Miracle-dz/UniFAR-FTUs huggingface dataset:
Walking the Tightrope: Navigating Faculty Status as a Mandatory Reporter in the #MeToo Era

Abstract One of the goals of the #MeToo movement is to elevate the voices of survivors. Course content related to sexual victimization often stresses the importance of understanding the experiences of victims of crime, and it is likely that such discussions result in student disclosures of sexual victimization to faculty. At the same time, institutions of higher education have created policies, such as making professors “mandatory reporters,” which require them to report disclosures to Title IX coordinators irrespective of a student’s desire to report. This essay will unpack the complicated nature of supporting survivors on campus in the #MeToo era: Where on the one hand, victims are encouraged to tell their story, but on the other hand, institutional policies may remove their agency if they do. As #MeToo has advanced our understanding of sexual victimization experiences, it should also help improve our policies responding to those empowered by the movement.
Im/migrant children’s stories in elementary school: caring and making space in the classroom

ABSTRACT Family separation policies’ impacts on children’s education are one of the critical issues of our time. In this article, I draw on ethnographic data collected over two years in one Northeastern town in the United States. More than fifty im/migrant children were observed inside kindergarten, first and second grade classrooms. For this article, I focus on the experience of four immigrant teachers in dual-language education and im/migrant children in their classrooms as they tried to engage with narratives of trauma and separation. I argue that teachers struggled to understand who was in a position to speak of im/migrant children’s pain and suffering in the classroom, while students wanted to tell their stories. I call this dynamic constrained care. I also describe ways in which children tried to make space for their stories and the teachers’ reactions.
Ageing European lobsters (Homarus gammarus) using DNA methylation of evolutionarily conserved ribosomal DNA

Crustaceans are notoriously difficult to age because of their indeterminate growth and the moulting of their exoskeleton throughout life. The poor knowledge of population age structure in crustaceans therefore hampers accurate assessment of population dynamics and consequently sustainable fisheries management. Quantification of DNA methylation of the evolutionarily conserved ribosomal DNA (rDNA) may allow for age prediction across diverse species. However, the rDNA epigenetic clock remains to be tested in crustaceans, despite its potential to inform both ecological and evolutionary understanding, as well as conservation and management practices. Here, patterns of rDNA methylation with age were measured across 5154 bp of rDNA corresponding to 355 quality‐filtered loci in the economically important European lobster (Homarus gammarus). Across 0‐ to 51‐month‐old lobsters (n = 155), there was a significant linear relationship between age and percentage rDNA methylation in claw tissue at 60% of quality‐filtered loci (n = 214). An Elastic Net regression model using 46 loci allowed for the accurate and precise age estimation of individuals (R2 = 0.98; standard deviation = 1.6 months). Applying this ageing model to antennal DNA from wild lobsters of unknown age (n = 38) resulted in predicted ages that are concordant with estimates of minimum size at age in the wild (mean estimated age = 40.1 months; range 32.8–55.7 months). Overall, the rDNA epigenetic clock shows potential as a novel, nonlethal ageing technique for European lobsters. However, further validation is required across a wider range of known‐age individuals and tissue types before the model can be used in fisheries management.
Visual Simulation of Soil-Structure Destruction with Seepage Flows

This paper introduces a method for simulating soil-structure coupling with water, which involves a series of visual effects, including wet granular materials, seepage flows, capillary action between grains, and dam breaking simulation. We develop a seepage flow based SPH-DEM framework to handle soil and water particles interactions through a momentum exchange term. In this framework, water is seen as a seepage flow through porous media by Darcy's law; the seepage rate and the soil permeability are manipulated according to drag coefficient and soil porosity. A water saturation-based capillary model is used to capture various soil behaviors such as sandy soil and clay soil. Furthermore, the capillary model can dynamically adjust liquid bridge forces induced by surface tension between soil particles. The adhesion model describes the attraction ability between soil surfaces and water particles to achieve various visual effects for soil and water. Lastly, this framework can capture the complicated dam-breaking scenarios caused by overtopping flow or internal seepage erosion that are challenging to simulate.
Automatic Seed Generation based Hybrid Fuzzing for Code Coverage Efficiency

