Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence Maximization within Period under Different Advance Publicity

As social network platforms are becoming more and more popular, many activities spread event information through these platforms so that the number of participants during the event can reach the maximum. Note that as time goes by, the event information may be forgotten and repeatedly seen by the users. So the participation intention of a user for the event will change between active and inactive. Therefore, we formulate an influence maximization problem concerning the previous publicity and volatility of user behaviors given a specific period, and propose a fluctuation-aware independent-cascade model to simulate the influence diffusion. Upon the diffusion model, we explored the effects of different advance publicities for influence maximization.
Distributed Stochastic Bandits with Hidden Contexts

We study distributed stochastic multi-arm contextual bandit with unknown contexts, in which M agents work collaboratively to choose optimal actions under the coordination of a central server to minimize the total regret. In our model, an adversary chooses a distribution on the set of possible contexts and the agents observe only the context distribution and the exact context is unknown to the agents. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism as in weather forecasting or stock market prediction. Our goal is to develop a distributed algorithm that selects a sequence of optimal actions to maximize the cumulative reward. By performing a feature vector transformation and by leveraging the UCB algorithm, we propose a UCB algorithm for stochastic bandits with context distribution. We provide regret and communication bounds for linearly parametrized reward functions. Finally, we validated the performance of our algorithm using simulations on synthetic data and real world Movielens dataset.
API Traffic Anomaly Detection in Microservice Architecture

In the current Digital Age, data is an important asset that is constantly targeted in cyberattacks. Attackers make use of vulnerabilities in the application design to perform data theft. Therefore, there is a need to implement an intrusion detection mechanism that is specific to the application architecture. The Microservices Architecture is predominantly used by organizations to develop their software applications. This application design architecture is a group of individual services that interact through Application Programming Interfaces (APIs). As the number of API endpoints increases, there is an increase in the attack surface for hackers to exploit the application. The activity at these endpoints and API calls can be monitored to check for anomalies, which indicates abnormal behaviour. An API call refers to a request made to an API endpoint. Multiple API calls among the services generate API traffic in the application. This traffic can be analyzed for detecting unusual behaviour. In this paper, a machine-learning based technique, API Traffic Anomaly Detection (API-TAD), that detects anomalies in API traffic at two levels – a generalized level, and an application-specific level is proposed. This makes it a more efficient and accurate anomaly detection, not only in the network layer of the OSI model, but also in the application layer.
Towards Generalizing Machine Learning Models to Detect Command and Control Attack Traffic

Identifying compromised hosts from network traffic traces has become challenging because benign and malicious traffic is encrypted, and both use the same protocols and ports. Machine learning-based anomaly detection models have been proposed to address this challenge by classifying malicious traffic based on network flow features learned from historical patterns. Previous work has shown that such models successfully identify compromised hosts in the same network environment in which they were trained. However, cyber incident response teams often have to look for intrusions in foreign networks, and we have found that learned models often fail to generalize to different network conditions. In this paper, we analyse the root cause of this problem using five network traces collected from different years and teams of Locked Shields, the world’s largest live-fire cyber defence exercise. We then explore techniques to make machine learning models generalize better to unknown network environments and evaluate their accuracy.
Should I Regret More? A Regret-based Multi-round Learning with Behavioral Human Players in a Multi-Target Security Game

We consider a security game in a setting consisting of an attacker and multiple defenders. Both defenders and attacker have limited budgets such that they can select subsets of the nodes to defend and attack, respectively. Each node has a certain gain and loss to the attacker and the defender, respectively. Each node has also a probability of being successfully compromised, which is a function of the investments in that node by the defenders. For such games, we investigate the impacts of behavioral probability weighting on the strategies of the players; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We explore the multi-round game under regret-matching and prove the convergence to correlated equilibrium in our game. We then show the effect of the behavioral bias on the convergence of the regret-matching learning. Finally, we show via numerical experiments that the regret-matching algorithm is effective for both behavioral and rational defenders (i.e., it helps both classes of defenders to converge to better investments over time)
Controlling the Dynamics and Stabilization of the Altitude and Attitude of An Underactuated Tri-rotor UAV

