Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of an Adaptive Learning Chatbot on Students' Learning Outcomes Based on Learning Styles

Intelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommending the most appropriate learning objects for learners has always been a challenge in the field of e-learning. This challenge has driven educators and researchers to implement new ideas to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). In this paper, we propose and develop a new personalization approach for recommendation that implements the adaptation of learning objects according to the learners’ learning style mainly focused on the use of a chatbot, named LearningPartnerBot, which will be integrated into the Moodle platform. We use the Felder-Silverman Learning Styles Model to determine learners’ learning styles in order to recommend learning objects, and also to overcome the cold start problem. A chatbot is an automated communication tool that attempts to imitate a conversation by detecting the intentions of its user. The proposed LearningPartnerBot should be able to answer learners’ questions in real time and provide a relevant set of suggestions according to their needs.
A numerical method for shape finding of cable structures

A cable element has been developed in our papers for flexible cable structures with pulley. This element is formulated by modified functional of variational principle and has the same features as usual finite element based on the displacement method in which displacements are unknown variables. In this paper some functions are appended for shape finding of many cable structures such as cable net, suspension bridge and cable stayed bridge. Some numerical examples are to show the accuracy and efficiency of the proposed numerical method.
Scheduled smooth MIMO robust control of aircraft verified through blade element SIL testing

This paper demonstrates a multi-input multi-output (MIMO) robust control approach where multiple scheduled designs are merged to produce a smooth control law. The design is verified using software-in-the-loop (SIL) testing based on blade element theory (BET) for highly realistic flight simulations. An inner-loop attitude controller balances performance and robustness, achieving a fast response time, low overshoot, good noise rejection and minimal lateral–longitudinal coupling. The controllers are formed at several predetermined grid points so the design covers a wide flight envelope. Blade element SIL testing shows that the flight control system preserves stable flight and follows the references well, even under tough weather conditions. The proposed strategy is also compared with a classical autopilot design procedure and is seen to be superior.
Starbursts and active nuclei in galaxies

Starbursts and active galactic nuclei (AGN) are predominantly driven by galaxy mergers, i.e., the collision of two galaxies, but isolated galaxies, e.g., our Milky Way, can also be subject to these phenomena. Therefore this thesis is divided into two parts: in partwe investigate starbursts and AGN due to galaxy mergers and in part II we examine the Galactic Center (GC), i.e., the center of our Milky Way. In part I we will focus in particular on the timing of starbursts and AGN activity during a merger event. Recent observations indicate a time lag between these two phenomena in the sense that first the merger produces a starburst, followed by, after a time span of 200-300 million years, an episode of AGN activity. To explain this time lag we develop a scenario where the galaxies’ gas, after having formed stars, first forms an accretion disk around the central black hole due to its angular momentum. The gas then loses its angular momentum due to viscous forces and is finally accreted by the black hole, causing the galactic nucleus to become active. Thus the observed time lag is related to the viscous timescale the gas needs, after having formed stars, to move through the accretion disk before reaching the black hole. We develop a new subgrid model to account for the gas forming an accretion disk and for the subsequent evolution of this disk. Our numerical simulations of galaxy mergers reproduce the observed time lag between starburst and AGN activity and fulfill further observational constraints, like the well known correlation between black hole mass and the stellar velocity dispersion of the host galaxy. In part II we will investigate the center of the Milky Way. The GC hosts a black hole that is surrounded by a gaseous disk, the so-called circumnuclear disk (CND). This disk has an inner cavity with a radius of 1 parsec which is populated by a stellar cluster that launches a strong spherical wind. The GC is currently in a quiescent state where no significant star formation or black hole accretion is taking place, but there is evidence that the GC experienced a starburst and AGN activity several million years ago. We will perform magnetohydrodynamical simulations of the CND to investigate the interaction of the stellar cluster’s wind with the CND. It has previously been argued that this wind is responsible for the quiescent state of the GC by pushing the disk’s gas outwards and thus preventing accretion of gas onto the black hole. Our results disprove this scenario, invoking only the wind actually leads to a collapse of the inner cavity within a short period of time. However, including the effects of magnetic fields, which are observed in the CND, stabilizes the inner cavity against collapse. Thus our results explain why the GC is currently in a quiescent state. Furthermore we discuss whether the fading of the stellar winds will subsequently lead to the accretion of gas by the black hole, which in turn could trigger an episode of star formation and AGN activity.
Deep Learning for Fake News Detection: Literature Review

