Miracle-dz/UniFAR-FTUs huggingface dataset:
An ETSI-Compliant Architecture for the Element Management System: The Key for Holistic NFV Management

The Element Management System (EMS) is defined by the ETSI NFV reference architecture as responsible for the instrumentation of Virtualized Network Functions (VNF). The EMS acts as a gateway to each function, executing monitoring and control requests received from the VNF Manager (VNFM) and Operation and Business Support Systems (OSS/BSS). Surprisingly, the EMS has been either entirely ignored or only employed in very restricted settings. In this work, we propose an ETSI-compliant architecture that allows the development of EMS solutions that can be used to manage arbitrary VNF instances while abstracting particular protocols and technologies, thus enabling the interoperability among heterogeneous systems (VNF, VNFM, and OSS/BSS). The architecture was implemented as the Holistic, Lightweight, and Malleable EMS Solution (HoLMES). Evaluation results are presented showing the overhead of using HoLMES as a gateway for typical management operations. The slight increase in execution times is a fair price to pay for the benefits that the holistic solution represents in terms of the effectiveness of NFV management.
A Novel Sensor for Real-Time Pressure Monitoring in Colonoscopy Training

Colonoscopy is the most frequently employed method for screening and diagnosing colorectal cancers. A significant factor resulting in an incomplete colonoscopy procedure is direct trauma to the colon due to large forces applied during the insertion of a colonoscope which causes significant pain and discomfort to the patient. Acquiring the necessary skills for performing a successful procedure is technically challenging and requires substantial training. The current learning techniques primarily focus on completing the procedure without any feedback of the intensity of the pressure transmitted from the colonoscope to the colon wall. In this paper, we present a pressure-sensing sleeve that can be used in an interactive simulator-based training environment capable of providing real-time feedback on the pressure transmitted by the scope to the simulator colon. The sensor was designed using flex PCBs and utilizing the piezo-resistive behavior of carbon-filled polymers to reliably measure pressures up to the safe limit of 34 kPa.
A Lightweight Heuristic for Micro-Services Placement and Chaining in Low Latency Services

The rise of novel Low-Latency (LL) applications, such as cloud gaming or the metaverse, imposes rigorous end-to-end LL constraints. Decomposing Virtualized Network Functions (VNFs) into micro-services has proven its effectiveness to reduce the Service Function Chaining (SFC) latency thanks to key characteristics: lighter entities, less resource consumption, and a strong capacity to operate in parallel. However, to make such a promising technology actually deployed in real operated networks, novel dedicated placement and chaining methods are required. Current solutions either do not fit with tied LL constraints or exhibit a prohibitive computation time by relying on exact resolution methods. In this paper, we propose a heuristic method dedicated to the placement and chaining of micro-services. Its purpose is to maximize the deployment of SFCs while respecting the required LL by considering intrinsic features of micro-services and integrating suitable load balancing, which makes it highly scalable. A comprehensive evaluation campaign highlights that generated solutions achieve results that are at most a factor of 1.1 to the optimal with an execution time up to 20,000 times faster.
Data Fabrics for Multi-Domain Information Systems

Data exchange in information systems that span multiple policy domains typically rely on network middleware that can abstract the management of underlying heterogeneous communication protocols. This also involves issues in managing interoperability, scalability, and privacy that arise in the movement of data from one domain to another information domain. The Data Fabric is an emerging approach to systematically build and design such middleware systems to support multi-domain exchange at scale. In this paper, we discuss and compare two key data-centric approaches: 1) application layer topic-based messaging and name-based networking in a multi-cloud environment. We implement and deploy these two approaches (using Kafka and NDN) and we compare the performance in terms of object transfer latency and CPU and memory utilization. We find that NDN networking has superior latency performance and lower resource usage. We believe that this advantage derives from the fact that named-based messaging operates at the network level, while topic-based messaging operates at the application level.
Empirical Analysis of the Fine-Tuning for Unsupervised Anomaly Detection in the ICT System

Many unsupervised anomaly detection (UAD) methods for ICT systems that use only normal data collected from routers or servers, such as traffic volumes and text logs, have been developed to detect anomalies. Since the normal states of the ICT systems change due to the addition or deletion of devices, configuration changes, and OS updates, system operators need to collect enough normal data to learn various normal states whenever there is a change. Therefore, UAD methods cannot be applied until new data is gathered and a new normal state is identified. Using fine-tuning, one of the transfer learning techniques might reduce the duration of collecting normal state data. However, most of the existing papers focus on transferring knowledge of supervised models on image classification tasks, which are completely different from UAD in ICT systems. This paper analyzes a fine-tuning architecture to improve UAD in an ICT system with a small amount of normal data. By preparing five datasets, comprehensive experiments were conducted, and it was found that the fine-tuning architecture has the possibility to improve the accuracy of anomaly detection with a small amount of data for the ICT system dataset and scenarios.
Numerical Embedding of Categorical Features in Tabular Data: A Survey

