Miracle-dz/UniFAR-FTUs huggingface dataset:
CUSTOMER CHURN PREDICTION ANALYSIS IN A TELECOMMUNICATION COMPANY WITH MACHINE LEARNING ALGORITHMS

The purpose of this study is to provide a descriptive analysis of the assessment of machine learning algorithms to an effective customer churn prediction (CCP) methodology. In the rapidly developing field of Customer Relation Management (CRM), to propose a convenient CCP methodology for retaining the customers who tend to churn, a set of data-mining analyses has been conducted to predict customer churn from a bulky dataset from customers with specific attributes in a telecommunication company by using machine learning (ML) algorithms built in an open-source data mining software, WEKA. Throughout the study, a set of experimental analyses regarding customer churn prediction are conducted by using residential, corporate, and combined datasets with the number of incidences of 195712, 32905, and 228617 respectively a private telecommunication company in Turkey. Six data mining algorithms have been evaluated to predict the customer churn status: Logistic Regression, Naive Bayes, J48, and ELM schemes such as RandomForest, Bagging and Boosting. RandomForest uses RandomTree, whereas Bagging uses J48 as a base learner. The experimental analyses are conducted with real-world datasets acquired from the company's historical database to validate some decision trees' effectiveness and ensemble ML classifiers to determine the likelihood of future churning customers based on such data mining analyses implemented for CCP. The results show that the J48 ou tperforms Naïve Bayes based on all datasets, and it provides very similar results as the Logistic Regression classifier scheme. Besides, since Bagging has not solved the large-sized database and J48 has given similar accurate results in the residential and complete data sets, the J48 decision tree classifier can be chosen and Bagging for customer churn prediction.
Traitor Tracing Revisited: New Attackers, Stronger Security Model and New Construction

In Crypto 94, Chor, Fiat, and Naor [8] ﬁrst introduced the traitor tracing ( TT ) systems, which aim at helping content distributors identify pirates. Since its introduction, many traitor tracing schemes have been proposed. However, we observe until now almost all the traitor tracing systems using probabilistic public key (and secret key) encryption as the the content distribution algorithm, they do not consider this basic fact: the malicious encrypter can plant some trapdoor in the randomness of the ciphertexts and later he can use this trapdoor or the delegation of the trapdoor to construct decoding pirates, He can sell them to the black market and get his own beneﬁts. At ﬁrst sight, this new attack model is too strong to capture the real attack scenarios. But we think it is valuable at least for the following two reasons: (1) Note in many modern content distribution systems, there are at least existing three diﬀerent roles: the content provider, the content distributer and the content consumer. In this framework, the encrypter is not necessarily the content provider (or content owner). He can be a malicious employee in the content provider corporation, it can also be the malicious content distributer corporation or its malicious employee. In all these cases, the encrypter has its own beneﬁts and has the potential intention to plant some trapdoor in the randomness of ciphertexts. (2) Also note in the related work, there is a conclusion that traitor tracing and diﬀerential privacy can have directly inﬂuence on each other, while diﬀerential privacy ( DP ) is at the heart of constructing modern privacy preserving systems. But if we consider this new insider attacker (the encrypter), at least some part arguments on the relationship between traitor tracing and diﬀerential privacy in [9][18] need more consideration.
Assessing and Comparing Participatory Governance in Energy Transition: Evidence from the 27 European Union Member States

Abstract The Energy Union governance regulation (CE 2018/1999) set new participatory procedures and governance standards for energy planning of Member States. This paper aims at identifying the contribution of this new mode of regulation in developing multi-sector, multi-level, and multi-stakeholder involvement and dialogue in the formulation of Energy Union plans. The article refines the concept of participatory governance in the energy sector and provides an operational and multidimensional definition. It innovatively compares the formulation process of the 27 Member States’ energy plans (draft and final) to highlight national evolution in participatory governance dimensions, cross-country convergence, and variations in alignment with EU regulation. Despite the significant differences in the governance of national energy planning, the empirical evidence highlights the contribution of EU governance regulation in promoting, along different dimensions, the participatory processes of all Member States.
Geomatics for Integrated Coastal Zone Management: multitemporal shoreline analysis and future regional perspective for the Portuguese Central Region

