Miracle-dz/UniFAR-FTUs huggingface dataset:
New Finite-Volume Relaxation Methods for the Third-Order Differential Equations

We propose a new method for numerical solution of the third-order differential equations. The key idea is to use relaxation approximation to transform the nonlinear third-order differential equation to a semilinear second-order differential system with a source term and a relaxation parameter. The relaxation system has linear characteristic variables and can be numerically solved without relying on Riemann problem solvers or linear iterations. A non-oscillatory finite volume method for the relaxation system is developed. The method is uniformly accurate for all relaxation rates. Numerical results are shown for some nonlinear problems such as the Korteweg-de Vires equation. Our method demonstrated the capability of accurately capturing soliton wave phenomena. AMS subject classifications: 5L30, 76M12, 35Q53
Retention of Customer Loyalty in Banking Sector Through Mining Techniques: A Review

Every citizen of the country has between associated with any one financial sector of their parent country. In that association, the financial institutions like banks play a vital role in customer service. The customer service may vary from bank to bank depends upon the customer needs. In order to provide the service in much better way the bank keeps track of their customers in a close observation. In most of the banking sectors customers are keeps minimum two account, one loan and another one savings respectively. The data mining techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. Customer retention is the most important factor to be considered in today’s aggressive business environment. The bankrupt is a significant problem in banking sector. Fraud prevention is a great challenge, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. Here almost all the banks need to service their customer with full loyalty. Since they are the assets. The classified data provides a wise decision making for the top officials. In this paper various research work done by different researchers are considered for analysis and make an outcome for further exploration.
A Review of Big Data Clustering Methods and Research Issues

: Data mining is a method for knowledge discovery from a dataset. The world today is moving toward data-driven in all ramifications, ranging from education, health care, security, customers’ management, smart city, etc. Unsupervised learning like clustering is the most big-data mining technique used for grouping large dataset when there is no prior information about the classes in the dataset. The use of the internet of things (wearable, sensors, RFID) and social networks has drastically increased data in the cyber-physical world resulting in what is called Big Data. With the increase in big data as a result of cloud computing, it has proliferated research on knowledge discovery on these avalanche of big data. Clustering is used to extract valuable hidden information from massive complex data. Clustering as unsupervised learning has an advantage over supervised learning when it comes to knowledge discovery in a huge dataset without a prior knowledge of the groups. In this review, we discussed big data mining techniques and narrowed it to clustering method. We also discussed different clustering approaches, and similarities measures used in clustering algorithms. Finally, we discussed the strength and weaknesses of clustering approaches and the research issues in clustering big data for information discovery.
Evaluation of the Structural Response of Reinforced Concrete Beams Produced with River Gravel as Coarse Aggregate in Building Construction

An increase in demand for concrete to meet global needs has been accompanied by an increase in global concerns due to an increase in demand for the non-renewable resources that are and comprise the constituents of concrete. To address these concerns and mitigate the impact of their depletion, researchers have investigated the intrinsic properties of a wide range of available materials and assessed their contribution when mixed with concrete. This paper presents the findings of an experimental study that was carried out to assess the structural response of concrete beams made with river gravel as coarse aggregate. Six reinforced beams (100 x 150 x 1100 mm) and six cubes were cast to investigate the specimens' flexural and compressive behavior. The failure modes, bending, and shear capacity were investigated in this study. According to the findings of the study, river gravel used as coarse aggregate in concrete has a roughly equivalent or slightly lower structural performance than conventional coarse aggregate, indicating the feasibility of river gravel as coarse aggregate for building construction.
On the way from invention to innovation: the role of applicant and inventor team characteristics

This paper investigates the impact of applicant and inventor team composition on patent commercialization in form of product creation. It outlines the importance of applicant and inventor team characteristics, i.e. specifically, size and internationality, on the speed of market authorization of a patent-related product and on the product quality. The analysis is performed for the European pharmaceutical industry. The product data is taken from the European Medicines Agency website for the period 2010-2019. Manual patentproduct concordance is established with the help of the Pat-INFORMED database from the World Intellectual Property Organization and the Health Canada database. The created dataset presents combined data on patent and product characteristics. Results from an accelerated failure time model show that larger applicant teams as well as the presence of international applicants and inventors decelerate the market authorization of patent-related products. Results of the probit analysis show that larger inventor teams lead to patents of higher quality.
Expressive Skinning Methods for 3D Character Animation

