Miracle-dz/UniFAR-FTUs huggingface dataset:
Using sparse representations for exemplar based continuous digit recognition

This paper introduces a novel approach to exemplar-based connected digit recognition. The approach is tested for different sizes of the exemplar collection (from 250 to 16,000), different length of the exemplars (from 1 to 50 time frames) and state-labeled versus word-labeled decoding. In addition, we compare the novel method for selecting exemplars, based on Sparse Classification, with a conventional K-Nearest-Neighbor approach. For word-labeled decoding we developed a Viterbi search that applies minimum and maximum duration constraints. It appears that Sparse Classification outperforms KNN, while state-labeled decoding provides better performance than word-labeled decoding. In all conditions the performance increases with the size of the collection. However, the optimal window length is 10 frames for state-labeled decoding, but 35 frames for word-labeled decoding.
Bringing Education to Children of Lesser Gods: Bharti Foundation's Satya Bharti School Programme

Quality education is the fulcrum of social development. Guided by his firm beliefs, Sunil Bharti Mittal set up Bharti Foundation to provide and expand creating quality education in the country. According to him, “Bharti Enterprises, in particular through Bharti Foundation, has taken upon itself the mandate to take quality education to the rural hinterland. Additionally, it imparts quality education and training to the youth to realize their true potential and become an important contributor to economic growth. This 'cause' is shared across all the Bharti Group Companies through acts of volunteering, mentoring and donation. ” Bharti Foundation's CSR is more governed by lofty ideals of improvement in elementary education for supplementing government efforts, which is inspired by its founder's firm belief that it is the quality education which holds key to alleviating society's all problems. The case pertains to the activities of Bharti Foundation for promoting elementary education as a positive force for country's social development.
CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Convolutional Neural Networks (CNN) are natural based classification algorithm that combine Multiple Layer Perceptron (MLPs). Meanwhile, support vector machines (SVM) is a mathematical-based classification algorithm that naturally have supervised learning models. In some research related to image processing, each algorithm has its owned supremacy as well as the drawback. None of the previous studies compare both algorithm when they are utilized to detect nodule located in the pulmonary or lung images produced by Computed Tomography (CT) scan. Hence, this research comparing the two algorithms in case of lung nodule detection in CT images, since detecting lung nodule in CT images is still challenging. SVM-based classifier is preceded by feature extraction as its common behavior of mathematical based classifier. There are three algorithms use to conduct feature extraction process, namely Hu moment invariant, Haralick and Color Histogram extraction. In the opposite, CNN-based classifier consists of three layers convolution for training and testing steps. The result shows that SVM has better results than CNN in case of computing speed. Meanwhile have a better accuracy in detecting lung nodule. The results of the test analysis show that the extractor feature when preprocessing conduct before being classified by SVM makes the computing process faster. The accuracy of SVM-based classifier can be improved by adjusting some computation variables in feature extraction stages, such as adding more bins in the color histogram extraction. Those adjustment will lead to more computation times. Keywords—CNN; SVM; lung nodule; computed tomography images
Chicken infectious anemia virus vaccination induces immune disorders and viral persistency in infectious bursal disease virus-infected young chicks

Infectious bursal disease (IBD) is one of the major causes of economic losses in the chicken industry. Vaccination is the main tool against the disease, and the susceptible birds should be vaccinated as soon as the maternal antibody (MA) becomes low enough to allow the vaccine to break through. Estimation of vaccination time is currently performed by Deventer formula which uses initial anti-IBDV titer and antibody half-life to predict the titer. Considering the increased growth rate of chicken in the last decades and the wide variations of MA, we have examined the effects of chick’s weight gain on MA decline and the use of weight in predicting IBD vaccination time. The virus neutralization test and ELISA results demonstrated that fast-growing birds had a faster rate of antibody decline whereas slow-growing birds demonstrated a slower rate. Based on the effect of weight-gain on maternal antibody decline, a new formula for predicting IBD vaccination time was introduced and tested. The predicted IBD vaccination time made by this weight formula showed higher correlation with the measured ELISA titers in the experiment. Chicken infectious anemia virus (CIAV) is another cause of immunosuppression in chicken which is characterized by increased pathogenicity of secondary infectious agents, sub-optimal antibody responses and mortality. CIAV subclinical infections can result in immunosuppression and enhancement of pathogenicity of co-infecting agents such as infectious bursal disease virus (IBDV). Effects of pathogenic CIAV and IBDV coinfection on chick’s health and immune responses are investigated in different studies.
Inclination Towards Entrepreneurship Among Universiti Pendidikan Sultan Idris Students

