Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Student Modelling in an Online Reading Platform

Use of technology-enhanced education and online learning systems has become more popular, especially after COVID-19. These systems capture a rich array of data as students interact with them. Predicting student performance is an essential part of technology-enhanced education systems to enable the generation of hints and provide recommendations to students. Typically, this is done through use of data on student interactions with questions without utilizing important data on the temporal ordering of students’ other interaction behavior, (e.g., reading, video watching). In this paper, we hypothesize that to predict students’ question performance, it is necessary to (i) consider other learning activities beyond question-answering and (ii) understand how these activities are related to question-solving behavior. We collected middle school physical science students’ data within a K12 reading platform, Actively Learn. This platform provides reading-support to students and collects trace data on their use of the system. We propose a transformer-based model to predict students' question scores utilizing question interaction and reading-related behaviors. Our findings show that integrating question attempts and reading-related behaviors results in better predictive power compared to using only question attempt features. The interpretable visualization of the transformer’s attention can be helpful for teachers to make tailored interventions in students’ learning.
The Increasing Capacity of Rice Farmers through the Role Agricultural Extension

Agricultural extension agents are agents of change who are directly related to farmers The purpose of the extension is to change the behavior of farmers in terms of knowledge, attitudes, and skills so that farmers have a more prosperous life. Rice farmers in West Tojo District carry out farming activities that still rely on hereditary habits, causing farmers to lack the capacity to run food crop businesses, both technically in cultivation, harvesting, and marketing. The objectives of the study are (1) to determine the level of capacity of rice farmers in Kabalo Village Tojo Una-Una District and, (2) to determine the effect of the role of extension agents on increasing the capacity of rice farmers in Kabalo Village Tojo Una-Una District. This research was conducted from March 2020 to August 2020 in Kabalo Village West Tojo District Tojo Una-Una Regency. This study used a population sample of 56 rice farmers. Based on the results of this study, the role of agricultural extension agents had an effect on the success rate of rice farmers in Kabalo Village. Through increasing the role of extension workers, both facilitators, motivators, mediators, advisors, and communicators, could increase the capacity of farmers in managing capital, utilizing technology, labor, and marketing processes that benefit farmers.
Transfer Learning for Color Constancy via Statistic Perspective

Color Constancy aims to correct image color casts caused by scene illumination. Recently, although the deep learning approaches have remarkably improved on single-camera data, these models still suffer from the seriously insufficient data problem, resulting in shallow model capacity and degradation in multi-camera settings. In this paper, to alleviate this problem, we present a Transfer Learning Color Constancy (TLCC) method that leverages cross-camera RAW data and massive unlabeled sRGB data to support training. Specifically, TLCC consists of the Statistic Estimation Scheme (SE-Scheme) and Color-Guided Adaption Branch (CGA-Branch). SE-Scheme builds a statistic perspective to map the camera-related illumination labels into camera-agnostic form and produce pseudo labels for sRGB data, which greatly expands data for joint training. Then, CGA-Branch further promotes efficient transfer learning from sRGB to RAW data by extracting color information to regularize the backbone's features adaptively. Experimental results show the TLCC has overcome the data limitation and model degradation, outperforming the state-of-the-art performance on popular benchmarks. Moreover, the experiments also prove the TLCC is capable of learning new scenes information from sRGB data to improve accuracy on the RAW images with similar scenes.
What We (Do Not) Know About Punishment Across Organizational Boundaries

Though organizational scholars have studied punishment for decades, recent examples of punishment in organizations cannot be fully explained by the scholarly literature. This may be because much of our prior understanding of punishment has been based on studies in highly bounded organizations, but with the shift to remote work arrangements and contract or freelance work, modern organizations are becoming increasingly boundaryless. Does punishment in bounded organizations look different than punishment in boundaryless organizations? To answer this question, we review and categorize the literature on punishment in organizations according to the boundedness of the organization it examined. We find that though there are similarities in punishment in bounded and boundaryless organizations, punishment in boundaryless organizations involves different actors, punishing different situations, for different reasons, using different methods. However, many questions about punishment in boundaryless organizations remain, including about the pervasiveness, motivations, and impacts that punishment has in boundaryless organizations.
Enhancing the Security of Data Stored in the Cloud using customized Data Visualization Patterns

