Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Point Cloud Adversarial Sample Classification Algorithm Based on Self-Supervised Learning and Information Gain

The advancement of computing power has facilitated the development of three-dimensional point cloud neural networks, and the issue of adversarial samples in point clouds has garnered increasing attention. However, Current adversarial training methods often result in reduced classification effectiveness of the model. Additionally, the scarcity of point cloud data poses challenges in acquiring sufficient training samples. This paper proposes a novel algorithm for classifying 3D point cloud adversarial samples, employing a combination of self-supervision and information gain. The algorithm first utilizes a self-supervised learning model to extract potential information from the data. Then, it employs information gain to screen the features of the adversarial samples and concentrate on the beneficial classification information. The selected features are fed back to the classification model through joint training, aiming to further enhance classification accuracy. In comparison to the standard training approaches of Pointnet, Pointnet++, and Dgcnn on the Modelnet40 and ScanObjectNN datasets, the proposed algorithm exhibits significant improvements in classification effectiveness. Specifically, on the ModelNet40 dataset, the algorithm achieves improvements of 0.70, 0.88, and 0.07 in classification effectiveness over PointNet, PointNet++, and DGCNN, respectively. Similarly, on the ScanObjectNN dataset, the algorithm demonstrates improvements of 0.06, 0.54, and 1.72 in classification effectiveness for the three networks, respectively. These results demonstrate that the algorithm is capable of overcoming the limitations of traditional adversarial training, which tends to reduce the generalization performance of the model, and further enhances the classification accuracy.
Trajectory and Offloading Policy Optimization in Age-of-Information-Aware UAV-Assisted MEC Systems

Unmanned Aerial Vehicles (UAVs) have been studied in Mobile Edge Computing (MEC) networks in many researches due to their distinctive advantages, especially in emergency and dynamic scenarios. In this paper, we propose a UAV-assisted MEC system in the disaster scenario, where the UAVs are deployed to undertake the computing and relaying tasks. To ensure the freshness of data as well as the energy efficiency of the system, we jointly minimize the Age of Information (AoI) of the User Equipment (UE) and the energy consumption of the UAVs by optimizing the UAVs' trajectories and the offloading strategies. The non-convex optimization problem is solved in two steps: firstly we propose a Multi-Agent Deep Reinforcement Learning (MADRL) based algorithm to find the optimal trajectory, and then we use a traversal-based algorithm to optimize the offloading policy greedily. Numerical simulations are carried out to verify the validity of the proposed algorithm. It is shown that it has better performance than the baseline algorithms, and is highly reliable in random environments.
Active Dual Band Frequency Selective Surface for 2.4 GHz Wi-Fi and Wi-Fi 6E

Frequency selective surfaces are essential components to future smart city and smart building network infrastructure, allowing for range extension via reflective surfaces, security of sensitive communications, and interference management between adjacent access points. In this paper, an active dual band bandstop for the 2.4 GHz Wi-Fi and Wi-Fi6E bands controlled by DC bias is presented with design and simulated data. The presented structure is designed to be integrated into large buildings for Wi-Fi management and incorporates signal polarization variations due to mounting location. The direction of the DC bias determines which band, or band portion, of the two Wi-Fi signals is blocked in the vertical polarization and controls the selection in the Wi-Fi6E band for horizontal polarization.
Effect of water quality on macrobenthic fauna in Achenkovil River, Southern-Western Ghats, Kerala, India

