Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms.
Applications of national land cover maps in United States forestry

Land cover maps derived from satellite imagery have a long and varied history of uses in United States forestry science and management. This article reviews recent developments concerning the use of national- to continental-scale land cover maps for inventory, monitoring, and resource assessment in the U.S. Forest Service. The use of mid-scale digital resolution information (from 10 to 30 meters) is ideal for many forest applications from stand exams to watershed assessments of numerous forest related attributes. Forest and landscape patterns can be meaningfully assessed at those spatial scales as well, and consistent national land cover maps are required for conducting consistent national assessments of forest patterns. National and continental strategies for land cover mapping should recognize that almost all forest inventory, monitoring, and assessment applications require map comparisons over time, and an ideal temporal frequency for most applications is no more than five years.
The Effects of an Urban Renewal Plan on Detailed Air Flows in an Urban Area

Using a computational fluid dynamics (CFD) model, the effects of building complexes constructed under an urban renewal plan on air flows in an urban area were investigated. For this, the geographic information system (GIS) data were used as the input data of the CFD model and four experiments were numerically simulated for different inflow directions (westerly, southerly, easterly, and northerly cases). Before constructing building complexes under the urban renewal plan, wind speed at the pedestrian level was very low around buildings because of decrease in wind speed by the drag effect of the densely distributed low-rise buildings. As the high-rise buildings were constructed and building density decreased by the urban renewal plan, wind speed at the pedestrian level increased compared to that before the urban renewal plan because the drag effect by the buildings decreases and the channeling effect satisfying the mass continuity partially appeared at the spaces among the high-rise buildings. At the upper levels, wind speed partially increased inside the high-rise buildings due to the channeling effect but it remarkably decreased across a vast extent of the downwind regions due to the generation of the recirculation zone and the drag effect of the high-rise buildings.
Money Laundering Detection Using A Transaction-Based Graph Learning Approach

Money laundering poses a pervasive threat to the stability and integrity of global financial systems. Since traditional anti-money laundering (AML) methods predominantly rely on rule-based systems and statistical approaches, it has limitations to capture the intricate and interconnected relationships that is inherent in money laundering networks. In response to this challenge, this paper proposes an innovative approach to enhance money laundering detection through transactions. We begin by constructing network graphs from a large dataset of bank transactions. Drawing insights from language modeling and supervised learning, we transform these graphs into directed node representations that effectively encode these intricate relationships and community structures within the transaction network. Subsequently, we utilize Random Forest (RF) to predict suspicious behavior associated with money laundering. Additionally, we address the specific challenges posed by highly imbalanced classes in the context of money laundering detection through both oversampling and undersampling experiments to overcome these challenges. The predictive performance of the RF model with oversampling yielded an accuracy of 86%, whereas when undersampling was applied, the accuracy increased to 92%.
Intelligent Detection Method for Avian Nests on Transmission Lines Based on Edge Computing and YOLOv5

The safety of transmission lines is a prerequisite for the secure and stable operation of the power grid. Avian nests can severely affect the safety of overhead power transmission lines. Considering the issues such as the large number of parameters in existing avian nest detection and tracking algorithms, relatively complex networks, and high computational requirements, which are not conducive to deployment on embedded edge nodes, this paper proposes a lightweight avian nest detection model based on YOLOv5. Experimental results show that YOLOv5 can rapidly and accurately detect avian nests on transmission lines, achieving an accuracy of up to 90.15% and a detection speed of 34.2 frames per second. Compared with commonly used detection algorithms such as Faster-RCNN, SSD, YOLOv3, and YOLOv4, the YOLOv5 demonstrates stronger competitiveness in terms of avian nest recognition accuracy and speed. Therefore, the YOLOv5s algorithm ensures real-time detection while enhancing the detection accuracy of ambiguous fault target images, meeting the demand for unmanned aerial vehicles equipped with edge devices to conduct avian nest inspections on transmission lines.
Radiocarbon microanalysis on ice impurities for dating of Alpine glaciers