—Cloud Computing is getting popularized with the invention of latest technologies like Big Data, Artificial Intelligence, Data Science etc. The biggest challenge faced by researchers is efficient ways of accessing the data and acquiring the required results. The efficiency of the system will help the researchers to go one step further in the field of cloud computing. Alongside of storing the data in an optimal way, one biggest challenge faced by the researchers is security. How best security can be enhanced for this data in order to protect the end system from data thefts and illegal attacks. In this paper the proposed research concentrates on customized data visualization techniques that have been developed in order to store the data and also enhance the data security. These visualization patterns are dynamic in nature and can be further extended based on the need and level of the security required by the application. The proposed research in this paper will help researchers to implement the data visualization techniques with enhanced security in the real time data stored in the cloud from unauthorized access and various attacks like Malware etc. and these data patterns are dynamic in nature which will be selected based on the number of fragments need to be stored pertaining to particular cluster or region. The patterns will be selected based on two factors basically, one is the number of fragments and another important factor is how many nodes are available in the pattern. This proposed research will give an additional strength to the Cloud Computing Platforms like AWS and Google Cloud where the customers can feel that their data is in safe hands. Today when we are living in the data world, the need of this system is very much essential as it enhances the security of the data.
Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.
Effect of Radical Laparoscopic Surgery and Conventional Open Surgery on Surgical Outcomes, Complications, and Prognosis in Elderly Patients with Bladder Cancer

Background Bladder cancer is a common malignant tumor of the urinary system in the clinic. It has multiple lesions, easy recurrence, easy metastasis, poor prognosis, and high mortality. Objective The aim of this study is to investigate the impact of laparoscopic radical cystectomy (LRC) and open radical cystectomy (ORC) on the surgical outcome, complications, and prognosis of elderly patients with bladder cancer. Materials and Methods One hundred elderly bladder cancer patients who underwent surgery in our hospital from June 2019 to June 2021 were selected for the retrospective study and were divided into 50 cases each in the ORC group and the LRC group according to the different surgical methods. The ORC group was treated with ORC, and the LRC group implemented LRC treatment. The differences in surgery, immune function, recent clinical outcomes, and complications between the two groups were observed and compared. Results The mean operative time, mean intraoperative bleeding, intraoperative and postoperative transfusion rate, and transfusion volume of patients in the LRC group were statistically significant when compared to the ORC group. The differences in the meantime to resume eating, time to get out of bed, mean number of days in hospital after surgery, and the amount of postoperative numbing analgesics used by patients in the LRC group after surgery were statistically significant compared to the ORC group (P < 0.05). There was no statistically significant difference in the comparison of immune function between the two groups before surgery (P > 0.05), while the comparison of CD8+ and B cells 1 week after surgery of the LRC group was significantly better than that of the ORC group (P < 0.05), and the operation time of the LRC group was longer than that of the ORC group (P < 0.05). Statistical analysis of postoperative complications showed that the overall incidence of postoperative complications in the LRC group was significantly lower than that in the ORC group (16.67% vs. 46.67%) (P < 0.05). Conclusion LRC has less surgical trauma and intraoperative bleeding, faster postoperative recovery, and fewer postoperative complications, providing some reference for clinical surgery for elderly bladder cancer patients.
Enhancing Electrochemical Performance of Co(OH)2 Anode Materials by Introducing Graphene for Next-Generation Li-ion Batteries

