Among the four nanocomposite resins tested in this study, except for Filtek Z350 XT’s ΔE00 values (>1.8) in the clinically unacceptable range, the remaining three kinds of resin ΔE00 values (<1.8) were all in the clinically acceptable range. The hardness of the four nanocomposite resins increased after aging. The results of the present study revealed that there was no correlation between ΔE00 and hardness change percentage in the four nanocomposite resin materials after aging.