Miracle-dz/UniFAR-FTUs huggingface dataset:
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This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion’s convexiﬁcation by transforming the cross-entropy loss into a risk averting error (RAE) criterion. To alleviate numerical difﬁculties, a normalized RAE (NRAE) is employed. The convexity region of the cross-entropy loss expands as its risk sensitivity index (RSI) increases. Making the best use of the convexity region, our method starts training with an extensive RSI, gradually reduces it, and switches to the RAE as soon as the RAE is numerically feasible. After training converges, the resultant deep learning machine is expected to be inside the attraction basin of a global minimum of the cross-entropy loss. Numerical results are provided to show the effectiveness of the proposed method.
Sentiment Analysis of the Covid-19 Booster Vaccination Program as a Requirement for Homecoming during Eid Fitr in Indonesia

The COVID-19 vaccination program was carried out to overcome the pandemic. In addition to vaccination, there is a booster vaccine program which is also an obligation for the public to be followed but has not received a good response from the public. Indonesia's government is making booster vaccination a mandatory requirement for mass homecoming in the Islamic celebration day of Eid Fitr, known as mudik Lebaran. This study aims to find out public opinion and perception regarding the booster vaccination program for mudik Lebaran using sentiment analysis. This study uses eight classification modeling: Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, Random Forest, K-Nearest Neighbor, AdaBoost, and XGBoost. The best classification modeling is SVM with the best accuracy score 88% and the F1 score 88%. Then this SVM model is used to predict the sentiment of 30,582 tweet data from March 22 to May 02, 2022. The results are 11,507 giving negative sentiment (37.63%) and 19,075 giving positive sentiment (62.37%). This result shows that the government's strategy in accelerating the COVID-19 booster vaccination program was well accepted by making it a requirement for mudik Lebaran. Furthers analysis with visualization of time series, shows that the sentiment had eveloved. In the first week, negative sentiment prevailed due to reactions to this policy. The Indonesian people compare the policy of the MotoGP event in Mandalika which does not require a vaccine booster. After that, in the following weeks the positive sentiment prevailed because the community realized that boosters were important to maintain their health and that of their families back home. This research shows the importance of time series visualization because sentiment can change over time. © 2023 Little Lion Scientific.
Robust formation control in SE

This paper presents a novel control protocol for distance and orientation formation control of rigid bodies, whose sensing graph is a static and undirected tree, in the special Euclidean group SE (3). The proposed control laws are decentralized, in the sense that each agent uses only local relative information from its neighbors to calculate its control signal, as well as robust with respect to modeling (parametric and structural) uncertainties and external disturbances. The proposed methodology guarantees the satisfaction of inter-agent distance constraints that resemble collision avoidance and connectivity maintenance properties. Moreover, certain predeﬁned functions characterize the transient and steady state performance of the closed loop system. Finally, simulation results verify the validity and eﬃciency of the proposed approach.
ENHANCING CREDIT RISK MANAGEMENT IN THE BANKING SECTOR THROUGH MACHINE LEARNING-BASED PREDICTIVE MODELS

Credit risk plays a vital role in the functioning of the banking sector, as banks extensively engage in providing loans, credit cards, mortgages, and other financial products. However, the increasing number of credit card users has led to a rise in credit card default rates, posing challenges for banks. To address this issue and effectively control credit risk, leveraging data analytics becomes crucial. This study aims to predict loan defaults, enabling banks to proactively mitigate potential losses by offering alternative options to borrowers. To achieve this objective, we propose a system that utilizes various machine learning classification algorithms. Specifically, we explore and compare the performance of Logistic Regression, XGBoost, k-Nearest Neighbors, Neural Network, and Random Forest models in predicting credit risk. Our findings reveal that the Random Forest model demonstrates exceptional accuracy, with a forecast accuracy of 98% in assessing the credit risk of credit card users.
Patients with Liver Cirrhosis Normal-appearing Cerebral White Matter in Proton MR Spectroscopy of Magnetization Transfer Ratio Values and

