Miracle-dz/UniFAR-FTUs huggingface dataset:
Wildlife conservation, human welfare and the failure of protected areas

The establishment and expansion of protected areas in Africa have been motivated by the aspiration of increased wildlife abundance. However, the increasing poaching pressure on this continent has led to the perception that protected areas have failed in preserving wildlife. This paper presents a bio-economic model in order to explain the economic factors and mechanisms which may have caused this failure. The analysis focuses on a hunter-agrarian economy where an opportunity cost of habitat protection, due to less land for agricultural cultivation, pasture, and wildlife hunting, is present. An expansion of the protected area restricts the local people’s user rights to land. Depending on the economic conditions in agriculture and wildlife hunting, this policy may reduce the degree of wildlife conservation. This result contrasts the theoretical findings of fishery economics, where a marine reserve increases the aggregate fish stock. In addition to the conservation effect of protected areas, this paper investigates the impact on human welfare of restricting the user rights to wildlife and land. I would like to thank the Norwegian Research Foundation and The European Commission’s BIOECON programme for funding for this paper.
KERNEL METHODS ON HISTOGRAM INTERSECTION MEASUREMENT FOR SCENE AND OBJECT CLASSIFICATION

Histogram intersection(HI) measurement is proved to be an effective similarity measurement in recent works. Meanwhile, HI enjoys a linear computational complexity. Kernel methods on inner product spaces are commonly used in pattern recognition problems because of their generally satisfactory performance. In this paper, we proposed to apply some nonlinear transformation functions to spread out the original HI measurement. We choose three transformation functions which are motivated by kernel methods on inner product spaces. We present three specific kernels and apply them to scene and object classification tasks. Experiments are conducted on two widely used datasets including Scene-15 datasets and Oxford flowers. In our experiments, the proposed kernels demonstrate promising performance in comparison with HI kernel and other kernels. Specifically, compared with the original HI kernel, the proposed methods achieve about 2.8% improvement when the codebook size is 200 and 2.3% improvement when the size is 400 with hard assignment on Scene-15 datasets.
Back care education on peasant farmers suffering from chronic mechanical low back pain

Objective: Low back pain (LBP) is a common health problem that cuts across various occupations. The objective of this study was to determine the effect of back care education on farmers suffering from chronic mechanical LBP. Methods: Male peasant farmers suffering from chronic LBP were purposively recruited and randomly assigned to back care education group (BG) and control group (CG). The participants in the BG were trained in back care education five times within eight weeks while participants in CG received convectional medical care. Pre- and post-intervention pain intensity and disability level was assessed using chronic pain questionnaire. Data obtained were analysed using descriptive and inferential statistics. Results: The pre-treatment pain intensity of participants in BG was found to be significantly higher than those of participants in the control group, while there was no significant difference in pre-treatment functional disability scores between the groups. At the end of 8 weeks of training the pain intensity and functional disability scores of the BG were significantly lower than those of the CG. Conclusion: Back care education caused a reduction in pain intensity and functional disability among farmers with chronic mechanical LBP. It is recommended that back care education should be used to reduce back pain and disability among farmers.
The Importance of Family Meals and Sedentary Behavior in Understanding Childhood Depression and Obesity

The ongoing obesity epidemic within the United States is a problem that has received a lot of attention, but is still inadequately understood. Understanding the epidemic requires examining BMI from a larger perspective, with an ecological mindset (Rosenkranz & Dzewaltowski, 2008). A bidirectional relationship between depression and obesity, which has been found in the past, might be due to family meals. Sedentary behaviors has been linked to both outcomes, and is predicted by family meal frequency, suggesting that it may play a mediational role. In order to help clarify the relationship between depressive symptoms and obesity, a sample of 120 youth (55.8% males) between the ages of eight and 14 years old (M = 11.90, SD = 1.87) was examined. The sample was predominantly Caucasian (36.7%) and African American (33.3%). Preliminary analyses found that there were differences in the outcome variables between males and females, and between participants from the two recruitment sites. Regression analyses and Sobel tests with bootstraps were conducted to test the theoretical model, one for each mediational pathway. Evidence was found for a mediational relationship between family meal frequency and depressed mood, through sedentary behaviors (indirect effect b = -0.04, 95% CI [-0.11, -0.01]). No evidence was found for the same relationship but with BMI as the outcome instead of depressed mood, as the indirect effect was non-significant (b = -0.01, 95% CI [-0.07, 0.03]). Implications for future research and intervention development are discussed.
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

