Miracle-dz/UniFAR-FTUs huggingface dataset:
Movement Attentive Graph Embedding for Improving Next Point of Access Prediction

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%.
Resource Allocation for Multi-cell Semantic Communication based on Deep Reinforcement Learning

Semantic communication is considered to be able to break through the limitations of Shannon's law and truly realizes the 6th generation mobile communication technology (6G). Most of the current semantic communication researches focus on signal detection, semantic extraction and recovery. The resource allocation in semantic communication system has not been completely considered. Meanwhile, with the development of communication system, the communication environment is increasingly complex, so that the high dynamic resource allocation problem is difficult to be solved by classical methods. Therefore, this paper formulates to solve multi-object, non-convex, dynamic resource allocation problems in multi-cell semantic communication systems through deep reinforcement learning. Based on Transformer model, we realize the transformation from bit information transmission rate to semantic information transmission rate. At the same time, we introduce semantic throughput (STP) as a metric of system semantic transmission rate. Moreover, we use deep reinforcement learning to solve sub-channel allocation and power allocation problems in dynamic systems. Compared with the traditional resource allocation method, that of semantic communication in this paper has larger STP under the same semantic confidence.
SOIL MOISTURE EXTRACTION PATTERNS FROM IRRIGATED AND DRYLAND ARABLE CROPS IN CANTERBURY

Soil moisture extraction patterns as measured by neutron probe are reported for barley, wheat, field pea, sugar beet and potato crops grown at Lincoln over a number of years. The barley was grown as a dryland crop and the wheat, peas and sugar beet were irrigated to provide sufficient soil moisture for growth. The potatoes were grown under three irrigation regimes; full, half, and none. If necessary, substantial amounts of water were extracted from depths of at least a metre. Where irrigation was applied, extraction tended to be confined to the upper profile. Barley extracted a substantial proportion of its water from near the top of the profile while sugar beet and unirrigated potatoes extracted water evenly throughout the profile. The results for sugar beet suggest a substantial extraction of water from deeper than a metre. Frequent small irrigations, which confine extraction to surface layers where nutrients are more concentrated, are likely to be more effective than infrequent large irrigations.
The Realtime Yoga Assistance System

Yoga, an ancient practice originating from India, has gained immense popularity worldwide due to its numerous physical and mental benefits. Through asana and other breathing practices, it is utilized to harmonize the body. The restrictions of standard ways of learning yoga, which frequently relies on hiring instructors or self-learning through reading materials are the issue this study attempts to address. These methods might not be feasible for many people in the present world. Therefore, there is a need to digitalize the yoga learning process, utilizing technology to provide practitioners with scalable and practical ways to practice yoga efficiently. The importance of this study lies in its ability to offer a uniform framework for identifying yoga asanas, facilitating effective instruction, and clear communication. The creation of precise performance tracking, 3D visualization, and recommendation can also improve clients' safety and individualized performance. The web application aims to solve the problem of yoga asana classification and performance tracking by using machine learning algorithms. In the end, the application implemented using modern technologies to achieve accuracy levels of better than 82% for all machine learning models. These results have significant implications for various stakeholders in the field of yoga, including practitioners and instructors.
Machine learning regressor for the prediction of the SPAD value of indoor basil with RGB monitoring

Information from digital images can be automatically collected and analyzed using computer vision (CV), which allows to solve complex problems through self-learning given by artificial neural networks.The objective of this study was the evaluation of a dynamic strategy of nutrient solution management and the creation and validation of a ML algorithm to obtain a digital RGB image regressor for indoor-grown basil. The algorithm has the purpose of analyzing the images returning the prediction of the SPAD value, therefore a relative measure of the nutritional status of the plants. In commercial application, the regressor could allow fertilization to be remotely steered to avoid or remedy nutrient deficiencies. The experiment concerned the indoor growth of Ocimum Basilicum (cv. “Genovese”), for 22 days after transplanting. During the growth cycle, five nutritional regimens were applied: surplus (160% and 130%), optimal (100%) and deficit (70% and 30%) starting from a slightly modified Hoagland nutrient solution and the experimental scheme used was randomized blocks. The architecture of the regression algorithm has foreseen the implementation of 13 layers, four validation metrics have been foreseen, such as MSE, MAE, R2 and r. The algorithm was trained for 1200 epochs with a learning rate of 0.001. Performance was encouraging, achieving low (dimensionless) MSE and MAE values of 2.92 and 1.32, respectively, and R2 and r coefficients of 0.94 and 0.97, respectively. The biomass analysis demonstrates the possibility of carrying out a dynamic strategy of nutrient management with a significant reduction in the use of fertilizers. The results obtained by the algorithm are promising, also in consideration of the discrete number of images in the dataset and the few layers, the computational lightness of which the algorithm is composed.
Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.
Characterization of Spirulina-Alginate Beads Formed Using Ionic Gelation