ABSTRACT Cenci, L. , Disperati, L. , Sousa, L.P., Phillips, M. and Alves, F.L. , 2013. Geomatics for Integrated Coastal Zone Management: multitemporal shoreline analysis and future regional perspective for the Portuguese Central Region. Proceedings 12th International Coastal Symposium (Plymouth, England), Journal of Coastal Research, Special Issue No. 65, pp. 1349–1354, ISSN 0749-0208. --> Shoreline mapping and change detection are critical for Integrated Coastal Zone Management (ICZM) and all that it represents. This research utilized previous studies that combined both Remote Sensing and Geographical Information System (GIS) techniques to assess, map and forecast shoreline evolution from short-term perspectives. The study area is located in the central region of Portugal, between the counties of Ovar and Marinha Grande (circa 140 km) and the time period assessed was from 1984 to 2011. Historical data were used to calculate advance and retreat rates in order to support environmental scenarios for the Portuguese Central Region's Coastal Management Plan. To ensure accuracy, a repeatable procedure was validated using Landsat TM and ETM+ satellite images, which were subsequently enhanced and elaborated by Remote Sensing analyses to detect and extract shorelines. They were subsequently integrated within an Esri ArcGIS software application (DSAS - Digital Shoreline Analysis System) to determine and predict rates of coastline change. Graphical DSAS plots identified coastline phases and shifts and were used to simulate the 2022 coastline scenario. These results will be integrated into the Coastal Zone Management Plan (Horizon – 2022). Importantly this methodological planning approach provides visual coastline change information for regional decision-makers and stakeholders.
Rendaku is not blocked by two nasal consonants: A reply to Kim (2022)

Rendaku is a morphophonological process in Japanese, in which the first consonant of the second element of compounds becomes voiced (e.g. /ao-sora/ → [ao-zora] ‘blue sky’). In a recent paper, Kim (2022) argues that the presence of two nasals—but not one nasal—in the second element reduces the probability of Rendaku application (e.g. /umi-kamome/ → [umi-kamome], *[umi-gamome] ‘seagull’). This claim has an important consequence for grammatical theories, as it would constitute a case in which the influences of two segments add up to block a phonological process, a pattern that is referred to as “super-additive counting cumulativity. ” We have reexamined the empirical basis of this claim, and have found that there are only four relevant lexical items. Moreover, two of these items actually undergo Rendaku almost all the time, and there are thus only two items whose Rendaku is clearly blocked. The empirical basis of the theoretical claim based on Rendaku thus seems to be weak at best. With this, we move on to the main contribution of this paper, which is a new nonce-word experiment that aimed to resolve the conflicting results from the previous studies on this topic. The results of the current experiment show that the applicability of Rendaku is not reduced by the presence of two nasal consonants.
Economic Growth, Trade, and Foreign Direct Investment Interrelationships in South Asian Countries

This paper surveys the complex relationships between economic growth and Foreign Direct Investment (FDI) in South Asian countries, taking into account the specific conditions and environments that influence foreign investment in each country's economy. The study analyzes annual data from 1991-2020 focuses on India, Pakistan, and Bangladesh and uses co-integration and granger causality tests to determine that every country is at a distinct stage of outreach based on factors such as population, resources, and international trade. Results declare that FDI, economic development, and exports have robust relationship. The study concludes that creating a favorable environment for investors can increase foreign investment, leading to increase per capita GDP growth within the country
Non-Circular Signal DOA Estimation with Nested Array via Off-Grid Sparse Bayesian Learning

For the traditional uniform linear array (ULA) direction of arrival (DOA) estimation method with a limited array aperture, a non-circular signal off-grid sparse Bayesian DOA estimation method based on nested arrays is proposed. Firstly, the extended matrix of the received data is constructed by taking advantage of the fact that the statistical properties of non-circular signals are not rotationally invariant. Secondly, we use the difference and sum co-arrays for the nested array technique, thus increasing the array aperture and improving the estimation accuracy. Finally, we take the noise as part of the interest signal and iteratively update the grid points using the sparse Bayesian learning (SBL) method to eliminate the modeling errors caused by off-grid gaps. The simulation results show that the proposed algorithm can improve the accuracy of DOA estimation compared with the existing algorithms.
NON-DESTRUCTIVE IDENTIFICATION OF MICROMETER-SCALE MINERALS AND THEIR POSITION WITHIN A BULK SAMPLE