This paper describes our submission to the shared task on “Multi-hop Inference Explanation Regeneration” in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of ‘spurious chains’ leading to ‘semantic drifts’, we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system was ranked first w.r. t the mean average precision (MAP) metric outperforming the second best system by 14.95 points.
Local Ideal Point Method for GIS-based Multicriteria Analysis: A Case Study in London, Ontario

GIS-based multicriteria analysis (GIS-MCA) is a procedure for transforming and combining geographic data and value judgments (preferences) to evaluate a set of alternatives with respect to relevant criteria. Ideal Point Method (IPM) is one of the most often used GIS-MCA techniques. It has been applied in many research/planning areas including environmental planning, urban/regional planning, waste management, water resource management and agriculture. One of the limitations of IPM is that it has conventionally been used as a global approach based on the implicit assumption that its parameters do not vary as a function of geographic space. The conventional IPM assumes a spatial homogeneity of its parameters within the whole study area. This thesis proposes a new IPM called local IPM. The local version of IPM is developed by localizing two parameters (criterion weights and ideal/nadir points) based on the range-sensitivity principle. The IPM methods are used to evaluate and analyse the spatial patterns of the quality of employment in London, Ontario. The case study shows that there are significant differences between the spatial patterns generated by the local and conventional IPM. The local IPM not only can display the general ‘spatial trend’ of the quality of employment in London, but also is able to highlight the areas with relatively high quality of employment in different neighborhoods. Furthermore, the local IPM provides a tool for visualizing and exploring spatial patterns. The parameters influencing the local IPM results can be mapped and further examined with GIS.
Practical Research on the College Art Design Experiment Center under the Studio Production Mode

In the teaching of art design in China, the “studio” teaching mode has become a brand-new teaching mode, which has been effectively applied in practical teaching. Enterprises and universities cooperate with each other to create a studio, and use this as a platform and link to complete education. Abandoning the teaching methods and measures of internship training in the past teaching process, the perfect combination of theory and practice is realized. In teaching, through theory-oriented practice, practice verification theory. Such an education method can better achieve the purpose of training, and provide more versatile artistic talents with comprehensive quality, strong professional operation ability and innovative ability for the society. Based on the analysis of the malpractices of traditional laboratory construction and management model, this paper constructs an open experimental teaching platform based on “studio system” according to the characteristics and requirements of art disciplines, and from the laboratory opening, management mechanism and teaching system. In the other aspects, the research and practice of the open experimental teaching management mode under the “studio system” platform was carried out.
ACTIVE AGEING LEVEL IN THAILAND: A COMPARISON BETWEEN FEMALE AND MALE ELDERLY

Background: Active ageing is a global goal in today's ageing world for meeting the challenges of elderly and for improving their quality of life. Determinant factors of active ageing and their indicators need to be better understood for developing policies and programs focused on active ageing in an ageing society. Thailand has become an ageing society and is going to increase her elderly population but determinant factors and level of active ageing are not known clearly. Methods: Using exploratory factor analysis on data from the 2011 Survey of Older Persons in Thailand (n=23,801 elderly), attempts have been made to find the determinant factors of active ageing level. Similarity of factor structure (relationships of indicator variables with determinant factors) of active ageing for female and male elderly has been tested. Also, attempt has been made to find the directions of relationships of active ageing level with important indicators (determinant factors). Results: Results revealed different factor structure of active ageing for female and male elderly. This study found six determinant factors of active ageing for each gender of elderly in the broader cultural context of Thailand. Based on the determinant factors (their scores, and index value), active ageing index (AAIranges from 0 to 1) has been estimated. The mean active ageing level for female and male elderly found as 0.66 and 0.62, respectively. Conclusion: Active ageing level of elderly yet to be improved in Thailand as active ageing level in Thailand is not high (evidenced as far behind the goal). For increasing active ageing level of elderly in Thailand policy should be focused for elderly to fulfill health needs, to promote longer working lives, toarrange lifelong a learning program, and to improve financial/economic conditions.
A Novel Method for Resource Allocation in Cloud Computing Using Virtual Machines

