Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Backdoor Attacks

In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. In this work, we present a different approach to the backdoor attack problem. Specifically, we show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data--and thus impossible to"detect"in a general sense. Then, guided by this observation, we revisit existing defenses against backdoor attacks and characterize the (often latent) assumptions they make and on which they depend. Finally, we explore an alternative perspective on backdoor attacks: one that assumes these attacks correspond to the strongest feature in the training data. Under this assumption (which we make formal) we develop a new primitive for detecting backdoor attacks. Our primitive naturally gives rise to a detection algorithm that comes with theoretical guarantees and is effective in practice.
Deteksi Sampah di Permukaan dan Dalam Perairan pada Objek Video dengan Metode Robust and Efficient Post-Processing dan Tubelet-Level Bounding Box Linking

Indonesia, as a maritime country, has a significant portion of its territory covered by water. Ineffective waste management has resulted in a considerable amount of trash in Indonesian waters, leading to various issues. The development of an automated trash-collecting robot can be a solution to address this problem. The robot requires a system capable of detecting objects in motion, such as in videos. However, using naive object detection methods in videos has limitations, particularly when image focus is reduced and the target object is obstructed by other objects. This paper's contribution provides an explanation of the methods that can be applied to perform video object detection in an automated trash-collecting robot. The study utilizes the YOLOv5 model and the Robust&Efficient Post Processing (REPP) method, along with tubelet-level bounding box linking on the FloW and Roboflow datasets. The combination of these methods enhances the performance of naive object detection from YOLOv5 by considering the detection results in adjacent frames. The results show that the post-processing stage and tubelet-level bounding box linking can improve the quality of detection, achieving approximately 3% better performance compared to YOLOv5 alone. The use of these methods has the potential to detect surface and underwater trash and can be applied to a real-time image-based trash-collecting robot. Implementing this system is expected to mitigate the damage caused by trash in the past and improve Indonesia's waste management system in the future.
A Dual Formulation for Probabilistic Principal Component Analysis

In this paper, we characterize Probabilistic Principal Component Analysis in Hilbert spaces and demonstrate how the optimal solution admits a representation in dual space. This allows us to develop a generative framework for kernel methods. Furthermore, we show how it englobes Kernel Principal Component Analysis and illustrate its working on a toy and a real dataset.
The influence of the headmaster’s reward system toward the teachers work motivation in the Senoir High School 1 of Tanete Rilau of Barru Regency

This study aims to find out and explain the influence of the headmaster's reward system toward the teachers’ work motivation. This study uses a quantitative associative approach using descriptive statistical data analysis techniques with percentage, mean and standard deviation and inferential statistical analysis using data normality test, product moment correlation, simple linear regression, and homogeneity test. The results of this study suggest that the headmaster's reward system has a significant effect on the work motivation of the teacher. This can be seen from the results of the data analysis, the level of influence of the headmaster's reward system to the work motivation of teachers in the SeniorHigh School1 of Tanete Rilau of Barru Regency, which is classified as in the medium category. 800x600 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:. 0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif;}
Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis (preprint)

We use data from 107 Italian provinces to characterize and compare mortality patterns in the first two COVID-19 epidemic waves, which occurred prior to the introduction of vaccines. We also associate these patterns with mobility, timing of government restrictions, and socio-demographic, infrastructural, and environmental covariates. Notwithstanding limitations in the accuracy and reliability of publicly available data, we are able to exploit information in curves and shapes through Functional Data Analysis techniques. Specifically, we document differences in magnitude and variability between the two waves; while both were characterized by a co-occurrence of 'exponential' and 'mild' mortality patterns, the second spread much more broadly and asynchronously through the country. Moreover, we find evidence of a significant positive association between local mobility and mortality in both epidemic waves and corroborate the effectiveness of timely restrictions in curbing mortality. The techniques we describe could capture additional signals of interest if applied, for instance, to data on cases and positivity rates. However, we show that the quality of such data, at least in the case of Italian provinces, was too poor to support meaningful analyses.
A discretization-invariant extension and analysis of some deep operator networks

