Miracle-dz/UniFAR-FTUs huggingface dataset:
Interview ラン・ラン

Brian Thompson is a Senior Nutrition Officer at the Food and Agriculture Organization of the United Nations (FAO). FAO is an intergovernmental organization, it has 191 Member Nations, two associate members and one member organization, the European Union. As a knowledge organization, FAO creates and shares critical information about food, agriculture and natural resources in the form of global public goods. FAO plays a connector role, through identifying and working with different partners with established expertise, and facilitating a dialogue between those who have the knowledge and those who need it. By turning knowledge into action, FAO links the field to national, regional and global initiatives in a mutually reinforcing cycle. Its mandate is to raise levels of nutrition and standards of living. What is the size of the malnutrition problem and what regions are most affected? There are persistently high levels of undernutrition. Nearly 870 million people in the world go to bed hungry (1 in 8 people) according to the recently released report of The State of Food Insecurity in the World 2012 (SOFI) and 2 billion have some form of micronutrient malnutrition – this constitutes a crime against humanity and is a responsibility for all of us. Prevalence of undernourishment in developing countries has declined over the past two decades, from 23 to 15 percent. In terms of total numbers in 1990– 92, around 980 million individuals were estimated to be undernourished. The number dropped to 901 million in 1999– 2001, to 885 million in 2006–06 and to 852 million in 2007–09. The financial crisis, economic downturn, persistent food price volatility, drought and other repercussions of climate change since 2006–08 may have prevented any further significant improvements in the number of people who are undernourished in developing countries since then. Africa has by far the highest prevalence, at around 23 percent in 2010–12 but though it is down from what it was in 1990–92 (27 percent) the numbers have risen from 175 million to 239 million with nearly 20 million added in the past four years. In subSaharan Africa, the modest progress achieved in recent years up to 2007 was reversed, with hunger rising 2 percent per year since then. In Asia, both prevalence and numbers dropped over the same period from 24 percent (739 million) to 14 percent (563 million). Latin America and the Caribbean (LAC) boasts the lowest rate of undernourishment (8 percent) among developing country regions but the rate of progress has slowed recently. Countries considered as leastdeveloped countries and lowincome economies have the highest prevalence rates of all around 30 percent but down from the 40 percent levels of twenty years ago.
A Study on Mixed-Initiative for Fostering Creativity in Game Design

Mixed-initiative systems highlights the collaboration between humans and computers in fostering the generation of more interesting content in game design. In light of the ever-increasing cost of game development, providing mixed-initiative tools can not only significantly reduce the cost but also encourage more creativity amongst designers. This study focused on the improvement of mixed-initiative aspects for a previously developed tool called Evolutionary Dungeon Designer or Eddy for short. Eddy placed a focus on using its genetic algorithms to produce content that adhere to game design patterns. Using the feedback from Eddy 2.0’s user study and studying other related works, new features have been planned and implemented to improve the elements of mixed-initiative in Eddy for version 3.0. The results primarily feature a new and improved workflow as well as enhancing a room’s context with the addition of a logical world grid. This in turn improves the program’s intuitiveness and makes it possible for the designer to create more complex designs of their preference while maintaining the general functionalities of the original Eddy. The study was evaluated with a small scale user study of five users at a game development company with most of the feedback being positive in nature. From the feedback more features have been planned for a future version of Eddy being version 4.0.
Accessible Digital Musical Instruments - a Survey of Inclusive Instruments Presented at the NIME, SMC and ICMC Conferences

This paper describes a survey of accessible Digital Musical Instruments (ADMIs) presented at the NIME, SMC and ICMC conferences. It outlines the history of research concerned with facilitating inclusion in music making and discusses advances, current state of developments and trends in the field. Based on a systematic analysis of DMIs presented at the three conferences, seven control interface types could be identified: tangible, nontangible, audio, touch-screen, gaze, BCMIs and adapted instruments. Most of the ADMIs were tangible interfaces or physical controllers. Many of the instruments were designed for persons with physical disabilities or children with health conditions or impairments. Little attention was paid to DMIs for blind users. Although the haptic modality could play an important role in musical interaction in this context, relatively few of the ADMIs (26.7%) incorporated vibrotactile feedback. A discussion on future directions for inclusive design of DMIs is presented.
An analysis of software defect prediction studies through reproducibility and replication