Based on the 4th Industrial Revolution, numerous ICT technologies are developing, and for this reason, IoT devices are formed around us. Accordingly, hackers are inflicting financial and physical damage to our lives by using software vulnerabilities in IoT devices around us based on intelligent hacking technology. Automated security vulnerability response systems are required to respond to attacks through continuously occurring software vulnerabilities. In this paper, we analyze the hybrid fuzzing system that complements the technical limitations of the existing fuzzing technology as the base technology for an automated security vulnerability response system. In addition, we propose a hybrid fuzzing system based on an automatic seed generation mechanism for coverage efficiency in order to find vulnerabilities inherent in software quickly and efficiently.
Prediction of IDH Mutation Status of Glioma Based on Multimodal MRI Images

Isocitrate dehydrogenase (IDH) mutation status is one of the important indicators for the diagnosis and treatment of glioma, the gold standard to identify IDH mutant gliomas is to perform immunohistochemistry (IHC) or gene sequencing on tissue samples obtained through biopsy or surgical resection. Recent studies demonstrated that IDH mutation status can be predicted non-invasively through multimodal magnetic resonance imaging (MRI) images, while the possible modal mismatch of multimodal images in clinical practice limits the practical application. In this paper, a multi-modal MRI data set is established. Based on this dataset, a variety of multi-modal fusion networks are designed to predict the IDH mutation state, and we analyzed the interpretability of the results by Gradient-weighted Class Activation Mapping (Grad-CAM). The best model can achieve an AUC of 0.87, which is more accurate than mainstream fusion methods and can tolerate small modal mismatches.
PhysioAR: An augmented reality system applied in respiratory physiotherapy for hypertensive patients

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Approximately 1.13 billion people suffer from arterial hypertension (AH), a condition that stands out among all other CVD risk factors. One of the main non-pharmacological treatments for AH is the performance of physical activities. Studies have also demonstrated the possibility of blood pressure control through the application of breathing exercises. Such exercises can be performed even at home. Due to the limitations concerning physical exercise for patients with uncontrolled hypertension, clinicians often have their patients conduct breathing exercises before a physiotherapy session for blood pressure reduction purposes. However, more tools are needed to assist clinicians with this approach. Considering these circumstances, this paper introduces PhysioAR, an augmented reality system designed to assist in respiratory therapy treatments for hypertensive patients. Using three-dimensional virtual environments and specific hardware for vital sign measurements, PhysioAR enables the creation and application of respiratory therapy sessions by a physiotherapist professional. At the end of its development, a group of usability experts evaluated the system through a questionnaire based on Nielsen's heuristic evaluation of user interfaces. After evaluation, PhysioAR obtained an average performance of 4.3 by the Likert scale. All evaluators agreed that using the system was a pleasant experience, which suggests that PhysioAR has potential for physiotherapy clinics. The questionnaire application exposed some user experience problems. This demonstrated the effectiveness of a questionnaire-based evaluation to identify possible system problems.
Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model

This paper presents a novel deep learning-based framework for translating a motion into various styles within multiple domains. Our framework is a single set of generative adversarial networks that learns stylistic features from a collection of unpaired motion clips with style labels to support mapping between multiple style domains. We construct a spatio-temporal graph to model a motion sequence and employ the spatial-temporal graph convolution networks (ST-GCN) to extract stylistic properties along spatial and temporal dimensions. Through spatial-temporal modeling, our framework shows improved style translation results between significantly different actions and on a long motion sequence containing multiple actions. In addition, we first develop a mapping network for motion stylization that maps a random noise to style, which allows for generating diverse stylization results without using reference motions. Through various experiments, we demonstrate the ability of our method to generate improved results in terms of visual quality, stylistic diversity, and content preservation.
Developing Intelligent Feeding Systems based on Deep Learning

1The system can reduce the calculating workload of the IoT development board, as well as lowering the power consumption and guard the pool against water pollution. The intelligent feeding system offered by this study can effectively ease the workforce of the aquaculture industry. In the future, cage culture can also implement such a method to increase the safety of the operators. According to the experimental result of this study, the approach is feasible.
An Effective Algorithm based on Search Economics for Multi-Objective Optimization

An effective multi-objective search algorithm based on a new meta-heuristic algorithm, named search economic (SE), is presented in this study. The basic idea of SE is to first partition the solution space into a certain number of regions to keep the diversity. Then, it will determine the later search directions by the so-called expected value that is composed of the objective values of the best-so-far solution of each region, the searched solutions, and the number of searches invested on a region. More important, the proposed algorithm will invest limited computing resources on promising regions to find a better Pareto optimal set (POS). Different from other search economics-based algorithms, the proposed method uses two transition operators of differential evolution and adds a self adaptive mechanism to tune its parameters. Experimental results show that the proposed algorithm outperforms all the other metaheuristic algorithms compared in this study in most cases in the sense that it can get a more uniformly distributed POS and a smaller distance to the Pareto optimal front.
A Taxonomy of IS Certification's Characteristics