Spirulina (blue-green algae) is one of the cheapest sources of protein and essential vitamins. However, bitterness and bad flavor of spirulina protein may limit its use in food products. In this study, spirulina was encapsulated using ionic gelation to facilitate protein delivery. The objective was to study the effects of different types of gelation methods on particle size, texture, morphology, and crude protein content of the beads. Spirulina protein was encapsulated in alginate using both internal and external gelation methods and varying concentrations of sodium alginate and calcium chloride. A total of six different treatments were evaluated, and characterization of the beads included crude protein content, hardness measured using a texture analyzer, and thickness and width of the beads. The morphology was also studied using a scanning electron microscope (SEM). Results from the texture analysis show that the hardness of the external gelation beads is three times that of the internal gelation beads, and an increase in the alginate percentage in both gelation methods increased the firmness of the beads. The crude protein content was significantly higher with the beads formed using external gelation than with internal gelation. The SEM micrographs also show that the surface morphology of the beads produced with internal gelation has a more heterogeneous structure. Overall, the beads formed with external gelation were superior to those formed with internal gelation methods. Results from this study suggest that alginate is suitable for encapsulation of spirulina protein and these hydrogel beads could be used to enhance the protein delivery and facilitate the design of foods with alternative sources of protein.
A Fast Power Allocation Algorithm for 2D Visible Light Positioning System

This paper investigates a visible light positioning (VLP) system that utilizes received signal strength (RSS) measurements to perform 2D positioning. A power allocation problem is proposed to minimize the power consumption of the system while guaranteeing the position accuracy. Moreover, an iterative algorithm is introduced to tackle the proposed problem within the framework of alternating direction method of multipliers (ADMM). Simulation results demonstrate that the ADMM-based algorithm is able to achieve the globally optimal solution for the proposed power allocation problem with significantly reduced processing time compared to the existing algorithms, which is of great significance in guaranteeing the actual positioning performance of the power allocation result.
A survey on the evolution of bootkits attack and defense techniques

According to the boot process of modern computer systems, whoever boots first will gain control first. Taking advantage of this feature, a malicious code called bootkit can hijack the control before the OS bootloader and bypass security mechanisms in boot process. That makes bootkits difficult to detect or clean up thoroughly. With the improvement of security mechanisms and the emergence of UEFI, the attack and defense techniques for bootkits have constantly been evolving. We first introduce two boot modes of modern computer systems and present an attack model of bootkits by some sophistical samples. Then we discuss some classic attack techniques used by bootkits from their initial appearance to the present on two axes, including boot mode axis and attack phase axis. Next, we evaluate the race to the bottom of the system and the evolution process between bootkits and security mechanisms. At last, we present the possible future direction for bootkits in the context of continuous improvement of OS and firmware security mechanisms.
Dynamic and Tectonic Landscapes in Eastern Oregon Reveal Neogene to Quaternary Rearrangement of Topography.

