Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Game-Based Learning Judgment System for Online Education Environments Based on Video Lecture: Minimum Learning Judgment System

We propose a minimum learning judgment system that is appropriate for online learning environments, and we verify this minimum learning judgment system through various experiments. By focusing on the learning effort, this system can easily and quickly determine whether learners have exerted the minimum effort required for learning. To do this, the system automatically generates a word game and determines whether minimum learning has taken place through the results of the word game. To verify the minimum learning judgment system, we conducted a comparative experiment on the importance of high-frequency words, a word count verification test for word games, and a judgment criteria verification test based on the length of a video lecture. Results of the experiments show that high-frequency words can be used as a feature to determine minimum learning. The appropriate number of words in the word game for the minimum learning judgment was found to be seven, and the results showed that the video length did not affect the minimum learning criteria. In addition, the minimum learning judgment accuracy result was 82%. This is not considered very high judgment accuracy, but the accuracy of the judgment is positive considering the aim of this study.
Establishment of PCR assay for differentiation of Muscovy duck parvovirus from goose parvovirus

To establish a method to differentiate Muscovy duck parvovirus(MDPV) from goose parvovirus(GPV),primers GPVF/R1 and MDPVF1/R1 were designed according to the genomic sequences of GPV and MDPV to amplify the virus culture and nucleotide of GPV,MDPV,duck plague virus(DPV),duck hepatitis virus(DHV),duck reovirus(DRV), canine parvovirus(CPV) and feline panleucopenia virus(FPLV). Primers MDPVF1/R1 could only amplify a 900 bp fragment from MDPV,and primers GPVF1/R1 could only amplify a 465 bp fragment from GPV.The results indicated that the established PCR could be used to differentiate GPV from MDPV.
Interactive intrinsic video editing

Separating a photograph into its reflectance and illumination intrinsic images is a fundamentally ambiguous problem, and state-of-the-art algorithms combine sophisticated reflectance and illumination priors with user annotations to create plausible results. However, these algorithms cannot be easily extended to videos for two reasons: first, näively applying algorithms designed for single images to videos produce results that are temporally incoherent; second, effectively specifying user annotations for a video requires interactive feedback, and current approaches are orders of magnitudes too slow to support this. We introduce a fast and temporally consistent algorithm to decompose video sequences into their reflectance and illumination components. Our algorithm uses a hybrid ℓ2ℓp formulation that separates image gradients into smooth illumination and sparse reflectance gradients using look-up tables. We use a multi-scale parallelized solver to reconstruct the reflectance and illumination from these gradients while enforcing spatial and temporal reflectance constraints and user annotations. We demonstrate that our algorithm automatically produces reasonable results, that can be interactively refined by users, at rates that are two orders of magnitude faster than existing tools, to produce high-quality decompositions for challenging real-world video sequences. We also show how these decompositions can be used for a number of video editing applications including recoloring, retexturing, illumination editing, and lighting-aware compositing.
Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis

Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis Yuanjun Gao Advances in techniques have been producing increasingly complex neural recordings, posing significant challenges for data analysis. This thesis discusses novel statistical methods for analyzing high-dimensional neural data. Part one discusses two extensions of state space models tailored to neural data analysis. First, we propose using a flexible count data distribution family in the observation model to faithfully capture over-dispersion and under-dispersion of the neural observations. Second, we incorporate nonlinear observation models into state space models to improve the flexibility of the model and get a more concise representation of the data. For both extensions, novel variational inference techniques are developed for model fitting, and simulated and real experiments show the advantages of our extensions. Part two discusses a fast region of interest (ROI) detection method for large-scale calcium imaging data based on structured matrix factorization. Part three discusses a method for sampling from a maximum entropy distribution with complicated constraints, which is useful for hypothesis testing for neural data analysis and many other applications related to maximum entropy formulation. We conclude the thesis with discussions and future works.
The Use of Transition Signals in Efl Academic Writing Context: a Corpus Study

For foreign language learners, especially in university, academic writing is essential. Students are required to analyse, compare, and inform through academic writing. Moreover, when they reach the end of their study, they should write a thesis as part of a requirement to graduate. Thus, thesis writing is a major challenge for students. Halliday and Hassan as cited in Hinkel (2001) emphasise that academic writing should achieve cohesiveness to make it well-constructed and understandable. Transition signal is one of prominent cohesive devices that should be taken into account in academic writing. This study aims to identify students’ problems and tendency in using transition signals in academic writing. The data are gained from university students’ academic writing product which is built in a form of corpus. The data are then analysed by using corpus software (ant conc.3.2.4). Its implication on English Language Teaching (ELT) concerning the teaching of transition signals will also be addressed
The Prelimery Application of ISSR Marker in Taxonomy of Pleurotus Species