We present a generalized version of the discretization-invariant neural operator and prove that the network is a universal approximation in the operator sense. Moreover, by incorporating additional terms in the architecture, we establish a connection between this discretization-invariant neural operator network and those discussed before. The discretization-invariance property of the operator network implies that different input functions can be sampled using various sensor locations within the same training and testing phases. Additionally, since the network learns a ``basis'' for the input and output function spaces, our approach enables the evaluation of input functions on different discretizations. To evaluate the performance of the proposed discretization-invariant neural operator, we focus on challenging examples from multiscale partial differential equations. Our experimental results indicate that the method achieves lower prediction errors compared to previous networks and benefits from its discretization-invariant property.
Single shot diagnosis of ion channel dysfunction from assimilation of cell membrane dynamics

Many neurological diseases originate in the dysfunction of cellular ion channels. Their diagnosis presents a challenge especially when alterations in the complement of ion channels are a priori unknown. Current approaches based on voltage clamps lack the throughput necessary to identify the mutations causing changes in electrical activity. Here, we introduce a single-shot method for diagnosing changes in the complement of ion channels from changes in the electrical activity of a cell. We developed data assimilation (DA) to estimate the parameters of individual ion channels and from these parameters reconstruct the ionic currents of hippocampal CA1 neurons to within 11% of their actual value. DA correctly predicts which ionic current is altered and by how much after we blocked the BK, SK, A and HCN channels with selective antagonists of known potency. We anticipate our assay will transform the treatment of neurological disease through comprehensive diagnosis and drug screening.
The Strategy of Management of Mangrove Forests Area as a Sustainable Production Area in Coastal of Takalar Regency, South Sulawesi

A sustainable production forest is a forest that can produce economically valuable and sustainable products, meaning that these products are used for life today and in the future. Mangrove forests products and services consist of wood and non-timber. Such as wood and aquatic organisms living in it as a source of income and livelihood for the community around the mangrove forest area. The data collection techniques used were adjusted to the required data, including observation, documentation techniques, in-depth interviews, and focus group discussions. The number of samples used was 60 people. It was determined using a SWOT analysis to specify the management of mangrove forests strategy as a sustainable production forest area. Furthermore, internal (Internal Factor Analysis Strategic) and external (External Factor Analysis Strategic) factors are calculated. Based on this, the strategy taken is (1) developing the sustainable and economically valuable mangrove forest resources potential through cultivation and fishing that is environmentally friendly, (2) increasing the role of NGOs to increase knowledge and community awareness in mangrove management, (3) developing the potential of mangrove forests as an eco-tourisms area, (4) empowering communities through groups of fishermen/farmers to create a home-scale industry based on mangrove resources.
Application for Image Processing in a Receiver Operating Character-Based Testing Review

The image processing technology is currently developing quite rapidly. Implementation of technology based on artificial intelligence systems has entered various sectors and platforms, mostly in the health and agriculture sectors. There are challenges in developing image processing applications, especially on mobile platforms, including processing a large amount of data that involves millions of pixels that must be recognized. To measure the quality of the results of the process certainly requires a reliable framework. In testing the quality of training data in the database and case studies, many image processing techniques utilize the Receiver Operating Character (ROC) approach’s reliability. In some cases, this ROC approach is very different depending on the data model, output, and algorithm used. This study aims to review ROC’s use as an approach used in testing systems on developed mobile applications. The case studies used in this research are image processing in agriculture cases for pests and disease recognition in cocoa fruits and chili leaves, which is then implemented in a mobile application. Testing entities in the ROC approach are analyzed based on actual and predicted conditions. The measurement results show that the ROC approach is applied in both test cases. It can only produce accuracy measurements on the value of accuracy, sensitivity, and precision. The values on chili leaves are accuracy 60%, sensitivity 60%, and precision 100%. The accuracy is 66.7% for cacao fruit objects, sensitivity 66.7%, and precision 100%.
The benefits of prosocial power motivation in leadership: Action orientation fosters a win-win