Context. Software defect prediction is essential in reducing software development costs and in helping companies save their reputation. Defect prediction uses mathematical models to identify patterns associated with defects within code. Resources spent reviewing the entire code can be minimised by focusing on defective parts of the code. Recent findings suggest many published prediction models may not be reliable. Critical scientific methods for identifying reliable research are Replication and Reproduction. Replication can test the external validity of studies while Reproduction can test their internal validity. Aims. The aims of my dissertation are first to study the use and quality of replications and reproductions in defect prediction. Second, to identify factors that aid or hinder these scientific methods. Methods. My methodology is based on tracking the replication of 208 defect prediction studies identified in a highly cited Systematic Literature Review (SLR) [Hall et al. 2012]. I analyse how often each of these 208 studies has been replicated and determine the type of replication carried out. I use quality, citation counts, publication venue, impact factor, and data availability from all the 208 papers to see if any of these factors are associated with the frequency with which they are replicated. I further reproduce the original studies that have been replicated in order to check their internal validity. Finally, I identify factors that affect reproducibility. Results. Only 13 (6%) of the 208 studies are replicated, most of which fail a quality check. Of the 13 replicated original studies, 62% agree with their replications and 38% disagree. The main feature of a study associated with being replicated is that original papers appear in the Transactions of Software Engineering (TSE) journal. The number of citations an original paper had was also an indicator of the probability of being replicated. In addition, studies conducted using closed source data have more replications than those based on open source data. Of the 4 out of 5 papers I reproduced, their results differed with those of the original by more than 5%. Four factors are likely to have caused these failures: i) lack of a single version of the data initially used by the original; ii) the different dataset versions available have different properties that impact model performance; iii) unreported data preprocessing; and iv) inconsistent results from alternative versions of the same tools. Conclusions. Very few defect prediction studies are replicated. The lack of replication and failure of reproduction means that it remains unclear how reliable defect prediction is. Further investigation into this failure provides key aspects researchers need to consider when designing primary studies, performing replication and reproduction studies. Finally, I provide practical steps for improving the likelihood of replication and the chances of validating a study by reporting key factors.
The Image of the Monolingual Dictionary Across Europe. Results of the European Survey of Dictionary use and Culture

The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section.
DETECTION OF SURFACE AND SUBSURFACE DEFECTS OF APPLES USING STRUCTURED-ILLUMINATION REFLECTANCE IMAGING WITH MACHINE LEARNING ALGORITHMS

Machine vision technology coupled with uniform illumination is now widely used for automatic sorting and grading of apples and other fruits, but it still does not have satisfactory performance for defect detection because there are a large variety of defects, some of which are difficult to detect under uniform illumination. Structured-illumination reflectance imaging (SIRI) offers a new modality for imaging by using sinusoidally-modulated structured illumination, to obtain two sets of independent images, i.e., direct component (DC), which corresponds to conventional uniform illumination, and amplitude component (AC), which is unique to structured illumination. The objective of this study was to develop machine learning classification algorithms by using DC and AC images and their combinations for enhanced detection of surface and subsurface defects of apples. A multispectral SIRI system under illumination of two phase-shifted sinusoidal patterns was used to acquire near-infrared images from ‘Delicious’ and ‘Golden Delicious’ apples with various types of surface and subsurface defects. DC and AC images were extracted through demodulation of the acquired images, and were then enhanced using bi-dimensional empirical mode decomposition (BEMD) and subsequent image reconstruction. Defect detection algorithms were developed, by using random forest (RF), support vector machine (SVM) and convolutional neural network (CNN), for DC, AC, and ratio (AC divided by DC) images and their combinations. Results showed that AC images were superior to DC images for detecting subsurface defects and DC images were overall better than AC for detecting surface defects, whereas ratio images were comparable to, or better than, DC and AC images for defect detection. The ensemble of DC, AC and even ratio images resulted in significantly better detection accuracies over using them individually. Among the three classifiers, CNN performed the best with 98% detection accuracies for both varieties of apples, followed by SVM and RF. This research demonstrated that SIRI, coupled with a machine learning algorithm, can be a new, versatile and effective modality for fruit defect detection.
Are Octave, Scilab and Matlab Reliable?