Tabular data, which are also known as structured data, is one of the most common forms of data and can be frequently seen in fields such as healthcare, finance, recommendation systems and network security. In contrast to homogeneous data like image, audio and text, tabular data is usually heterogeneous and shows a small sample size and weak correlations between features. Besides, tabular data may contain not only numerical features but also categorical ones that can not be handled directly by the majority of those popular learning algorithms. The aforementioned characteristics make decision tree ensemble methods, such as random forests and gradient boosting trees, more suitable for processing tabular data than other models. In this context, it is essential to transform categorical features into numerical ones in the setting of category embedding. This paper surveys embedding methods for handling low-cardinality categorical features in tabular data. Specifically, these methods are put into two categories, namely, supervised and unsupervised encoding, which each is introduced in terms of the strategies of category encoding. Moreover, the characteristics of each method are summarized. On the basis of the summarization, further directions of category embedding are suggested within the setting of representation learning.
Automated Line Detection on Lateral Flow Assays: A Paradigm Shift in Rapid Diagnostic Testing

Lateral flow assays (LFAs) provide rapid, accurate, and affordable results for disease diagnostics in resource-limited and point-of-care settings. However, the manual interpretation of LFA results can introduce human errors and biases. In this paper, we detail an automated line detection system that can read and interpret LFA results based on the image taken using a smartphone or any other digital imaging device. The proposed system can self identify the test and control lines of the LFA and can be used to quantify the analyte post calibration The automated line detection system reduces the reliance on trained personnel and enhances the accuracy and reliability of LFA readouts. This work details the various steps from image acquisition to detection of the test and control lines of a sensitive LFA and also evaluated an increase in sensitivity of detection due to the readout automation. In conclusion, the automated line detection can generate more accurate and consistent data than manual interpretation, which can be used to make better decisions for patient care.
Technical note: Late Pliocene age control and composite depths at ODP Site 982, revisited

Ocean Drilling Program (ODP) Site 982 provided a key sediment section at Rockall Plateau for reconstructing northeast Atlantic paleoceanography and monitoring benthic δ18O stratigraphy over the late Pliocene to Quaternary onset of major Northern Hemisphere glaciation. A renewed hole-specific inspection of magnetostratigraphic reversals and the addition of epibenthic δ18O records for short Pliocene sections in holes 982A, B, and C, crossing core breaks in the δ18O record published for Hole 982B, now imply a major revision of composite core depths. After tuning to the orbitally tuned reference record LR04, the new composite δ18O record results in a hiatus, where the Kaena magnetic subchron might have been lost, and in a significant age reduction for all proxy records by 130 to 20 ky over the time span 3.2–2.7 million years ago (Ma). Our study demonstrates the general significance of reliable composite-depth scales and δ18O stratigraphies in ODP sediment records for generating ocean-wide correlations in paleoceanography. The new concept of age control makes the late Pliocene trends in SST (sea surface temperature) and atmospheric pCO2 at Site 982 more consistent with various paleoclimate trends published from elsewhere in the North Atlantic.
Effect of Dimensionality Reduction on Uncertainty Quantification in Trustworthy Machine Learning

Machine learning (ML) is a commonly employed computer-assisted tool for ECG diagnosis with above 85 % correct. However, the interpretability of the prediction has become a barrier for the clinical use. To enhance the trustworthiness of ML systems, this paper integrates the trust score framework with the existing ECG classification. At present, the trust score framework is effective for low-dimensional data, but it does not offer significant enhancement over the model confidence for feature spaces with high dimensions. Moreover, the ECG data is a time series data with duration about one or half minute with sampling time about 3 ms. In other words, each signal usually contains several thousand or more sample points. As a result, this paper investigated effect of six popular dimensionality reduction (DR) techniques, including linear and kernel-based principal component analysis (KPCA), independent component analysis (ICA), Autoencoder, uniform manifold Approximation and projection (UMAP), and local linear embedded (LLE). The experimental results showed that UMAP with 2 dimensions can achieve result as well as ICA with 25 dimensions in tracking the model tendency and the other four DR methods has better performance with 25 dimensions.
How Perceived Value and Customer Engagament Drive Purchase Intention in Livestream Shopping

