Miracle-dz/UniFAR-FTUs huggingface dataset:
DIGITAL STORYTELLING AS AN EFFECTIVE FRAMEWORK FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING SKILLS

Computational Thinking (CT) is widely recognized as a way of thinking –including a set of essential thinking skills-that should be mastered by the whole literate population, through compulsory education along with reading, writing and mathematics. CT is described as a set of significant aspects such as abstraction, algorithmic problem solving, data representation and data analysis. Diverse studies have been conducted in order to find effective educational practices and frameworks to help K-12 students to develop their computational thinking skills and many proposals have been formed. Despite this fact, a proposal identifying Digital Storytelling (DST) as a framework for the development of CT skills by K-12 level students has not yet been reported. This is the contribution of this paper. In fact, by analyzing secondary data emerging from the literature regarding CT and DST, a specific framework highlighting the relationship between the abilities of CT and the skills cultivated in DST for each stage of digital story development are proposed. It is worth mentioning that DST has emerged as a powerful teaching and learning tool across all disciplines by providing various direct and indirect advantages for the learners. Thus, the purpose of this paper is to clarify the contribution of digital storytelling (DST) as an effective educational framework to the development of specific aspects of computational thinking at K-12 level students.
Efficacy of Temozolomide in Treatment of Recurrent Glioma

Objective To evaluate the efficacy of Temozolomide in the treatment of recurrent glioma.Methods Thirty-five patients with recurrent glioma were enrolled in our study. According to the history of temozolomide administration,35 patients were divided into group F (the patients first treated with temozolomide),group R (the patients re-treated with temozolomide),and group N (the patients without chemotherapeutic history). Clinical evaluations and assessments of tumor response were performed every 2 months.Results In group F,the complete response(CR) was not observed,partial response (PR) in 6 cases,stable disease(SD)in 10 cases,and 5 cases developed progressive disease(PD). In group R,the complete response(CR) was not observed,partial response (PR) was observed in 2 cases,stable disease(SD)in 4 cases,and 9 cases developed progressive disease(PD). In group N,the complete response(CR) was not observed,partial response (PR) was observed in 1 cases,stable disease(SD)in 5 cases,and 9 cases developed progressive disease(PD). Therein,(PR+SD) in group F was significantly higher than group R and group N respectively (both P0.05). Cases of 6 month overall survival in group F was more than those in group R (P0.05) and group N (P0.01). Conclusion Initial use of temozolomide is effective for recurrent glioma,but the curative effectiveness of temozolomide re-administration is not obvious.
A novel content-based image retrieval system based on Bayesian logistic regression

In this work, a novel content-based image retrieval (CBIR) method is presented. It has been implemented and run on “Qatris IManager” [14], a system belonging to SICUBO S.L. (spin-off from University of Extremadura, Spain). The system offers some innovative visual content search tools for image retrieval from databases. It searches, manages and classiﬁes images using four kinds of features: colour, texture, shape and user description. In a typical CBIR system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This discrepancy between low-level features and high-level concepts is known as the semantic gap. The search method presented here, is a novel supervised image retrieval method, based in Bayesian Logistic Regression, which uses the information from the characteristics extracted from the images and from the user’s opinion who sets up the search. The procedure of search and learning is based on a statistical method of aggregation of preferences given by Arias-Nicolás et al. [1] and is useful in problems with both a large number of characteristics and few images. The method could be specially helpful for those professionals who have to make a decision based in images, such as doctors to determine the diagnosis of patients, meteorologists, trafﬁc police to detect license plate, etc.
Visual environment recognition for robot path planning using template matched filters

A visual approach in environment recognition for robot navigation is proposed. This work includes a template matching filtering technique to detect obstacles and feasible paths using a single camera to sense a cluttered environment. In this problem statement, a robot can move from the start to the goal by choosing a single path between multiple possible ways. In order to generate an efficient and safe path for mobile robot navigation, the proposal employs a pseudo-bacterial potential field algorithm to derive optimal potential field functions using evolutionary computation. Simulation results are evaluated in synthetic and real scenes in terms of accuracy of environment recognition and efficiency of path planning computation.
How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright. Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law—the fair use doctrine—has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems.
Link-Prediction and its Application in Online Social Networks