Abstract Aim The present study investigated the influence of environmental variables on macrobenthic fauna in the Achenkovil River, Southern Western Ghats, Kerala. The knowledge of aquatic ecosystems in many remote areas is very scanty and no one has yet made an attempt to study the macrobenthic fauna of Achenkovil river. Since, macrobenthos play an important role in the benthic community structure a better knowledge of their ecology and distributional patterns in remote freshwater ecosystems would be of great interest for a better understanding of their functioning. Methods A bimonthly sampling was conducted from February 2019 to January 2020. Water samples for physicochemical analysis were carried to the laboratory in clean polyethylene bottles. The macrobenthic fauna was collected using Van Veen grab (0.025m2). Multivariate statistical technique such as Principal Component Analysis was used to monitor the variation in environmental parameters. The diversity of macrobenthic fauna was studied using Simpson_1-D, Shannon_H, Evenness_e^H/S, and Margalef’s indices. We analyzed the relationship between environmental variables and macrobenthic fauna, using Canonical Correlation Analysis and Pearson correlation. Results A total of 3563 individuals belonging to 9 orders, 32 families, 32 genera, and 32 species were identified. The most dominant order was Ephemeroptera followed by Coleoptera and Trichoptera and the least dominant was Zygoptera. The maximum number of macrobenthic fauna was observed in station 1(S1), (1461 Ind/m2) and the least in station 9 (S9), (37 Ind/m2). Station 1 (S1) is a pristine forest area, so this area is considered the reference site. The macrobenthic fauna in the pristine headwater forests (S1) was much more diverse than in the river mouth (S9) which is a highly impacted region. This region is facing various kinds of anthropogenic stress resulting in pollution and deterioration of water quality. The macrobenthic community structure in the Achenkovil River was significantly associated to the variations in water quality due to various natural as well as anthropogenic stresses. Conclusions It can be concluded that Ephemeroptera, Coleoptera, and Trichoptera were the most dominant groups in the undisturbed habitats in the river during the study period. The predominance of Chironomidae species, especially from the Chironomus genus in the midstream and downstream segments of the river is possibly due to their ability to adapt to various environmental conditions or habitats and their tolerance to the low oxygen content in anoxic conditions of the bottom sediment. The lowest abundance of macrobenthos at all the studied stations was observed at station 9 (S9) during dry seasons. S9 is a highly impacted region, characterized by high temperature, turbidity, TDS, pH, conductivity, nitrate, and phosphate values. For this reason, anthropogenic stress and changing environmental conditions are the main factors affecting the distribution of the macrobenthic fauna of the Achenkovil River basin, Kerala.
Global Trends of Educational Data Mining in Online Learning

Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field.
Halia: Toward Full-Coverage Network Function Offloading in the Data Plane

Offloading network functions (NFs) to data plane switches brings remarkable performance benefits. In such offloading, NFs are required to process all the flows of interest (i.e., full coverage) to preserve the quality of services. However, existing solutions fail to guarantee full coverage for NFs. Thus, NFs may miss some essential flows, leading to accuracy drops. In this paper, we propose Halia, a framework that makes NF offloading decisions while ensuring full coverage for NFs. Specifically, Halia formulates the problem of NF offloading as an optimization problem. It encodes the requirement of full coverage as a constraint. Thus, its decisions activate enough NF instances in the substrate network to achieve full coverage. We have implemented Halia and conducted experiments under multiple realistic network topologies to evaluate Halia. The experimental results indicate that compared to existing solutions, Halia achieves full coverage and high scalability in large-scale networks.
Online Learning in Matching Games for Task Offloading in Multi-Access Edge Computing

In multi-access edge computing (MEC), mobile users (MUs) can offload computation tasks to nearby computational resources, which are owned by a mobile network operator (MNO), to save energy. In this work, we investigate two important challenges of task offloading in MEC: (i) The techno-economic interactions of the MNO and the MUs. The MNO faces a profit maximization problem, whereas the MUs face an energy minimization problem. (ii) Limited information at the MUs about the MNO's communication and computation resources and the task offloading strategies of other MUs. To overcome these challenges, we model the task offloading problem as a matching game between the MUs and the MNO including their techno-economic interactions. Furthermore, we propose a novel Collision-Avoidance Task Offloading Multi-Armed-Bandit (CA-TO-MAB) algorithm, that allows the MUs to learn the amount of available resources at the MNO and the task offloading strategies of other MUs in an online, fully decentralized way. We show that by using CA-TO-MAB, the cumulative revenue of the MNO can be increased by 25% and, at the same time the energy consumption of the MUs can be reduced by 6% compared to state-of-the-art online learning algorithms for task offloading. Furthermore, the communication overhead can be reduced by 55% compared to a non-learning game-theoretic approach.
Leaf area index estimation from the time-series SAR data using the AIEM-MWCM model