In this article, an Adaptive Sliding Mode Control (ASMC) method is proposed for controlling the dynamics and stabilization of the altitude and attitude of an underactuated tri-rotor Unmanned Aerial Vehicle (UAV). The model of UAV is highly nonlinear and complex in nature with the six degree of freedom (6-DOF) and subdivided in to fully actuated and underactuated subsystems which is controlled by the sliding surface of ASMC. Furthermore, the stability of a system is validated using Lyapunov stability theory. Simulation results showed that the proposed control scheme enabled the tri-rotor UAV to fly effectively when compared with Adaptive Robust Backstepping (ARB) control. Lastly, an external disturbance is added in the system to check the effectiveness of the proposed scheme in which a wind disturbance test is done with the proposed controller that shows the robustness, fast error convergence and good transient behavior.
Prevalence of sarcopenia in the elderly adult population in Costa Rica

Aim: Sarcopenia is the progressive loss of mass and skeletal muscle strength. Its prevalence varies in the different reviews carried out, this by the different measurement methods. Costa Rica does not have data on its prevalence . The aim of the study was to determine the prevalence of sarcopenia and associated factors in the elderly population included in the Costa Rican Study of Longevity and Healthy Ageing. Methods: A subgroup of 2516 people were analyzed, using the European Working Group on Sarcopenia in Elderly People diagnostic criteria that include low muscle mass and low muscle strength. Muscle mass was assessed using calf circumference < 31 cm and grip strength using a dynamometer, was low strength if < 27 kg in men or < 16 kg in women. Results: The prevalence of sarcopenia was 10.26%. There was an increasing trend of sarcopenia by age group, it was more prevalent in women. Some characteristics of Costa Ricans with sarcopenia were that most of them had less income, lived accompanied, were perceived as unhealthy, and had incomplete primary education. The weight, brachial, abdominal, and calf circumference were lower in people with sarcopenia. Sarcopenia was positively associated with age (OR 3.164; CI 1.962-5.101), education (OR 4.264; CI 1.547-11.753), perceived as unhealthy (OR 1.691; CI 1.133-2.525), the antecedent of an ischemic vascular event (OR 3.221; CI 1.566-6.628), arthritis (OR 1.648; CI 1.044-2.601), and falls (OR 1.676; CI 1.143-2.458). Conclusions: The prevalence of sarcopenia was 10.26%, similar to other countries in Latin America. It increases with age.
PADAYON SINING: A CELEBRATION OF THE ENDURING VALUE OF THE HUMANITIES

Filipino cuisine reflects the rich cultural heritage of the Filipinos. It is an integral part of Filipino culture. However, due to many influences, the taste for Filipino cuisine has begun to be replaced by the preference for foreign food. In many social gatherings, in restaurants, as well as in many households, Filipino food is being demoted to a position of inferiority. Likewise, the way Filipino food is presently prepared has been modified to cater to the foreign taste. We argue that the colonization of the Filipino palate indicates a colonization of a lifeworld. Many Filipinos have lost their understanding of their own culture as they have become attracted to foreign ways. This paper intends to present a Habermasian philosophical discourse of the colonization of the Filipino food as a factor undermining Filipino culture. The first part of the paper will be a brief discussion of Filipino food as a reflection of authentic Filipino culture and how the Filipino cuisine is slowly relegated into the background by the foreign cuisine. The second part of the paper involves a philosophical analysis, utilizing the Habermasian framework, of the effects of the alienation of the Filipino from his or her own lifeworld. In this section, an exposition of how cultural identities are dissolved in the interplay of cultures will be discussed. The preservation of the Filipino lifeworld is crucial to every Filipino. It will determine his or her attitude in confronting personal, national, and global issues now and in the future. While it is true that culture is continuously evolving, we argue that its essentials must remain intact in order to preserve the vitality of the Filipino lifeworld.
Learning CNNs from weakly annotated images

Age prediction from facial images is a long standing problem. Various automated facial recognition applications and models have been developed and researched to tackle it. State of the art models for age prediction and face recognition are based on convolutional neural network. The main weakness of such models is the requirement of large annotated datasets. Many state of the art models are learned from automatically annotated IMDB database containing over 500k images. The major weakness of the IMDB database is a large amount of noisy labels produced by the used annotation algorithm. The thesis aims to develop an annotation algorithm which produces a cleaner annotation of the IMDB database. The proposed algorithm builds identity models of the actors appearing in the database and uses the identity models to assign age to the correct faces, and as such accurately annotate the data. We present the proposed annotation method for building the identity models and the fine tuning of the necessary parameters surrounding the annotation. We evaluate the model on two manually independent datasets, and compute two different types of errors: overlooked and misclassified samples. For the purpose of manual annotation we develop a tool which is used for efficient collection of the labels from the data.
Fuzzy Membership Function Based on Structural Information of Data

