Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Artificial Intelligence in Biometrics

AI-enabled biometric systems can help improve traditional systems and methods as they capture data, which can have great benefit in AI. As AI improves its performance, speed, accuracy, and security, it is being used in industries and research. A.I.-based biometric authentication capability Biometrics is identification based on their special and peculiar physical, behavioral, or biological characteristics. Recognition in biometrics included emotion's influence on thinking and behavioral intentions for utilitarian-type customers in high-involvement A.I.-grabbing situations. A.I. systems can help reduce “human error” incidents and aid in faster decision-making, making it difficult for hackers to breach into systems. AI systems, such as facial recognition identification, are increasingly being introduced, creating unprecedented difficulties for criminal procedure rights. Artificial intelligence-based face and music biometric technologies are impacting customer behavior and adding to the atmospheric literature.
Point Cloud Extraction of Tower and Conductor in Overhead Transmission Line Based on PointCNN Improved

The point cloud deep learning algorithm PointCNN was improved using a clustering algorithm, to replace the way random sampling of point clouds that the original algorithm used in hierarchical convolution. The improved algorithm was applied to the task of automatic point cloud identification for key objects in Transmission channels, such as tower and conductor. The results showed that the overall recognition accuracy of the original algorithm was 89.17% and the overall recognition accuracy of the improved algorithm was 91.83%, the improved deep learning algorithm was proven to get better results in the recognition for tower and conductor point clouds.
Resource Allocation Using Deep Reinforcement Learning in GEO Multibeam Satellite System

Future Internet of Things (IoT) is becoming more and more common in multi-beam satellite communications. However, due to the constrained system resource, the next generation of Geostationary Earth Orbit (GEO) multibeam satellite systems will focus on achieving flexible joint power and bandwidth allocation. Therefore, a GEO multi-beam satellite resource optimization scheme is proposed in this paper. We first model the optimization problem as a Markov decision process (MDP) due to the continuity and variation of the system resource. Then we propose a deep reinforcement learning (DRL) algorithm based on Asynchronous Advantage Actor-critic (A3C) to jointly allocate power and bandwidth whose goal is to satisfy the beam traffic demand. The algorithm aims to increase the system throughput and user fairness. The simulation results demonstrate that our proposed algorithm achieves significant advantages over the existing algorithms.
Gait Planning and Foot-Terrain Mechanical Model Analysis of Hexapod Robot

In this paper, we first study the single stiffness-damping model, analyze the corresponding relationship between the single stiffness-damping model and the stress integral model of foot-terrain interaction, then derive the stiffness-damping composite model, and finally establish the mechanical model of foot-terrain interaction based on the stiffness-damping composite model. Taking the hexagonal hexapod robot model as the main research object, the kinematics planning, coordinate system and state of walking in soft soil or desert are analyzed. The gait planning and the change of robot foot-terrain force are analyzed and simulated separately that the robot walking on flat terrain and crossing obstacle, crossing ditch and climbing slope in the unstructured terrain. The simulation results and curve analysis prove that the correctness of tripod gait planning strategy and foot-terrain force change in the flat terrain, and the correctness of obstacle crossing, ditch crossing and slope climbing in the unstructured terrain.
Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment_ a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients. © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
High-speed Current Sampling Circuit Designed for COT Applications

A high-speed current sampling circuit is designed for a COT (Constant On Time) Buck converter. A charge pump and LDO (Low Dropout Regulator) are used to power the circuit, and the circuit is able to operate properly during soft-start, protection phase, and high duty cycle while ensuring a high bandwidth. The simulation results show that the circuit can operate normally at a Buck converter switching frequency of 1.6MHz and a duty cycle of 95%, with a gain of 26.6dB and a -3dB bandwidth of 13.42MHz, enabling fast and loss-free sampling of current information.
Theoretical investigation of energy levels and transition data for S II, Cl III, Ar IV

Aims. The aim of this work is to present accurate and extensive results of energy spectra and transition data for the S II, Cl III, and Ar IV ions. These data are useful for understanding and probing physical processes and conditions in various types of astrophysical plasmas.Methods. The multiconfiguration Dirac–Hartree–Fock (MCDHF) and relativistic configuration interaction (RCI) methods, which are implemented in the general-purpose relativistic atomic structure package GRASP2K, are used in the present work. In the RCI calculations the transverse-photon (Breit) interaction, the vacuum polarization, and the self-energy corrections are included.Results. Energy spectra are presented comprising the 134, 87, and 103 lowest states in S II, Cl III, and Ar IV, respectively. Energy levels are in very good agreement with NIST database recommended values and associated with smaller uncertainties than energies from other theoretical computations. Electric dipole (E1), magnetic dipole (M1), and electric quadrupole (E2) transition data are computed between the above states together with the corresponding lifetimes. Based on internal validation, transition rates for the majority of the stronger transitions are estimated to have uncertainties of less than 3%.
UDE-based Consensus Tracking Control of Multi-Agent Systems with Actuator Faults and External Disturbances