The use of Deep Learning (DL) for identifying false or misleading information, known as fake news, is a growing area of research. Deep learning, a form of machine learning that utilizes algorithms to learn from large data sets, has shown promise in detecting fake news. The spread of fake news can cause significant harm to society economically, politically, and socially, and it has become increasingly important to find ways to detect and stop its spread. This paper examines current studies that use deep learning methods, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), as well as the multi-model approach, to detect fake news. It also looks at the use of word embedding models to convert text to vector representations and the datasets used for training models. Furthermore, the paper discusses the use of the attention mechanism in conjunction with deep learning to process sequential data.
Exploring the Successions in Microbial Community and Flavor of Daqu during Fermentation Produced by Different Pressing Patterns

Daqu can be divided into artificially pressed daqu (A-Daqu) and mechanically pressed daqu (M-Daqu) based on pressing patterns. Here, we compared the discrepancies in physicochemical properties, volatile metabolites, and microbiota features between A-Daqu and M-Daqu during fermentation and further investigated the factors causing those differences. A-Daqu microbiota was characterized by six genera (e.g., Bacillus and Thermoactinomyces), while five genera (e.g., Bacillus and Thermomyces) dominated in M-Daqu. The flavor compounds analysis revealed that no obvious difference was observed in the type of esters between the two types of daqu, and M-Daqu was enriched with more alcohols. The factors related to differences between the two types of daqu were five genera (e.g., Hyphopichia). The functional prediction of microbial communities revealed that the functional discrepancies between the two types of daqu were mainly related to ethanol metabolism and 2,3-butanediol metabolism. This study provided a theoretical basis for understanding the heterogeneity of daqu due to the different pressing patterns.
The Influence of Health Education on Breast Self-Examination with Video Media on Knowledge and Attitudes of Female Students at the Institut Kesehatan Immanuel Bandung

Breast cancer is the most common type of cancer in Indonesia and one of the main causes of cancer mortality, according to GLOBOCON (2020) data. The purpose of this study is to evaluate the knowledge and attitudes of female students at the Immanuel Health Institute in Bandung after receiving BSE health education through video media. Pretest-posttest design with one group is used in this pre-experimental style of study. Utilizing a proportionate stratified random sampling procedure, the sample consists of 68 female students. The Wilcoxon signed rank test was utilized during the analysis process. The results of the study obtained that the majority of respondents' knowledge before being given education was less (39.7%) after being given education, most of them were good (72.1%). The attitude of the respondents before being given the majority of negative education was (54. 4%) after being given the majority of positive education (75.0%). The results of the Wilcoxon statistical test showed knowledge p-value 0.000 (p<0.05) and attitude p-value 0.000 (p<0.05). It can be concluded that there is an effect of providing health education on breast self-examination with video media on the knowledge and attitudes of students at the Institut Kesehatan Immanuel, Bandung. Suggestions for female students to routinely do BSE every month after menstruation.
Style Adaptation Based On Image Processing Methods Using Cyclegan

: Cycle-Consistent Generative Adversarial Networks (CycleGANs) are able to provide a highly under-constrained mapping between input and output data samples, i.e., source and target data domain, in cases when the aligned dataset is unavailable, in an unsupervised training fashion, using cycle-consistency loss mechanisms. On the other hand, most image-to-image and speech-to-speech translation tasks use the aligned, i.e., paired input-output training datasets. A large amount of data is necessary to train such architectures, while one of the domains could be scarce. Several possible improvements to the original CycleGAN architecture are analysed in this paper for the cases when only a small percentage of training samples are aligned among source and target data domains. A semi-supervised approach is proposed to achieve better translation accuracy and prevent overfitting of the scarce data domain discriminator during initial training iterations. The training database is augmented by adding samples generated by inverse CycleGAN mappings after several training epochs (when the network is sufficiently trained) into the training pool of the discriminator of scarce, i.e., reduced data domain. An additional optimization constraint is also proposed, aligning probability distributions of feature maps belonging to the same-depth neural network layers of direct GAN encoder and inverse GAN decoder, to reinforce resemblance among object representations in various data domains. Significantly better performances are obtained using proposed improvements in both image-to-image and speech-to-speech translation tasks, by observing standard qualitative and quantitative measures, in comparison to the baseline CycleGAN training approach.
Extreme Early Image Recognition Using Event-Based Vision