Real-time three-dimensional (3D) character animation has benefited greatly from advancements in skinning and surface deformation. While both fields offer tremendous techniques for controlling character shape over time, few methods address the needs of both. I propose such a method to enable greater artistic control and expressive potential. First, I introduce a skinning algorithm to address common artifacts and offer configurable shape behavior around joints. Second, I discuss embedding surface deformations in the skinning process. Both of these operate by adjusting scale vectors to provide the character’s final shape. These vectors run from attachment points located within the mesh volume to vertex positions. Smooth skinning is achievable by combining a rigid transformation on the attachment point and a blended transformation on the scale vector. Further adjustments to scale vectors can produce surface deformation. This representation permits a variety of surface deformation methods. To demonstrate the effectiveness of the approach, I have written a graphics processing unit (GPU)-accelerated implementation of the deformation method, a rendering pipeline built to support and compare various skinning methods, and an input system well-suited for authoring character animations. Future work includes furthering shape control in the skinning method, developing more expressive deformation tools, and incorporating virtual reality (VR) support to provide an embodied interface for 3D animation using my deformation methods.
Data-Based Support in the Development of Press Systems Using the Example of Sheet Metal Forming

The market pressure and the products of competitors are decreasing the duration of product life cycles. Besides that, special customer requirements and rising product complexity result in an increase of the effort and the costs of the development process. In order to minimize the effort of the development process as well as to handle the uncertainties which come along with the planning and selection of manufacturing systems a data-based configuration tool was developed for the use in the field of sheet metal forming. Based on the product data of existing press systems, the tool is able to predict the fundamental configuration of a newly developed press system. The utilization and functionality of the tool is illustrated on two exemplary press systems.
DESENVOLVIMENTO E CARACTERIZAÇÃO DE QUEIJO TIPO COTTAGE ADICIONADO DE Lactobacillus delbrueckii UFV H2b20 E DE INULINA

x 1. INTRODUCAO GERAL 1 2. REFERENCIAS BIBLIOGRAFICAS 3 CAPITULO 1 4 DESENVOLVIMENTO E ESTUDO DE PARÂMETROS QUALITATIVOS DE QUEIJO TIPO COTTAGE SIMBIOTICO 4 1. INTRODUCAO 4 2. MATERIAL E METODOS 13 2.1. Producao de cultura estoque de L. delbrueckii UFV 2b20 . 13 2.2. Producao do concentrado de celulas viaveis de L. delbrueckii UFV H2b20 14 2.3. Producao do queijo tipo Cottage simbiotico 14 2.4. Viabilidade de L. delbrueckii UFV H2b20 ao longo da vidade-prateleira do queijo Cottage simbiotico 17 2.5. Quantificacao da substância prebiotica inulina no queijo tipo Cottage simbiotico 17 2.6. Analises fisico-quimicas e microbiologicas do queijo 18 2.7. Avaliacao sensorial do queijo tipo Cottage simbiotico 19 2.8. Analises estatisticas 19
The Effect of Manipulating Task and Environment Constraints on Performance of Selected Fundamental Movement Skills

The aim of this study was to investigate the effect of task and environment manipulation on the performance of selected basic motor skills. For this purpose, 30 children aged 4 to 6 years were selected using the available sampling method. First, the pre-test of over hand throwing was performed using the accuracy of throwing and kicking, and then the participants were randomly divided into 3 groups: manipulation of task restraints, environment constraints, and control. According to the research plan, the intervention groups performed the program for 6 weeks and two 60-minute sessions each week. The task constraint group practiced according to the task ecological analysis method, the environment constraint manipulation group practiced in the based on enrichment of the environment and the control group had no activity. After the last training session, a post-test was performed and the results were recorded. The data were analyzed with analysis of covariance test and Bonferroni was used for paired comparisons. The results showed that there was a significant difference between the remaining mean scores of kicking and over hand throwing of the subjects in terms of group membership (experimental and control groups) (P <0.05). Also, the results of Bonferroni post hoc test showed that the groups of manipulation of task constraints and performance environment had better than the control group in the studied skills, but the difference between the two groups was not statistically significant (P <0.05). The finding generally support and provide an insight into the idea of the effect of constraints manipulation on performance. Therefore, when teaching and working with children, manipulation of task constraints and environment can be used to improve the performance of children's fundamental movement skills.
The Application of Compost Extract Enriched with Eggshell Flour on the Growth and Production of Sweet Corn Plants (Zea mays saccharata)