ABSTRACT The leaf area index (LAI) inversion accuracy of the water cloud model (WCM) based on the SAR images is low. The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces, without taking into account their interactions. Based on this, this study proposes an efficient LAI estimation method that combines the advanced integral equation model (AIEM) and modified water cloud model (MWCM). Images collected by GaoFen-3 synthetic aperture radar (GF-3 SAR) in the Xiangfu area in the east of Kaifeng City, Henan Province, are processed with a modified SAR-BM3D method and used as test data of the proposed method. The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle. The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEM-MWCM method inversion is better than that of the Dubois-MWCM model. The R2 value of the proposed method is higher than 0.8, and its root mean squared error (RMSE) is lower than 0.3.
Evaluation of Clinical Competence and Job Satisfaction and Their Related Factors Among Emergency Nurses in Palestinian Hospitals

Introduction Nurses are responsible for providing primary care to millions of patients, and emergency nurses serve on the frontline in providing care for mildly to severely critically ill patients. Objectives This study aims to assess and evaluate the clinical competence and its related factors among emergency nurses in Palestinian hospitals. Methods A cross-sectional study was conducted among 170 nurses working in the emergency units of hospitals in the West Bank, Palestine. Data collection utilized the Nurse Competence Scale, and the Job Satisfaction Scale. Results The results showed that only 33(19.4%) of the participants exhibited a good level of clinical competence, with none reaching the very good level. Among them, the highest proportion of good level 60 (35.3%) was in the helping role, while the lowest 38 (22.4%) was in ensuring quality. About 35% of nurses reported very low or low job satisfaction, while 58% had high or very high job satisfaction. Additionally, the results revealed a statistically significant relationship between clinical competence and job satisfaction (P < 0.05). Both clinical competence and job satisfaction were positively correlated with age and experience. Conclusion The study found a significant relationship between demographic characteristics (e.g., education, experience, and marital status) and clinical competency among emergency nurses. Notably, less than half of the participants demonstrated good clinical competency, with none reaching a very good level. The highest scores were observed in the helping role domain, while the lowest were in the ensuring quality domain. Furthermore, clinical competence was found to be significantly associated with job satisfaction. Consequently, enhancing nurses’ clinical competency will lead to an improvement in the quality of patient care.
SEDMA: Self-Distillation with Model Aggregation for Membership Privacy

Membership inference attacks (MIAs) are important measures to evaluate potential risks of privacy leakage from machine learning (ML) models. State-of-the-art MIA defenses have achieved favorable privacy-utility trade-offs using knowledge distillation on split training datasets. However, such defenses increase computational costs as a large number of the ML models must be trained on the split datasets. In this study, we proposed a new MIA defense, called SEDMA, based on self-distillation using model aggregation to mitigate the MIAs, inspired by the model parameter averaging as used in federated learning. The key idea of SEDMA is to split the training dataset into several parts and aggregate multiple ML models trained on each split for self-distillation. The intuitive explanation of SEDMA is that model aggregation prevents model over-fitting by smoothing information related to the training data among the multiple ML models and preserving the model utility, such as in federated learning. Through our experiments on major benchmark datasets (Purchase100, Texas100, and CIFAR100), we show that SEDMA outperforms state-of-the-art MIA defenses in terms of membership privacy (MIA accuracy), model accuracy, and computational costs. Specifically, SEDMA incurs at most approximately 3 - 5% model accuracy drop, while achieving the lowest MIA accuracy in state-of-the-art empirical MIA defenses. For computational costs, SEDMA takes significantly less processing time than a defense with the state-of-the-art privacy-utility trade-offs in previous defenses. SEDMA achieves both favorable privacy-utility trade-offs and low computational costs.
Toward Mobility-Aware Edge Inference Via Model Partition and Service Migration

