Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Dependence among Rural Households in Southern Ghana: Implications for Conservation and Poverty Reduction

While it is widely acknowledged that an understanding of the determinants of rural households’ forest extraction and dependence on forest resources is important for policies on forest conservation and rural development, the factors that determine Ghanaian households’ dependence on forests are neither adequately explored nor well-understood. Against this background, this paper examines the extraction and dependence on forest resources among rural households in the forest communities of Southern Ghana. Data were collected through a household livelihood survey and in-depth interviews in two forest communities. Regression models were then used to investigate key factors that condition the households' dependence on forests in the study communities. The findings indicate that almost all households are engaged in forest extraction. The average overall contribution of forests to household income in the study communities was 21 percent and constituted the third largest contributor to household income following crop income and non-farm income. The findings also indicate that forests also play an essential safety net role in the face of unforeseen income shortfalls and ultimately, in poverty alleviation. The results further reveal that the rural household's extraction of forest resources and consequently its dependence on forests (livelihood strategy) are a function of its access to other livelihood assets, its vulnerability context as well as other context variables. Recommended policy interventions for forest conservation and sustainable rural development include securing the natural resource base, broadening poor people's livelihood options and improving access to education in rural communities.
Smart Choice – Content Based Clothing Recommendation System

Shopping has always been a basic necessity but also a time consuming nuisance. With the trending e-platforms, people are shopping on the go! In this ever growing technology, finding something with pattern of your choice is difficult. The plethora of options often gets the customers confused and eventually drops the idea. Our project will help the consumers to search for the clothes referring the pattern in the picture they upload on our platform. It aims at using image processing techniques to provide with much better results for our customers and aid them in shopping. Exploring choices become easy as the clothes having similar patterns will be shown as the result. Rather than searching for hours, our project will bring the searched results to them. The texture and print on the image of cloth will be analyzed for patterns like lines at different angles and circular designs. These identified patterns will help to find similar clothes which the customer may desire. Keywordss: Image Retrieval, Shopping, Content based image retrieval, Gabor function, Circle Hough Transform.
Devising News Recommendation Strategies with Process Mining Support

News media is in a digital transformation, disrupting their existing business models. Many news media houses are looking into recommender systems as a part of their digital strategies. However, the social role of journalism, existing publishing platforms and news as a continuous data stream infer particular challenges for applying standard recommender technologies. This paper explores how news recommendation can go beyond popularity and recency and take advantage of content quality metrics and interaction patterns. This knowledge is derived through adapting process mining for usage with web logs. The proposal is evaluated on real event logs from a German news publisher, revealing encouraging results.
Study on Brain Tumor Classification Through MRI Images Using a Deep Convolutional Neural Network

Brain tumor (Glioma) is one of the deadliest diseases that attack humans, now even men or women aged 20-30 are suffering from this disease. To cure tumor in a person, doctors use MRI machine, because the results of MRI images are proven to provide better image results than CT-Scan images, but sometimes it is difficult to distinguish between the MRI images having tumors with that images not having tumor from MRI image results. It is because of resulting contrast is like any other normal organ. However, using features of image processing techniques like scaling, contrast enhancement and thresh-holding based in Deep Neural Networks the scheme can classify the results more appropriately and with high accuracy. In this paper, this study reveals the nitty-gritty of Brain tumor (Gliomas) and Deep Learning techniques for better inception in the field of computer-vision.
ENHANCED CREDIT WORTHINESS OF BANK CUSTOMER IN NIGERIA USING MACHINE LEARNING AND DIGITAL NERVOUS SYSTEM

This paper presents an enhanced model of machine learning and digital nervous system based on credit worthiness of bank customer in Nigeria. The machine learning have the capability to determine the relevant features and customer’s credit worthiness in efficient manner. The Digital Nervous System (DNS) approach will enable the Credit Risk Management System (CRMS) to capture credit risk information and provide it where it is needed for decision making and when it is needed. The paper aimed at developing an enhanced model for detecting a credit worthiness in a bank. In recent years, customer’s credit worthiness is becoming more crucial for financial organizations. To deal with major problems like noise, data incompleteness and lack inconsistency in loan while building predictive models, this paper proposes a predictive model for detection of credit worthiness. For developing and testing of model a large, real and most recent dataset of credit card, obtained from UCL repository, is proposed. The paper makes it possible for the really availability of more comprehensive credit risk management information and to minimizes decision-making errors with respect to loans. The key focus of the work is on detection of credit worthiness which is defined as the probability of default on the loan or credit from financial organizations like banks. The efficiency of model is demonstrated using confusion matrix on the basis of prediction accuracy and other metrics, against benchmark classifies.
An Investigation on Special Antennae using Artificial Neural Networks