To satisfy the growing demand for high-performance batteries, diverse novel anode materials with high specific capacities have been developed to replace commercial graphite. Among them, cobalt hydroxides have received considerable attention as promising anode materials for lithium-ion batteries as they exhibit a high reversible capacity owing to the additional reaction of LiOH, followed by conversion reaction. In this study, we introduced graphene in the fabrication of Co(OH) 2 -based anode materials to further improve electrochemical performance. The resultant Co(OH) 2 /graphene composite exhibited a larger reversible capacity of ~1090 mAh g -1 , compared with ~705 mAh g -1 for bare Co(OH) 2 . Synchrotron-based analyses were conducted to explore the beneficial effects of graphene on the composite material. The experimental results demonstrate that introducing graphene into Co(OH) 2 facilitates both the conversion and reaction of the LiOH phase and provides additional lithium storage sites. In addition to insights into how the electrochemical performance of composite materials can be improved, this study also provides an effective strategy for designing composite materials.
Ablation Treatment Planning for Patients with Primary Aldosteronism

Microwave thermal ablation is a minimally invasive technique under investigation for treatment of adrenal adenomas. This therapy uses microwave antennas to deliver localised electromagnetic energy to tissue where the energy is absorbed as heat. A challenge for adrenal ablation is to focus the heating in small zones while minimizing thermal damage to the adjacent healthy tissue in such a way to preserve the adrenal function. This work aims to develop treatment planning to predict the ablation zone and deliver the thermal therapy safely and effectively.
Regulation and Function of Matrix Metalloproteinase-13 in Cancer Progression and Metastasis

Simple Summary MMP-13 is an enzyme that can digest and disrupt the tissue structures surrounding epithelial cells that line the surface of many internal organs, as well as the tissue structures surrounding endothelial cells that line the surface of blood vessels. The production of MMP-13 is tightly controlled in physiological conditions but is increased in various cancers and plays multiple roles in tumour progression and metastasis. This review summarises the current understanding of the regulation of MMP-13 production and discusses the actions of MMP-13 in cancer progression and metastasis. Abstract Matrix metalloproteinase-13 (MMP-13) is a member of the Matrix metalloproteinases (MMPs) family of endopeptidases. MMP-13 is produced in low amounts and is well-regulated during normal physiological conditions. Its expression and secretion are, however, increased in various cancers, where it plays multiple roles in tumour progression and metastasis. As an interstitial collagenase, MMP-13 can proteolytically cleave not only collagens I, II and III, but also a range of extracellular matrix proteins (ECMs). Its action causes ECM remodelling and often leads to the release of various sequestered growth and angiogenetic factors that promote tumour cell growth, invasion and angiogenesis. This review summarizes our current understanding of the regulation of MMP-13 expression and secretion and discusses the actions of MMP-13 in cancer progression and metastasis.
Sixteen-Year Monitoring of Particulate Matter Exposure in the Parisian Subway: Data Inventory and Compilation in a Database

The regularly reported associations between particulate matter (PM) exposure, and morbidity and mortality due to respiratory, cardiovascular, cancer, and metabolic diseases have led to the reduction in recommended outdoor PM10 and PM2.5 exposure limits. However, indoor PM10 and PM2.5 concentrations in subway systems in many cities are often higher than outdoor concentrations. The effects of these exposures on subway workers and passengers are not well known, mainly because of the challenges in exposure assessment and the lack of longitudinal studies combining comprehensive exposure and health surveillance. To fulfill this gap, we made an inventory of the PM measurement campaigns conducted in the Parisian subway since 2004. We identified 5856 PM2.5 and 18,148 PM10 results from both personal and stationary air sample measurements that we centralized in a database along with contextual information of each measurement. This database has extensive coverage of the subway network and will enable descriptive and analytical studies of indoor PM exposure in the Parisian subway and its potential effects on human health.
Analytical and Predictive Model for the impact of social distancing on COVID-19 pandemic

The coronavirus (COVID-19) as in the study of which had a starting point in China in 2019, has spread rapidly in every single country and has spread in millions of cases. The pandemic attracts lots of attentions due to major impacts not only on human health but on many other aspects including, social and political ones. This paper presents a robust data-driven machine learning analysis of COVID19 starting from data collection to the final step of knowledge extraction based on the selected research topics. The proposed approach evaluates the impact of social distancing on COVID19. Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore the social distancing aspects of COVID-19 pandemic. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. Also, it uses different Python libraries, Rattle, RStudio, Anaconda, and Jupyter Notebook. This study shows superior prediction performance comparing with the related approaches and the classical machine learning approaches.
P^3-Net: Part Mobility Parsing from Point Cloud Sequences via Learning Explicit Point Correspondence