Deep neural networks are deemed to be the cornerstone of a series of mobile intelligent systems, and their inference processes bring about a mass of computation-intensive tasks. To migrate the burden of inference computation from resource-constrained mobile devices, device-edge cooperative inference in mobile edge computing provides a fine-grained processing method. However, the geographical dispersion of resources and the mobility pattern of devices pose technical issues in the scheduling of co-inference systems, which have not been fully considered. In this paper, we propose a learning-based scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider a resource provisioning strategy based on the number of devices and a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Next, we propose an algorithm based on proximal policy optimization for each device to make the decision independently. Further, we adopt long short-term memory in the algorithm to capture the temporal characteristics of the system state. Experiments using a real-world network and computing trace demonstrate that the proposed algorithm can efficiently sense the mobile system to make decisions at various system scales and two mobility scenes. The average pipeline time of the proposed algorithm is only 67.63% of that of local processing, which is 97.50% of that of the omniscient algorithm.
계층적 Deformable Model의 설계와 분석

본 논문에서 제안하는 계층적인 형태의 deformable model 을 이용하면 기존의 deformable model 방법이 가지고 있던 여러 문제점을 해결할 수 있다. 특히 가장 큰 문제중의 하나인 초기위치를 찾는 문제나 적용시간이 오래 걸린다는 단점을 상당부분 해결할 수 있다. 또한 계층적인 형태를 사용하면 최종적으로 찾고자 하는 물체가 증가될수록 더 많은 시간상/공간상의 이익을 볼 수 있게 된다. 본 논문에서는 이처럼 계층적인 형태로 deformable model을 구성하는 방법과 계층적 deformable model을 영상에 적용하는 방법, 그리고 그 방법에 대한 공간적/시간적 복잡도 분석을 통해 그 효율성을 알아보았다.
Statistical Analyses of Satellite Cloud Object Data from CERES. Part V: Relationships between Physical Properties of Marine Boundary Layer Clouds

Abstract Relationships between physical properties are studied for three types of marine boundary layer cloud objects identified with the Clouds and the Earth’s Radiant Energy System (CERES) footprint data from the Tropical Rainfall Measuring Mission satellite between 30°S and 30°N. Each cloud object is a contiguous region of CERES footprints that have cloud-top heights below 3 km, and cloud fractions of 99%–100% (overcast type), 40%–99% (stratocumulus type), or 10%–40% (shallow cumulus type). These cloud fractions represent the fraction of ∼2 km × 2 km Visible/Infrared Scanner pixels that are cloudy within each ∼10 km × 10 km footprint. The cloud objects have effective diameters that are greater than 300 km for the overcast and stratocumulus types, and greater than 150 km for the shallow cumulus type. The Spearman rank correlation coefficient is calculated between many microphysical/optical [effective radius (re), cloud optical depth (τ), albedo, liquid water path, and shortwave cloud radiative forcing (.
Deep Reinforcement Learning Based Fast Anomaly Detection and Localization for Programmable Networks

The fast anomaly detection and localization is essential for network management, however, it is also very challenging for the current networks due to the lack of flexible control and telemetry capabilities. Fortunately, the maturity of Deep Reinforcement Learning (DRL) and programmable networking technologies could shed a light on realizing fast and intelligent anomaly detection and localization. In the paper, we design a fast anomaly detection and localization system for programmable networks by leveraging the in-band network telemetry and flexible control capabilities of programmable networks. Based on the system, we propose a DRL-based abnormal link detection and localization algorithm. It can iteratively infer abnormal links based on the ingress-to-egress performance metrics of flows and the one-hop performance metrics of the flows on the already identified abnormal links. The simulation results show that our proposals can detect and localize link anomalies in a matter of seconds to tens of seconds with low network telemetry overhead.
Efficacy and Safety of a High-Energy, Low-Protein Formula Replacement Meal for Pre-Dialysis Chronic Kidney Disease Patients: A Randomized Controlled Trial