This study aims to provide an overview and deployment of Artificial Neural Network(ANN) based microstrip antenna and arrays , with different configurations, feeding mechanisms, and performances. Their performance improvement methods, including bandwidth enhancement, size reduction, and gain improvement are also discussed based on available literature. This paper shows the designing of antennas by using Artificial Neural Networks with the help of different techniques. The techniques such as genetic algorithm, Levenberg-Marquardt algorithm, Neurospectral method, reflect array element modeling and pattern synthesis are discussed here . Keywords—Levenberg Marquardt; Artificial Neural Network; Neurospectral; reflect array element; pattern synthesis
Transcriptome analysis of miRNAs expression reveals novel insights into root formation under Root restriction cultivation in grapevine (Vitis vinifera L.)

Background: Root restriction cultivation not only promotes maturation and quality of fruit, but optimizes the architecture of root, especially in enhancing the occurrence of adventitious and lateral roots. While the molecular mechanism of this phenotype is still unknown. Results: In this study, the development of roots was observed at 12 different time points under Root restriction and normal cultivations (control). Root phenotype showed a significantly different feature start from the seventh sampling, mainly in the increasing of adventitious roots numbers, degeneration of root tip and subsequent mass of lateral roots development. The 7th and 12th sampling of two different cultivations, named nR7, nR12, RR7, and RR12, were selected for small RNA sequencing. 214,439,588 raw reads were obtained and 168,741,687 clean reads were remained after the quality control steps, and finally got a total of 153 known miRNAs, and 119 predicted novel miRNAs. The predicted novel miRNAs blasted with the miRVIT and miRBase databases simultaneously. 96 new family members of grapevine miRNA and 23 grapevine-specific novel miRNAs were obtained. Differentially expressed miRNAs (DEMs) analysis showed that 26 and 33 miRNAs were differentially expressed in two different cultivation models (RR7 vs nR7; RR12 vs nR12), while 26 and 32 DEMs were obtained in different root development stages (nR12 vs nR7; RR12 vs RR7). MiRNA expression levels analysis found that conserved miRNA s were in higher expression level than novel miRNAs. The predicted target genes of DEMs were annotated on a variety of biological processes, and 24 participated in root development. An analysis of vvi-miR160 family members revealed that vvi-miR160c was highly expressed in grapevine root, indicating the potential role of miR160c in root development, which in accordance with the previous report in Arabidopsis . Conclusions: Multiple miRNAs were jointly to regulate root development on Root restriction condition, mainly in lateral root development. And the specific expression of vvi-miR160c in the apex may be the main cause of apical degradation. Moreover, there were multiple miRNAs related to biotic or abiotic stresses, which indicated that multiple minor stresses exist in root development after Root restriction cultivation.
Greeny: NAO As Assistive Teaching Tool For Recycling in Early Childhood Education

Greeny is proposed as an assistive teaching tool for early childhood education to improve on the learning experience of the students, as well as facilitating the teaching and learning process for young children (from the age of 3 to 5 years old, or pre-school students) on the importance of saving the Mother Nature through recycling items. The undertaking of this approach is motivated by the interest of improving the teaching and learning experience in early childhood education, as well as improving the awareness of recycling and sustaining the environment. The approach will utilize the commercially available robot, which is NAO. Greeny will demonstrate to the young children on sorting recycle items into the right container and explaining the significance of recycling. Surveys are conducted as preliminary investigation to assess the recycling awareness in the public. An interactive module for the Greeny is to be developed and implemented to assist the teacher in teaching and learning process. Iterative development model is being used as the methodology for development. At the end of the development, testing will be done onto the prototype before being implemented for the teaching and learning. After completion of prototype testing, testing on young children will be done by evaluating the success and failure of sorting the recycle items into the right bin to assess on the grasp of understanding on the input gain from the teaching and learning with intervention of robot as assistive teaching tool.
Improving Efficiency and Accuracy of Classification and Clustering of a Text Documents with Feature Selection

The quality of the data is one of the most important factors influencing the performance of any classification or clustering algorithm. The attributes defining the feature space of a given data set can often be inadequate, which make it difficult to discover interesting knowledge or desired output. However, even when the original attributes are individually inadequate, it is often possible to combine such attributes in order to construct new ones with greater predictive power. Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, and noise from data to improving result comprehensibility. The goal of this thesis is to find out the best feature subset from the given features in order to improve the performance of classification and clustering techniques on complex, real world data. To partition a given document collection into clusters of similar documents a choice of good features along with good clustering algorithms is very important in clustering. The feature selection is an important part in automatic text categorization which can change the entire results of text clusters.
Equipping social robots with culturally-sensitive facial expressions of emotion using data-driven methods