In a word, no. In this article we show evidence that three platforms frequently used for solving Engineering problems have numerical pitfalls. These platforms are Octave, Scilab and Matlab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). They were submitted to two comprehensive tests, namely the data sets and functions provided by NIST (National Institute of Standards and Technology), and our proposal of a set of matrices and operations on them. NIST protocol includes the computation of basic univariate statistics (mean, standard deviation and ﬁrstlag correlation), linear regression, and extremes of probability distributions. Our set of operations include matrix inversion and the computation of the determinant and eigenvalues. The assessment is made comparing the results computed by the platforms, and assessing the number of correct digits with respect to certiﬁed values. Serious pitfalls are identiﬁed in seemingly easy tasks as, for instance, the ﬁrst-lag autocorrelation coefﬁcient. Whenever available, the results are compared with those provided by R, a FLOSS (Free/Libre Open Source Software), whose excellent numerical abilities have been reported elsewhere.
The Learning Exchange, a Community Knowledge Commons for Learning Networks: Qualitative Evaluation to Test Acceptability, Feasibility, and Utility

Background Learning Networks are distributed learning health systems that enable collaboration at scale to improve health and health care. A key requirement for such networks is having a way to create and share information and knowledge in furtherance of the work of the community. Objective We describe a Learning Exchange—a bespoke, scalable knowledge management and exchange platform initially built and tested for improving pediatric inflammatory bowel disease outcomes in the ImproveCareNow (ICN) Network—and assess evidence of its acceptability, feasibility, and utility in facilitating creation and sharing of information in furtherance of the work of the community and as a model for other communities. Methods Acceptability was assessed via growth in active users and activity. Feasibility was measured in terms of the percentage of users with a log-in who became active users as well as user surveys and a case study. Utility was measured in terms of the type of work that the Learning Exchange facilitated for the community. Results The ICNExchange has over 1000 users and supported sharing of resources across all care centers in ICN. Users reported that the Learning Exchange has facilitated their work and resulted in increased ability to find resources relevant to local information needs. Conclusions The ICNExchange is acceptable, feasible, and useful as a knowledge management and exchange platform in service of the work of ICN. Experience with the ICNExchange suggests that the design principles are extensible to other chronic care Learning Networks.
Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends

The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.
Emotions in Wikipedia: the role of intended negative events in the expression of sadness and anger in online peer production

ABSTRACT The online encyclopaedia Wikipedia has strict guidelines for ensuring the objectivity of its content and unbiased language of its articles. Wikipedia also provides articles on negative events such as disasters. Depending on the event, the Wikipedia community may express certain emotions when writing Wikipedia articles on those events. So far, it is unclear which psychological mechanisms are reflected in the potential emotional expressions. Based on appraisal theories of specific emotions and on previous research on intentional harm, we investigated whether intentional negative events such as terrorist attacks elicited different emotional expressions than unintentional negative events, such as natural or man-made disasters. We analysed N = 330 Wikipedia articles with automatic linguistic text analyses and found that Wikipedia articles on man-made attacks (e.g. terrorist attacks, shooting rampages) contained more anger-related content than Wikipedia articles on man-made disasters (e.g. ship accidents, train accidents) and natural disasters (e.g. hurricanes, flooding); F (2, 327) = 127.78, p < . 001, = . 439. Wikipedia articles on man-made attacks also contained fewer sadness-related words than articles on natural and man-made disasters, F (2, 327) = 19.01, p < . 001, = . 104. We discuss the implications of these findings with respect to theory and practical applications.
Learning object metadata surrogates in search result interfaces: user evaluation, design and content

The purpose of this research was to evaluate user interaction with learning object metadata surrogates both in terms of content and presentation. The main objectives of this study were: (1) to review the literature on learning object metadata and user-centred evaluation of metadata surrogates in the context of cognitive information retrieval (including user-centred relevance and usability research); (2) to develop a framework for the evaluation of user interaction with learning object metadata surrogates in search result interfaces; (3) to investigate the usability of metadata surrogates in search result interfaces of learning object repositories (LORs) in terms of various presentation aspects (such as amount of information, structure and highlighting of query terms) as a means for facilitating the user relevance judgment process; (4) to investigate in-depth the type of content that should be included in learning object metadata surrogates in order to facilitate the process of relevance judgment; (5) to provide a set of recommendations—guidelines for the design of learning object metadata surrogates in search result interfaces both in terms of content and presentation. [Continues. ]
Automatic Indexing of Patent Right-claiming Document Based on Deep Learning