This study was undertaken to explore the inclination towards entrepreneurship among university Pendidikan Sultan Idris students The population for the study was UPSI’s final year students from faculty of business and economic. Entrepreneurship course was introduced as a core subject in the areas of business programme. This course is made compulsory for all business undergraduate’s students. The total population in this study was 181.The respondent were selected by using simple random sampling. Descriptive analysis (such as frequencies, means and standard deviations) were used to analyze the respondents’ demographic characteristics such as age, race, religion, educational level and working experience. Descriptive analysis (frequencies, percentages, means) and inferential analysis ( one-way ANOVA) were employed to analyze the data. In this study it was discovered that there are two aspect of demographic that was qualification and programmed of study that showed a relationship and has a positive significant on inclination towards entrepreneurship. These findings support previous studies that were carried out on demographic characteristics and entrepreneurial inclination by Mohar Yusof et al (2008). The study also showed a strong significant relationship between family background and entrepreneurial inclination. It was suggested that to attract more student involve in entrepreneurship, UPSI should design entrepreneurial curriculum and content, provide training programmes for faculty members, establish the club of entrepreneurship in universities and establish student one-stop business advice and guidance centers It could be concluded that the role of universities in promoting entrepreneurship, entrepreneurial curriculum and content and role models had significant impact on the inclination of the students towards entrepreneurship. The role model such as friends and lecturers are the main factor which influenced students on inclination towards entrepreneurship. In the part of the universities in promoting entrepreneurship, it showed that the majority of the students supported the entrepreneurship and business educational programmes in campus. While on the part of the entrepreneurial curriculum and content, the course could develop entrepreneurial knowledge and skills.
Dynamic Lifetime Reliability and Energy Management for Network-on-Chip based Chip Multiprocessors

DYNAMIC LIFETIME RELIABILITY AND ENERGY MANAGEMENT FOR NETWORK-ON-CHIP BASED CHIP MULTIPROCESSORS Milad Ghorbani Moghaddam, B.S., M.S. Marquette University In this dissertation, we study dynamic reliability management (DRM) and dynamic energy management (DEM) techniques for network-on-chip (NoC) based chip multiprocessors (CMPs). In the rst part, the proposed DRM algorithm takes both the computational and the communication components of the CMP into consideration and combines thread migration and dynamic voltage and frequency scaling (DVFS) as the two primary techniques to change the CMP operation. The goal is to increase the lifetime reliability of the overall system to the desired target with minimal performance degradation. The simulation results on a variety of benchmarks on 16 and 64 core NoC based CMP architectures demonstrate that lifetime reliability can be improved by 100% for an average performance penalty of 7.7% and 8.7% for the two CMP architectures. In the second part of this dissertation, we rst propose novel algorithms that employ Kalman ltering and long short term memory (LSTM) for workload prediction. These predictions are then used as the basis on which voltage/frequency (V/F) pairs are selected for each core by an e ective dynamic voltage and frequency scaling algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Secondly, we investigate the use of deep neural network (DNN) models for energy optimization under performance constraints in CMPs. The proposed algorithm is implemented in three phases. The rst phase collects the training data by employing Kalman ltering for workload prediction and an e cient heuristic algorithm based on DVFS. The second phase represents the training process of the DNN model and in the last phase, the DNN model is used to directly identify V/F pairs that can achieve lower energy consumption without performance degradation beyond the acceptable threshold set by the user. Simulation results on 16 and 64 core NoC based architectures demonstrate that the proposed approach can achieve up to 55% energy reduction for 10% performance degradation constraints. Simulation experiments compare the proposed algorithm against existing approaches based on reinforcement learning and Kalman ltering and show that the proposed DNN technique provides average improvements in energy-delay-product (EDP) of 6.3% and 6% for the 16 core architecture and of 7.4% and 5.5% for the 64 core architecture.
Neurochemistry of the hepatic encephalopathy