Social robots must be able to generate realistic and recognizable facial expressions to engage their human users. Many social robots are equipped with standardized facial expressions of emotion that are widely considered to be universally recognized across all cultures. However, mounting evidence shows that these facial expressions are not universally recognized – for example, they elicit significantly lower recognition accuracy in East Asian cultures than they do in Western cultures. Therefore, without culturally sensitive facial expressions, state-of-the-art social robots are restricted in their ability to engage a culturally diverse range of human users, which in turn limits their global marketability. To develop culturally sensitive facial expressions, novel data-driven methods are used to model the dynamic face movement patterns that convey basic emotions (e.g., happy, sad, anger) in a given culture using cultural perception. Here, we tested whether such dynamic facial expression models, derived in an East Asian culture and transferred to a popular social robot, improved the social signalling generation capabilities of the social robot with East Asian participants. Results showed that, compared to the social robot's existing set of facial ‘universal’ expressions, the culturally-sensitive facial expression models are recognized with generally higher accuracy and judged as more human-like by East Asian participants. We also detail the specific dynamic face movements (Action Units) that are associated with high recognition accuracy and judgments of human-likeness, including those that further boost performance. Our results therefore demonstrate the utility of using data-driven methods that employ human cultural perception to derive culturally-sensitive facial expressions that improve the social face signal generation capabilities of social robots. We anticipate that these methods will continue to inform the design of social robots and broaden their usability and global marketability.
Role of UPQC in Distributed Generation Power System: A Review

The ever increasing share of renewable energy sources (RERs) in the today’s scenario, the power grids are suffering from poor power quality due to the intermittent nature of wind and solar based power generating units. The led to extensive research in the field of power quality especially in voltage and frequency regulations Distributed generation involving RERs has become more popular in recent years due to technological advancement and has been started increasingly used in industry. It has become more important to understand the integration of these systems through PE interface with the existing electric power systems networks. At the same time, high frequency switching of Power Electronic interface has caused major Power Quality concerns, which has been tackled with the help of Custom power devices interfaces that has allowed DG to offers various benefits like ability to provide ancillary services, increased energy efficiency, increased functionality through improved power quality and voltage/VAR support, improved electrical system reducing the fault contributions, and flexibility in operations with various other DE sources. DG also allows the customer to have a choice while it reduces the overall interconnection costs. This paper focuses on widespread use of DG through various Renewable Energy Sources, Power Quality issues associated with the use of Power Electronic interface and use of various Custom Power Devices to improve Power Quality. It particularly evaluates the role of UPQC DG in various modes of DG in follow standards.
COLGALT1 is a Prognostic Biomarker in Clear Renal Cell Carcinoma Correlated with Immune Infiltrates: A Study Based on TCGA Data

Background: COLGALT1, as one gene enriched in metabolic pathways, which may be related to the tumorigenesis and progression. We aim to explore the potential value of COLGALT1 in clear cell renal cell carcinoma (ccRCC) through the study.Methods: We searched The Cancer Genome Atlas (TCGA) database to collecte ccRCC patients’ information including clinicopathologic parameters and COLGALT1 gene expression. We also validiated the COLGALT1 mRNA expression by qRT-PCR. Then, We evaluated the relationship between COLGALT1 and overall survival (OS) by the Cox regression analyses. Gene Set Enrichment Analysis (GSEA) was utilized to compare between tissues with different COLGALT1 expression levels. Microsatellite Instability (MSI), Tumor Mutational Burden (TMB) and Neoantigen were evaluated through different tools. By TIMER, correlations between COLGALT1 and immune cell infiltrations were analyzed. The ESTIMATE algorithm was used to calculate the estimate, stromal and immune scores for ccRCC. Finally, CIBERSORT was carried out to explore the connection between the COLGALT1 and the tumor immune microenvironment.Results: Significant gene expression of COLGALT1 was identified between normal and ccRCC tissues. Multivariate analysis indicated that high expression of COLGALT1 was linked to poor OS (P = 0e+00). GSEA results demonstrated that high COLGALT1 expression was associated with metabolic pathways. COLGALT1 was identified to be one independent prognostic factor through the univariate and multivariate Cox regression analyses. One nomogram was integrated including both the clinicopathologic variables and COLGALT1 expression to provide a quantitative approach to clinicians for predicting prognostic risk. Futher more, we find out some genes which are significantly correlated with COLGALT1. Besides, MSI and TMB showed strong correlations with COLGALT1 in ccRCC. Also, the correlations between COLGALT1 with immune infiltrations were found in ccRCC. Finally, immune microenvironment including immune checkpoint molecules, immune cells and mismatch repair protein were proved to be linked to COLGALT1 in ccRCC.Conclusions: Our results revealed that COLGALT1 could act as a favorable prognostic factor for ccRCC. Besides, this study also provided one method to determine the immune infiltration of patients and some signal pathways which are potential regulated by COLGALT1 in ccRCC.
Passive Localization of Standard WiFi Devices