The main objective of this thesis is the deployment of radiometric dating on Alpine ice archives, where conventional, stratigraphical dating techniques fail. To this end a 14C analysis of particulate and, in a novel attempt, also of dissolved organic carbon contained in the ice matrix is developed. Aimed at minimizing the required ice sample size, the designed ice processing line allows for handling of up to 700 g ice and is shown to involve blank levels small enough to ensure reliable 14C analyses down to a modern carbon fraction of fm = 0.33 at a minimum sample mass of about 20 µg carbon. Deployment of the system at the high altitude drilling area Colle Gnifetti (Monte Rosa, 4 550 m.a.s.l.) and, in contrast, to an ice core at the low altitude Vadret dal Corvatsch ice cap (Upper Engadin, 3 433 m.a.s.l.) reveals a quite significant 14C excess, not explained by any blank contribution and in contrast to the expected age range. The hypothesis that in situ produced 14C might contribute to the 14C content of dissolved organic carbon reveals strong short comings in applying this carbon fraction for radiometric dating of glacier ice, but also the new possibility to infer past accumulation changes. The particulate fraction, mostly unaffected by excess 14C, allows to connect an upper age limit of (7.5 ± 0.2) ka with the base of the Vadret dal Corvatsch, suggesting a minimal glacier extent during that time. For the Colle Gnifetti drill sites a tentative upper age constrain indicates pre-Holocene ice remains in the basal layer, while an enhanced 14C profile in the dissolved organic carbon fraction at core bottom suggests a large in situ produced 14C contribution most likely as a result of low accumulation during much colder periods.
Design and Stability Analysis of an Agricultural Sprayer UAS Integrated with an Anti-Sloshing Tank

The efficiency of the precision spraying application employing Unmanned Aircraft Systems (UAS) is significantly influenced by disturbances that impact the system, consequently affecting trajectory accuracy. For this purpose, a hexagonal tank with a capacity of $10 \mathrm{~L}$, featuring two perforated plates, has emerged as the most optimal solution for UAS with an MTOW of $25 \mathrm{~kg}$, aimed at minimizing the adverse effect of liquid sloshing. Computational Fluid Dynamics (CFD) analysis is conducted using SolidWorks Flow Simulation software. In order to identify the forces and moments acting inside the tank and their impact on UAS dynamics, a simulated scenario involving a sudden change in flight path is employed. The results show that the introduction of perforated plates, combined with the control system’s work, effectively mitigates fluid motion, leading to improvements in trajectory tracking, spray performance, and overall safety.
We need to talk: a qualitative inquiry into pathways to care for young men at ultra-high risk for psychosis

Introduction It is known from the literature that men are slower to seek help and staying engaged in mental health care compared to women. Seeing that in psychosis, men more often than women have insidious onsets but also a more malign illness course, it is important to find ways to improve timely help-seeking. The aim of this study was to explore barriers and facilitators for help-seeking in young male persons struggling with early signs of psychosis. Methods Qualitative interviews with nine young men who suffer from a first episode of psychosis or psychosis risk symptoms. Results Male stereotypical ideals, significant others, and knowledge of symptoms and where to get help as well characteristics of symptom trajectories appeared to be important determinants of help-seeking behavior. Discussion Interviews indicated that help-seeking in the participants was delayed first, because of reluctancy to disclose distress and second, because significant others were unable to accurately recognize symptoms. Information, awareness, and easy access to care remain important in early detection and intervention in psychosis and psychosis risk. However, more emphasis should be placed on de-stigmatizing mental health problems in men and aiming information specifically at them.
Penerapan Algoritma Euclidean Distance Untuk Pemilihan Paket Internet Berdasarkan Wilayah

Abstrak Banyaknya operator telekomunikasi seperti Telkomsel, Indosat, XL, dan Three yang menawarkan berbagai macam paket internet untuk smartphone, hal ini akan mempersulit para konsumen dalam menentukan pilihan yang tepat sesuai dengan kriteria yang di inginkan. Hal ini disebabkan kurangnya pengetahuan konsumen tentang kecepatan data setiap operator di wilayah tempat tinggalnya, kurang pahamnya konsumen dalam hal pembagian kuota internet yang sesuai dengan kehendak konsumen dan tarif operator mana saja yang murah namun tetap diberikan layanan akses internet yang cukup bagus , oleh sebab itu dibuatlah sebuah penerapan algoritma Euclidean Distance untuk pemilihan paket internet yang tepat sesuai dengan lokasi tempat tinggal dan kriteria yang petani inginkan. Dengan menggunakan paket internet yang dijual di BIP Ponsel Banjarbaru, sistem yang dibangun akan membantu konsumen untuk memilih paket internet mana yang cocok untuk mereka gunakan, sesuai dengan kriteria tarif, kuota, pembagian waktu, kecepatan dan masa aktif. Dari pengujian nilai akurasi sistem rekomendasi (F1) menggunakan metode precision dan recall test, sistem yang dibangun memperoleh nilai sebesar 0.760, nilai ini cukup tinggi karena apabila nilai F1 semakin mendekati angka 1 maka semakin tinggi pula nilai tingkat akurasi dari sistem rekomendasi. Kata Kunci : Paket Internet, Analytical Hierarchy Process, Euclidean Distance, precision and recall test Abstract The number of telecom operators such as Telkomsel, Indosat, XL, and Three are offering various packages for smartphones, it will be more difficult for consumers to make the right choice according to the desired criteria. This is due to a lack of consumer knowledge about the data rates of each operator in the region where he lived, less pahamnya consumers in terms of distribution of quotas of the Internet in accordance with the will of the consumer and operator rates which are cheap but still given an internet access service is pretty good, so it made an implementation of the algorithm Euclidean Distance to the selection of internet packages appropriate to the location of residence and criteria that farmers want. By using the internet packages sold in BIP Mobile Banjarbaru system established will help consumers to choose the internet package which is suitable for their use, in accordance with the criteria of tariffs, quotas, sharing time, speed and active period. Accuracy of testing the value of a recommendation system (F1) using precision and recall test, a system built obtain a value of 0760, this value is quite high because if the value of F1 is getting very close to 1, the higher the value of the accuracy of the system recommendation. Keywords : Internet Package, Analytical Hierarchy Process, Euclidean Distance, precision and recall test
Changes in the Networks of Attention across the Lifespan: A Graphical Meta-Analysis