The pathogenesis of hepatic encephalopathy (HE) in cirrhosis is multifactorial and the role of ammonia remains controversial. Experimental studies conducted in animal (rat) models of HE, in combination with pharmacological approaches, were used to test the hypothesis that during HE, chronic exposure to elevated ammonia concentrations alters cerebral oxygenation, compromises lactate transport between astrocytes and neurons, and impairs uptake of neurotransmitters. It was also hypothesised that HE impairs glymphatic clearance mechanisms, either as a cause or a consequence of the disease, which exacerbates the detrimental central nervous effects of the accumulated toxins. The results of the experiments described in this thesis suggest that in HE: a) ammonia compromises cerebral oxygenation, but does not affect cerebrovascular reactivity, b) ammonia mediates cortical hemichannel dysfunction and impairs channel-mediated lactate release, potentially interfering with the astrocyte-neuron lactate shuttle, c) hyperammonemia results in a significant increase in cortical extracellular glutamate concentration, which is exacerbated under hypoxic conditions, and d) efficacy of glymphatic clearance is affected in discrete regions of the brain, which aligns with specific cognitive/behavioral impairments. These findings provide the first evidence of a critical pathophysiological role of ammonia in inducing neuronal energy deficit in HE due to impaired cerebral oxygenation, compromised hemichannel-mediated lactate transport between astrocytes and neurons and affected glymphatic clearance.
The Hazards of Misusing the Smart Contract: An AHP Approach to Its Risk

This article explores four critical groups of systematic risk embedded in smart contract employment using the analytic hierarchy process (AHP). The four principal risk analysis groups include: 1) transparency in the light of corporate governance 2) IT security 3) contract management automation and 4) legality. The AHP assists both decision-makers and stakeholders alike in the evaluation process essential for identifying potential technological constraints posed within a permissioned blockchain environment using peer-to-peer format in the absence of digital currency. Based upon critical assessment, the AHP methodology enables pairwise comparisons among different features and consequently increases the knowledge regarding these attributes in light of the software’s risk assessment.
Generative contracts

A quality-driven approach to software development and testing demands that, ultimately, the requirements of stakeholders be validated against the actual behavior of an implementation under test (hereafter IUT). That is, there needs to be a systematic (ideally objective and automated) approach to the validation of the requirements of the stakeholder against the actual behavior of an IUT. To do so, it is necessary to have a specification language from which tests can be generated and executed 'against' an actual IUT (as opposed to a model of the latter). Corriveau and Arnold have recently described at length elsewhere such an approach (in the form of a contract-based specification language called ACL (Another Contrcat Language)) and its corresponding tool, the Validation Framework (hereafter VF). Simultaneously, in recent years, the software product line approach, initiated by Parnas back in the 1970s, has emerged as a promising way to improve software productivity and quality. A product line (also called a 'product family' or 'system family') arises from situations when we need to develop multiple similar products (e.g., for a library, or for different clients, or from a single system over years of evolution). The problem we address in this dissertation can be summarized in one question: how can ACL/VF support domain engineering and application engineering? In the specific context of ACL/VF, this question can be broken down into two more immediate ones: 1) how can ACL (i.e., the requirements modeling language) be 'augmented' to support some modeling of variability? 2) how can such augmented ACL models be used, together with some specification of a configuration of feature values, to generate a domain member contract , that is, the set of contracts associated with a specific member of a domain? The solution we propose adopts Cleaveland's template-based general approach to variability. We explain at length and demonstrate with two case studies how to go from the traditional feature diagram and feature grammar used in System Family Engineering and Software Product Lines to a) domain contracts capturing commonalities between the requirements contracts of a domain and b) variability contracts capturing how features and their relationships (captured in the feature grammar) can affect these domain contracts. Domain and variability contracts are ultimately captured in XML files relying on XSLT to specify how variability is to be resolved to generate a specific member contract. This generative process uses off-the-shelf XML technologies to generate ACL contracts that indeed can be input, compiled and run in ACL/VF. Our solution is domain independent and we believe that being template-based it is not specific to ACL and can be generalized (though this claim is left as future work).
Time Lag between Deforestation and Bird Extinction in Tropical Forest Fragments

Abstract: Tropical forests are becoming increasingly fragmented, threatening the survival of the species that depend on them. Small, isolated forest fragments will lose some of their original species. What is uncertain is how long this process of faunal relaxation will take. We compiled data on birds in five tropical forest fragments in Kakamega Forest, Kenya, of known date of isolation. We then predicted the original and eventual species richness of these fragments and, from this difference, the eventual species losses. Expressing the losses to date as a fraction of eventual losses suggests that faunal relaxation approximates an exponential decay with a half‐life of approximately 50 years for fragments of roughly 1000 ha. In other words, in the first 50 years after isolation, tropical forest fragments of this size suffer half of the total number of extinctions that they are likely to experience. This result sets the time scale over which humanity must take conservation action in fragmented tropical forests, may aid efforts to set priorities, and indicates how high the future global extinction rate will be.
Knowledge Discovery from Financial Text