The main objective of this paper is to address the robust consensus tracking control problem for multi-agent systems, despite the presence of actuator faults and external disturbances. By integrating the techniques of neural network (NN) and uncertainty and disturbance estimator (UDE), a distributed controller is formulated for each follower agent. At first, UDE is constructed to estimate the external disturbances of the follower agent. The estimation performance is adjusted by tuning a simple parameter through a trade-off between the ability of the controller and cooperative performance. Then, NN is utilized to estimate the leader’s uncertainties and generate a high-order reference signal for the followers. Meanwhile, the adaptive update laws which are designed for the parameter estimates of NN are formulated and executed online. Based on the Lyapunov stability theorem, asymptotically consensus tracking can be achieved with the undirected and connected communication topology. Finally, some numerical simulations are conducted to validate the effectiveness of the proposed control scheme, and the results are presented.
Analytical Methods for Environmental Risk Assessment of Acid Sulfate Soils：A Review

Assessment of acid sulfate soil risk is an important step for acid sulfate soil management and its reliability depends very much on the suitability and accuracy of various analytical methods for estimating sulfide-derived potential acidity, actual acidity and acid-neutralizing capacity in acid sulfate soils. This paper critically reviews various analytical methods that are currently used for determination of the above parameters, as well as their implications for environmental risk assessment of acid sulfate soils.
Boosting impulse control in addiction: Pharmacological neuroimaging studies

BACKGROUND Impulsive decision making is a hallmark of frequently occurring addictive disorders including alcohol dependence (AD). Therefore, ameliorating impulsive decision making is a promising target for the treatment of AD. Previous studies have shown that modafinil enhances cognitive control functions in various psychiatric disorders. However, the effects of modafinil on delay discounting and its underlying neural correlates have not been investigated as yet. The aim of the current study was to investigate the effects of modafinil on neural correlates of impulsive decision making in abstinent AD patients and healthy controls (HC). METHODS A randomized, double-blind, placebo-controlled, within-subjects cross-over study, using functional Magnetic Resonance Imaging (fMRI) was conducted in 14 AD patients and 16 HC subjects. All subjects participated in two fMRI sessions in which they either received a single dose of placebo or 200 mg of modafinil two hours before the session. During fMRI, subjects completed a delay discounting task to measure impulsive decision making. RESULTS Modafinil improved impulsive decision making in AD, which was accompanied by enhanced recruitment of frontoparietal regions and reduced activation of the ventromedial prefrontal cortex. Moreover, modafinil-induced enhancement of functional connectivity between the superior frontal gyrus and ventral striatum was specifically associated with improvement in impulsive decision making. CONCLUSION These findings indicate that modafinil can improve impulsive decision making in alcohol dependent patients through an enhanced coupling of prefrontal control regions and brain regions coding the subjective value of rewards. Therefore, the current study suggests that modafinil is a promising treatment option for AD.
Translational Issues in Psychological Science Bilingualism Alters the Neural Correlates of Sustained Attention

The present study examined whether monolingual and bilingual language experience — including ﬁ rst and second language pro ﬁ ciency, exposure, and age of acquisition — modify the neural mechanisms of attention during nonverbal sound discrimination. English mono-linguals and Korean – English bilinguals performed an auditory two-stimulus oddball task while their electroencephalogram was recorded. Participants heard a series of two different tones (high-pitch tone vs. low-pitch tone), one of which occurred less frequently (deviant trials) than the other (standard trials), and were asked to mentally count the number of infrequent tones. We found that in the early time window, bilinguals had larger amplitudes than monolinguals in response to both standard and deviant trials, suggesting that bilinguals initially increased attention to identify which of the two tones they heard. In the later timewin-dow, however, bilinguals had a smaller event-related potential (ERP) effect (deviant minus standard trials) relative to monolinguals, suggesting that bilinguals used fewer cognitive resources for the infrequent stimuli at later stages of processing. Furthermore, across the entire sample, increased exposure to the native language led to larger early, middle, and late ERP effects. These results suggest that native language exposure shapes perceptual processes involved in detection and monitoring. Knowing more than one language may alter sustained attentional processes, with implications for perception and learning. What is the signi ﬁ cance of this article for the general public? This study examined the effect of bilingualism on the neural correlates of sustained attention, which is necessary for learning and completing day-to-day activities. Our results provide neural evidence that speaking multiple languages shapes the way non-verbal sounds are processed.
Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition

