Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstructing the Ontology of the Tang Dynasty A pilot study of the Shakespearean-garden approach

We propose the Shakespearean-garden approach towards domain ontology construction in this paper. In sum, we suggest that domain lexica can be extracted and obtained for non-standard knowledge backgrounds. Once the comprehensive lexica are collected, a lexical interface between wordnet and our Sinica BOW can be applied. It will allow each lexical item to a conceptual location on Sinica BOW. With the WordNet and SUMO interface, as well as our bilingual correspondence program, each domain lexica can be mapped to a set of SUMO conceptual nodes. These nodes will each be linked to the ontology. We show that the domain ontologies can be constructed directly from synset-ontology pairs, or from the lexical information taken from Wordnet.
Towards a Model of Advanced Learners’ Vocabulary Acquisition: An Investigation of L2 Vocabulary Acquisition and Retention by Taiwanese English Majors

Abstract Development of vocabulary is one of the fundamental elements for second language acquisition; it is important to meaning transfer and successful communication as well (Barcroft 2004). The purpose of the research reported in this paper was to investigate to what extent the characteristics of vocabulary affect Taiwanese English language majors’ second-language vocabulary acquisition and retention. This research builds upon similar research conducted by Willis and Ohashi (2012), which found a relationship between acquisition and a word’s frequency, cognateness, and length for Japanese learners of English. Our results indicate a significant relationship between acquisition and a word’s frequency, level of polysemy, and part of speech. Correlation results show that polysemy and frequency are both strongly related to acquisition. Nevertheless, a negative correlation between phonemes and acquisition was also shown. The findings indicate that high frequency words and polysemous words are both easily acquired and retained; in contrast, words with more phonemes require more effort by Taiwanese English language majors to learn and retain. Findings from a multiple regression analysis further suggest that high frequency polysemous nouns are easily acquired and retained by Taiwanese English language majors. Pedagogical implications concerning the variables investigated are provided.
Applying CPM-GOMS Analysis for Predicting and Explaining Two-Handed Korean Text Entry Task on Mobile Phone

In this study, we employ CPM-GOMS analysis for explaining physical and cognitive processes and for quantitatively predicting when users are typing Korean text messages on mobile phones using both hands. First, we observe the behaviors of 10 subjects, when the subjects enter keypads with both hands. Then, basing upon MHP, we categorize the behaviors into perceptual, cognitive, motor operators, and then we analyze those operators. After that, we use the critical paths to model three task sentences. Also, for the sake of comparison between the actual data and the results predicted from our CPM-GOMS model, we empirically tested 30 subjects and concluded that there were no significant differences between the predicted values and the actual data. With the CPM-GOMS model, we can observe the human information processes composed on the physical and cognitive processes. Also we verified that the CPM-GOMS model can be well applied to predict the users' performance when they input text messages on mobile phones using both hands by comparing the predicted total task time with the real execution time.
INFLUENCE OF AUXINS ON ROOTING STEM CUTTINGS OF MORINGA OLEIFERA LAM IN DRY AND WET SEASONS

The study was designed to evaluate the influence of three types of auxins: Indole Butyric Acid (IBA), Naphthalene Acetic Acid (NAA) and, Indole Acetic Acid (IAA) at four concentrations (0mg/l, 100mg/l, 300mg/l and 500mg/l) and two seasons (dry and wet) on rooting mature stem cuttings of Moringa oleifera. The experiment was 3×4×2 factorial laid in a completely randomized design with three replicates. The findings of this study show that, M oleifera can be propagated vegetatively by rooting the stem cuttings with auxins. The results of the developmental phases of the stem cuttings prior to rooting significantly (P>0.05) showed variations due to auxin concentrations and seasons. Stem cuttings treated with the higher concentration of the three auxins (500ppm of IBA, NAA and IAA) showed that wet season stem cuttings sprouted earlier (18-27 days after planting, DAP) than the dry season stem cuttings (24-30 DAP). The results also showed that auxin application and seasons influenced higher bud development into leaves in the wet than in the dry season (2.0% 83.4% wet season and, 10% 56.08% dry season). It was also observed that, untreated (control) stem cuttings and those treated with 100mg/l and 300mg/l did not root in the dry season trial. While lateral roots were observed on stem cuttings across the treatments in the wet season, only those treated with 500mg/l of the auxins produced tap roots with IAA proving most effective. Therefore, rooting stem cuttings of M. oleifera with 500mg/l of IAA during the wet season is recommended.
Threshold Voltage Modeling for Nanometer Scale Junction Less Double Gate MOSFET

