Miracle-dz/UniFAR-FTUs huggingface dataset:
Pelleting DDGS Diets for Poultry Introduction

ALTHOUGH DDGS IS AN ECONOMICAL ENERGY AND DIGESTIBLE NUTRIENT SOURCE in poultry diets, diet inclusion rates are often limited to less than 10 percent of the diet because of concerns of achieving desired pellet quality and pellet mill throughput. As a result, the ability of feed manufacturers and poultry producers to capture greater economic value from using higher dietary inclusion rates is diminished because of the diet inclusion constraints imposed on DDGS to meet desired pellet quality and production ef ciencies in commercial feed mills.
Matrix capsules with EM routing

A capsule is a group of neurons whose outputs represent different properties of the same entity. Each layer in a capsule network contains many capsules. We describe a version of capsules in which each capsule has a logistic unit to represent the presence of an entity and a 4x4 matrix which could learn to represent the relationship between that entity and the viewer (the pose). A capsule in one layer votes for the pose matrix of many different capsules in the layer above by multiplying its own pose matrix by trainable viewpoint-invariant transformation matrices that could learn to represent part-whole relationships. Each of these votes is weighted by an assignment coefficient. These coefficients are iteratively updated for each image using the Expectation-Maximization algorithm such that the output of each capsule is routed to a capsule in the layer above that receives a cluster of similar votes. The transformation matrices are trained discriminatively by backpropagating through the unrolled iterations of EM between each pair of adjacent capsule layers. On the smallNORB benchmark, capsules reduce the number of test errors by 45% compared to the state-of-the-art. Capsules also show far more resistance to white box adversarial attacks than our baseline convolutional neural network.
FORWARD SATURATION BASED MODEL CHECKING

Formal methods are gaining importance as safety critical, distributed and embedded systems are becoming widespread. By using formal verification we can find errors or we can prove the correctness in an early stage of the design. Model checking is an automatic verification method to check discrete, finite state models. In the last 20 years many model checking algorithms appeared: in this paper we focus on one of them, the so-called saturation algorithm. Saturation is a symbolic state space generation and model checking algorithm, which is efficient for globally asynchronous, locally synchronous (GALS) models. Former work presented structural model checking algorithms using saturation and constrained saturation, which were based on the classical backward traversal of the state space. In this paper we introduce saturation model checking based on forward state traversal. We hope this research direction to further improve the efficiency of model checking algorithms.
Denoising Convolutional Autoencoders for Noisy Speech Recognition

We propose the use of a deep denoising convolutional autoencoder to mitigate problems of noise in real-world automatic speech recognition. We propose an overall pipeline for denoising, experiment with a variety of network architectures and conﬁguration parameters, and report results on an intermediate reconstruction error metric. Our experiments show that optimal conﬁguration for convolutional denoising varies signiﬁcantly from that for standard CNN tasks such as image recognition. Further, the proposed method easily beats a simple baseline and anecdotally displays interesting behavior. A number of avenues for further research are discussed.
English Language Guiding Platform Based on Pattern Recognition and Cloud Computing Simulation

In order to adapt to the rapid development of the network society, schools need to effectively improve college students' comprehensive ability of English application and rapidly improve students’ listening and speaking skills. The campus teaching platform based on network regards enhancing students' English learning initiative as the core, enhancing students' autonomous English learning interest and ability as the standard, in order to improve the efficiency of students' English learning, and to better serve the purpose of teaching. In this paper, we briefly describe the main problems that need to be solved, and outline the overall situation of the system, and analyze the functional requirements of the system. On the basis of requirement analysis, and according to constructivism theory, we analyze the requirement data of English learners, and fully apply computer network technology and multimedia technology to establish an open network English teaching platform. The English teaching platform regards the administrator as the main data manager, and teachers mainly go through the system to obtain student learning data. Students can obtain rich video, image, voice, text and other resources through the system. Students can always get help from teachers and classmates, while using the Internet review to consolidate classroom knowledge through computer technology to monitor the learning of students, which has played a role in promoting learning effectiveness and efficiency.
LOWER BOUNDS FOR GROMOV WIDTH OF THE U(n)-COADJOINT ORBITS