Seven primers synthesized based on 16-mer oligonucleotide(TATG)_(4) were screened with the DNA from 22 Pleurotu strains and one Agaricus bisporus strain.120 bands were amplified by primer P0 and P1.Strains were classified into five groups by UPGMA.The 1.3 kb band of No.17 strain were obtained with 1.5% agarose gel and sequenced.The(TATG)_(4) motif was found and reached the teast limit of repetition of a motif.The Cluster-analysis result showed plentiful variability within closely related strains and suggested ISSR is useful in taxonomy of Pleurotus strains.
Visual analytics of topics in twitter in connection with political debates

CARVALHO, E. J. Visual analytics of topics in Twitter in connection with political debates. 2017. 81 p. Master dissertation (Master student Program in Computer Science and Computational Mathematics) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2017. Social media channels such as Twitter and Facebook often contribute to disseminate initiatives that seek to inform and empower citizens concerned with government actions. On the other hand, certain actions and statements by governmental institutions, or parliament members and political journalists that appear on the conventional media tend to reverberate on the social media. This scenario produces a lot of textual data that can reveal relevant information on governmental actions and policies. Nonetheless, the target audience still lacks appropriate tools capable of supporting the acquisition, correlation and interpretation of potentially useful information embedded in such text sources. In this scenario, this work presents two system for the analysis of government and social media data. One of the systems introduces a new visualization, based on the river metaphor, for the analysis of the temporal evolution of topics in Twitter in connection with political debates. For this purpose, the problem was initially modeled as a clustering problem and a domain-independent text segmentation method was adapted to associate (by clustering) Twitter content with parliamentary speeches. Moreover, a version of the MONIC framework for cluster transition detection was employed to track the temporal evolution of debates (or clusters) and to produce a set of time-stamped clusters. The other system, named ATR-Vis, combines visualization techniques with active retrieval strategies to involve the user in the retrieval of Twitter’s posts related to political debates and associate them to the specific debate they refer to. The framework proposed introduces four active retrieval strategies that make use of the Twitter’s structural information increasing retrieval accuracy while minimizing user involvement by keeping the number of labeling requests to a minimum. Evaluations through use cases and quantitative experiments, as well as qualitative analysis conducted with three domain experts, illustrates the effectiveness of ATR-Vis in the retrieval of relevant tweets. For the evaluation, two Twitter datasets were collected, related to parliamentary debates being held in Brazil and Canada, and a dataset comprising a set of top news stories that received great media attention at the time.
Development and application of Taq Man-MGB fluorescence quantitative RT-PCR assay for detection of porcine reproductive and respiratory syndrome virus

A quantitative Taq Man reverse transcription-polymerase chain reaction(RT-PCR) for detecting PRRSV was deve-loped using specific primers and fluorogenetic probe based on the ORF7 gene of porcine reproductive and respiratory syndrome virus(PRRSV). The established method was highly specific,sensitive and reproducible.It had a linear dynamic range of the 101 copies/μL to 107 copies/μL and could detect the least 10 copies of target DNA and 1 TCID50 virus.The assay was succesffuly used to test on serum and tissue samples collected from experimentally infected pigs at various times following infection.Pigs infected with the fifth passage PRRSV exhibitedd a rapid increase in virus load in serum on day 3 and reached a peak of 1010 copies/mL on day 7,while pigs infected with the sixty-fifth passage virus had a lower virus load which peaked at 106 copies/mL on day 21.The virus concentrations in the lung were 109 copies/g,which was much higher than that in other tissues.
Designing for student-facing learning analytics

Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.
Optimization Method of Fusing Model Tree into Partial Least Squares

Partial Least Square (PLS) can’t adapt to the characteristics of the data of many fields due to its own features multiple independent variables, multi-dependent variables and non-linear. However, Model Tree (MT) has a good adaptability to nonlinear function, which is made up of many multiple linear segments. Based on this, a new method combining PLS and MT to analysis and predict the data is proposed, which build MT through the main ingredient and the explanatory variables(the dependent variable) extracted from PLS, and extract residual information constantly to build Model Tree until well-pleased accuracy condition is satisfied. Using the data of the maxingshigan decoction of the monarch drug to treat the asthma or cough and two sample sets in the UCI Machine Learning Repository, the experimental results show that, the ability of explanation and predicting get improved in the new method.
Intimate control for physical modeling synthesis.