The elasticity and the lack of upfront capital investment offered by cloud computing is appealing to any businesses. In this paper, we discuss how the cloud service provider can best multiplex the available virtual resources onto the physical hardware. This is important because much of the touted gains in the cloud model come from such multiplexing. Virtual Machine Monitors (VMMs) like Xen provide a mechanism for mapping Virtual Machines (VMs) to Physical Resources [3]. This mapping is hidden from the cloud users. Users with the Amazon EC2 service [4], for example, do not know where their VM instances run. It is up to the Cloud Service Provider to make sure the underlying Physical Machines (PMs) has sufficient resources to meet their needs VM live migration technology makes it possible to change the mapping between VMs and PMs While applications are running [5], but,a policy issue remains as how to decide the mapping adaptively so that the resource demands of VMs are met while the number of PMs used is minimized.
A review on advanced technique for assessment the quality of milk and milk products

This paper presents the advanced instrumental analytical techniques used in milk and milk products analysis. Milk is a complex matrix consisting of different components and because of this, it often becomes impossible to accurately analyze one component in the presence of others using the classical method of analysis. The major advantages of advanced instruments analysis are more sensitive and rapid. Milk and milk products are analysed for a variety of reasons, e.g. compliance with legal and labelling requirements, assessment of product quality, composition, determination of nutritive value, detection of adulteration, and in research & development. The present paper briefly discussed various application of GCMS, HPLC, AAS, FTIR, DLS, NALDI-TOF and MALDI-TOF in dairy sector.
A tumor suppressor enhancing module orchestrated by GATA4 denotes a therapeutic opportunity for GATA4 deficient HCC patients

Rationale: Effective targeting therapies are limited in Hepatocellular carcinoma (HCC) clinic. Characterization of tumor suppressor genes (TSGs) and elucidation their signaling cascades could shed light on new strategies for developing targeting therapies for HCC. Methods: We checked genome-wide DNA copy number variation (CNV) of HCC samples, focusing on deleted genes for TSG candidates. Clinical data, in vitro and in vivo data were collected to validate the tumor suppressor functions. Results: Focal deletion of GATA4 gene locus was the most prominent feature across all liver cancer samples. Ectopic expression of GATA4 resulted in senescence of HCC cell lines. Mechanistically, GATA4 exerted tumor suppressive role by orchestrating the assembly of a tumor suppressor enhancing module: GATA4 directly bound and potently inhibited the mRNA transcription activity of β-catenin; meanwhile, β-catenin was recruited by GATA4 to promoter regions and facilitated transcription of GATA4 target genes, which were TSGs per se. Expression of GATA4 was effective to shrink GATA4-deficient HCC tumors in vivo. We also showed that β-catenin inhibitor was capable of shrinking GATA4-deficient tumors. Conclusions: Our study unveiled a previously unnoticed tumor suppressor enhancing module assembled by ectopically expressed GATA4 in HCC cells and denoted a therapeutic opportunity for GATA4 deficient HCC patients. Our study also presented an interesting case that an oncogenic transcription factor conditionally functioned as a tumor suppressor when recruited by a TSG transcription factor.
Secure Multi Owner Data Sharing With Virtual Password Mechanism in Cloud

Cloud computing affords an economical and proficient solution for sharing group resources among cloud users mainly because of the character of low maintenance. But sharing data is a challenging issue in untrusted cloud storage for preserving data and identity privacy since membership of group changes often. In secure multiowner data sharing scheme, any cloud user can share data with others by using group signature and dynamic broadcast encryption techniques. Generally communication should not be limited in a circle. So, we propose a secure multi-owner multi group data sharing scheme with virtual password mechanism in the cloud. Hence group to group communication is introduced such that owner can send their data to other user from different group with the knowledge of group manager. This shows that any data owner can communicate with other members of any other group. To enhance the security of registered users by extending existing concept, we introduce color authentication scheme. In this proposed system we use virtual password mechanism nothing but graphical password to make our registration and login process more secured. In our proposed scheme secret keys and encryption keys aresent to the registered users through email which makes our login process more secured. Each time secret, encryption and decryption keys should be used to upload, download and share the documents so that data stored in the cloud is much more secured.
Using Probabilistic Tag Modeling to Improve Recommendations

Predicting users’ interest in products is at the core of recommender systems. is requires understanding of users’ tastes as well as the characteristics of the products. In this study, we focus on automatically discovering latent product features from algorithmically dened tags. Tags not only help understand implicit tastes of users but also facilitate discovery and categorization of products in large inventories. Product tags also provide structured information to rene the similarity between products in a content-based recommender system. A framework is presented for modeling product tags using search query data. User search queries are modeled as an undirected network; nodes represent the search queries and the similarities between nodes are obtained from user feedback. eries that have similar meanings are clustered using community detection algorithms and the central node in each cluster is used as a tag. We use a probabilistic model to relate products and users with these tags. Tag proles of products and users are then used with the recommendation engine. e framework is employed to tag courses on Udemy, a global marketplace for learning and teaching online. We demonstrate signicant improvements to Udemy’s recommendations using this novel method.
Improved Learning Vector Quantization for Mixed-Type Data