MORRISS, MATTHEW CONNOR. Dynamic and Tectonic Landscapes in Eastern Oregon Reveal Neogene to Quaternary Rearrangement of Topography. (Under the direction of Karl W. Wegmann). Eastern Oregon contains the deepest canyon in North America, where the Snake River cuts the 2300 m deep Hells Canyon. A landscape that can maintain such a topographic features with high relief is likely undergoing relatively recent deformation. Study of the Burnt River, a tributary to the Snake, yielded data on river incision in eastern Oregon, indicating that Quaternary faults are a first order control on landscape development. Through 1:24,000-scale geologic mapping, a 500,000 year record of fluvial incision along the Burnt River was constructed and is chronologically anchored by optically stimulated luminescence dating and tephrochronology analyses. A conceptual model of fluvial terrace formation was developed using these ages and likely applies for other non-glaciated catchments in eastern Oregon. Mapped terraces, inferred to have formed during glacial-interglacial cycles, provide constraints on rates of incision of the Burnt River. Incision through these terraces indicates that the Burnt River is downcutting at a rate of 0.15– 0.3 m kyr. This incision appears to reflect a combination of local base level adjustments tied to movement along the newly mapped Durkee fault and regional base level control imposed by the downcutting of the Snake River. Deformation of terraces as young as 38.7 ± 5.1 ka indicates Quaternary activity along the Durkee fault and when combined with topographic metrics (slope, relief, hypsometry, and stream-steepness) indicates a landscape in disequilibrium. In addition to deformation associated with the newly mapped Durkee fault, lithospheric dynamics (delamination and crustal foundering) initiated in the Miocene Epoch may be responsible for continued regional deformation of the Earth’s surface. © Copyright 2015 Matthew Connor Morriss All Rights Reserved Dynamic and Tectonic Landscapes in Eastern Oregon Reveal Neogene to Quaternary Rearrangement of Topography by Matthew Connor Morriss A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science Marine, Earth, and Atmospheric Science Raleigh, North Carolina 2015
Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation

In this paper, we investigate the problem of state estimation for a class of non-linear systems with non-Gaussian measurement noise. Based on the maximum correntropy criterion (MCC), an adaptive maximum correntropy unscented kalman filter (AMCUKF) is derived by introducing a weighted combined cost function and an adaptive kernel function bandwidth. The filter solves the numerical problem of the existing maximum correntropy unscented Kalman filter (MCUKF) when the measured value contains large outliers and the problem of performance degradation caused by improper kernel bandwidth selection. Finally, taking the aero-engine state estimation problem as an example, the filtering performance of different filters is compared, which shows that the filter proposed in this paper has advantages in dealing with non-linear and nonGaussian systems.
Generalization of Reinforcement Learning through Artificial Potential Fields for agricultural UGVs

The increasing global demand for food, coupled with factors such as the shrinking agricultural workforce and the need for environmentally friendly practices has led to the emergence of Agriculture 4.0. In this context, Unmanned Ground Vehicles (UGVs) have become integral to smart farming, offering efficient and environmentally sustainable solutions compared to traditional machinery. One of the most used path planning algorithms for ground robots is the Artificial Potential Field (APF), whose effectiveness can be compromised by the insurgence of minimum potential points. Hence, a method combining Deep Reinforcement Learning with APF is here proposed. The algorithm is tested on the numerical model of a tracked UGV. Numerous simulations demonstrate its capability to guide the ground robot model through complex environments while ensuring collision avoidance. Specifically, the presented algorithm is able to overcome the generalization limit typical of Reinforcement Learning (RL) and the local minima problem characteristic of APF methods.
Completing the Puzzle: A Cluster of Hunting Dogs with Tick-Borne Illness from a Fishing Community in Tobago, West Indies

Eight hunting dogs were visited by a state veterinarian on the island of Tobago, Trinidad and Tobago, West Indies, as owners reported anorexia and paralysis in five of their dogs. The veterinarian observed a combination of clinical signs consistent with tick-borne illness, including fever, anorexia, anaemia, lethargy and paralysis. Blood and ticks were collected from each dog and submitted to a diagnostic laboratory for analysis. Microscopic analysis revealed a mixed infection of intracytoplasmic organisms consistent with Babesia spp. (erythrocyte) and Ehrlichia spp. (monocyte), respectively, from one dog, while a complete blood count indicated a regenerative anaemia (n = 1; 12.5%), non-regenerative anaemia (n = 4; 50%), neutrophilia (n = 3; 37.5%), lymphocytosis (n = 2; 25%), thrombocytopaenia (n = 3; 37.5%) and pancytopaenia (n = 1; 12.5%). DNA isolated from the eight blood samples and 20 ticks (16 Rhipicephalus sanguineus and 4 Amblyomma ovale) were subjected to conventional PCR and next-generation sequencing of the 16S rRNA and 18S rRNA gene for Anaplasma/Ehrlichia and Babesia/Theileria/Hepatozoon, respectively. The DNA of Ehrlichia spp. , closely related to Ehrlichia canis, was detected in the blood of three dogs (37.5%), Anaplasma spp. , closely related to Anaplasma marginale, in two (25%), Babesia vogeli in one dog (12.5%) and seven ticks (35%) and Hepatozoon canis and Anaplasma spp. , in one tick (5%), respectively. These findings highlight the need to test both the vector and host for the presence of tick-borne pathogens when undertaking diagnostic investigations. Further studies are also warranted to elucidate the susceptibility of canids to Anaplasma marginale.
Compliant-Control-Based Assisted Walking with Mobile Manipulator