Information system (IS) certifications are considered as a powerful tool for improving the security and trustworthiness of an IS. However, organizations struggle to evaluate and finally adopt appropriate IS certifications. To enhance our understanding of the evaluation of IS certifications, we iteratively develop a taxonomy of key characteristics of IS certifications using inductive and deductive approaches, including expert surveys and interviews. Our taxonomy consists of 17 subjective and objective dimensions, comprising 46 characteristics of IS certifications. Our taxonomy highlights interesting insights into the key characteristics of IS certifications, such as certification targets, scopes, and pricing models, among others, that organizations should consider throughout evaluation. We provide a more fine-grained perspective on IS certifications, guiding future research assessing certification adoption and effectiveness, and supporting organizations in selecting appropriate IS certifications.
Political Economics System in Macroeconomics in the New Era

The thesis first combs the mechanism of regional industrial homogeneity influencing economic fluctuations from a new political economy from both the micro and macro perspectives. Once again, the paper uses data from the three industries, sub-industries, and national and regional economic fluctuations in 30 provinces and cities in Mainland China from 1952 to 2020 to describe Chinese macroeconomic fluctuations industrial homogeneity characteristics. Finally, the paper uses panel data and an error correction model to empirically study the impact of regional industrial homogeneity on economic fluctuations from the new political economy's perspective. The research shows that from the perspective of the same structure of the three industries, the more the industrial structure in the first region converges, the more severe the economic fluctuations. The impact of the same structure of regional industries on economic fluctuations is long-term. Secondly, according to the phased estimation results of the three industries' basic model, the influence of the same structure of the tertiary industries in the region on economic fluctuations has always existed. Still, the effects of different periods show differences. Thirdly, no matter what method is used to measure economic fluctuations or industrial homogeneity, regional industrial homogeneity will aggravate regional economic fluctuations. The thesis compares the impact of the same structure of the industrial industry and the same structure of three industries on economic fluctuations. This paper verifies the impact of the same structure of regional industries on economic fluctuations from the perspective of a new political economy, the impact of the same structure of institutional industries on economic fluctuations. The exact structure of non-institutional industries has a more significant impact on economic fluctuations.
Sensitive Information Detection Using HMM&SVM

With the use of electronic data, the protection and detection of sensitive information is an important issue for businesses and individuals. However, most sensitive detection methods focus on traditional machine learning methods like CNN and RNN, which are traditional and inefficient compared to HMM and SVM. This paper analyzes text information, using HMM and SVM to detect the sensitive information in the text file. The conclusion is drawn out that besides traditional machine learning methods, SVM and HMM models can also help us do the classification problem to extract the focused information, which cannot be exposed to the public.
Practical Knowledge of Preschool Teachers: A Case Study

This study explored the practical knowledge of the teacher of unit teaching and the teacher of thematic teaching, and the similarities and differences of practical knowledge. This study adopts the case study of qualitative research. The data was conducted by collecting interviews, observing classrooms, and teaching logs. The participants were two preschool teachers, each serving their own preschools. The findings revealed that unit teaching and theme teaching include the four aspects of knowledge on educational goals, knowledge on the curriculum and teaching, knowledge on learners, and knowledge on class management. Both teachers included balanced development of children in their knowledge on educational goals, but their knowledge on the curriculum and teaching, knowledge on learners, and knowledge on class management differed. The “unit teaching” teacher claimed that children are like blank paper and are easily affected by the family. Unit teaching emphasizes prelesson planning and an instructional teaching strategy as well as a clear reward and punishment mechanism and collective action. By contrast, the “thematic teaching” teacher considered children independent individuals with strong curiosity. She emphasized timely adjustment of lesson plans and a guided instruction strategy as well as timely reminders and student autonomy. Based on the research results, it can provide reference for teacher education programs, preschool teachers.
An Effective Optimizer based on Global and Local Searched Experiences for Short-term Electricity Consumption Forecasting