We use the Gelfand-Tsetlin pattern to construct an effective Hamiltonian, completely integrable action of a torus T on an open dense subset of a coadjoint orbit of the unitary group. We then identify a proper Hamiltonian T -manifold centered around a point in the dual of the Lie algebra of T . A theorem of Karshon and Tolman says that such a manifold is equivariantly symplectomorphic to a particular subset of R2D. This fact enables us to construct symplectic embeddings of balls into certain coadjoint orbits of the unitary group, and therefore obtain a lower bound for their Gromov width. Using the identification of the dual of the Lie algebra of the unitary group with the space of n× n Hermitian matrices, the main theorem states that for a coadjoint orbit through λ = diag(λ1, . . . , λn) in the dual of the Lie algebra of the unitary group, where at most one eigenvalue is repeated, the lower bound for Gromov width is equal to the minimum of the differences λi − λj , over all λi > λj . For a generic orbit (i.e. with distinct λi’s), with additional integrality conditions, this minimum has been proved to be exactly the Gromov width of the orbit. For nongeneric orbits this lower bound is new.
220 Monetary policy regimes and the volatility of Long-Term interest rates

This paper addresses two important questions that have, so far, been studied separately in the literature. First, the paper aims at explaining the high volatility of long-term interest rates observed in the data, which is hard to replicate using standard macro models. Building a small-scale macroeconomic model and estimating it on U.S. and U.K. data, I show that the policy responses of a central bank that is uncertain about the natural rate of unemployment can explain this volatility puzzle. Second, the paper aims at shedding new light on the distinction between rules and discretion in monetary policy. My empirical results show that using yield curve data may facilitate the empirical discrimination between different monetary policy regimes and that U.S. monetary policy is best understood as originating from a discretionary regime since 1960.
Ontology Based Automatic Text Mining Using TF and IDF Algorithms for Summarization of Multiple Files

DOI: 10.21276/sjeat.2018. 3.6. 8 Abstract: In the present world, due to tremendous development in technology, a huge amount of information is available everywhere. Therefore, it is difficult for the users to understand the main content of the entire document as it takes a lot of time. In this work we use extractive text summarization which uses a method to give the version of summary for one or more file or document. Here we give an approach that maps sentences to nodes of a hierarchical ontology. Ontology explains what exists in a particular domain. For the ontology creation, vocabularies are collected. It is used as background knowledge and helps to find the related meaning of the terms which occur in the source documents. Text mining is the technique from which high quality information is derived from text. Clustering is a significant task. The clustering method groups similar or related terms into a single group. In the first stage, data collection takes place. The preprocessing stage includes stemming and stop words removal.TF-IDF process occurs after which clustering takes place. In the ontology creation, first the determination of the main sub topics of the article of interest is done. We classify sentences to nodes which have a predefined hierarchical ontology. Each ontology node has bag-of-words from a web search. We represent sentences by sub trees that permit to apply measures of similarity and find relations between sentences. The ontology used in this work is not domainspecific; it does not require labelled data. this work can be extended to topics focused on summarization framework to news articles or blogs and to also to various machine learning approaches
No . 07-09 Distributional and Efficiency Impacts of Increased U . S . Gasoline Taxes

We examine the impacts of increased U.S. gasoline taxes with a model that links the markets for new, used, and scrapped vehicles. Parameters for the household demand side of the model derive from an estimation procedure that integrates individual choices for car ownership and miles traveled. The model considers both shortand long-run impacts of policy changes, and recognizes the considerable heterogeneity among households and cars. We find that each cent-per-gallon increase in the price of gasoline reduces the equilibrium gasoline consumption by about . 2 percent. Impacts on the used car market change significantly over time. Taking account of revenue-recycling, the impact of a 25-cent gasoline tax increase on the average household is about $30 per year (2001 dollars). Distributional impacts depend importantly on how additional revenues from the tax increase are recycled. If revenues are recycled in equal amounts to each household, the average household in each of the bottom four income deciles experiences a welfare gain from a gasoline tax increase. On the other hand, if revenues are recycled in proportion to income, only very poor households (those in the lowest decile) and very rich households (those in the highest) stand to gain.
Study the Spectral Properties of the Molecule Trimethyl Indium ( TMIn ) Using Semiempirical Quantum Programs