BACKGROUND AND PURPOSE: Hepatic encephalopathy in cirrhotic patients may be the clinical manifestation of disturbed cerebral cell volume homeostasis. The aim of this study was to investigate the presence of signiﬁcant changes in magnetization transfer ratio (MTR) values, which could reﬂect an increase in free water within the brain of patients with liver cirrhosis, and to correlate these ﬁndings with minimal hepatic encephalopathy and proton MR spectroscopy ( 1 H-MRS) abnormalities. METHODS: Twenty-four patients with liver cirrhosis and eight healthy control volunteers were included in the study. MR imaging studies included conventional T1-and T2-weighted imaging, 1 H-MRS, and magnetization transfer imaging. MTR and 1 H-MRS values were obtained from normal-appearing white matter and were correlated with each other and with the presence of minimal hepatic encephalopathy. RESULTS: 1 H-MRS showed a decrease in choline and myo -inositol and an increase in glutamine (cid:49) glutamate with respect to creatine (cid:49) phosphocreatine. MTR values were signiﬁcantly decreased in cirrhotic patients when compared with healthy control volunteers, although this decrease was not signiﬁcantly higher in the patients with minimal hepatic encephalopathy. The decreases in MTR values correlated with increases in glutamine (cid:49) glutamate. CONCLUSION: The MTR decrease in patients with liver cirrhosis may be caused by low-grade astrocytic swelling produced as a response to the osmotic stress occurring in these patients. However, in this cross-sectional study, we did not ﬁnd a correlation between MTR decrease and the presence of minimal hepatic encephalopathy, probably because of limitations in MTR
Edinburgh Research Explorer Hiding opinions from machine learning

Recent breakthroughs in machine learning and big data analysis are allowing our online activities to be scrutinized at an unprecedented scale, and our private information to be inferred without our consent or knowledge. Here, we focus on algorithms designed to infer the opinions of Twitter users toward a growing number of topics, and consider the possibility of modifying the profiles of these users in the hope of hiding their opinions from such algorithms. We ran a survey to understand the extent of this privacy threat, and found evidence suggesting that a significant proportion of Twitter users wish to avoid revealing at least some of their opinions about social, political, and religious issues. Moreover, our participants were unable to reliably identify the Twitter activities that reveal one’s opinion to such algorithms. Given these findings, we consider the possibility of fighting AI with AI , i.e., instead of relying on human intuition, people may have a better chance at hiding their opinion if they modify their Twitter profiles following advice from an automated assistant. We propose a heuristic that identifies which Twitter accounts the users should follow or mention in their tweets, and show that such a heuristic can effectively hide the user’s opinions.Altogether,our study highlights the risk associated with developing machine learning algorithms that analyze people’s profiles, and demonstrates the potential to develop countermeasures that preserve the basic right of choosing which of our opinions to share with the world.
Differential Associations between Trauma Types and Borderline Personality Disorder from the Perspective of Emotion Dysregulation

Background: Borderline Personality Disorder (BPD) is characterized by pervasive instability in a range of areas including interpersonal relationships, self-image, and affect. Extant studies have consistently identi�ed signi�cant correlations between childhood trauma and BPD. While exploring this trauma-BPD link, a number of cross-sectional studies commonly emphasize the role of emotion dysregulation (ED). A better understanding of the association between trauma and BPD is essential in formulating early, effective intervention approaches, and in addressing varied adverse impacts. Methods: We analyzed a subset of data collected for a larger community-based longitudinal study. Given that our current focus on trauma and ED, only those participants who completed the baseline assessment measures of trauma and ED (N=144) were included for the primary analyses. We conducted stepwise multivariate linear models to examine the differential relationships between BPD features, ED, and speci�c traumas. A path analysis with latent factors using the structural equation modeling (SEM) method was performed to test the indirect effect from trauma to BPD features via ED. Results: Linear regression models revealed that emotional abuse (relative to other trauma types) was signi�cantly associated with high BPD features. The SEM, by constructing direct and indirect effects simultaneously, showed that (1) ED partially mediated the path from trauma to BPD features; and (2) trauma played a complex role in which the direct effect remained signi�cant even after accounting for the indirect effect through ED. Conclusions: Our results highlight a most consistent association between emotional abuse and trauma, indicating its unique role in understanding BPD features in the context of childhood adversity. Further, shame-related negative appraisal and ED were found critical when examining the association between trauma and BPD, possibly providing promising treatment targets for future practices.
Edinburgh Research Explorer The complexity of a language is shaped by the communicative needs of its users and by the hierarchical nature of their social inferences

Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray & Grace (2007) argue that even compositionality, traditionally regarded as a universal and fundamental feature of human languages, may have only culturally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community’s needs.
OF TEACHERS IN SELECTED PUBLIC AND PRIVATE PRIMARY SCHOOLS IN KENYA