The pandemic COVID-19 makes Indonesians accustomed to doing various activities from home including shopping. Livestreaming shopping can attract higher purchase intentions than the usual way of selling. The current research aims to determine the influence of consumer perceived value on the purchase intention of Live services with the mediation of customer engagement. The population consisted of Indonesians who watched live streams on Live Shopping feature and interacted with streamers on live streaming. Using the targeted sampling method and calculation of percentage estimates, the minimum number of respondents for this study was 385. The research uses SEM-PLS to analyze data collected. The results of the study indicate that not all perceived value variables are significant to purchase intention. In addition, the mediating impact of customer engagement only mediates through perceived individual and social value. This research provides insight for online shop that use live streaming to attract buyers, especially local consumers in Indonesia.
An Application of a Diffusive Reduced-Gravity Model to Deep Circulation above Various Forms of Bottom Topography

Abstract A diffusive reduced-gravity model is applied to the deep circulation over a diversity of idealized bottom topography such as a basinwide uniform slope, a Gaussian sea mountain, or their complex. Although different circulations are generated by different forms or locations of bottom topography, a perturbation method with respect to the height of topography enables one to systematically interpret how these kinds of bottom topography modify the basic circulation on a flat bottom (the Stommel–Arons scheme). For instance, a slope deepening westward rotates a northeastward deep flow in the Stommel–Arons scheme eastward, whereas the opposite slope rotates the deep flow northward. Accordingly, it is the orientation of the slope that steers the deep flow rather than hypsometry. On the other hand, topography like a sea mountain forces a dipolar distribution of stretching along the basic flow without a sea mountain or a depression. This dipolar forcing turns out to induce two regimes of the circulation depe.
5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

—Over the past several years, the mobile security community has discovered a wide variety of exploits against link and session-establishment protocols. These exploits can be implemented on software-defined radios (SDRs) that disrupt, spoof, or flood layer-3 (L3) messages to compromise security and privacy, which still apply to the latest 5G mobile network standard. Interestingly, unlike the prior generations of closed (proprietary) mobile network infrastructures, 5G networks are migrating toward a more intelligent and open-standards-based fully interoperable mobile architecture, called Open RAN or O-RAN . The implications of transitioning mobile infrastructures to a software-defined architectural abstraction are quite significant to the INFOSEC community, as it allows us to extend the mobile data plane and control plane with security-focused protocol auditing services and exploit detection. Based on this design, we present 5G-S PECTOR , the first comprehensive framework for detecting the wide spectrum of L3 protocol exploits on O-RAN. It features a novel security audit stream called M OBI F LOW that transfers fine-grained cellular network telemetry, and a programmable control-plane xApp called M OBIE X PERT . We present an extensible prototype of 5G-S PECTOR which can detect 7 types of cellular attacks in real-time. We also demonstrate its scalability to 11 unknown attacks as well as 31 real-world cellular traces, with effective performance (high accuracy, no false alarms) and low ( < 2% CPU, < 100 MB memory) overhead.
OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION

: Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.
Modeling Particulate Matter (PM10) in Makkah, Saudi Arabia - A View Point of Health Impact

The core aim of this paper is to investigate the health impacts of PM10 in Makkah (March 2012 to February 2013). The annual average of PM10 concentration was 195 μg/m, which is greater than both EC and PME annual standards. Daily average concentrations also exceeded PME and EC standards. Furthermore, health assessment is carried out using AirQ2.2. 3 model to estimate the number of hospital admissions due to respiratory diseases. The cumulative number of average hospital admission due to respiratory illnesses during the study period was 112665, cumulative number of cases per 100,000 was 2504 and the concentrationresponse coefficient was 2.342 (95% CI 1.899 2.785) per 10 μg/m increase of PM10. The violation of daily and annual air quality standars and the results of AirQ2.2. 3 suggest potential negative health impact for the residents, especially for more vulnerable groups, such as old aged, children and people with asthma and other respiratory diseases.
Complementa la dosis baja de ketamina al bloqueo paravertebral