Membership function based on affinity among samples considers not only the distance between samples and its center, but also the relationship among samples. However it ignores the structural distribution of samples in different classes and the influences of between-class distance on membership. Therefore, this paper proposed an improved membership function, named fuzzy membership function based on structure information of data, which incorporates structural distribution and between-class distance into the calculation of membership. The main idea is to find the optimal hyper-sphere for the positive and negative samples respectively by support vector data description method and give a reduction ratio for samples out of sphere according to the between-class distance. Numerical experiments demonstrate that the proposed membership function is consistent with the practical application, and it can significantly improve the classification performance of fuzzy support vector machine.
Nano-Clay Platelet Integration for Enhanced Bending Performance of Concrete Beams Resting on Elastic Foundation: An Analytical Investigation

Acknowledging the growing impact of nanotechnologies across various fields, this engaging research paper focuses on harnessing the potential of nano-sized materials as enhancers for concretes. The paper emphasizes the strategic integration of these entities to comprehensively improve the strength and performance of concrete matrixes. To achieve this, an analytical study is conducted to investigate the static behavior of concrete beams infused with different types of clay nano-platelets (NC’s), employing quasi-3D beam theory. The study leverages the effective Eshelby’s homogenization model to determine the equivalent elastic characteristics of the nanocomposite. The intricate interactions of the soil medium are captured through the use of a Winkler–Pasternak elastic foundation. By employing virtual work principles, the study derives equations of motion and proposes analytical solutions based on Navier’s theory to unravel the equilibrium equations of simply supported concrete beams. The results shed light on influential factors, such as the clay nano-platelet type, volume percentage, geometric parameters, and soil medium, providing insights into the static behavior of the beams. Moreover, this research presents previously unreported referential results, highlighting the potential of clay nano-platelets as reinforcements for enhancing structural mechanical resistance.
The Development of Germination Tests and Breaking Dormancy Techniques of Bitter Seeds (Andrographis paniculata (Burm. f.) Wall. ex Nees)

Bitter seed (Andrographis paniculata (Burm. f.) Wall. ex Nees) is a medicinal species used in phyto pharmacy and known as ‘the king of bitterness’. Bitter seeds contain andrographolide, a diterpenoid lactone which is widely used in Indian medicine for snake bites and for the treatment of hepatitis. The research was conducted at the Seed Management Unit experimental field of the Indonesian Spice and Medicinal Plants Research Institute (IMACRI) and at the Seed Quality Testing Laboratory of IPB University, Indonesia, from January until May 2021. The objectives of the study is to determine the best germination test and effective breaking dormancy techniques to increase the germination percentage of bitter seeds. The study was arranged in a completely randomized design with two-factors. The first factor was seed treatments, i.e., control/untreated, chemical scarification with 5% H2SO4 for 10 minutes, and 5% H2SO4 followed by a 100 ppm GA3 soaking treatment. The second factor conducted was germination test methods according to International Seed Testing Association, i.e., germinating the seeds on the top of paper and in pleated paper. Our study demonstrated that seed treatment with H2SO4 can break seed dormancy and speed up seed germination. Bitter seed germination on the pleated paper was higher than with the top-of-paper method. Bitter seed germination with the top-of-paper method was at 65 DAS and the final germination was at 80 days after sowing (DAS), and the final germination was 55.2%. With the pleated paper method, the seeds germinated at 30 DAS and the final germination was at 70 DAS, and the final the germination of 59.2%.
Understanding the evolution of channeling and fracturing in tight rocks due to fast fluid flow

Fluid induced brittle deformation of porous medium is a phenomenon commonly present in everyday life. From an espresso machine to volcanoes it is possible to see traces of this phenomenon. In a rectangular Hele‐Shaw cell we inject air into a loose porous medium. Then, we monitor this system using optical imaging using a high speed camera (1000 fps) and 4 high frequency resolution accelerometers. Using the numerical and experimental acoustic emissions, different sources of the recorded signal (vibrations due to air, changes in the effective stress due to fluid‐solid interactions) are analyzed. We found that, the peaks in the low frequency range (f < 20 kHz) diminishes while the medium fractures. Furthermore, we propose a new signal localization method based on energy amplitude attenuation and inversed source amplitude comparison. Furthermore, using optical and acoustic datasets and numerical simulations, the mechanics leading Type‐A and Type‐B earthquakes are explained.
The oblique chiral nematic phase in calamitic bimesogens