The aim of this study was to investigate the application of compost extract enriched with eggshell flour on the growth and production yield of sweet corn plants. This research was conducted from July to September 2021 at the Academic Farm of the Faculty of Agriculture, Tadulako University, Central Sulawesi Province, Indonesia. This study used a randomized block design with a factorial pattern. The first factor was the time of eggshell flour addition consisting of 5 treatments, i.e., W1 = 1 week after planting (WAP); W2 = 2 WAP; W3 = 3 WAP; W4 = 4 WAP; W5 = 5 WAP. The second factor was corn compost extract application consisting of 2 treatments, i.e., A1 = application on the soil; A2 = application on the plants. Therefore, 10 treatment combinations were obtained, and each treatment combination was repeated 3 times to obtain 30 experimental units. The results showed that there was no interaction effect between the application of corn compost extract and the time of eggshell flour addition in all measured parameters. The treatment that resulted in better growth and yield of sweet corn was the combination treatment of 1 WAP and application through the soil on all measured parameters, The time of eggshell flour addition of 1 WAP gave a better effect on the growth and yield of sweet corn plants on the parameters of plant height at 28 days after planting (DAP), stem circumference at 49 DAP, length and diameter of the corncob with husks, and weight of the corncob without husks, and Application of corn compost extract treatment through the soil gave a better effect on the growth of sweet corn plants on the parameters of plant height at 28 DAP, stem circumference at 42 and 49 DAP and number of leaves at 14 DAP.
Fire in A Crowded Theater: A Descriptive Investigation of the Emergence of Panic

More than 160 people were killed in a fire in the Beverly Hills Supper Club near Cincinnati, Ohio, in 1977, an incident that appeared to be a classic case of panic involving a breakdown of social order and ruthless competition for exits. This paper analyzes transcripts of police interviews with club patrons to describe flight from the show room in which most of the deaths occurred. The descriptive analysis reveals that escape from the building was initially orderly and non-competitive; however, when escape became urgent, competition for the remaining exit possibilities developed. Even then the behavior did not appear to be in total disregard of social order; social norms and structural ties continued to constrain behavior.
Caring about Sharing: User Perceptions of Multiparty Data Sharing

Data sharing between companies is typically regarded as one-size-ﬁts-all in practice and in research. For instance, the main source of information available to users about how a company shares their data is privacy policies. Privacy policies use ambiguous terms such as ‘third-parties’ and ‘partners’ with regard to who data is shared with. In the real-world, data sharing has more nuance than is captured by these over-arching terms. We investigate whether users perceive different data sharing scenarios differently through an online survey with scenarios that describe speciﬁc types of multiparty data sharing practices. We determine users’ perceptions when explicitly presented with how their data is shared, who it is shared with, and why. We show that users have preferences and that variations in acceptability exist which depend on the nature of the data sharing collaboration. Users caring about sharing, necessitates more transparent sharing practices and regulations.
Gait pattern detection for amputated prosthetic using fuzzy algorithm

Conventional gait rehabilitation treatment does not provide quantitative and graphical information on abnormal gait kinematics, and the match of the intervention strategy to the underlying clinical presentation may be limited by clinical expertise and experience. Amputated patient with prosthetic leg suffered with gait deviation due to variety causes commonly alignment and fitting problem. Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. The work included in this project focuses on developing a system to measure the angular displacement of human joint of lower part with patients having this problem and then applying gait phase detection using intelligent algorithm. The developed prototype has three inertial measurement units (IMU) sensor to measure and quantify body gait on thigh, shank and foot. The data from specific placement sensor on body part was evaluated and process in Arduino and MATLAB via serial communication. IMU provides the orientation of two axes and from this, it determined elevated position of each joint by using well established trigonometry technique in board to generate displacement angle during walking. The data acquired from the motion tests was displayed graphically through GUI MATLAB. A fuzzy inference system (FIS) was implementing to improve precision of the detection of gait phase from obtained gait trajectories. The prototype and FIS system showed satisfactory performance and has potential to emerge as a tool in diagnosing and predicting the pace of the disease and a possible feedback system for rehabilitation of prosthetic patients.
Runtime Detection of Executional Errors in Robot-Assisted Surgery