Alongside the continuous development of Internet technologies, traditional social networks are running online to provide more services so as to unite the community. In the meantime, conventional web-based information systems are trying hard to utilise social networking elements to develop a virtual community so as to increase their popularity. The combination of these two domains has become what people knew as the ‘online social networks’. There is much to do to reveal the knowledge behind the screen as massive amounts of user-generated data is created every second. Many people from different disciplines are using their tools and techniques to analyse and build knowledge to try understanding the evolution of it. Link Prediction, with the essence of calculating similarities of two nodes, is one of the most common techniques to analyse an online social network. It is worth mentioning that while using Link Prediction to explain online social network, we consider it as a graph with nodes and edges connecting one another where nodes represent individuals and edges represent the relations between them. Link Prediction can be utilised in many ways in this domain, where one of the most common ways is predicting links/edges that may appear in the future of an evolving network where links/edges represent connections. The meaning of these connections vary under different circumstance, such as an academia social network where they may represent co-author relationships among researchers. Therefore, one of the most common applications of Link Prediction in an online social network will be the recommendation system. Many works have been done to analyse social-oriented online networks and many turns into applications with great success such as Facebook and Twitter. However, this thesis concentrates on investigating a particular type of online social network where there is still a large gap waiting to be filled - the online academia social network. The objective of this thesis is to provide a more sensible way for people to understand the evolution of this network and develop models and algorithms that solving issues in regards to the needs of the users in this system of finding valuable research partners. Further the object is to building up an environment for future researchers to share knowledge and to carry on the work as a community. To be specific, this thesis contains four main chapters, and they are connected in some ways to develop solutions for the issues coming out during the research processes.
Factors Related To HPV Vaccine Practice Among Adult Women

ABSTRACT Cervical cancer is one of most common diseases among women worldwide. Human papilloma virus (HPV) is known as precursor of cervical cancer. Cervical cancer can be prevented effectively by practicing hpv vaccine. But the coverage of HPV vaccine is remain low. The objection of study was to analyze factors related to HPV vaccine pratice among adult women. This study used case control design with sample size 25 for each group. Sample case was women who took HPV vaccine in IBI Kota Kediri on 2013, while sample control was neighboor from the sample case who didn’t take HPV vaccine. The independent variabels were age, education level, marital status, income level, knowledge, family support, family history of cervical cancer and the dependent variable was HPV vaccine practice. Those variables was analyzed with chi square or Fisher’s exact with significancy level at 95%. The result showed that there were correlation between education level (p = 0.006; c = 0.346), knowledge (p = 0.001; c = 0.464), and family support (p = 0.000; c = 0.516) with HPV vaccination practice. While there were no correlation between age (p = 0.275), marital status (0.490) and income level (p = 0.098) and family history of cervical cancer (p = 1.000) with HPV vaccination practice. Based on data from this study can be concluded that family support and knowledge had average strenght correlation withHPV vaccine practice among adult women. So, the intervention should be focused in increasing knowledge among women and their family about the important of HPV vaccine as a cervical cancer prevention. Keywords : practice, preventive, HPV, vaccine, adult women
Monetary Policy Surprises and the Responses of Asset Prices: An Event Study Analysis

The monetary policy shocks have been widely regarded to have effects on the financial markets. Before the 2008 financial crisis, the Federal Reserve adjusted the federal funds target rate to implement the monetary policy. This paper uses event studies to examine the relationship between the Federal Reserve’s interest rate decisions and the asset prices. We find that treasury bills and exchange rates of the developed countries were significantly influenced by the Fed’s unexpected monetary policy shocks from the year 1989 to 2008. However, in the same period, exchange rates of the emerging markets responded weakly to the policy surprises. We also observe that international equity markets and commodity prices were not sensitive to the rate decisions of the Federal Reserve. In addition, Treasury bill yields responded significantly to the anticipated and unanticipated rate decisions in both pre and post FOMC meeting days. We also show that the Fed’s unexpected monetary policy had significant 5-day post-announcement impacts on prices of almost all assets.
The dynamic study on the behavioral effect on rats with Parkinson's disease treated by Levodopa