While deep learning algorithms have advanced to a great extent, they are all designed for frame-based imagers that capture images at a high frame rate, which leads to a high storage requirement, heavy computations, and very high power consumption. Unlike frame-based imagers, event-based imagers output asynchronous pixel events without the need for global exposure time, therefore lowering both power consumption and latency. In this paper, we propose an innovative image recognition technique that operates on image events rather than frame-based data, paving the way for a new paradigm of recognizing objects prior to image acquisition. To the best of our knowledge, this is the first time such a concept is introduced featuring not only extreme early image recognition but also reduced computational overhead, storage requirement, and power consumption. Our collected event-based dataset using CeleX imager and five public event-based datasets are used to prove this concept, and the testing metrics reflect how early the neural network (NN) detects an image before the full-frame image is captured. It is demonstrated that, on average for all the datasets, the proposed technique recognizes an image 38.7 ms before the first perfect event and 603.4 ms before the last event is received, which is a reduction of 34% and 69% of the time needed, respectively. Further, less processing is required as the image is recognized 9460 events earlier, which is 37% less than waiting for the first perfectly recognized image. An enhanced NN method is also introduced to reduce this time.
COARSE GRAINED SIMULATIONS AND TURBULENT MIXING

Mixing of materials by the small scales of turbulent motion is a critical element of many flow systems of interest in engineering, geophysics and astrophysics. Numerical simulation plays a crucial role and turbulent mixing predictability is a major concern. Small-scale resolution requirements focus typically on those of continuum fluid mechanics described by the NavierStokes equations; different requirements are involved depending on the regime considered and on the relative importance of coupled physics such as multi-species diffusion and combustion as determined by Knudsen, Reynolds, Schmidt, Damkohler, and other characteristic non-dimensional numbers. Direct numerical simulation (DNS) resolving all relevant physical space/time scales, is prohibitively expensive in the foreseeable future for most practical flows and regimes of interest at moderate-to-high Re. On the other end of the simulation spectrum are the Reynolds-Averaged Navier-Stokes (RANS) approaches ‐ which focus on statistical moments for an ensemble of realizations and model the turbulent effects. In coarse grained simulation (CGS) large energy containing structures are resolved, smaller structures are spatially filtered out, and unresolved subgrid scale (SGS) effects are modeled; this includes classical LES strategies [1] with explicit closure SGS models, and implicit LES (ILES) [2], relying on SGS modeling implicitly provided by physics capturing numerical algorithms. The CGS strategy of separating resolved / unresolved physics constitutes the viable intermediate approach between DNS and RANS to address practical geometries and multiphysics. Complex turbulent flow applications unavoidably involve under-resolved simulations, and robustness of CGS predictions is a frequently unsettled issue. The validity of the scale separation assumptions in CGS needs to be carefully tested when potentially important SGS flow physics is involved, specifically, for turbulent wall bounded flows, and for turbulent material mixing ‐ the focus area of the proposed mini-symposia. We examine our understanding of coarse-grained turbulent mixing, its theoretical basis, verification, validation, and predictability aspects, and progress addressing difficult open issues in complex non-equilibrium applications involving single- as well as multi-phase turbulent flow.
How does Digital Trust Make Up for the Digital Divide in Elderly Care Services

Under the double impact of aging and the digital divide, China is facing a severe pension problem. Promoting smart pension services can help solve this problem. This paper mainly discusses how to promote the use intention of intelligent elderly care service through digital trust. In this study, 403 valid questionnaires were obtained through questionnaire survey. The results showed that respondents' perceived ease of use of digital tools and applications significantly affected their digital trust and willingness to use them. However, considering digital trust, digital risk has no significant effect on willingness to use. This study suggests that strengthening the digital knowledge and skills of the elderly and strengthening their trust in smart elderly care services can help improve their willingness to use, and resolve the dual impact of aging and the digital divide.
On Annual Cycle of Monogenean Parasites Infestation in Freshwater Fish Pangasius pangasius

Annual cycle on the infestation of monogenean parasites found in fresh water fish Pangasius pangasius is statistically analysed and presented in this communication. During routine course of investigations for the presence of helminth parasites harbouring freshwater fishes found in Muzaffarnagar, annual data was collected for these parasites. Results are well presented in the form of tables. Statistical analysis of the data is carried for density, intensity, rate of infestation and the same has been correlated for annual cycle
Cartesian constrained time-optimal point-to-point motion planning for robots: the waiter problem

Time-optimal point-to-point motion is of significant importance for maximizing the productivity of robot systems. This type of motion planning for robots is however a complex problem and is therefore often solved in two phases. First, a high level planner determines a geometric path ignoring the system dynamics but taking into account geometric path constraints. Second, an optimal trajectory along the geometric path is determined taking into account system dynamics and limitations. Since the dynamics along a geometric path can be described in terms of a scalar path coordinate s and its time derivatives [1], the decoupled approach simplifies the motion planning problem to great extent. In recent work it was shown for a simplified robotic manipulator that the path following problem with Cartesian acceleration constraints can be cast as a convex optimization problem [2], which allows for the highly efficient computation of the global optimum.
First steps toward formal controller synthesis for bipedal robots