Physical modeling synthesis has proven to be a successful method of synthesizing realistic sounds, but providing expressive controls for performance remains a major challenge. This thesis presents a new approach to playing physical models, based on multidimensional signals. Its focus is on the long-term research question, “How can we make a computermediated instrument with control intimacy equal to the most expressive acoustic instruments? ” In the material world, the control and sounding properties of an instrument or other object are intimately linked by the object’s construction. Multidimensional signals, used as connections between a gestural controller and a physical model, can in principle provide the same intimacy. This work presents a new, low-cost sensor design capable of generating a 2D force signal, a new implementation of the 2D digital waveguide mesh, and two experimental computer music instruments that combine these components using different metaphors. The new instruments are evaluated in terms of intimacy, playability and plausibility. Multidimensional connections between sensors and a physical model are found to facilitate a high degree of control intimacy, and to reproduce as emergent behavior some important phenomena associated with acoustic instruments.
HERO: Hierarchcal key management protocol for heterogeneous wireless sensor networks

Early researches focused on the security of homogenous sensor networks. However, recent works have demonstrated that the presence of heterogeneous sensor nodes gives better performance than homogenous ones in terms of energy consumptions, storage overhead, and network connectivity. In this paper, we propose a hierarchical key management scheme named HERO which is based on random key pre-distribution. HERO aims to construct a secure tree instead of complete connected graph as in existing schemes. Thanks to this realistic assumption, our key management scheme reduces considerably the number of pre-loaded keys assigned to each node while maintaining high security level at the same time. The preliminary simulation results using TOSSIM demonstrates that our scheme outperforms existing ones with respect to storage overhead.
Experience of radiotherapy in head and neck

Abstract Objective: The aim of this manuscript was to understand and describe the experience of radiotherapy on the perspective of a woman with oral cancer. Methods: This is a qualitative, exploratory, descriptive study, with theoretical reference of medical anthropology and instrumental case study methodology. Data collection was performed in the oncology sector of a public university hospital in the Southeast region of Brazil, State of Minas Gerais, Brazil and at the participant’s residence, during from March to August, 2016. The case selected was of a female patient, diagnosed with Spinocellular Carcinoma in oral cavity, submitted to radiotherapy. Five semi-structured interviews and participant observation were performed and audio-taped. Results: Three thematic units are presented: the radiotherapy trajectory, family support and refuge in God and the importance of the multiprofessional team. The adverse reactions such as odynophagia, dysgeugia and weight loss were the ones that stood out the most, being causes of the social isolation of the participant, nonetheless the patient showed optimism and hope, despite the social isolation. Conclusion: The qualitative methodology allowed the whole trajectory of radiotherapy follow-up which made it possible to understand the treatment experience from the perspective of the participant. Despite the adverse reactions, no negative experience with radiotherapy was found. New studies using the qualitative methodology are suggested with groups presenting similar characteristics of the participant, thus providing a better understanding of the topic addressed, as well as the identification and adoption of strategies that aim at improving care and the quality of life of this population, as well.
Regulatory Science on AI-based Medical Devices and Systems

AI-based medical and healthcare devices and systems have unique characteristics including 1) plasticity causing changes in system performance through learning, and need of creating new concepts about the timing of learning and assignment of responsibilities for risk management; 2) unpredictability of system behavior in response to unknown inputs due to the black box characteristics precluding deductive output prediction; and 3) need of assuring the characteristics of datasets to be used for learning and evaluation. The Subcommittee on Arti cial Intelligence and its Applications in Medical Field of the Science Board, the Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan, examined “new elements speci c to AI” not included in conventional technologies, thereby clarifying the characteristics and risks of AI-based technologies. This paper summarizes the characteristics and clinical positioning of AI medical systems and their applications from the viewpoint of regulatory science, and presents the issues related to the characteristics and reliability of data sets in machine learning.
A Malicious Web Site Identification Technique Using Web Structure Clustering