The abundance of on-line electronic financial news articles has opened up new possibilities for intelligent systems that could extract and organize relevant knowledge automatically in a usable format. While most typical information extraction systems require a hand-built dictionary of templates and, subsequently, are subject to ceaseless modification to accommodate new patterns that are observed in the text, in this research, we propose a novel text-based decision support system (DSS) that will (i) extract event sequences from shallow text patterns and (ii) predict the likelihood of the occurrence of events using a classifier-based inference engine. We investigated more than 2,000 financial reports with 28,000 sentences. Experiments show the DSS outperforms other similar statistical models.
Clustering Patterns and Correlates of Multiple Health Behaviors in Middle-aged Koreans with Metabolic Syndrome

Objectives: The objective of the study was to examine the clustering patterns and correlates of multiple health behaviors (MHBs) in middle-aged Koreans with metabolic syndrome (MetS). Methods: Data on sociodemographics, clinical characteristics, health behaviors (vegetable intake, physical activity, cigarette smoking, and alcohol consumption), and psychological characteristics were collected by a self-reported survey and medical examination from 331 individuals with MetS. Clustering of MHBs was examined by measuring 1) the ratios of observed and expected prevalence of MHBs, and 2) the prevalence odds ratios. A binomial logistic regression were conducted. Results: Men were more likely than women to engage in multiple unhealthy behaviors. Clustering of smoking and heavy drinking was exhibited in the participants. Women with high vegetable intake were more likely to be physically inactive, and those with inadequate vegetable intake were more likely to be physically active. Those with lower self-regulation were more likely to engage in unhealthy behaviors. Conclusions: The findings support the multiple health behavior approach as opposed to the individual health behavior approach. Emphasis of self-regulation is necessary in developing multiple behavior intervention for individuals with MetS.
Effects of essential oils from African basil on fermentation of Andropogon gayanus grass in the Artificial Rumen (RUSITEC)

Kouazounde, J. B. , Gbenou, J. D. , He, M. , Jardim, T. , Jin, L. , Wang, Y. , Beauchemin, K. A. and McAllister, T. A. 2015. Effects of essential oils from African basil on fermentation of Andropogon gayanus grass in the Artificial Rumen (RUSITEC). Can. J. Anim. Sci. 95: 425–431. Essential oils (EO) from African basil (Ocimum gratissimum) have shown the potential to modify rumen microbial fermentation and reduce ruminal methane production from grass forages in in vitro batch cultures. However, it is not known whether the effects of EO on rumen microbial fermentation attenuate over time. The objective of this study was to examine the effects of African basil EO at 0 (control), 100, 200 and 400 mg L−1 incubation medium on microbial fermentation and methane production in the Rumen Simulation Technique (RUSITEC) using Andropogon gayanus grass as a substrate. African basil EO quadratically affected (P<0.05) methane production gas production and the pH of fermenter liquid. Total volatile fatty acid (VFA) production w.
Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. We model this problem as a Markov decision process (MDP), which takes both temporal and spatial features into consideration. We propose a novel deep reinforcement learning algorithm called Loss-Reduced Reinforcement Pricing (LRP), which builds upon the deterministic policy gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, LRP is embedded with a novel network architecture to incorporate the dependence of neighboring regions, for reducing the training loss in Q-function learning. We conduct extensive experiments to evaluate the performance of the LRP algorithm, based on trajectory data from Mobike, a major Chinese dockless bike sharing company. Results show that LRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas, and can even make additional profit with the given budget. We further propose the first hybrid rebalancing system, which take advantages of both the truck-based and user-based approaches, and outperforms each individual approach.
Who Should They Relate To? A Study For the Identification and Analysis of Criteria to the Partners’ Selection in Inter-Organizational Networks

The selection of partners is strategic in inter-organizational networks. One of the most important aspects is the definition of criteria for selection, that are the minimal characteristics required from those prospected. This study aimed to identify the most important criteria for the selection of members in horizontal inter-organizational networks, also describing their influence on network activities. First, we applied 120 questionnaires to managers of inter-organizational networks to identify the degree of importance of criteria previously treated in the literature. After, we interviewed 16 managers enabling us to identify other criteria, as well as understanding their influence on network activities. All of the 20 criteria from the literature were considered with significant importance by managers and the following criteria were added: trustworthiness, entrepreneur’s profile and company lifetime. The results aim to contribute to the selection of partners and provide information for the construction of the inter-organizational networks literature.