High-energy, low-protein formulas (HE-LPFs) are commonly used as oral nutritional supplements (ONSs) to help provide extra calories to patients who are adhering to a low-protein diet (LPD) after diagnosis with chronic kidney disease (CKD). This randomized controlled trial aimed to evaluate the efficacy and safety of an HE-LPF as either a partial or a total replacement for one meal in pre-dialysis CKD patients. Stage 4–5 CKD patients received either a once-daily HE-LPF (HE-LPF group) or normal food (control group) for a period of 4 weeks while following an LPD. Overall, 73 patients who completed the study were included in the intention-to-treat population. After analyzing the 3-day food records, the HE-LPF group experienced a significant decrease in the percentage of energy derived from protein (p < 0.05) and an increase in the percentage of energy derived from fat (p < 0.05) compared to the control group. The two groups had no significant differences in body weight, body composition, grip strength, renal function, electrolytes, or metabolic markers. The HE-LPF group had a high adherence (94.9% at week 4), and no adverse effects were observed. HE-LPFs are safe to employ as meal replacements for pre-dialysis CKD patients adhering to an LPD.
Phylogenetic Comparative Methods Demonstrate the Non-Directional Evolution of Genomic Features in the Streptococcus Genus

Streptococcus, a genus of Gram-positive bacteria, encompasses many species with diverse ecological roles, from commensals in the human microbiota to pathogens causing a spectrum of infections. Understanding the biology and evolution of Streptococcus is crucial for unraveling its significance in health and disease. In this paper, I shall study the trait evolution of 4 genomic features - Genome size, Genomic GC content, Genomic repeat fraction, and Number of coding genes in the Streptococcus genus. Using phylogenetic generalized least squares, I find a strong positive correlation between genome size and the number of coding genes, while other features are unrelated.
Inhibitory Activity of Natural cis-Khellactone on Soluble Epoxide Hydrolase and Proinflammatory Cytokine Production in Lipopolysaccharides-Stimulated RAW264.7 Cells

The pursuit of anti-inflammatory agents has led to intensive research on the inhibition of soluble epoxide hydrolase (sEH) and cytokine production using medicinal plants. In this study, we evaluated the efficacy of cis-khellactone, a compound isolated for the first time from the roots of Peucedanum japonicum. The compound was found to be a competitive inhibitor of sEH, exhibiting an IC50 value of 3.1 ± 2.5 µM and ki value of 3.5 µM. Molecular docking and dynamics simulations illustrated the binding pose of (−)cis-khellactone within the active site of sEH. The results suggest that binding of the inhibitor to the enzyme is largely dependent on the Trp336–Gln384 loop within the active site. Further, cis-khellactone was found to inhibit pro-inflammatory cytokines, including NO, iNOS, IL-1β, and IL-4. These findings affirm that cis-khellactone could serve as a natural therapeutic candidate for the treatment of inflammation.
THE IMPACT OF TAX E-FILING SYSTEM QUALITY ON TAXPAYER SATISFACTION: PERCEIVED USEFULNESS AS MEDIATOR

Tax e-filing system quality is vital, directly shaping tax process efficiency and user convenience. This study aims to examine the effect of tax e-filing system quality on taxpayer satisfaction using perceived usefulness as a mediating variable. To gather data, 300 questionnaires were distributed to taxpayers, resulting in participation from 230 respondents.The findings of our study demonstrated a positive effect of tax e-filing system quality on both user satisfaction and perceived usefulness, and that perceived usefulness has a positively significant effect on user satisfaction. Furthermore, this study confirmed that perceived usefulness as a mediating variable in the e-filing system quality and user satisfaction relationship. The study enhances tax system theory by revealing the mediating role of perceived usefulness, and it practically suggests improving e-filing systems for increased taxpayer satisfaction and efficient tax collection.