Three Posnerian networks of attention (alerting, orienting, and executive control) have been distinguished on the bases of behavioural, neuropsychological, and neuroscientific evidence. Here, we examined the trajectories of these networks throughout the human lifespan using the various Attention Network Tests (ANTs), which were specifically developed to measure the efficacy of these networks. The ANT Database was used to identify relevant research, resulting in the inclusion of 36 publications. We conducted a graphical meta-analysis using network scores from each study, based on reaction time plotted as a function of age group. Evaluation of attentional networks from childhood to early adulthood suggests that the alerting network develops relatively quickly, and reaches near-adult level by the age of 12. The developmental pattern of the orienting network seems to depend on the information value of the spatial cues. Executive control network scores show a consistent decrease (improvement) with age in childhood. During adulthood (ages 19–75), changes in alerting depend on the modality of the warning signal, while a moderate increase in orienting scores was seen with increasing age. Whereas executive control scores, as measured in reaction time, increase (deterioration) from young adulthood into later adulthood an opposite trend is seen when scores are based on error rates.
An Open Set Specific Emitter Identification Method Using Deep Feature Embedded Discriminator

Specific emitter identification (SEI) can verify the identity of transmitter devices. It is playing an increasingly important role in maintaining internet of things (IoT) security by allowing legitimate devices to access the core network while preventing rogue devices intrusion. In recent years, more and more methods have applied deep learning to SEI. However, most of the existing works make the assumption of closed sets, which results in classifiers that can only identify a limited set of known devices. When closed set classifier is faced with rogue devices, it will misclassify them as legitimate devices, thereby endangering IoT security. In this paper, an open set SEI method based on deep features has been proposed, which can complete the identification of known devices and the detection of rogue devices by combining the appropriate transmitter deep feature distance measurement method and extreme value theory. Experiments on intercom dataset collected by universal software radio peripheral (USRP) show that our proposed method is better than other open set SEI methods. Furthermore, our method is more robust to noise. The proposed method can effectively solve the problem of open set SEI, and the detection of rogue devices is more accurate.
Enhancing Official Policy Document Classification with BERT and Keyword Augmentation

Official policy document (OPD) classification is a kind of special text classification. Good policy classification can bring great convenience to the people. However, on many local government websites, such policies are not well separated, which brings trouble to enterprises and individuals. For this reason, the job of policy classification becomes very necessary. Although many government officers have used different models to classify relevant policies, there are many cases in which the actual effect is not ideal. There are two main reasons for this situation. First, Chinese text classification has its own unique difficulties compared with English text classification. The second is the lack of data. The authors in this paper propose a method to enhance policy text classification using BERT. The proposed method uses keyword extraction and data augmentation techniques to improve the accuracy of the policy classification model. Specifically, the method extracts keywords from OPD using Jieba's keyword extraction module and utilizes data augmentation to increase the size and diversity of the training dataset. The experimental results show that compared with other traditional classification models, the proposed method achieves better performance. Overall, this work demonstrates the potential of BERT-based methods for policy text classification and provides insights into the effective use of keyword extraction and data augmentation in natural language processing tasks.
How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data