In recent years, there have been more and more applications of deep learning in natural language processing, and people have paid more and more attention to the value embodied in patent. In this paper, based on the value of right-claiming document in the patent, a deep learning tool word2vec is used to convert text information into a set of word embeddings. The word embeddings carry semantic information, so that the quantified metrics the relationships between words. Then, the kmeans clustering method is used to extract words whose distance between words is closer to the center of the cluster, so as to achieve the purpose of automatic indexing the right-claiming document. Keywords—deep learning; word2vec; k-means; automatic indexing; keyword extraction
Crowdsourcing mode-based learning activity flow approach to promote subject ontology generation and evolution in learning

ABSTRACT Subject ontology can help implement the structured organization of knowledge for online learners and thus plays an important role in the learning process. However, building ontologies by experts is time-consuming, and the adaptation of such ontologies to different contexts might be a problem. Crowdsourcing, which allows users to build and refine ontologies during their learning process, has exhibited potential for solving this problem. In this study, a crowdsourcing mode-based learning activity flow approach is proposed to guide the learning of online learners while promoting the generation and evolution of subject ontologies using the learners’ contributions to the learning content. This flow makes full use of the learners’ wisdom during the learning process to promote self-regulated learning as well as the generation and evolution of the ontology. Based on the proposed approach, a learning support system was developed and an experiment conducted involving a Chinese lesson on “The Liangzhou Poem”. In the experiment, student participants built 722 triples, of which 584 evolved as formal items in the subject ontology. Moreover, all learners were able to construct a well-organized knowledge graph. Students in both high- and low-scoring groups contributed valuably to the knowledge generation and evolution of the subject ontology. Furthermore, while the widths of the knowledge constructed by students in high- and low-scoring groups were similar, their depths were substantially different. During this process, the crowdsourcing-based activity flow system achieved satisfactory technique acceptance, which means that the proposed approach and system are useful for the effective generation of subject ontologies while helping learners acquire knowledge.
Statistical Model for Yield Estimation of ‘Gala Red Lum’ Apples after Bloom in Northern India

The objective of this paper is to develop the best fitted model for estimating yield in Gala Red Lum after bloom . The data used for this research were primary data collected from high density apple block of SKUAST-Kashmir (HDP, Plate-1). The study was undertaken at experimental field of Division of Fruit Science, SKUAST-Kashmir, Srinagar, J&K, Northern India, during the years 2015 and 2016. The measurements of various tree/fruit characteristics in Gala Red Lum were recorded. The data recorded was pooled to average out the on /off bearing effect. Regression technique was used to find the relationship between the yield and various biometrical characters of apple trees. Models were first developed on the whole population and then on sample. The best fitted model was Y= -FCT+FCA-FCT*FCA and was validated by using k-fold cross validation and bootstrap validation techniques. Thus, the developed model can be used for estimating the yield in Gala Red Lum after bloom .
Technology Enhanced Learning Environments

Introduction Consider a school in a remote community several hours traveling time from the nearest metropolitan center. Regular excursions to the city are limited to a few times in the year when the weather and traveling conditions are favourable. Being an isolated community with resource-based economy, it is not large enough to support a full K-12 school system. As a result students are arranged in split-classes as follows: K – 2, 3 – 6, 7 – 9 and 10 – 12. Nonetheless, the enterprising people of the community are determined to provide the best possible education for their children.
A Lexicographic Approach to Teaching the English Article System: Help or Hindrance?

This article reports on changes in EFL learners' article choice performance before and after receiving lessons on the main rules applicable to article usage combined with dictionary consultation guidance. A sample of 43 Korean college students undertook the same forced-choice elicitation task once as a diagnostic test and again as a post-intervention test at three-month intervals. Unlike the diagnostic test, in which the participants were only asked to choose the correct articles, the post-intervention test asked them to give written accounts of their decision-making procedures as well. The analyses of the diagnostic test results, specifically the items requiring the indefinite article or the zero article, demonstrated EFL learners' struggle with indeterminate nominal numbers, underlining the importance of clear lexicographic treatment of such information. Further, the post-intervention test and the written think-aloud data analyses suggested that although using a bilingualised dictionary for nominal countability is useful in general, dictionary consultation can sometimes impede users from using articles correctly. Specific problem areas are discussed.