Ubiquitous wireless indoor localization can be achieved by leveraging the widespread deployment of WiFi systems. Most existing WiFi-based localization solutions are based on received signal strength (RSS) fingerprinting, which requires a database of the RSS values in the application environment to be built and maintained. The latest 802.11ac WiFi standard offers channels with wide bandwidths, which enables accurate timing-based positioning. This paper presents a passive localization system in which multiple sniffers monitor the WiFi traffic and locate the standard WiFi transmitters based on the time-of-arrival measurements. Multiple implementation issues are addressed, including sniffer clock synchronization and hardware delay calibration. The proposed system is evaluated experimentally using a prototype developed by us. It is shown that the positioning accuracy is significantly improved over existing systems.
Polyglot Text Classification with Neural Document Models

Polyglot Text Classification with Neural Document Models Suchin Gururangan Chair of the Supervisory Committee: Noah A. Smith Computer Science Sometimes, annotating data for text classification is expensive, so one must rely on techniques like parameter sharing and semi-supervised learning to improve classification performance in low-resource environments. In this thesis, I combine a generative, neural document model (Card et al., 2018) and multilingual word vectors (Ammar et al., 2016) to perform text classification on documents in eight languages. The model I propose jointly trains on labeled and unlabeled data from multiple languages, and incorporates additional document-level metadata, such as language ID, in its generative story. Through a series of experiments, I show that the model significantly outperforms monolingual baselines in low-resource environments.
Investigation of Opinions of Students Participating Mobile Programming Course in Blended Learning Environment

The aim of this study is to examine students' views on the blended learning environment prepared for the mobile programming course. For this purpose, a blended learning environment has been designed using the Moodle learning management system to be applied in mobile programming education. The participants of the study consisted of 30 students studying in the computer programming program of a vocational school in the spring semester of 2016-2017 academic year. The case study model, one of the qualitative research models, was used in the study. The data obtained from the students were analyzed by content analysis method. The results of the study show that the blended learning environment prepared for mobile programming contributes to the learning of the students. However, the majority of the students stated that they did not encounter any problems in the blended learning environment, that they wanted to learn different courses and subjects in the blended learning environment and that they met the expectations of the blended * İletişim: Cemal Hakan Dikmen, Afyon Kocatepe Üniversitesi, Dinar Meslek Yüksekokulu, c.hakan. dikmen@gmail.com
Systems, methods, and apparatus for controlling the power-on or boot sequence of an integrated circuit based on power harvesting conditions

Comprises an integrated circuit such as a system-on-a-chip using the information relating to the whole sequence or in part, the operation from the harvested power and power harvesting conditions to change the boot sequence, in some embodiments, the apparatus on the basis of the information power . In some embodiments, the method the power-use information relating to the power harvesting and energy harvesting conditions in order to change the whole sequence or a boot sequence.
A Case of Systemic Sclerosis with ILD and Pulmonary Tuberculosis - A Rare Case Report

LCSSC: Limited Cutaneous Sclerosis; DCSSC: Diffuse Cutaneous Sclerosis; SSC: Systemic Sclerosis; ILD: Interstitial Lung Disease; Pulmonary Hypertension; PFTs Pulmonary Function Tests; HRCT: High-Resolution Computed Tomography; PFTs: Pulmonary Function Tests; BAL: Bronchoalveolar Lavage; NSIP: Nonspecific Interstitial Pneumonitis
A Lightweight Algorithm against Replica Node Attack in Mobile Wireless Sensor Networks using Learning Agents

The node replication attack is considered one of the most dangerous attacks against wireless sensor networks (WSNs). In this attack, an adversary captures one or more normal nodes of the network, extracts its key materials, generates several replicas, and deploys them in the network. In this paper, we propose a novel, intelligent, and lightweight algorithm using learning agents and watchdog nodes (WNs) to detect replica nodes in mobile WSNs. In the proposed algorithm, there are a few WNs, each one equipped with a learning agent that monitors the network traffic and nodes’ movements to identify potential replica nodes in the network. We use the fact that the number of times that a replica node meets a WN is probably more than that of a normal node during a specific monitoring interval of the network. The proposed algorithm is simulated using the J-SIM simulator, and its performance is evaluated in terms of replica node–detection probability and false detection probability through experiments. Experiment results show that the proposed algorithm can detect 100% of the replica nodes, while its false detection probability is less than 0.01.