espanolObjetivo: El cancer de mama es el tumor maligno mas frecuente entre las mujeres del mundo desarrollado. En Espana se diagnostican alrededor de 16.000 casos/ano, con incidencia maxima entre 45-65 anos. Nuestro objetivo fue evaluar la eficacia de la adicion de dosis bajas de ketamina para complementar la analgesia postoperatoria proporcionada por el bloqueo paravertebral en la cirugia oncologica de mama no reconstructiva. Material y metodos: Estudio de cohortes, retrospectivo, descriptivo y observacional de 62 pacientes sometidos a cirugia oncologica de mama no reconstructiva, a los que se les realizo bloqueo paravertebral mediante triple puncion en el extremo inferior de las apofisis transversas de T2-T4-T6. A un grupo se le anadio ketamina intravenosa a dosis bajas y al otro nada. Se les sedo con propofol en perfusion continua mediante bomba durante la cirugia y se recogio la necesidad de analgesia postoperatoria en las 72 horas posteriores a la cirugia. El estudio se realizo de acuerdo con los principios de la declaracion de Helsinki, y los datos recogidos han sido tratados conforme la Ley Organica 15/1999, de 13 de diciembre, de proteccion de datos de caracter personal. Resultados: Se concluyo que la adicion de dosis bajas de ketamina disminuyo la necesidad de analgesia postoperatoria en las 72 horas posteriores a la cirugia, siendo esta estadisticamente significativa a las 48 y 72 horas (p EnglishObjective: Breast cancer is the most frequent malignant tumor among women in the development world. In Spain, they are diagnosed around 16,000 cases/year, with peak incidence between con 45-65 years old. Our objective was to evaluate the effectiveness of additional low ketamine doses to complement the postoperative analgesia provided by the paravertebral block in breast cancer surgery. Material and methods: Cohort, retrospective, descriptive and observational study of 62 patients undergoing oncologic surgery of breast to wich we performed paravertebral blockade by triple puncture in the lower end of the transverse processes of T2-T4-T6. A group was added intravenous ketamine at low doses and the other nothing. We sedated them with propofol in continuous perfusion by pump and we collected the need of postoperative analgesia in the 72 h after surgery. Results: It was concluded that the addition of low-dose ketamine decreased the need for postoperative analgesia in the 72 hours after the surgery being statistically significant at 48 to 72 hours (p
INDIVIDUAL DIFFERENCES IN NON-NATIVE PHONETIC PERCEPTION: POTENTIAL LINKS TO NATIVE PERCEPTION, ATTENTION, & MEMORY

Adults differ considerably in their ability to distinguish non-native sounds. The predictors of these individual differences remain poorly understood. We investigated whether differences in the discrimination of unfamiliar non-native phonemes were predicted by differences in performance on two native phonetic perception tasks (2AFC and VAS) and on two non-linguistic cognitive tasks. While preregistered analyses showed no significant predictors of non-native discrimination, exploratory analyses revealed a potential role of consistent native perception and cognitive factors in promoting successful non-native perception. These findings contribute to our understanding of individual differences in non-native phonetic perception, and have implications for encouraging successful acquisition of new languages in adulthood.
Diadromous fishes from Saint-Pierre and Miquelon archipelago: diagnoses, taxonomy, nomenclature and distribution

spe-Abstract . – saint-Pierre and Miquelon is a French self-governing overseas territory near the canadian province of newfoundland and Labrador. seven diadromous species occur in the rivers and ponds of this archipelago: Anguilla ���t�ata (Lesueur, 1817), O�me�u� m��dax (Mitchill, 1814), Salm� �ala� Linnaeus, 1758, Salvelinu� f�ntinali� (Mitchill, 1814), Apelte� quad�acu� (Mitchill, 1815), Ga�te���teu� aculeatu� Linnaeus, 1758 and Pun-gitiu� pungitiu� (Linnaeus, 1758). For each species, we review nomenclature, give all arguments delineating the taxon according to an integrative approach (morphology, molecular, time divergence, cytogenetics
The Influence of Leader Member Exchange on Innovative Work Behavior (A Study of High School Teachers)

This research aimed to examine the influence of leader-member exchange on innovative work behavior among high school teachers in Bantul Regency. This ex-post facto research used a quantitative approach involving 259 teachers. The instruments used in this study were the scales of leader-member exchange and innovative work behavior. Reliability tests were carried out on several dimensions of LMX. On the affection dimension, the score was 0.741, on the loyalty dimension it was 0.806, on the contribution dimension it was 0.704, and on the professional respect dimension it was 0.729. The score of overall LMX was 0.914, and the score of innovative work behavior was 0.906. According to the overall analysis, leader-member exchange has an influence on innovative work behavior. Furthermore, the partial data analysis has found that: (1) the affective dimension of LMX influences innovative behavior with an influence size of 25.7%. (2) the loyalty dimension of LMX has no effect on innovative work behavior. (3) the contribution dimension of LMX influences innovative work behavior with an influence size of 5.1%. (4) the professional respect dimension of LMX does not affect innovative work behavior.