Most of the existing transistors used in fabrication of integrated chips are with junctions. The device scaling is growing the channel length between junctions in devices are scaling gone down to 10 nm. For smooth functioning of the device extremely high doping concentration gradients become necessary. These junction are becoming increasingly difficult to fabricate. The newer devices have been proposed and demonstrated in which there are no junctions and no gradients of doping. One such device is the Double Gate Metal Oxide Semiconductor Field Effect Transistor (DG-MOSFET) without junction which shows remarkable performance against short channel effects such as Drain Induced Barrier Lowering (DIBL), threshold voltage roll-off, etc. In this paper, a two dimensional model of threshold voltage is considered to study the performance of DG-MOSFET. The model used shows how the DG-MOSFET parameters such as the silicon thickness, oxide thickness, drain bias, and channel length, affect the threshold voltage degradation.
Physical parameters based on Fe-Ti oxide compositions and their correlation with geochemical signatures of mid-Miocene rhyolites, eastern Oregon

This Thesis is brought to you for free and open access. It has been accepted for inclusion in University Honors Theses by an authorized administrator of PDXScholar. For more information, please contact pdxscholar@pdx.edu. Recommended Citation Orr, Aleysa A. , "Physical Parameters based on Fe-Ti Oxide Compositions and Their Correlation with Geochemical Signatures of MidMiocene Rhyolites, Eastern Oregon" (2019). University Honors Theses. Paper 744.
Domestic Violence and Child Protection : Challenging directions for practice May 2007

In section one, this paper contends that simply ‘grafting’ domestic violence onto the existing child protection system can push an already vulnerable situation towards system failure, when judged against the criteria for a functional system (Checkland and Poulter 2006). It argues that issues specific to domestic violence need to be addressed if an effective intervention for children affected by domestic violence is to occur3. Four issues are explored, along with the negative and unintended consequences that have arisen. The discussion invites a re-consideration of the practice of referring or notifying all children affected by domestic violence to statutory child protection agencies which often, although not always, occurs due to the requirements of mandatory notification. 5
The comparative investigation of the antioxidant activities of some species belonging to the Lamiaceae and Poaceae families

Bitkilerin antioksidan bilesikleri aroma, renk, koku ve koruyucu ozellikleri nedeniyle tibbi ve endustriyel kullanim amaciyla yogun olarak arastirilmaktadir. Bu calismada, Lamiaceae’den 16 tur ve Poaceae’den 14 tur, Kuzeydogu Akdeniz bolgesinden toplandi ve analiz edildi. Bitki orneklerinin kuru agirlik ve su durumlari belirlendi. Bu iki familyanin uyeleri arasinda klorofil ve karoten degerleri bakimindan onemli bir fark olmadigi belirlendi. Lamiaceae turlerinde ortalama ksantofil icerigi (236 µg g -1 FW) Poacea turlerinden (142 µg g -1 FW ) ve total antioksidatif kapasite Lamiacea turlerinde (5.19 mg g -1 FW) Poaceae turlerinden (3.49 mg g -1 FW) daha yuksek bulundu. Buna karsin toplam cozunur fenolikler Poaceae turlerinde ortalama 1.83 mg g -1 FW, Lamiaceae turlerinde 1.67 mg g -1 FW olarak olculdu. Superoksit dismutaz enzim aktivitesi Poaceae turlerinde daha yuksek bulundu. Bulgular iki familya arasinda onemli farkliliklar bulunabilecegini ve biyokimyasal kaynaklarin belirlenmesi bakimindan karsilastirmali calismalarin cogaltilmasi gerektigini ortaya koymaktadir.
A COGNITIVE-PRAGMATIC APPROACH TO METAPHOR AND METONYMY IN BRAND NAMES : A CASE STUDY OF FILM TITLES

This paper analyzes the cognitive and pragmatic roles of conceptual metaphors and metonymies in brand names. By investigating a corpus of 535 Oscar film titles, it argues that metaphor and metonymy are two powerful naming strategies for film branding. Abundant evidence reveals that film titles are rich in metaphor and metonymy and relevant cognitive operations are activated to generate the drawing of inferences from the semantic cues provided by metaphorical and metonymic film titles. Specifically, in metaphorical titles, the correlation operation can limit the risk of negative inference generations and the comparison operation can enhance the degree of lexical richness that helps film plot conceptualization; in metonymic titles, the domain expansion operation can highlight film plot information, and the domain reduction operation can present abundant film contents which makes film title more economical. In this way, metaphor and metonym are taken as safe and effective film branding and marketing strategies. The cognitive analysis of film title contributes to studies in brand names, verifies and supplements cognitive-pragmatic theories and also sheds light on cognitive research in advertising discourse.
SWARM TECHNOLOGIES FOR FUTURE SPACE EXPLORATION MISSIONS