ABSTRACT The discovery of the oblique chiral (or, the twist-bend, NTB) nematic phase predicted for bent-core mesogens has engendered much interest due to its unique structure and physical properties, and the possibility of use in the next generation of fast electro-optic technology. Bimesogenic calamitic as well as bent-core mesogens are found to form the NTB phase. Here, we report direct measurements of the temperature dependence of the conical tilt and the evidence of volcano-like orientational distribution of molecules in the NTB phase. Optical and x-ray scattering investigations of two single-component calamitic bimesogens and their mixtures show that, while the Maier–Saupe orientational distribution function (ODF) is valid for the higher temperature nematic phase, a generalised expansion in terms of even Legendre functions is needed for the NTB phase. Temperature dependence of the ODFs and the order parameters 〈P2(cosβ)〉, 〈P4(cosβ)〉, and 〈P6(cosβ)〉 has been measured in both phases. The parameters 〈P2(cosβ)〉 and 〈P4(cosβ)〉 increase/decrease in the N/NTB phase with decreasing temperature, while 〈P6(cosβ)〉 remains vanishingly small for all samples. The value of 〈P4(cosβ)〉 becomes negative in the NTB phase confirming a conical distribution of molecules as they follow a helical trajectory keeping the local director tilted at an angle α wrt the macroscopic director. The heliconical tilt calculated from ODFs, exhibits a power law behaviour with temperature, vanishing at the transition to the N phase. Graphical Abstract
Mesures associées à une forme de Dirichlet. Applications

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An Evolutionary Account of Guilt?

Grant Ramsey and Michael Deem argue that appreciating the role that empathy plays in posttransgression guilt leads to a more promising account of the emotion’s evolutionary origins. But because their proposal fails to adequately distinguish guilt from shame, we cannot say which of the two emotions we are actually getting an evolutionary account of. Moreover, a closer look at the details suggests both that empathy may be more relevant for our understanding of shame’s evolutionary origins than guilt’s, and that guilt is unlikely to be an adaptation.
TOPOLOGICAL DATA ANALYSIS OF COMMUTING PAIRS OF SU(2)

Topology is a branch of mathematics that studies the properties of objects that remain unchanged when the object is stretched, bent, or twisted. In recent years, topological methods have become increasingly popular in data analysis, as they provide a powerful tool for extracting meaningful information from complex datasets. One key concept in topology is homology, which measures the number of holes in an object of a given dimension. Homology provides a way to distinguish different shapes and structures and has been used to analyze a wide range of datasets, from images and videos to gene expression data and networks. Topological Data Analysis (TDA) is a field that uses algebraic topology to analyze complex data sets. One of the main techniques in TDA is Persistence Homology, which assigns a measure of persistence to topological features in a data set. In this project, we apply Persistence Homology to a data set of points of commuting pairs of matrices in the Lie group SU(2). We used the Ripser software to compute the persistence homology of our data set. Ripser is a C++ program that computes persistence homology using the Vietoris-Rips filtration. Our method uses a combination of topological and algebraic techniques to construct a simplicial complex from a point cloud and then computes the persistent homology of this complex to identify the topological features of the space. Our results show that there is a connected component in the space of commuting pairs of matrices in SU(2), but no higher-dimensional features such as loops. We compare our results with previous work in the field and identify several limitations and challenges that need to be addressed in future research.
Psychology Is a Property of Persons, Not Averages or Distributions: Confronting the Group-to-Person Generalizability Problem in Experimental Psychology

When experimental psychologists make a claim (e.g., “Participants judged X as morally worse than Y”), how many participants are represented? Such claims are often based exclusively on group-level analyses; here, psychologists often fail to report or perhaps even investigate how many participants judged X as morally worse than Y. More troubling, group-level analyses do not necessarily generalize to the person level: “the group-to-person generalizability problem. ” We first argue for the necessity of designing experiments that allow investigation of whether claims represent most participants. Second, we report findings that in a survey of researchers (and laypeople), most interpret claims based on group-level effects as being intended to represent most participants in a study. Most believe this ought to be the case if a claim is used to support a general, person-level psychological theory. Third, building on prior approaches, we document claims in the experimental-psychology literature, derived from sets of typical group-level analyses, that describe only a (sometimes tiny) minority of participants. Fourth, we reason through an example from our own research to illustrate this group-to-person generalizability problem. In addition, we demonstrate how claims from sets of simulated group-level effects can emerge without a single participant’s responses matching these patterns. Fifth, we conduct four experiments that rule out several methodology-based noise explanations of the problem. Finally, we propose a set of simple and flexible options to help researchers confront the group-to-person generalizability problem in their own work.