Objective:To observe the behavioral effect on rats with parkinson disease treated by 50 mg/kg dose of L-dopa(L-dihydroxy-phenylalanine). Methods:The PD model rats were established through stereotaxic injection of 6-OHDA,and the successful models of PD were randomized into two groups:the PD control group and the LD model group,then the LD model group was treated intraperitoneally with 50 mg/kg dose of L-dopa for 8 weeks.Then at different time(2,4,6,8 weeks),the behavioral changes of PD models(include the rotation behavior induced by APO,the initiating time of rotation,the lasting time of rotation,the maximum number of rotation,and the score of abnormal involuntary movement) were observed.Results:As the time given L-dopa went by ①the rotation behavior induced by APO gradually increased,and reached the peak at 6 weeks,compared with 2 weeks,there was difference(P0.05);②the initiating time of rotation gradually decreased,but there was no difference(P0.05);③the lasting time of rotation gradually increased,and 6,8 weeks compared with 2,4 weeks,there were significant difference(P0.05);④the maximum number of rotation gradually increased,and 6 weeks compared with 2 weeks,there were difference(P0.05);⑤the score of abnormal involuntary movement gradually increased,and 6,8 weeks compared with 2 weeks,there were difference(P0.05 or P0.01),8 weeks compared with 4 weeks,there was also difference(P0.05). Conclusion:The 50 mg/kg dose of levodopa has neurotoxic effect on rats with PD,the models display the typical and stable behavior,so it is suitable for the side-effect study of LD.
Study on Integrated Teaching Mode in Cases of Pharmacology of Traditional Chinese Medical Formulae

Objective:To probe into difference of traditional and integrated teaching mode Cases in Traditional Chinese Medicine in pharmacology of traditional Chinese medical formulae.Methods:integrated teaching mode was professed by in experimental class,but the control class adopted traditional teaching mode.In the end,estimation of different mode was made by examination result.Results and Conclusion:examination result in examination result surpassed than control class(P0.05),hingting integrated teaching mode Cases in Traditional Chinese Medicine better than traditional ways.
Cluster forming based on spatial information using HMAC in WSN

The wireless sensor can be considered as one of the most major technologies through using it in different application such as environmental applications, military applications, commercial applications, health applications and agriculture applications. Due to the sensor networks may be deployed in open area (uncontrolled area), especially in military applica­tions. In such situ­ations, the nodes are vulnerable to be captured. Moreover, data/control packets may be inter­cepted and/or modified due to the transmission nature. Consequently, security services such as authentication and encryption done by key manage­ment which is not trivial task. The key management is important to maintain the network op­erations. The proposed scheme the nodes based on spatial information with data sequence then sends encryption message to each other in secure way by HMAC for providing safe links between the nodes in the network and forming the clusters. Consequently, the proposed scheme covers the security goals and au­thentication of each node.
Improving Small File Management in Hadoop

Hadoop, considered nowadays as the de-facto platform for managing big data, has revolutionized the way customers manage their data. As an open-source implementation of map-reduce, it was designed to offer a high scalability and availability across clusters of thousands of machines. Through it two principals’ components, which is HDFS for a distributed storage and MapReduce as the distributed processing engine, companies and research studies are taking a big benefit from its capabilities. However, Hadoop was designed to handle large size files, so when it comes to a large number of small files, the performance can be heavily degraded. The small file problem has been well defined by researchers and Hadoop community, but most of the proposed approaches only deal with the pressure caused on the NameNode memory. Certainly, grouping small files in different possible formats, that are most of time supported by the actual Hadoop distribution, reduce the metadata entries and solve the memory limitation, but that remain only a part of the equation. Actually, the real impact that organizations need to solve when dealing with lot of small files, is the cluster performance when those files are processed in Hadoop clusters. In this paper, we proposed a new strategy to use efficiently some one of the common solution that group files in a MapFile format. The core idea, is to organize small files files based on specific attributes in MapFile output files, and use prefetching and caching mechanisms during read access. This would lead to less calls of metadata from the NameNode, and better I/O performance during MapReduce jobs. The experimental results show that this approach can help to obtain better access time when the cluster contain massive number of small files.
PENGEMBANGAN APLIKASI AUGMENTED REALITY BERBASIS ANDROID PADA GEDUNG MIKROSKIL