Bipedal robots are prime examples of complex cyber-physical systems (CPS). They exhibit many of the features that make the design and verification of CPS so difficult: hybrid dynamics, large continuous dynamics in each mode (e.g., 10 or more state variables), and nontrivial specifications involving nonlinear constraints on the state variables. In this paper, we propose a two-step approach to formally synthesize control software for bipedal robots so as to enforce specifications by design and thereby generate physically realizable stable walking. In the first step, we design outputs and classical controllers driving these outputs to zero. The resulting controlled system evolves on a lower dimensional manifold and is described by the hybrid zero dynamics governing the remaining degrees of freedom. In the second step, we construct an abstraction of the hybrid zero dynamics that is used to synthesize a controller enforcing the desired specifications to be satisfied on the full order model. Our two step approach is a systematic way to mitigate the curse of dimensionality that hampers the applicability of formal synthesis techniques to complex CPS. Our results are illustrated with simulations showing how the synthesized controller enforces all the desired specifications and offers improved performance with respect to a controller that was utilized to obtain walking experimentally on the bipedal robot AMBER 2.
Print-Camera Resistant Image Watermarking With Deep Noise Simulation and Constrained Learning

In this article, an effective print-camera (P-C) resistant image watermarking scheme is proposed. To achieve watermark robustness, most of existing works try to simulate P-C noise by a sophisticated math model. However, the diversity of P-C noises in the real world is ignored, and the watermarked image may not attain a good balance between high robustness and low distortion. To address the problem, we construct an efficient end-to-end network architecture for watermark embedding and extraction. To be specific, a deep noise simulation network (NSN) is designed to simulate the fusion process of real P-C noises, which can help to generate high-robust watermarked image. Also, a multitask loss function based on just-noticeable-difference (JND) is proposed to conduct constrained learning for residual image containing watermark information, thus, the distortion of generated watermarked image can be significantly reduced. Experimental results show that our scheme can achieve high robustness against P-C process while maintaining a satisfactory watermark capacity and visual quality of watermarked image.
Infrared anti-reflection film device for electromagnetic interference shielding

Abstract. The infrared anti-reflection film is prepared by ion beam assisted thermal evaporation deposition technology. The film has good firmness and can meet the needs of wet transfer. By adjusting the number of layers of the graphene mesh, the electromagnetic shielding and optical transparency properties are simultaneously improved. The infrared anti-reflection film and graphene mesh are combined to design and prepare a compatible electromagnetic shielding infrared anti-reflection film device with sandwich structure. The test results of the device show that the peak transmittance of the graphene mesh/infrared film/substrate/infrared film combination structure in the 3 to 5 μm band is 95.06%, and the average transmittance is 93.40%. The peak shielding effectiveness (SE) in the 12 to 18 GHz frequency band is 14.50 dB, and the average SE is 12.98 dB. It shows that the film device of this structure maintains the high transmittance of the infrared anti-reflection film and has good electromagnetic shielding effectiveness.
Spatiotemporal stimulus effects on response properties of neurons in the primary somatosensory cortex of owl monkeys

59 Introduction . 60 Do Widespread Spatial Interactions Exist in Area 3b? 61 How Might Temporal Factors Influence Spatial Interactions? 62 How Will the Current Study Extend Previous Studies? 63 Materials and Methods . 65 Preparation for Recording . 65 Stimulation Procedures . 67 Data Acquisition . 68 Histology . 68 Data Analysis . 69 Spike Sorting . 69 Peak Firing Rate. 69 Response Latency . 71 Firing Rate Modulation Index . 72 Response Field . 73
Application of a controlled outside cold airflow by a PID controller to improve the performance of a household refrigerator

The present paper aims to prove the efficiency of using the cold to improve the performance of a household refrigerator. It is produced naturally in countries that are characterized by a severe wintry climate. The cold airflow is spread out inside a cavity covering the side wall of the appliance, which is connected to the inlet and outlet ducts. For that purpose, a Simulink model is proposed to model this installation. The internal air temperature is computed according to the evaporator temperature and the outside cold airflow that is also computed according to the outside temperature and controlled by a PID controller. The simulation results show that when the internal air temperature is higher than the desired one and the outside temperature is low enough, the controlled cold airflow used as a second cooling source allowed to speed-up the cooling inside the refrigerator compartment of about 36.21% and to reach an energy saving of about 36.23% compared with the classical thermostatic control.