Using the conventional techniques of mineralogy, it has been a challenge to determine mineral identity, crystal orientation and spatial position of micrometer-sized crystals that are embedded in a rock, sediment or soil. Traditionally, the individual grains must be extracted and analyzed separately. Crushing or disintegrating a sample annihilates any possibility for gathering information from the texture of the porous media or the mineral assemblage close to the grains in question. A new method using three-dimensional X-ray diffraction (3DXRD) microscopy can be successfully applied to natural materials. We combined X-ray microtomography (XMT) and 3DXRD to investigate a sample of very fine-grained chalk containing fracture minerals. The XMT technique provides three-dimensional images of the particles and pore structure at very high resolution (350 nm voxel dimension) on samples less than 500 μm in diameter. The minor phases present as crystals in fractures were determined nondestructively with 3DXRD microscopy. The chalk fragment investigated is composed predominantly of randomly oriented nanometric crystals of calcite that produce powder rings where no texture can be observed. Superimposed on this pattern, Bragg diffraction peaks from the other crystalline phases were observed. Individual crystals of barite and pyrite only a few micrometers in diameter are present in the fractures. Magnetite, celestine and siderite, other minerals that might have been expected based on the XMT absorption contrast, were not identified. The crystal shape and in-fracture location, derived from the microtomograms, and the mineral identity, derived from 3DXRD, allowed us to propose that the fractures are original in these tiny drill cuttings; they were not induced by drilling and filled with drilling mud particles, thus allowing reliable estimates to be made of rock porosity and permeability.
Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to address the challenges of centralized data processing and user privacy concerns prevalent in previous methods. Word embeddings capture semantic relationships and contextual information, enabling a more nuanced understanding of text data, while emotional features derived from text extend the analysis to encompass the affective dimension, enhancing cyberbullying identification. Federated learning, a decentralized learning paradigm, offers a compelling solution to centralizing sensitive user data by enabling collaborative model training across distributed devices, preserving privacy while harnessing collective intelligence. In this study, we conduct an in-depth investigation into the fusion of word embeddings, emotional features, and federated learning, complemented by the utilization of BERT, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) models. Hyperparameters and neural architecture are explored to find optimal configurations, leading to the generation of superior results. These techniques are applied in the context of cyberbullying detection, using publicly available multi-platform (social media) cyberbullying datasets. Through extensive experiments and evaluations, our proposed framework demonstrates superior performance and robustness compared to traditional methods. The results illustrate the enhanced ability to identify and combat cyberbullying incidents effectively, contributing to the creation of safer online environments. Particularly, the BERT model consistently outperforms other deep learning models (CNN, DNN, LSTM) in cyberbullying detection while preserving the privacy of local datasets for each social platform through our improved federated learning setup. We have provided Differential Privacy based security analysis for the proposed method to further strengthen the privacy and robustness of the system. By leveraging word embeddings, emotional features, and federated learning, this research opens new avenues in cyberbullying research, paving the way for proactive intervention and support mechanisms. The comprehensive approach presented herein highlights the substantial strengths and advantages of this integrated methodology, setting a foundation for future advancements in cyberbullying detection and mitigation.
The presence of native aromatic species in understorey plant communities predicts reduced infestation by olive fruit flies in olive orchards on Lesvos Island

The olive fruit fly (Bactrocera oleae) (Rossi) is the most economically important insect pest in many olive cultivation areas worldwide. However, levels of infestation by this pest show remarkable variability, even among orchards in close proximity to one another. While the factors driving this variability remain uncertain, there is evidence that the presence of native plants species can influence pest populations in agro‐ecosystems via various mechanisms, suggesting that variation in the composition of understorey plant communities might plausibly play a role. In this study, we first used field trapping and plant community surveys to explore the effect of the native understorey plants Cistus creticus (Cistaceae), Lavandula stoechas (Lamiaceae), Origanum onites (Lamiaceae) and Sarcopoterium spinosum (Rosaceae) on the abundance of B. oleae across 40 olive orchards on Lesvos Island (Greece). These four plant species are widely spread in many olive orchards. Our results revealed a relationship between the plant community and the intensity of flies captured in McPhail traps, and identified the plant species C. creticus and S. spinosum whose presence was negatively correlated with fly abundance (estimate = −0.047, −0.091, respectively). The subsequent laboratory behavioural experiments revealed that exposure to olfactory cues from C. creticus and S. spinosum reduced fly attraction to odours from the olive host (Olea europaea) by 10%. These results thus reveal a relationship between the infestation levels by B. oleae and the composition of the plant community in olive orchards on Lesvos and suggest a plausible mechanism via which olfactory cues from native aromatic species might influence fly recruitment. More generally, these findings highlight the potentially important role of understorey communities in mediating resistance to B. oleae and other agricultural pests.
Numerical investigation of the coagulation mixing between dust and hygroscopic aerosol particles and its impacts