In this paper, a new approach involving the use of a mobile manipulator to assist humans with mobility impairments to walk is proposed. First, in order to achieve flexible interaction between humans and mobile manipulators, we propose a variable admittance controller that can adaptively regulate the virtual mass and damping parameters based on the interaction forces and the human motion intention predicted using the fuzzy theory. Moreover, a feedforward velocity compensator based on a designed state observer is proposed to decrease the inertia resistance of the manipulator, effectively enhancing the compliance of the human–robot interaction. Then, the configuration of the mobile manipulator is optimized based on a null-space approach by considering the singularity, force capacity, and deformation induced by gravity. Finally, the proposed assisted walking approach for the mobile manipulator is implemented using the human–robot interaction controller and the null-space controller. The validity of the proposed controllers and the feasibility of assisted human walking are verified by conducting a set of tests involving different human volunteers.
You Are What You Buy: Personal Information Extraction From Anonymized Data

The exponential growth of data in the information age poses several threats to the privacy and safety of digital service users. Existing legislation, such as the GDPR in Europe and the CCPA in California, defines frameworks and guidelines to promote personal privacy but leaves freedom in the choice of means to achieve privacy. Data anonymization techniques remove information that can be used to identify individuals from the dataset, either through suppression, generalization, anatomization, permutation, or perturbation. Information suppression remains the most common, safe, and widely applicable anonymization method, though at a high data utility cost. In this paper, we argue that even information suppression may not be sufficient in some cases. We study the case of a dataset that describes the shopping habits of a grocery store’s customers. All identifiers and quasi-identifiers are removed from the dataset by suppression. However, by augmenting the data in a novel multi-step, iterative process, and building a neural network enriched with prior knowledge, we show that most sensitive information can be retrieved with an accuracy of 80%.
Advancing Human Motion Recognition with SkeletonCLIP++: Weighted Video Feature Integration and Enhanced Contrastive Sample Discrimination

This paper introduces ‘SkeletonCLIP++’, an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, ‘Weighted Frame Integration’ (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, ‘Contrastive Sample Identification’ (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model’s ability to distinguish between closely related actions. Incorporating the ‘BERT Text Encoder Integration’ (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model’s performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. ‘SkeletonCLIP++’ thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.
Temperature Compensation of Interferometric and Polarimetric Fiber-Optic Current Sensors With Spun Highly Birefringent Fiber

We theoretically and experimentally investigate intrinsic temperature compensation of interferometric and polarimetric fiber-optic current sensors with a coil of spun highly birefringent fiber operated in reflection mode. The interferometric sensor recovers the differential magneto-optic phase shift of the left- and right-handed circular (or slightly elliptical) polarization states in the fiber by means of non-reciprocal phase modulation, whereas the polarimetric sensor employs a simple passive polarization analyzer. We show that the two sensor types exhibit substantial differences in their response to temperature changes. The main parameters that determine the sensors’ sensitivity to temperature are, besides the fiber's Verdet constant, the intrinsic birefringence of the spun fiber, the retardation of the fiber retarder at the coil entrance that generates the elliptical polarization states, and the angle α between the principal axes of the retarder and spun fiber. In particular, fringe contrast changes at varying fiber birefringence make the polarimetric sensor version significantly more sensitive to temperature. Furthermore, whereas in the interferometric sensor, the contribution of the fiber birefringence to the temperature dependence disappears for special fiber orientations (α = 0°, 90°), such an arrangement is not possible for the polarimetric sensor. Also, the response to changes in the retardation of the retarder shows different patterns for the two sensor types. In spite of the differences, we achieve intrinsic temperature compensation well within ±0.2% between –40 °C and 85 °C for both sensors by using the retarder contribution to balance the contributions from the Verdet constant, the fiber birefringence, and potential further effects of temperature.