This study aims to determine the spectral properties of the trimethyl Indium molecule (TMIn) using the Semi-empirical quantum programs [HyperChem8.0, WinMopac7.21] by (MNDO-PM3) (Modified Neglect of Differential Overlap-Parameterization Model3). This method characterized by the flexibility, the simplicity and the quick performance. The equilibrium geometry and the potential curve were calculated and then from the curve, the total energy of the molecule at the equilibrium distance was calculated. In addition, the dissociation energy, the electronical energy, the core-core repulsion, the ionization energy, the zero point energy, the energy gap, the frequencies and basic vibrational modes have been calculated. The study’s results were in agreement with the previous research.
DETERMINANTS OF FARMLAND PRICES IN GERMANY

Valuing land is a critical issue for farm credit, especially during periods of agricultural reform. If banks determine the loan availability for agricultural land primarily on the basis of the land’s its value (agronomic characteristics and commodity price levels), agro-political reform could influence land value estimates, bank loan procedures, and, consequently, farmers’ access to credit for land purchases. To the extent that non-agricultural factors are incorporated into agricultural land valuation, agropolitical reform could lose its impact on land price estimates, and in turn farm credit availability. Farmland price studies have used a variety of methods and data from the United States, Canada, and Argentina. Most of the research has been conducted in countries characterised by relatively free land markets. In addition, these countries have large rural areas where agricultural production occurs as a primary activity. Analysis of farmland prices in countries characterized by small geographic area, high population density, limited rural area, and regulated land markets has been less common. Consequently, the relative importance of agricultural and non-agricultural actors in determining farmland prices in these countries is not well understood. Population settlement patterns, agricultural land availability, farm-processor transportation costs, and processor dependence on
Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets

With the advances in information and communication technologies, social media and microblogging platforms serve as an important source of information. In microblogging platforms, people can share their opinions, complaints, sentiments and attitudes towards topics, current issues and products. Sentiment analysis is an important research direction in natural language processing, which aims to identify the sentiment orientation of source materials. Twitter is a popular microblogging platform, where people all over the world can interact by user-generated text messages. Information obtained from Twitter can serve as an essential source for several applications, including event detection, news recommendation and crisis management. In sentiment classification, the identification of an appropriate feature subset plays an important role. LIWC (Linguistic Inquiry and Word Count) is an exploratory text analysis software to extract psycholinguistic features from text documents. In this paper, we present a psycholinguistic approach to sentiment analysis on Twitter. In this scheme, we utilized five main LIWC categories (namely, linguistic processes, psychological processes, personal concerns, spoken categories and punctuation) as feature sets. In the experimental analysis, five LIWC categories and their ensemble combinations are taken into consideration. To explore the predictive performance of different feature engineering schemes, four supervised learning algorithms (namely, Naive Bayes, support vector machines, k-nearest neighbor algorithm and logistic regression) and three ensemble learning methods (namely, AdaBoost, Bagging and Random Subspace) are utilized. The experimental results indicate that ensemble feature sets yield higher predictive performance compared to the individual feature sets.
LTE Performance Evaluation Based on two Scheduling Models LTE downlink and uplink analysis

This paper presents a detailed analysis on the Long Term Evolution performance in both downlink and uplink directions emphasizing the most important aspects that influence the performance indicators. Round Robin and Weighted Round Robin scheduling strategies, in time domain and time-frequency domain, are used in different scenarios concerning antenna configuration, number of users and types of services in order to evaluate cell throughput, average user throughput and cell capacity. The control channels bring some limitations in the number of users served and on the actual transmission bandwidth when time-frequency domain packet scheduling is implemented and all these are reflected in the simulation results. This paper offers an image of the LTE network performance in various scenarios, the most important aspect being the cell capacity evaluation with a certain minimum or expected service throughput.
Reconnaissance d’actions 3D par codage parcimonieux sur l’espace de Kendall

Les représentations de formes ainsi que leur évolution temporelle, appelées trajectoires, reposent souvent sur des variétés non-linéaires. Ceci engendre une contrainte supplémentaire pour l’usage des techniques d’apprentissage automatique conventionnelles. Cet article adapte la méthode classique de codage parcimonieux et apprentissage de dictionnaire à l’espace de formes de Kendall et illustre son utilisation sur la reconnaissance d’actions 3D à partir de données squelettiques. En se basant sur la géométrie Riemannienne de l’espace des formes, une formulation intrinsèque du codage parcimonieux est d’abord proposée pour des formes statiques afin de contourner le problème de non-linéarité de l’espace des formes. Les trajectoires sont ainsi représentées dans un espace vectoriel par des séries temporelles parcimonieuses. Enfin, nous adoptons deux techniques de classification : un LSTM bidirectionnel appliqué directement sur ces séries temporelles et un SVM linéaire précédé par l’application d’une pyramide temporelle de Fourier sur celles-ci. Les expérimentations effectuées sur trois bases de données montrent la supériorité de l’approche proposée par rapport aux approches Riemanniennes de la littérature, et sa compétitivité vis-à-vis des autres approches récentes.
Designing a Hybrid Controller for Non-minimum Phase Quadruple Tank System with Model Uncertainties