Building datasets for autonomous vehicles has become an essential element of robotics research. Numerous datasets were published, pushing the state-of-the-art forward. This article investigates the problem of building reliable perception platforms that can accurately capture and process sensor data, ensuring the integrity and quality of the datasets generated. We propose using version control systems to enhance dataset acquisition's efficiency, reliability, and scalability. We present a method that can launch the system and record data while logging the exact state of the system simultaneously, making the setup and the data generated with it more reliable and reproducible. The main contribution of this paper is a systematic method that applies to existing and operating perception platforms used to collect data. Our framework is based solely on standard tools and is independent of the chosen sensor suite or host system. Implementing our method for existing perception platforms is possible, and to facilitate this, we open-source all the software used to operate our perception platform at: https://www.github. com/ipb-car.
RELIABILITY OF INTERNATIONAL BENEFIT TRANSFER IN CULTURAL ECONOMICS: NON-MARKET VALUATION OF THEATER IN DENMARK AND POLAND

:: Cultural goods provide numerous non-market benefits to society. Estimates of the benefits are needed for benefit-cost analyses, helping to inform cultural policy decisions and aiming at the efficient allocation of public funds. The non-market benefits cannot be assessed through market transactions. While original non-market valuation studies require substantial budgets and time, a benefit transfer approach offers an alternative. It enables the application of empirical estimates from existing original studies conducted at one site to approximate the value at another site. This study provides the first international benefit transfer for performing arts and examines the reliability of various benefit transfer approaches. We use empirical data from two separate stated preference valuation surveys conducted in Denmark and in Poland. Our results suggest that the benefit function transfer accounting for differences in purchasing power parity between the countries can generate transfer errors as low as 3-6%, indicating high reliability of the transferred values
A literature review of parameter tuning within the context of big data processing frameworks

This essay explores the challenges and solutions in automatic machine learning (ML)-based configuration tuning of Big Data processing frameworks. The use of Big Data processing systems has increased due to the availability of high performance computing resources and the realization of the value of data in making business decisions. However, configuring the parameters of these systems is difficult due to the high diversity of workloads, rapid growth of data, changing software and hardware infrastructures, and inter-dependency between the configurations. The literature has extensively explored ML-based tuners to solve the auto-tuning problem. In this essay we focus on answering four survey questions related to model generalization, search-space pruning, workload characterization, and tuning benchmarks. We follow a Systematic Literature Review (SLR) plan to answer the survey questions. As part of our SLR plan, we finalized 47 research papers and articles (published between 2012 and 2022) from different database sources. Using these papers, we perform an extensive literature review and summarize our
IMPLEMENTATION OF RELIGIOUS CHARACTER EDUCATION THROUGH SCHOOL CULTURE TRANSFORMATION

This article discussed the implementation of religious character education through the transformation of school cultural values. This study aimed to describe the transformation of school cultural values in 1) management of curriculum and learning, 2) management of students, 3) management of educators and educational staff in realizing religious character education. This study used a qualitative approach. The subjects of this study were principals, educators, educational staff and students at Madrasah Ibtidaiyah Negeri Sambas in West Kalimantan Province-Indonesia. The results of the analysis showed that the implementation of character education habituation of cultural values such as religious values, maintaining cleanliness, honesty, togetherness, simplicity, and discipline that are systematically and continuously integrated into the management of curriculum, management of students, and management of educators and management educational staff, which will form character education so as to form a generation that is superior and emotionally intelligent, socially intelligent and spiritually intelligent. A superior generation is a generation that has a religious character in facing the future in order to build a nation of character.
Application of Drought Prediction Based on Deep Learning