Introduction Volume conduction models of the human head are used in various neuroscience fields, such as for source reconstruction in EEG and MEG, and for modeling the effects of brain stimulation. Numerous studies have quantified the accuracy and sensitivity of volume conduction models by analyzing the effects of the geometrical and electrical features of the head model, the sensor model, the source model, and the numerical method. Most studies are based on simulations as it is hard to obtain sufficiently detailed measurements to compare to models. The recording of stereotactic EEG during electric stimulation mapping provides an opportunity for such empirical validation. Methods In the study presented here, we used the potential distribution of volume-conducted artifacts that are due to cortical stimulation to evaluate the accuracy of finite element method (FEM) volume conduction models. We adopted a widely used strategy for numerical comparison, i.e., we fixed the geometrical description of the head model and the mathematical method to perform simulations, and we gradually altered the head models, by increasing the level of detail of the conductivity profile. We compared the simulated potentials at different levels of refinement with the measured potentials in three epilepsy patients. Results Our results show that increasing the level of detail of the volume conduction head model only marginally improves the accuracy of the simulated potentials when compared to in-vivo sEEG measurements. The mismatch between measured and simulated potentials is, throughout all patients and models, maximally 40 microvolts (i.e., 10% relative error) in 80% of the stimulation-recording combination pairs and it is modulated by the distance between recording and stimulating electrodes. Discussion Our study suggests that commonly used strategies used to validate volume conduction models based solely on simulations might give an overly optimistic idea about volume conduction model accuracy. We recommend more empirical validations to be performed to identify those factors in volume conduction models that have the highest impact on the accuracy of simulated potentials. We share the dataset to allow researchers to further investigate the mismatch between measurements and FEM models and to contribute to improving volume conduction models.
Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

Objective The aim of this study was to develop and validate a series of breast cancer-related lymphoedema risk prediction models using machine learning algorithms for early identification of high-risk individuals to reduce the incidence of postoperative breast cancer lymphoedema. Methods This was a retrospective study conducted from January 2012 to July 2022 in a tertiary oncology hospital. Subsequent to the collection of clinical data, variables with predictive capacity for breast cancer-related lymphoedema (BCRL) were subjected to scrutiny utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The entire dataset underwent a randomized partition into training and test subsets, adhering to a 7:3 distribution. Nine classification models were developed, and the model performance was evaluated based on accuracy, sensitivity, specificity, recall, precision, F-score, and area under curve (AUC) of the ROC curve. Ultimately, the selection of the optimal model hinged upon the AUC value. Grid search and 10-fold cross-validation was used to determine the best parameter setting for each algorithm. Results A total of 670 patients were investigated, of which 469 were in the modeling group and 201 in the validation group. A total of 174 had BCRL (25.97%). The LASSO regression model screened for the 13 features most valuable in predicting BCRL. The range of each metric in the test set for the nine models was, in order: accuracy (0.75–0.84), sensitivity (0.50–0.79), specificity (0.79–0.93), recall (0.50–0.79), precision (0.51–0.70), F score (0.56–0.69), and AUC value (0.71–0.87). Overall, LR achieved the best performance in terms of accuracy (0.81), precision (0.60), sensitivity (0.79), specificity (0.82), recall (0.79), F-score (0.68), and AUC value (0.87) for predicting BCRL. Conclusion The study established that the constructed logistic regression (LR) model exhibits a more favorable amalgamation of accuracy, sensitivity, specificity, recall, and AUC value. This configuration adeptly discerns patients who are at an elevated risk of BCRL. Consequently, this precise identification equips nurses with the means to undertake timely and tailored interventions, thus averting the onset of BCRL.
Assessing the taxonomic sufficiency of macroinvertebrate communities and their potential to distinguish the impacts of land use on lowland streams in Uruguay

Abstract: Aim Macroinvertebrates are extensively employed as bioindicators for evaluating how stream ecosystems respond to alterations in physical and chemical habitats caused by human activity. Although family-taxonomic level is usually used to identify macroinvertebrates in biomonitoring programs, coarser taxonomic resolution may provide similar information. We aim to evaluate taxonomic sufficiency by comparing macroinvertebrate communities at the order and family levels to differentiate the effects of different land uses. Methods We conducted colonization experiments using artificial substrates in streams representing three land use types: urban (U, n=3), intensive agriculture - dairy production (AD, n=4), and extensive cattle ranching (CR, n=4). Results We obtained similar results using both taxonomic levels, which effectively distinguished the impacts of different land uses. In the sites examined, the majority of indicator groups exhibited only one family per order, with the exception of Ephemeroptera (Leptophlebiidae, Baetidae, and Caenidae). Notably, Baetidae and Caenidae were more prevalent in streams associated with extensive cattle ranching (CR). Conclusions Although this study provides a clear precedent, further investigation of these systems and other land uses (e.g., afforestation) is needed to validate this pattern. Coarser taxonomic assessment holds potential as a cost-effective method for stream monitoring and assessment in Uruguay.