Power motivation is considered a key component of successful leadership. Based on its dualistic nature, the need for power (nPower) can be expressed in a dominant or a prosocial manner. Whereas dominant motivation is associated with antisocial behaviors, prosocial motivation is characterized by more benevolent actions (e.g., helping, guiding). Prosocial enactment of the power motive has been linked to a wide range of beneficial outcomes, yet less has been investigated what determines a prosocial enactment of the power motive. According to Personality Systems Interactions (PSI) theory, action orientation (i.e., the ability to self-regulate affect) promotes prosocial enactment of the implicit power motive and initial findings within student samples verify this assumption. In the present study, we verified the role of action orientation as an antecedent for prosocial power enactment in a leadership sample (N = 383). Additionally, we found that leaders personally benefited from a prosocial enactment strategy. Results show that action orientation through prosocial power motivation leads to reduced power-related anxiety and, in turn, to greater leader well-being. The integration of motivation and self-regulation research reveals why leaders enact their power motive in a certain way and helps to understand how to establish a win-win situation for both followers and leaders.
Exploring the impact of prior spontaneous miscarriage on stress among pregnant women during the first trimester: an observational study

Background Spontaneous miscarriage (SM) is the most common complication of pregnancy. Its psychological repercussions are widely documented but few studies have investigated its effect on women’s experience of a subsequent pregnancy. Aim To evaluate the impact of prior SM on the level of stress experienced by pregnant women during the first trimester of pregnancy. Design and setting Cross-sectional, observational study, which was conducted between June and October 2021 in France. Method A self-report questionnaire was distributed to women in the first trimester of pregnancy. Stress was assessed using the Antenatal Perceived Stress Inventory to yield an overall score and a score for three dimensions ('medical and obstetric risks or fetal health'; 'psychosocial changes during pregnancy'; and the ‘prospect of childbirth'). Women with a history of prior SM and those without were compared. Results In total, 93 women were included; 63 without and 30 with a history of prior SM. Prior SM was not associated with the overall score. The score for the dimension 'medical and obstetric risks or fetal health' was significantly higher in women with prior SM (3.00±0.86 versus 2.34±0.80; β = 0.61 [95% confidence interval {CI} = 0.25 to 0.96]; P = 0.001). Prior SM was significantly associated with the items 'the baby’s health' (P = 0.048) and 'the echography' (P = 0.002). Conclusion This study shows a significant impact of prior SM on the level of stress of pregnant women during the first trimester, particularly relating to the medical and obstetric risks or fetal health, underlining the need for appropriate psychological support to be provided to women who experience SM.
Whole-Body Vibration Response Through the Upper Limbs Associated With Reaching Movements and Posture

Exposure of human operators to whole-body vibration may induce health risks, interfere with manual operation, and compromise tasks performed in a vehicle [Griffin 1990], [Okunribido et al 2007]. For operator’s safety and performance enhancement, numerous investigations have attempted to identify the characteristics of WBV responses of seated individuals. WBV responses are functions of vibration variables [Griffin 1990] and postures [Lim et al 2004], [Kim and Bernard 2008]. As opposed to earlier studies involving limited static postures, the present work analyzes human responses for a large range of arm postures including two elbow flexion constraints in reaching movements.
Superlinear convergence of a reduced BFGS method with piecewise line-search and update criterion

We show the q-superlinear convergence of a reduced BFGS method for equality constrained problems, using eventually only one constraint linearization per iteration. The local method is globalized either with a standard arc-search or when an update criterion is satisfied, with a piecewise line-search. The aim of the latter technique is to realize generalized Wolfe conditions, which allow the algorithm to maintain naturally the positive definiteness of the generated matrices. We show that if the sequence of iterates converges, the convergence is q-superlinear. No assumption is made on the speed of convergence of the sequence of iterates or on the boundedness of the sequence of generated matrices. The main difficulty is to show that the ideal step-size is accepted after finitely many steps.
Application of bivariate negative binomial regression model in analysing insurance count data

Abstract In this paper we analyse insurance claim frequency data using the bivariate negative binomial regression (BNBR) model. We use general insurance data on claims from simple third-party liability insurance and comprehensive insurance. We find that bivariate regression, with its capacity for modelling correlation between the two observed claim counts, provides both a superior fit and out-of-sample prediction compared with the more common practice of fitting univariate negative binomial regression models separately to each claim type. Noting the complexity of BNBR models and their potential for a large number of parameters, we explore the use of model shrinkage methodology, namely the least absolute shrinkage and selection operator (Lasso) and ridge regression. We find that models estimated using shrinkage methods outperform the ordinary likelihood-based models when being used to make predictions out-of-sample. We find that the Lasso performs better than ridge regression as a method of shrinkage.
FAVIS: Fast and versatile protocol for non-destructive metabarcoding of bulk insect samples