Perkembangan multimedia yang semakin pesat memiliki dampak yang signifikan terhadap penyajian informasi. Seiring berkembangnya multimedia, objek 3D juga telah menjadi salah satu komponen multimedia. Teknologi Augmented Reality merupakan salah satu teknologi yang dapat menunjang fungsi dari objek 3D. Melalui penggabungan antara objek 3D dengan penyajian informasi serta interaksi terhadap objek 3D, maka seseorang akan lebih mudah menerima informasi. FindMi merupakan aplikasi pemodelan bangunan dalam bentuk 3D yang menggabungkan teknologi Augmented Reality dengan pemodelan gedung STMIK Mikroskil dalam bentuk 3 dimensi yang akan menghasilkan augmentasi objek 3D tersebut secara real time. Pengembangan FindMi menggunakan metodologi prototyping pada platform Android serta didukung library Vuforia-SDK. Dengan adanya aplikasi FindMi diharapkan akan mempermudah bagi mahasiswa untuk memperoleh informasi di lingkungan STMIK Mikroskil.
Central Venous Catheter Placement in Hemodialysis: Evaluation of Electrocardiography using a Guide-Wire

Internal jugular vein cannulation has become a routine and clinically important aspect of medical care in hemodialysis patients. Mismanagement in the location of a central venous catheter may occur in up to 20% of cases. The aim of the study was to evaluate the utility of endocavitary electrocardiography in right internal jugular vein placement of central venous catheters. We examined 327 central venous catheterizations performed in two Dialysis Units; all catheters were positioned using intra-atrial ECG monitoring by guide-wire and after, by catheter filled with NaCl solution. EC-ECG via guide-wire was successful on 321 occasions (98.1%). Correct placement of the catheter was confirmed by EC-ECG via catheter in each case, and by plain chest-X-ray only in the first hundred cases. In 314 patients (98.1%), insertion of the catheter was successful at the first attempt. In 6 catheterizations, no atrial trace was obtained due to atrial fibrillation in 4 cases, and in 2 cases technical error and guide-wire looping into a right jugular vein. Complications as a direct result of guide-wire or catheter placement were not observed. In our opinion this method can be used safely and makes radiological control usually unnecessary.
The Application of Integrated Management Information Systems of Community Service Program in Universitas Muhammadiyah Yogyakarta

Community service program is a compulsory subject for all students in Universitas Muhammadiyah Yogyakarta (UMY). The existing information system of community service program in UMY is still run conventionally starting from registration system, group division and work area announcement. Meanwhile, students should come and ask directly to Office of Research, Publication, and Community Service to get all the information related to the program. This current system is complicated not only for students but also for officer who manage all data about community service program in UMY. Manually administered management cause frequent errors in invading data and takes a long time to complete the activity. Researchers initiated solution to overcome this problem by implement an application of management information system. This research has developed a web-based application that can be used to manage registration, personal data filling, and periodic scheduling for students whom take the community service program. Various parameters implementation as success determinant of system creation has been applied, so obtained a decent system used. The application is based on windows operating system that easily could be accessed through web interface. System testing has been done both from users (students) side and admin. the form of the webbased application developed has followed the standard manual form so that the system can work effectively and efficiently.
j. Perinat. Med. The potential distribution generated by the fetal heart at the maternal 14(1986)435 abdomen

The pathways along which the electrical currents generated by the fetal heart are conducted to the surface of the maternal abdomen are not known. As a consequence, in recording the fetal electrocardiogram (FECG) it is hard to predict where electrodes should be placed in order to obtain an optimal signal. The FECG varies with gestation [2], and there is a large interindividual variation in the amplitude of the FECG and in the optimal recording site among subjects of the same gestational age.
Hemodynamic modelling of BOLD fMRI - A machine learning approach

Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequenc e of local neural activity. Several models have been proposed fo r this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These dis tributions are themselves stochastic, therefore we estimate t heir variance by epoch based leave-one-out cross validation, us ing a Metropolis-Hastings algorithm for sampling of the poster ior parameter distribution.