The purpose of the study was to establish teachers’ perception on their preparedness to implement the Competency Based Curriculum (CBC) in public and private primary schools in Kenya. CBC was rolled out by the Kenya Institute of Curriculum Development in January 2018 and since then, studies have indicated that teachers who are the key implementers of the curriculum are not adequately trained. The current study sought to establish teachers’ perception on their preparedness to implement the Competency Based Curriculum in selected public and private primary schools in Kenya. The study was anchored on instructional design theory and targeted 23,286 public primary schools and 9,058 private primary schools in Kenya. The actual sample was made of 19 public primary schools and 31 private primary schools. The study adopted a convergent parallel mixed methods research design using a cross sectional survey design for the quantitative method and phenomenological design for the qualitative method. Data were collected using questionnaires and focus group discussions. Descriptive and inferential statistics were generated using MS Excel 2020 and SPSS version 21. Qualitative data from the focus group discussions were analyzed thematically and report given in narrative form and direct quotes. The findings revealed among other factors that digital literacy is still a challenge among teachers and that the number of trained teachers in the selected schools was approaching expectation. Resources for CBC are there in a number of schools but quite inadequate in a number of public schools. The study recommended that the training of teachers at all levels should be on-going and that there is an urgent need to help teachers understand the use of CBC assessment rubrics.
CLASSIFICATION APPROACH TO PREDICT CUSTOMER DECISION BETWEEN PRODUCT BRANDS BASED ON CUSTOMER PROFILE AND TRANSACTION

Businesses need to be able to anticipate what products their customers will buy so that they can better respond to changing market demands and consumer tastes. The purpose of this study is to employ several machine learning models that can reliably estimate the customer's likelihood of purchasing the product given a customer's profile, transaction date, and other transaction information. This was achieved by training and evaluating different machine learning techniques, such as naive bayes, linear models, deep learning, and decision trees, on a dataset consisting of actual transaction data from three months of sales at a medium-scale grocery store in Bandung. Results indicated that naive bayes performed best as a prediction algorithm, this study shows that data mining can be used to predict grocery store datasets. This research provides insights into how machine learning can be used to improve businesses' ability to anticipate consumer behavior and respond to changing market demands. We also found that demographic factors like age and location, as well as contextual factors like time of week, significantly influenced customers' propensity to buy.
Prosodic Features of Jonglish Community: An Effort to A Glocalization

: The current study aims to glocalize the Jonglish community by comparing the pitch and intensity of the accents of native English speakers and Javanese speakers. Thirty-two university students whose first language was Javanese, coming from seven different regencies in Central Java, became the respondents. They are all first-semester students from various Java Island regencies, ranging in age from 19 to 21. The research instrument comprised an oral test of 10 academic vocabulary words and 5 phrases or sentences. Using PRAAT software, the oral test result is examined for the pitch and volume of the sound. The data show each respondent's and native speaker’s average, lowest, and maximum pitch and intensity. The average pitch of the respondents increased by 127.57 Hz, whereas a natura l speaker’s pitch is 198.25 Hz. The average Javanese accent's intensity was measured at 65.11 decibels, while a native English speaker's accent was measured at 70.85 decibels. According to the results, there is no discernible difference between the native English speaker and the Javanese accent in terms of pitch or intensity. Even if there are imperfections in individual sounds, listeners may still understand and perceive speech as fluent as long as the prosodic features are well-executed. Consequently, it is acceptable to speak English with a Jonglish accent. Additionally, it might have an impact on cross-cultural communication strategies, foreign language teaching techniques, and linguistic studies on English-Jonglish prosody blending
Implementation of EDEEC protocol for 4-Level Scalable Heterogeneous Wireless Sensor Networks

- Wireless Sensor Network consists of a large number of Sensor nodes, which firstly sense the data and then transmit the sensed data to the base station which is known as the Sink. Network lifetime is one of the key challenges for Wireless Sensor networks because of the limited battery life of the nodes. As we know sensor nodes are energy constrained devices and to increase the lifetime of the network it is very necessary to minimize the consumption of energy of nodes while sensing and transmitting the data. Clustering in Wireless networks is one of the pre-eminent ways to improve the lifetime of the network. In EDEEC, a clustering-based hierarchical model is used where data is aggregated in the cluster and sent to a higher-level cluster head where the cluster head is selected randomly on the basis of residual energy of the network. EDEEC works on 3-level heterogeneous wireless sensor networks in which there are three type of sensor nodes named as normal nodes, advanced nodes and super node, which still have scope of improvement because if the levels of heterogeneous wireless sensor networks are to be increased then more complexity will be there in the network and then a more stable behaviour of network is required. In this work it is proposed that performance of the network can be enhanced if the level of heterogeneity in the network increases because in the real world there can exist more than three types of nodes in the network. In this work EDEEC protocol has been implemented with four types of nodes that includes normal nodes, advance nodes, super nodes and ultra-super nodes and the nodes count varies from 100 to 1000 nodes to check the change in energy consumption and stability of the network.
Surface Ocean Cooling in the Eocene North Atlantic Coincides With Declining Atmospheric CO2