Understanding an articulated 3D object with its movable parts is an essential skill for an intelligent agent. This paper presents a novel approach to parse 3D part mobility from point cloud sequences. The key innovation is learning explicit point correspondence from a raw unordered point cloud sequence. We propose a novel deep network called P^3-Net to parallelize trajectory feature extraction and point correspondence establishment, performing joint optimization between them. Specifically, we design a Match-LSTM module to reaggregate point features among different frames by a point correspondence matrix, a.k. a. the matching matrix. To obtain this matrix, an attention module is proposed to calculate the point correspondence. Moreover, we implement a Gumbel-Sinkhorn module to reduce the many-to-one relationship for better point correspondence. We conduct comprehensive evaluations on public benchmarks, including the motion dataset and the PartNet dataset. Results demonstrate that our approach outperforms SOTA methods on various 3D parsing tasks of part mobility, including motion flow prediction, motion part segmentation, and motion attribute (i.e. axis & range) estimation. Moreover, we integrate our approach into a robot perception module to validate its robustness.
DD-SSD: Deep Detector for Strip Steel Defects

Recently, the detection of defects in raw materials became one of the important topics in deep learning. However, the lack of databases restricted the ability to build new models, since the improvement of performance requires more data. In this paper, the most recent database of strip steel defects is used to build a new technique for defects detection. Different pre-trained models were utilized for the feature extraction stage. Then, a deep neural network classifier was devised and trained on images for seven types of steel defects. The final model was able to classify the defects with a 98.43% accuracy, which was trained on the features extracted from the DesneNet201 pre-trained model. The results of the proposed framework outperformed the most recent work with a 2.93% of accuracy improvement.
Mechanistic Effects of Baicalein on Aqueous Humor Drainage and Intraocular Pressure

Elevated intraocular pressure (IOP) is a major risk factor for glaucoma that results from impeded fluid drainage. The increase in outflow resistance is caused by trabecular meshwork (TM) cell dysfunction and excessive extracellular matrix (ECM) deposition. Baicalein (Ba) is a natural flavonoid and has been shown to regulate cell contraction, fluid secretion, and ECM remodeling in various cell types, suggesting the potential significance of regulating outflow resistance and IOP. We demonstrated that Ba significantly lowered the IOP by about 5 mmHg in living mice. Consistent with that, Ba increased the outflow facility by up to 90% in enucleated mouse eyes. The effects of Ba on cell volume regulation and contractility were examined in primary human TM (hTM) cells. We found that Ba (1–100 µM) had no effect on cell volume under iso-osmotic conditions but inhibited the regulatory volume decrease (RVD) by up to 70% under hypotonic challenge. In addition, Ba relaxed hTM cells via reduced myosin light chain (MLC) phosphorylation. Using iTRAQ-based quantitative proteomics, 47 proteins were significantly regulated in hTM cells after a 3-h Ba treatment. Ba significantly increased the expression of cathepsin B by 1.51-fold and downregulated the expression of D-dopachrome decarboxylase and pre-B-cell leukemia transcription factor-interacting protein 1 with a fold-change of 0.58 and 0.40, respectively. We suggest that a Ba-mediated increase in outflow facility is triggered by cell relaxation via MLC phosphorylation along with inhibiting RVD in hTM cells. The Ba-mediated changes in protein expression support the notion of altered ECM homeostasis, potentially contributing to a reduction of outflow resistance and thereby IOP.
NeuralFieldEq.jl: A flexible solver to compute Neural Field Equations in several scenarios

In their works, Wilson & Cowan (1972) presented a model, later developed by Amari (1977), named Neural Field Equations (NFE). Bressloff (2011) and Coombes (2005), summarise the main theoretical results and enumerate a wide variety of neurological phenomena that can be described by these equations. Such as working memory (Laing et al., 2002), travelling waves, etc. One of the improvements to consider in the simpler NFE, is to take into account the time taken by the stimulus to travel from neurons at different points. This leads to the delayed equation,