Modern robotic platforms for in-situ space exploration are single-robots equipped with a number of specialized sensors providing scientists with unique information about a planet’s surface. However, there is a number of exploration problems where large spatial apertures of the exploration system are necessary, requiring a completely new perspective on in-situ space exploration and it’s required technologies. Large networks of robots, called swarm, pave the way: agents in a swarm span ad-hoc communication networks, localize themselves based on radio signals, share resources, process data and make inference over the network in a decentralized fashion. By cooperation, local information collected by agents becomes globally available. In this work we present our recent results in development of swarm technologies for future in-situ space exploration missions: a wireless system jointly used for communication and localization, and swarm navigation and exploration strategies to sample and reconstruct static spatial fields.
Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.
Controlling Tuberculosis in Community Corrections

The increasing incidence of tuberculosis infection and active disease in the United States raises significant issues because much of the increase has occurred in populations involved with the criminal justice system. Persons with TB, moreover, are often also infected with HIV. In addition, new strains of drug-resistant TB have appeared in part because of patients’ failure to follow prescribed courses of medication. Unless inmates with TB infection and disease are identified and appropriately treated while incarcerated, TB—including drug-resistant TB—can later develop among released inmates, who then may transmit the infection to their families, community corrections officials, and the general public.
Extracting automata from neural networks using active learning

Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying. In this paper, we present an active learning framework to extract automata from neural network classifiers, which can help users to understand the classifiers. In more detail, we use Angluin’s L* algorithm as a learner and the neural network under learning as an oracle, employing abstraction interpretation of the neural network for answering membership and equivalence queries. Our abstraction consists of value, symbol and word abstractions. The factors that may affect the abstraction are also discussed in the paper. We have implemented our approach in a prototype. To evaluate it, we have performed the prototype on a MNIST classifier and have identified that the abstraction with interval number 2 and block size 1 × 28 offers the best performance in terms of F1 score. We also have compared our extracted DFA against the DFAs learned via the passive learning algorithms provided in LearnLib and the experimental results show that our DFA gives a better performance on the MNIST dataset.
Path Sensitive Program Verification Based on SMT Solvers

With the rapid increase in size and complexity of software, more and more attention is paid to the software's trust. Verifying whether programs satisfy the properties described by assertions is a common method to guarantee trust of the software. Since path sensitive program verification cannot traverse all paths, it needs merge the path information, which causes a loss of precision. The study proposes a program verification method using SMT solvers, which can reduce the path search space and improve the precision at the same time. The method's main sprit is impacting the cycle path by using maximal strongly connected component and slicing the CFG according to the aim assertion. The study abstracts the path space using Boolean formulas and verifies the path by combining abstract interpretation and symbolic execution. The study has conducted experiments based on the F-Soft program verification platform and SMT solver Z3, and results show that this method performs well based on precision and effect.
Using typed messages and message filters for the realization of pervasive applications

This paper presents a software framework, called HDS, that tries to simplify the realization of distributed and adaptive applications by merging the client-server and the multi-agent paradigms and by implementing all the interactions among the processes of a system through the exchange of typed messages and the use of message filters for driving and dynamically adapting the behavior of the system. Typed messages and message filters are the elements that mainly characterize such a software framework. Typed messages can be considered an object-oriented "implementation" of the types of message defined by an agent communication language and so they are means that make HDS a suitable software framework both for the realization of multi-agent systems and for the reuse of multi-agent model and techniques in nonagent based systems. Message filters drive and adapt the behavior of a system by acting on the exchange of messages. On the one hand, message filters can constrain the exchange of messages, modify the flow of messages, and manipulate their content. On the other hand, processes can dynamically add and remove some message filters to adapt the behavior of a system to any hardware and software new configuration and to any new user requirement.
Enlightening Test-Driven with Formal , Formal with Test-Driven through Spec-Test-Go-Round