Despite significant developments in the design of surgical robots and automated techniques for objective evaluation of surgical skills, there are still challenges in ensuring safety in robot-assisted minimally-invasive surgery (RMIS). This paper presents a runtime monitoring system for the detection of executional errors during surgical tasks through the analysis of kinematic data. The proposed system incorporates dual Siamese neural networks and knowledge of surgical context, including surgical tasks and gestures, their distributional similarities, and common error modes, to learn the differences between normal and erroneous surgical trajectories from small training datasets. We evaluate the performance of the error detection using Siamese networks compared to single CNN and LSTM networks trained with different levels of contextual knowledge and training data, using the dry-lab demonstrations of the Suturing and Needle Passing tasks from the JIGSAWS dataset. Our results show that gesture specific task nonspecific Siamese networks obtain micro F1 scores of 0.94 (Siamese-CNN) and 0.95 (Siamese-LSTM), and perform better than single CNN (0.86) and LSTM (0.87) networks. These Siamese networks also outperform gesture nonspecific task specific Siamese-CNN and Siamese-LSTM models for Suturing and Needle Passing.
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

When training deep neural networks for classification tasks, an intriguing empirical phenomenon has been widely observed in the last-layer classifiers and features, where (i) the class means and the last-layer classifiers all collapse to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and (ii) cross-example within-class variability of last-layer activations collapses to zero. This phenomenon is called Neural Collapse (NC), which seems to take place regardless of the choice of loss functions. In this work, we justify NC under the mean squared error (MSE) loss, where recent empirical evidence shows that it performs comparably or even better than the de-facto cross-entropy loss. Under a simplified unconstrained feature model, we provide the first global landscape analysis for vanilla nonconvex MSE loss and show that the (only! ) global minimizers are neural collapse solutions, while all other critical points are strict saddles whose Hessian exhibit negative curvature directions. Furthermore, we justify the usage of rescaled MSE loss by probing the optimization landscape around the NC solutions, showing that the landscape can be improved by tuning the rescaling hyperparameters. Finally, our theoretical findings are experimentally verified on practical network architectures.
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance

In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system architecture that utilizes popular recommendation algorithms from the literature such as Collaborative Filtering, Content-based Filtering as well as various neural embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We showcase the applicability of our system architecture using two real-world use-cases, namely providing recommendations for the domains of (i) job marketplaces, and (ii) entrepreneurial start-up founding. We strongly believe that our experiences from both research- and industry-oriented settings should be of interest for practitioners in the field of real-time multi-domain recommender systems.
Evolving Curricula with Regret-Based Environment Design

It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces. By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of the paper is available at accelagent.github. io.
A modified elasto-dynamic model based static stiffness evaluation for a 3-PRS PKM

This paper proposes a modified elasto-dynamic model for a three-prismatic revolute spherical parallel kinematic machine, in which the flexibility of the prismatic revolute spherical limb structures are accounted in and modeled as a hollowed spatial beam with nonuniform cross section. The governing equations are derived through substructure synthesis and finite element formulation. The stiffness matrix of the platform is then extracted from global stiffness matrix and its characteristics at typical configurations are calculated to reveal complicated coupling effects of diagonal and nondiagonal elements of the stiffness matrix. The concept of principle stiffness and coupled stiffness are proposed and their distributions over the workspace are predicted with numerical simulations in a quick manner. Then the stiffness of the platform is physically interpreted as a kinematically unconstrained rigid body suspended by six screw springs with equivalent spring constants and pitches through eigenscrew decomposition. The distributions of screw spring constants over the workspace are then plotted to demonstrate a duality property. At last, the effects of some design variables such as structural and dimensional parameters on system rigidity performance are investigated with the purpose of providing useful information for the structural design and performance enhancement of the parallel kinematic machine.