Epidemic cyber incidents are caused by malicious websites using exploit kits. The exploit kit facilitate attackers to perform the drive-by download (DBD) attack. However, it is reported that malicious websites using an exploit kit have similarity in their website structure (WS)trees. Hence, malicious website identification techniques leveraging WStrees have been studied, where the WS-trees can be estimated from HTTP traffic data. Nevertheless, the defensive component of the exploit kit prevents us from capturing the WS-tree perfectly. This paper shows, hence, a new WS-tree construction procedure by using the fact that a DBD attack happens in a certain duration. This paper proposes, moreover, a new malicious website identification technique by clustering the WS-tree of the exploit kits. Experiment results assuming the D3M dataset verify that the proposed technique identifies exploit kits with a reasonable accuracy even when HTTP traffic from the malicious sites are partially lost. key words: website structure, malicious website, exploit kit, clustering
Comparative Histological Study on the Preventive Role of Acarbose and Calcipotriol/Betamethasone in Mouse Model of Psoriasis-Like Skin Inflammation Induced by Imiquimod

Introduction: Psoriasis is an autoimmune skin disorder that is symptomized by erythema and scaling. Recently, imiquimod (IMQ) has been used to induce skin inflammation in mice to create a psoriasis model.Aim of the Work: To assess the possible preventive potential of acarbose on psoriasis-like skin inflammation induced by IMQ in adult male mice, and to compare it to calcipotriol/betamethasone applied on the same psoriasis mouse model.Materials and Methods: Thirty four BALB/c mice were classified as the following groups: I(Control), II(IMQ): IMQ-model with topically applied Aldara cream, ІII(Acarbose): IMQ-model treated with Glucobay orally, ІV(Calcipotriol/Betamethasone): IMQ-model treated with Calcipoheal-Cort ointment and V(Acarbose+Calcipotriol/Betamethasone): IMQ-model treated with both Glucobay and Calcipoheal-Cort combined. Haematoxylin & Eosin stain and S100 immunohistochemical staining were performed along with electron microscopic study of skin sections. Mean epidermal thickness and mean number of S100 immuno-positive keratinocytes were measured and measurements were statistically analyzed.Results: Group II mice showed psoriasis-like signs of skin inflammation. Groups III and IV mice showed less signs of inflammation which were markedly attenuated by combination of both of the treatments. Group II demonstrated significant acanthosis and abundant infiltrates of inflammatory cells in the dermis and significantly numerous S100 immuno-positive keratinocytes. Groups III and IV showed significant improvement of these changes which were markedly diminished in group V as it showed almost normal histological structure.Conclusion: Acarbose had an overall beneficial effect on psoriasis-mimicking mouse model triggered by IMQ, almost comparable to that demonstrated by calcipotriol/betamethasone. However, combination of both treatments exerted a more powerful therapeutic effect.
My Algorithm: User Perceptions of Algorithmic Recommendations in Cultural Contexts

Automated functions for discovery and recommendations are important features of all the major players in digital media and culture. This paper sets out to explore algorithms in cultural contexts via the users of these services, addressing the following research question: How do users relate to online automated discovery and recommendation services for cultural products and services? Departing from relevant literature and analyses of Twitter streams, the paper explores how users respond and relate to the automation of cultural discovery and recommendation with specific attention to perceived quality and relevance, along with evaluations of pressures on individual privacy. Preliminary findings indicate how active users seem to take a personal interest in the recommendation algorithms, seeing them as part of their online identities. Accordingly, we find users referring to an automated system for targeted recommendations as “my algorithm”.
Parallel Scan for Stream Architectures

One of the most useful algorithmic primitives for parallel processing is scan (also known as prefix scan, prefix sum, prefix reduction, etc.). Because the computational granularity of concurrent scan tasks is so small, the memory bandwidth between physical processing elements and globally-visible storage banks is the limiting hardware resource. Current implementations of parallel scan for GPGPU stream architectures do not maximize memory bandwidth: they either make inefficient use of device memory accesses, are computationally-bound due to high dynamic instruction counts, or both. In this work we present three implementations of parallel scan that address these bandwidth inefficiencies. These new implementations are memory-bound, utilize 100% of achievable memory bandwidth, and only require the use of a constant amount of global device memory for the storage of intermediate results. On our target platform, all three provide a 1.7x performance speedup over the scan primitives provided by the CUDA Parallel Primitives (CUDPP) library, exhibiting up to a 64% reduction in dynamic instruction count. Our particular scan implementations are valuable in their own right, but more importantly we have developed a generalized design methodology that should allow us to construct bandwidth-optimal implementations for any stream device having similar machine and programming models.