The impression is that formal methods are at odds with popular approaches such as agile software development. However, these different approaches should instead be significant mutual complementary in their essential principles. It is an essential problem how to convince this point beyond apparently different communities that tend to be disjoint. This paper discusses this point by combining the principles of test-driven development and its extensions (such as “specification by example”) with formal specification methods. We constructed a language and prototype tool called JSTN that mixes specifications, test designs, and test cases with a model finder. Together, they enable interactive, quick feedback as well as a flexible blend of different principles. The role of this tool in education and enlightenment is evaluated through a seminar with over 60 industry engineers including many “agile people. ” 3
Extensions of distance and prototype methods for point patterns

of the Dissertation Extensions of distance and prototype methods for point patterns by David Mark Diez Doctor of Philosophy in Statistics University of California, Los Angeles, 2010 Professor Frederic Paik Schoenberg, Chair Many applications involve datasets that may be considered repeated observations of a point process over identical (or similar) spaces. In this work, distance measure techniques and their application to point pattern prototypes were expanded and developed for realizations of collections of point processes. These new methods readily extend to multiple dimensions. A combination of distance and prototype approaches were used to analyze neuronal spike data of cats in different behavioral states and across animals, and results are compared to previous time series analyses that used averaged frequency histograms. An R package has also been constructed to make these developments more widely available to researchers and practitioners, and this package is described in detail.
Vowel normalisation: Time-domain processing of the internal dynamics of speech

Human listeners can identify vowels regardless of speaker size, although the sound waves for an adult and a child speaking the ’same’ vowel would differ enormously. The differences are mainly due to the differences in vocal tract length (VTL) and glottal pulse rate (GPR) which are both related to body size. Automatic speech recognition machines are notoriously bad at understanding children if they have been trained on the speech of an adult. In this paper, we propose that the auditory system adapts its analysis of speech sounds, dynamically and automatically to the GPR and VTL of the speaker on a syllable-to-syllable basis. We illustrate how this rapid adaptation might be performed with the aid of a computational version of the auditory image model, and we propose that an auditory preprocessor of this form would improve the robustness of speech recognisers.
THE OPTIMAL PERFORMANCE OF MULTI-LAYER NEURAL NETWORK FOR SPEAKER-INDEPENDENT ISOLATED SPOKEN MALAY PARLIAMENTARY SPEECH

This paper describes speech recognizer modeling techniques which are suited to high performance and robust isolated word recognition in speaker-independent manner. In this study, a speech recognition system is presented, specifically for an isolated spoken Malay word recognizer which uses spontaneous and formal speeches collected from Parliament of Malaysia. Currently, the vocabulary is limited to ten words that can be pronounced exactly as it is written and controlled the distribution of the vocalic segments. The speech segmentation task is achieved by adopting energy based parameter and zero crossing rate measure with modification to better locates the beginning and ending points of speech from the spoken words. The training and recognition processes are realized by using Multi-layer Perceptron (MLP) Neural Networks with two-layer feedforward network configurations that are trained with stochastic error back-propagation to adjust its weights and biases after presentation of every training data. The Mel-frequency Cepstral Coefficients (MFCCs) has been chosen as speech extraction approach from each segmented utterance as characteristic features for the word recognizer. The MLP performance to determine the optimal cepstral orders and hidden neurons numbers are analyzed. Recognition results showed that the performance of the two-layer network increased as the numbers of hidden neurons increased. Experimental result also showed that the cepstral orders of 12 to 14 were appropriate for the speech feature extraction for the data in this study. KeywordsMulti-layer Perceptron, Feedforward, Mel-frequency Cepstral Coefficients, Hidden Neuron, Target vector.
Evaluating Seaweed as a Source of Protein in the Future of Food Production Worldwide 1 )

Alternative protein sources are constantly explored to secure the future food and protein demand. Among these sources, biomasses originating from algae[1]. Building on the United Nations Food and Agriculture Organization’s (FAO) food demand projections, it’s estimated that the world needs to close a 70 percent “food gap” between the crop calories available in 2006 and expected calorie demand in 2050 [12], in this review we evaluate seaweed as a source of protein as the future of food production worldwide. It is clear that seaweeds (microalgae) represent a sustainable source of various bioactive natural carotenoids and proteins [6,10]. However, several studies show that safety hazards for seaweed may include iodine, ANFs, heavy metals, radioactive isotopes, ammonium, dioxins, and pesticides[20, 21, 22]. At very high levels, most heavy metals can cause health problems. However, this is very uncommon [42]. Thus, this review suggest more research for cultivating seaweed without the presence of heavy metals or to experiment on how to extract heavy metals from seaweed. As seaweed is a very sustainable source of protein, we could use Seaweed as a source of protein for human consumption worldwide, however we need more research in the health hazards of heavy metals in seaweed and more research in techniques to prevent healthhazardsfrom the use of seaweed.