A statistical‐numerical aerosol parameterization was incorporated into the Community Multiscale Air Quality modeling system to study the coagulation mixing process focusing on a dust storm event that occurred over East Asia. Simulation results show that the coagulation mixing process tends to decrease aerosol mass, surface area, and number concentrations over the dust source areas. Over the downwind oceanic areas, aerosol concentrations generally increased due to enhanced sedimentation as particles became larger upon coagulation. The mixture process can reduce the overall single‐scattering albedo by up to 10% as a result of enhanced core with shell absorption by dust and reduction in the number of scattering particles. The enhanced dry deposition speed also altered the vertical distribution. In addition, the ability of aerosol particles to serve as cloud condensation nuclei (CCN) increased from around 107 m−3 to above 109 m−3 over downwind areas because a large amount of mineral dust particles became effective CCN with solute coating, except over the highly polluted areas where multiple collections of hygroscopic particles by dust in effect reduced CCN number. This CCN effect is much stronger for coagulation mixing than by the uptake of sulfuric acid gas on dust, although the nitric acid gas uptake was not investigated. The ability of dust particles to serve as ice nuclei may decrease or increase at low or high subzero temperatures, respectively, due to the switching from deposition nucleation to immersion freezing or haze freezing.
Comparison between vegetation change analysis in Kenya based on AVHRR and SeaWiFS images

An assessment of the suitability of the Advanced Very High Resolution Radiometer (AVHRR) vegetation index to estimate land degradation in semi‐arid areas has been carried out, comparing its behaviour with that of vegetation indices based on Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) images. Notwithstanding the importance of the classic Normalized Difference Vegetation Index (NDVI) indicator, based on red–NIR channels, several studies have identified some limitations related to its use, such as its dependence on the atmospheric profile, saturation problems, non‐linearity in biophysical coupling with Leaf Area Index (LAI) and canopy background contamination. The relatively recent Enhanced Vegetation Index (EVI) overcomes these limits, using the information related to the blue channel and a soil adjustment factor. SeaWiFS data allow the computation of both vegetation indices. On the other hand, the NDVI based on AVHRR can be computed back in time to the 1980s, allowing a sufficient time span to obtain information on the desertification trend of the considered region (northern Kenya). In conclusion, taking advantage of both datasets, the accuracy of a change detection technique based on the classic NDVI has been confirmed as suitable for revealing any desertification trend.
TAPHONOMIC SIGNATURES IN CONCENTRATIONS OF HELEOBIA STIMPSON, 1865 FROM HOLOCENE DEPOSITS OF THE SALADO RIVER BASIN, BUENOS AIRES, ARGENTINA: THEIR UTILITY IN PALEOENVIRONMENTAL RECONSTRUCTIONS

ABSTRACT Assemblages composed of Heleobia parchappii and H. australis constitute the dominant elements in Quaternary deposits of the Pampean Region in the province of Buenos Aires. This study describes and analyzes the degree of preservation of shells of both species recovered from nine Holocene localities in order to describe and quantify taphonomic alterations, evaluate if preservation varied during the Holocene, and assess their utility as paleoenvironmental bioindicators. Heleobia parchappii displayed better overall preservation than H. australis, with little evidence of fragmentation, limited principally to levels with highest densities. On a temporal scale, no significant differences in fragmentation were found, whereas corrasion and luster displayed temporal differences.
Early Progressive Peripheral Airway Dysfunction after Allogeneic Haematopoietic Stem Cell Transplantation is associated with chronic Graft vs Host disease not BOS-0p

Background: Current spirometric-based criteria for diagnosis of bronchiolitis obliterans syndrome (BOS) may miss early peripheral airway disease associate with disease onset. Multiple breath washout (MBW) and oscillometry offer improved sensitivity, but longitudinal changes occurring in allogeneic haematopoietic stem cell transplantation (HSCT) are unknown. Objective: In this longitudinal study of HSCT survivors, we investigated changes in nitrogen-based MBW, oscillometry and conventional lung function, from baseline (pre-transplant), over 36-months, and associations with BOS Stage 0p, a spirometry-defined risk classification for potential later BOS development, and chronic graft-vs-host disease (cGVHD). Study Design: Longitudinal observational study of allogeneic HSCT recipients from a single adult centre. All participants underwent spirometry, plethysmography, gas transfer capacity (DLCO), oscillometry and MBW at each study visit. Tests were performed pre-HSCT and 3 monthly thereafter over 36 months. Results: 64 of 69 recipients recruited were included in the final analysis. Across the entire cohort, deterioration in acinar ventilation inhomogeneity (Sacin) occurred as early as 90 days post-HSCT (0.224 z score change/month, p<0.001), prior to any significant change in spirometry or oscillometry. Progressive deteriorations in Sacin were associated with cGVHD status and grade but not BOS-0p status. Conclusions: Early progressive peripheral airway dysfunction occured following HSCT, and was best detected by Sacin from MBW. Distal acinar ventilation inhomogeneity(Sacin) deteriorated at an earlier stage than spirometry. Longitudinal deteriorations in Sacin were related to cGVHD, and independent of early changes in spirometry parameters. These findings suggest an important role of the lung in cGVHD, and provide important evidence to support future studies examining the prognostic utility of MBW in long-term monitoring of HSCT patients to provide an early effective signal of BOS.