With rapid growth of information technology, most of corporations have collected a large amount of digital data, such as data regarding employees, customers and transactions, etc. Thus, mining useful patterns from the data becomes an important issue. Learning Vector Quantization (LVQ) is a prototype-based classification technique and can process a large volume of data within reasonable computation time. However, traditional LVQ process only numeric data due to the use of Euclidean distance but cannot directly handle categorical data which must be converted in advance, by typically using 1-of-k method. Nevertheless, after the conversion, categorical data lose their semantic information, leading to reduced classification performance. In this work, we propose an Improved LVQ (ILVQ) to deal with mixed-type data by using distance hierarchy for expressing relationship between categorical data. Experimental results prove ILVQ is better than traditional LVQ in classifying mixed-type data. Keyword: Learning vector quantization (LVQ), Distance hierarchy, Mixed-type data, Categorical data and Classification. Improved Learning Vector Quantization for Mixed-Type Data
Unsupervised Single Image Hyperspectral Reconstruction

Reconstruction of the hyperspectral image from a compressively sensed image or an RGB image is a challenging task. The existing supervised regression methods have been shown to perform well in solving such ill-posed inverse problems as compared to traditional iterative methods, but their performance is data dependent and the reconstructed results exhibit inconsistent quality for different datasets. To overcome the drawbacks of supervised learning for hyperspectral reconstruction, we propose an unsupervised deep learning based pipeline that can reconstruct the hyperspectral image from a RGB image or compressively sensed (CASSI) data by solving an inverse optimization problem. We exploit the fact that the hyperspectral to RGB or CASSI image formation model is differentiable and can be used in a gradient-based learning algorithm. We utilize the imaging model to directly reconstruct the hyperspectral image while testing by optimizing in the neural network weight space. Our method is based on deep image prior and since it does not require a training step, it is robust to the problem of domain-shift. We show that even with a primitive network (UNet), our method competes well with much complex architectures that other supervised methods use.
Substance Misuse and Psychiatric Comorbidity: towards improved service provision

Much progress has been made in terms of improved diagnostic techniques for individual disorders, i.e. substance misuse or psychiatric disorders. Some progress has been made for the application of these assessment procedures for combined disorders, thereby providing prevalence rates which vary widely due to the methodological problems. Far less progress has been made in the understanding of the natural history and outcome of combined disorders. Relatively little research has been carried out on treatment strategies for people with dual disorders (including polysubstance abuse and dependence). As a consequence, appropriate multidisciplinary and multispecialty service developments, training and research have not been a focus. Awareness of the scale and nature of the problem is important for commissioners and providers so that resources are targeted appropriately.
Carban sequestration potential of cultivated land and Mango orchard in the plough layer of Alfisols occuring in Givedin district of Jharkhand

Conversion of forest area to agricultural land use led to an estimated loss of 36 Pg carbon (C) from soils between 1860 and 1960. Soil organic carbon (SOC) loss through cultivation can, however, be reduced and potentially fixed in soil through proper soil and land management. The objective of this study was, therefore, to determine the effect of crop cultivation vis-à-vis forest and plantation, on the SOC concentration, especially in SOC-poor plough layer, in Irga watershed Dhanwar (Typic Haplustalfs) and Birni (Typic Paleustalfs) soils predominate in the two administrative blocks within the watershed –, respectively. Three plots were chosen for each soil subgroup to represent three predominant land cover conditions viz. forest, plantation and mono-cropped area in Dhanwar and Birni. In general SOC stock was high in the mango orchard in Dhanwar (226 Mg C ha-1) and cultivated land in Birni (242 Mg C ha-1) and least in the plot under Eucalyptus plantation in Birni. In the 0-20 cm depth, mango orchard in Dhanwar and cultivated land in both the sites showed an increase in SOC pool in comparison to forests; (Eucalyptus plantation). The practice of paddy-fallow rotation and the application of animal manure led to an increase in the SOC pool in the plough layer of agricultural soils in the SOC-poor area; and thus have the potential to sequester C in the Irga watershed.