This paper explores the ability of deep learning models to predict drought phenomena. Taking the Songliao River Basin as an example, the Standardized Precipitation Evapotranspiration Index (SPEI) is used as the drought index predicted by the models, and the prediction accuracy of Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM), and Informer models for SPEI values at multiple time scales is analyzed and compared, and the prediction performance of the models is evaluated using NSE, RMSE, and MAE. The results show that (1) the prediction accuracy of the ARIMA, LSTM, and Informer models for SPEI values gradually improves as the time scale increases; (2) the prediction performance of all three models is optimal at the 24-month time scale, with NSE values of 0.928, 0.854, and 0.934; RMSE values of 0.185, 0.295, and 0.169; and MAE values of 0.104, 0.221, and 0.108, respectively; (3) The Informer model outperforms the ARIMA and LSTM models in forecasting at any time scale. Overall, the Informer model is more suitable for drought prediction in the Songliao River Basin.
Research on 3D Driveable Digital Human Generation System

The Metaverse has been gaining more attention from researchers worldwide. As the avatar of human in the Metaverse, three dimensional (3D) digital human has also attracted extensive attention. At present, the large-scale application of 3D digital human cannot be realized. The reason is that the construction cost of 3D avatar is too high, and it is mostly completed manually, especially for building a drivable 3D digital human. At present, the research on intelligent modeling of 3D digital human is modularized, lacking system integration to generate a drivable 3D digital human. This paper proposes a 3D drivable digital human generation method, and constructs an end-to-end testing system. The test proves that it can realize the generation of 3D drivable digital human based on single-view RGB image, and realize the driven of it by video from ordinary camera.
Analysis of Mixed Signal Low Power and Semiconductor Devices Applications Electronics and Computer system

This research study proposes an effective system function verification flow for mixed-signal SoC using a mixed-signal SoC project as an example. In this research work, the digital modules are emulated in a VCS environment, and all of the inherited analogue IPs is built in a virtuoso environment. The system function verification platform for this project generates digital stimulus using UVM (Universal Verification Methodology) and analog stimulation using Verilog-AMS. The entire circuit is converted to a spectre-netlist and incorporated into the verification platform. The project has rigorous standards for the simulation and development process. Using Synopsys' simulation tools VCS and XA, the system function is verified. The results obtained are accurate, demonstrating the efficiency of the flow. Flexible electronics that can operate in the GHz range are essential for devices like radars and wireless communication. Due to the better characteristics of AlGaN/GaN heterostructure, GaN-based transistors have received substantial research interest in rigid microwave electronics' high-frequency and high-power applications. Here, this study highlights the recent work on flexible GaN HEMT-based microwave electronics, including high-performance flexible GaN HEMTs on traditional plastic substrates, biodegradable cellulose nanofibril substrates, and flexible GaN HEMT-based microwave circuits.
Love and Dread in Cambodia: Weddings, Births and Ritual Harm under the Khmer Rouge

For a decade, the author followed Cambodian men and women to former wedding and birth sites from the Khmer Rouge period (1975-1979), filming their return to these locations. In the process, she uncovered evidence of the way severe dislocation, induced starvation and other murderous activities paved the way for reconstructed communes. Group marriages along with prescriptions for sex, pregnancies and births, were a central feature of the remaking of Cambodia society and contributed to the dissolution of the country's ritual practices. This 'ritualcide' caused a mass loss of spirit-protective places, objects, and arbitrators, and had a traumatic impact on Khmer society. Group marriages did, however, give spouses a reprieve from further dislocation. Approaching the process as an ethno-psychologist, LeVine argues that suffering was intensified by ritual tampering on the part of the Khmer Rouge. Such disruptions did not end in 1979, however, since Euro-American perspectives on trauma and reconciliation have also failed to accept spirit respect as a normative feature.
Performance Analysis of Criticality-Aware Out-of-Order Cores for Exploiting MLP

Modern out-of-order processors have improved their performance by leveraging instruction-level parallelism (ILP). Unfortunately, the performance of out-of-order cores are often limited by the latency gap between the cores and their memory hierarchies. One way to overcome this problem is extracting memory-level parallelism (MLP) by issuing long-latency memory instructions as fast as possible to reduce the effective stall cycles of load-dependent instructions. Prior works have suggested prioritizing critical instructions in slice-out-of-order cores, which comprises of multiple in-order issue queues, but they do not explore their effectiveness in out-of-order cores. In this paper, we analyze the performance impact of a criticality-aware out-of-order core that prioritizes critical instructions using iterative backward dependency analysis (IBDA). For this, we compare the performance of the proposed design with a conventional out-of-order core using a cycle-level x86 simulator. Our results show that prioritizing critical instructions with IBDA is not effective for boosting MLP in out-of-order cores, compared to the slice-out-of-order core designs.