The Eocene (56–34 million years ago) is characterized by declining sea surface temperatures (SSTs) in the low latitudes (∼4°C) and high southern latitudes (∼8–11°C), in accord with decreasing CO2 estimates. However, in the mid‐to‐high northern latitudes there is no evidence for surface water cooling, suggesting thermal decoupling between northern and southern hemispheres and additional non‐CO2 controls. To explore this further, we present a multi‐proxy (Mg/Ca, δ18O, TEX86) SST record from Bass River in the western North Atlantic. Our compiled multi‐proxy SST record confirms a net decline in SSTs (∼4°C) between the early Eocene Climatic Optimum (53.3–49.1 Ma) and mid‐Eocene (∼44–41 Ma), supporting declining atmospheric CO2 as the primary mechanism of Eocene cooling. However, from the mid‐Eocene onwards, east‐west North Atlantic temperature gradients exhibit different trends, which we attribute to incursion of warmer waters into the eastern North Atlantic and inception of Northern Component Water across the early‐middle Eocene transition.
Optimization of Surface-Assisted Laser Desorption/Ionization Mass Spectrometry The quest for the Holy Grail from the study of thermometer ions

In recent years, there has been a growing interest in the study of small molecules ( e.g. metabolites, lipids, drugs) as they play important roles in various research areas, including biomedicine, biotechnology, environmental science and drug discovery. Mass spectrometry (MS) has become a predominant technology to analyze these low molecular weight compounds. However, the analysis of small molecules by the established matrix-assisted laser desorption/ionization (MALDI) MS technique can be difficult because of the interference generated by the matrix in the low m/z range. Therefore, new innovating ionization techniques that do not require an organic matrix have been developed. Among these alternatives is surface-assisted laser desorption/ionization (SALDI) MS, which relies on nanostructured surfaces to promote the desorption/ionization process. Yet, the implementation of this novel ionization technique introduces new challenges, which include the understanding of the fundamental mechanisms that govern the SALDI process, and the optimization of the experimental factors. In particular, the design and optimization of the assisting nanosubstrates are not straightforward, because of the wide range of available nanomaterials (in terms of chemical nature and morphology), characterized by different properties, which impact the performance of the SALDI MS experiments. Thus, it is essential to find a model to compare the capabilities of the SALDI nanosubstrates, in order to optimize their characteristics based on the same reference. In this context, thermometer ions have been used as reference compounds to test novel instrumentation, new methodologies, or to tune instruments. In this article, we review the study of thermometer ions in SALDI MS, which have enabled to rationalize the effect of the experimental factors ( i.e. the nanosubstrate morphology and chemical nature, and the instrumental settings) on the performance of SALDI MS experiments. These studies allow the rational design of optimized nanosubstrates for advanced applications, such as SALDI MS imaging, which will be discussed at the end of this article.
Interactive comment on “Diurnal variation in middle atmospheric ozone by ground basedmicrowave radiometry at Ny-Ålesund over 1 year

microwave radiometry at Ny-Ålesund over 1 year Franziska Schranz1, Susana Fernandez2, Niklaus Kämpfer1, and Mathias Palm3 1Institute of Applied Physics, University of Bern, Bern, Switzerland 2Department of Physics, University of Santiago de Chile, Santiago, Chile 3Institute of Environmental Physics, University of Bremen, Bremen, Germany Correspondence to: Franziska Schranz (franziska.schranz@iap.unibe.ch)
Impact of Critical Barriers on the Stakeholder’s Adoption Behavior toward Green Building Technologies in Pakistan

The construction industry is becoming a main source of carbon emissions in Pakistan due to the country's rapid urbanization. To slow global warming, it is crucial to evaluate and cut construction's carbon emissions. Despite significant international efforts to encourage the use of green construction technology, which have not yet been extensively embraced because of obstacles, these carbon emissions are continuously rising. In this context, the current research looks at how barriers affected how stakeholders adopted green building technologies (GBTs) in Pakistan. Data are collected through a questionnaire from 349 stakeholders in the construction industry. Partial least square structural equation modeling is used for the empirical study. The main findings reveal that, out of the five types of barriers, innovation-related and economic barriers are the strongest influencing barriers, followed by socio-cultural and governmental barriers, resource-related and regulation-based barriers to the stakeholders’ adoption behavior of green building technologies in Pakistan. Based on the research's findings, various policy recommendations are made for the government policy-makers and other concerned stakeholders to ensure the effective deployment of green building technologies in Pakistan.