Insects are diverse and sustain essential ecosystem functions, yet remain understudied. Recent reports about declines in insect abundance and diversity have highlighted a pressing need for comprehensive large-scale monitoring. Metabarcoding (high-throughput bulk sequencing of marker gene amplicons) offers a cost-effective and relatively fast method for characterizing insect community samples. However, the methodology applied varies greatly among studies, thus complicating the design of large-scale and repeatable monitoring schemes. Here we describe a non-destructive metabarcoding protocol that is optimized for high-throughput processing of Malaise trap samples and other bulk insect samples. The protocol details the process from obtaining bulk samples up to submitting libraries for sequencing. It is divided into four sections: 1) Laboratory workspace preparation; 2) Sample processing—decanting ethanol, measuring the wet-weight biomass and the concentration of the preservative ethanol, performing non-destructive lysis and preserving the insect material for future work; 3) DNA extraction and purification; and 4) Library preparation and sequencing. The protocol relies on readily available reagents and materials. For steps that require expensive infrastructure, such as the DNA purification robots, we suggest alternative low-cost solutions. The use of this protocol yields a comprehensive assessment of the number of species present in a given sample, their relative read abundances and the overall insect biomass. To date, we have successfully applied the protocol to more than 7000 Malaise trap samples obtained from Sweden and Madagascar. We demonstrate the data yield from the protocol using a small subset of these samples.
United States Department of Agriculture nutrition assistance programs during the COVID-19 pandemic: A scoping review protocol

Objective The goal of this scoping review is to examine the published research on federal nutrition assistance programs administered by the United States (U.S.) Department of Agriculture during the COVID-19 pandemic, in the U.S., U.S. territories, and tribal nations. The review will identify the scope of the available research and provide research and policy recommendations. Introduction The COVID-19 pandemic made individuals more vulnerable to experiencing food insecurity. Federal nutrition assistance programs help to address food insecurity and have been rapidly adapting to meet food and nutrition needs among affected communities during the COVID-19 pandemic. It is important to understand the scope of the current research on this topic to help inform future research, practice, and policy recommendations. Inclusion criteria This review will include studies focused on federal nutrition assistance programs administered by the U.S. Department of Agriculture during the COVID-19 pandemic. The scoping review will consider all primary research designs. Methods Pubmed, CINHAL, Scopus, and Proquest’s Health Management databases will be used for the literature search. Only articles published in English since March 1, 2020 will be considered. Titles/abstracts followed by full-text articles will be reviewed to determine which articles meet the inclusion criteria and should be included in the review. Data will be extracted from each included article using a data extraction template in Covidence that will be developed by the study team. Data extracted will include information on key findings related to the review questions. At each step, two independent reviewers will be assigned to each article. Data will be summarized and presented in tables, charts, and narrative summary.
Does entrepreneurship education in China develop entrepreneurial intention? the role of self-efficacy and experience

Entrepreneurship education has attracted much attention in recent years. However, the relationship between entrepreneurship education and entrepreneurial intention has not achieved an agreement yet. To reconcile these conflicting conclusions, we explore the effect of entrepreneurship education on entrepreneurial intention from the content of the entrepreneurship education programs and different types of individuals who have participated in the program. Leveraging the self-efficacy theory and event system theory, we examine the mediation of entrepreneurial self-efficacy from five dimensions and the moderation of entrepreneurial experience. The sample of this study comprised 243 individuals who participated in entrepreneurship education in China (female = 40.3%, The majority of responders with an age range from 21 to 30 years). The results reveal that entrepreneurship education has a significantly positive influence on entrepreneurial intention (β = 0.331, p < 0.001). Entrepreneurial self-efficacies in searching (β = 0.382, p<0.001), planning (β = 0.249, p<0.001), and marshaling (β = 0.134, p<0.05) play mediating roles in the relationship between entrepreneurship education and entrepreneurial intention. We also find that entrepreneurial experience negatively moderates the relationship between entrepreneurship education and entrepreneurial intention (β = -0.212, p<0.05). The results have implications for entrepreneurship education scholars and policymakers in China.