A precise forecasting for the future short-term electricity consumption will be quite useful for making a good plan for the power demand management. Since deep neural network (DNN) provides an effective way for the short-term load forecasting, one of the focuses of this research is thus to use it to construct the prediction model. The gradient-based optimizers (GBOs) have been widely used in DNN algorithms in recent years; however, they make it easy for DNN to trap in poor local regions during the training process. In this research, we propose a new optimizer that is based on the searched experiences to solve this problem to enhance the performance of GBOs. More precisely, the proposed optimizer integrates the best position searched, Lévy flight, and gradient descent to preserve not only the diversification but also the intensification of search during the training process. To evaluate the performance of the proposed optimizer, we compare it with several state-of-the-art GBOs for DNN; namely, Adagrad, RMSprop, and Adam, for training a forecasting model for the short-term electricity consumption forecasting problem. The simulation results show that the proposed optimizer outperforms all the other GBOs in terms of the mean absolute percentage error.
Origin of electroneutrality in living system

Identifying the first chemical transformations, from which life emerged is a central problem in the theories of life’s origins. These reactions would likely have been self-sustaining and self-reproductive before the advent of complex biochemical pathways found in modern organisms to synthesize lipid membranes, enzymes, or nucleic acids. Without lipid membranes and enzymes, exceedingly low concentrations of the organic intermediates of early metabolic cycles in protocells would have significantly hindered evolvability. To address this problem, we propose a mechanism, where a positive membrane potential elevates the concentration of the organic intermediates. In this mechanism, positively charged surfaces of protocell membranes due to accumulation of transition metals generate positive membrane potentials. We compute steady-state ion distributions and determine their stability in a protocell model to identify the key factors constraining achievable membrane potentials. We find that (i) violation of electroneutrality is necessary to induce nonzero membrane potentials; (ii) strategies that generate larger membrane potentials can destabilize ion distributions; and (iii) violation of electroneutrality enhances osmotic pressure and diminishes reaction efficiency, thereby driving the evolution of lipid membranes, specialized ion channels, and active transport systems. Significance The building blocks of life are constantly synthesized and broken down through concurrent cycles of chemical transformations. Tracing these reactions back 4 billion years to their origins has been a long-standing goal of evolutionary biology. The first metabolic cycles at the origin of life must have overcome several obstacles to spontaneously start and sustain their nonequilibrium states. Notably, maintaining the concentration of organic intermediates at high levels needed to support their continued operation and subsequent evolution would have been particularly challenging in primitive cells lacking evolutionarily tuned lipid membranes and enzymes. Here, we propose a mechanism, in which the concentration of organic intermediates could have been elevated to drive early metabolic cycles forward in primitive cells with ion-permeable porous membranes under prebiotic conditions and demonstrate its feasibility in a protocell model from first principles.
Sexual dimorphism and the effect of wild introgressions on recombination in cassava (Manihot esculenta Crantz) breeding germplasm

Recombination has essential functions in meiosis, evolution, and breeding. The frequency and distribution of crossovers dictate the generation of new allele combinations and can vary across species and between sexes. Here, we examine recombination landscapes across the 18 chromosomes of cassava (Manihot esculenta Crantz) with respect to male and female meioses and known introgressions from the wild relative Manihot glaziovii. We used SHAPEIT2 and duoHMM to infer crossovers from genotyping-by-sequencing (GBS) data and a validated multi-generational pedigree from the International Institute of Tropical Agriculture (IITA) cassava breeding germplasm consisting of 7,020 informative meioses. We then constructed new genetic maps and compared them to an existing map previously constructed by the International Cassava Genetic Map Consortium (ICGMC). We observed higher recombination rates in females compared to males, and lower recombination rates in M. glaziovii introgression segments on chromosomes 1 and 4, with suppressed recombination along the entire length of the chromosome in the case of the chromosome 4 introgression. Finally, we discuss hypothesized mechanisms underlying our observations of heterochiasmy and crossover suppression and discuss the broader implications for plant breeding.
DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as Connectivity Map (CMap) and Library of Integrated Network-Based Cellular Signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed, and among them, a fundamental issue is to define the proper similarity between the transcriptional profiles to elucidate the drug mechanism of actions and identify new drug indications. Traditionally, this similarity has been defined in an unsupervised way, and due to the high dimensionality and the existence of high noise in those high-throughput data, it lacks robustness with limited performance. In our study, we present Dr. Sim, which is a general learning-based framework that automatically infers similarity measurement rather than being manually designed and can be used to characterize transcriptional phenotypic profiles for drug discovery with generalized good performance. We evaluated Dr. Sim on comprehensively publicly available in vitro and in vivo datasets in drug annotation and repositioning using high-throughput transcriptional perturbation data and indicated that Dr. Sim significantly outperforms the existing methods and is proved to be a conceptual improvement by learning transcriptional similarity to facilitate the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and usage of Dr. Sim is available at https://github.com/bm2-lab/DrSim/.