This paper is concerned with the design of a hybrid state-feedback sliding-mode controller using fuzzy logic for a multivariable laboratory process of quadruple tank system. This apparatus is set to operate in its non-minimum phase mode which is more challenging to control as compared to the minimum phase mode. In the proposed control strategy, the consequent part of the fuzzy rules consists of either a sliding-mode controller (SMC) or a state-feedback controller (SFC). The proposed method takes advantages of the fast transient response of the SMC and the zero steady-state errors in SFC. Experimental results confirm the effectiveness of the proposed method as compared to the standalone SMC and SFC methods, especially when there are uncertainties in the model of the system.
When to Form an Alliance? Emergent Entrepreneurs in the Internet Video Industry

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ON DUAL OF THE GENERALIZED SPLITTING MATROIDS

Given a binary matroid M and a subset T ⊆ E(M), Luis A. Goddyn posed a problem that the dual of the splitting of M , i.e., ((MT ) ∗) is not always equal to the splitting of the dual of M , ((M)T ). This persuade us to ask if we can characterize those binary matroids for which (MT ) ∗ = (M)T . Santosh B. Dhotre answered this question for a two-element subset T . In this paper, we generalize his result for any subset T ⊆ E(M) and exhibit a criterion for a binary matroid M and subsets T for which (MT ) ∗ and (M)T are the equal. We also show that there is no subset T ⊆ E(M) for which, the dual of element splitting of M , i.e., ((M ′ T ) ∗) equals to the element splitting of the dual of M , ((M∗) ′ T ).
TETEYEQ: Amharic Question Answering For Factoid Questions

The number of Amharic documents on the Web is increasing as many newspaper publishers started their services electronically. People were relying on IR systems to satisfy their information needs but it has been criticized for lack of delivering “readymade” information to the user, so that the Question Answering systems emerge as best solution to get the required information to the user with the help of information extraction techniques. The language specific issues in Amharic are extensively studied and hence, document normalization was found very crucial for the performance of our Question Answering system. The performance on normalized documents is found to be higher than on un-normalized ones. A distinct technique was used to determine the question types, possible question focuses, and expected answer types as well as to generate proper Information Retrieval query, based on our language specific issue investigations. An approach in document retrieval focuses on retrieving three types of documents (Sentence, paragraph, and file). An algorithm has been developed for sentence/paragraph re-ranking and answer selection. The named-entity-(gazetteer) and pattern-based answer pinpointing algorithms developed help locating possible answer particles in a document. The rule based question classification module classifies about 89% of the question correctly. The document retrieval component shows greater coverage of relevant document retrieval (97%) while the sentence based retrieval has the least (93%) which contributes to the better recall of our system. The gazetteer-based answer selection using a paragraph answer selection technique answers 72% of the questions correctly which can be considered as promising. The file based answer selection technique exhibits better recall (91%) which indicates that most relevant documents which are thought to have the correct answer are returned.
Who Really Cares about Ethics ? Corporate Social Responsibility and Consumer Purchase Intention

The concept of corporate social responsibility (CSR) is intrinsically linked to both ethics and the practice of public relations. While past research has shown us that it is no longer a question as to whether or not a corporation should be socially responsible, but is a requirement of organizations by consumers, modern research has yet to conclusively show that organizational involvement in socially responsible activities positively affects financial performance. This study extended the existing body of literature related to CSR and financial performance with the inclusion of consumers’ perspectives. In combination with Ajzen and Fishbein’s Theory of Reasoned Action model for predicting behavior, this study used interviews and surveys of consumers to show that a positive relationship exists between CSR and financial performance or that consumers are more likely to purchase a product if they perceive the company that makes it as socially responsible. Additionally, comparisons were made among populations within the study based on demographic data, resulting in conclusions about those populations that are more likely to purchase products based on CSR activities. Some qualitative data was also included in order to further explicate consumers’ understanding of CSR in general as well as in regards to specific organizational involvement in socially responsible activities.