Miracle-dz/UniFAR-FTUs huggingface dataset:
An Apriori Method for Topic Extraction from Text Files

In this data age peta-bytes of data is generated every day. One of the biggest challenge today is to convert this data into useful information, this is known as data mining. Important kinds of data include text-based data, audio-based data, imagebased data, video-based data etc. An important challenge in mining useful information from text-based data source (text mining) is topic modeling which is to find out the topic the text is talking about. The solution to this problem finds application, in clustering files based on the topic, pre-processing method in information retrieval, ontology of medical record etc. A lot of research work has gone into this area of topic modeling, and many approaches have been formulated. Some of these approaches take into account the occurrence and frequency of occurrence of words/terms, these models come under the Bag Of Words(BOW) approach. Others take into account the underlying structure in the corpus of text used, Wikipedia category graph is an example of this approach. This paper, provides an unsupervised solution to the above problem by extracting keywords that represent the topic of the text document. In our approach, topic modeling is carried out with a hybrid model which makes use of WordNet and Wikipedia Corpus. Promising experimental results have been obtained for well- known news dataset (BBCNews) from our model. We present the experimental result for our proposed approach along with the results of others in the same domain and show that our approach provides better results.
Electrophysical Modelling and Analysis of Axon in Neurons

Electrophysical modelling of axon is given based on voltage equilibrium and current continuity conditions. Inductance L and capacity C per unit length are obtained for lossless axon. Attenuation, phase rotation, and phase (signal) velocity are given for axon with loss. It is then presented that higher velocity and wideband signal transmission are realized by reduction of C and L by myelination and large cross section of axons. It is also presented that long distance transmission becomes available by reduction of serial resistance r and parallel conductance g by voltage dependent Na+ channels distributed at unmyelinated parts of axons. Key-Words: Electrophysical model of axon, Myelination and unmyelination in axon, Velocity and bandwidth, Attenuation, Phase rotation.
Using Domain Knowledge to Enhance Deep Learning for Emotional Intelligence

We propose a hierarchical classiﬁcation architecture for identifying granular emotions in unstructured text data. Whereas most existing emotion classiﬁers focus on a coarse set of emotions, such as Ekman’s six basic emotions (e.g., joy, anger, sadness) [2], we focus on a larger set of 24 granular emotions (e.g., irritation, envy, rage). Compared to coarse emotions, granular emotions are more speciﬁc in the information they convey (e.g., intensity, context). For example, sadness is a broad bucket of emotions that encompasses more speciﬁc granular emotions ranging from disappointment to neglect to sympathy. Individuals who are able to better recognize the nuance of their emotional state and capture it with more speciﬁc words [3] are typically characterized as having greater emotional intelligence [4, 1].
Effects of Component Densities and Nitrogen Fertilization on Efficiency and Yield of a Maize/Soyabean Intercrop

SUMMARY A field study was conducted in 1985 at two locations in Maryland, USA, to test the influence of environment (location), input level (N fertilization), management (component density combinations), and measure of performance on recommendations for a maize/soyabean intercrop. Location affected the degree of intercrop yield depression of each crop; the effect of N on relative competitive abilities; the maximum Land Equivalent Ratio (LER) and maximum modified Staple Land Equivalent Ratio (SLER); the difference between LER and SLER; the optimum density combinations and change in optima in response to N; and the N level at which the greatest land use efficiency was achieved. Given the complexity of the environmental effects, recommendations based on the trials have a very narrow range of applicability.
Identifying Potential Prognostic Biomarkers by Analyzing Gene Expression for Different Cancers

Cancer as everyone knows it is a very harmful and deadly disease which causes number of deaths world-wide and still there are no hundred percent cures for this disease. So for curing cancer scientist and researchers are working world-wide to find a probable cure for this deadly disease but still full diagnosis and recovery is not available. For providing some help and information for further analysis for scientists and researchers this work provides helpful hand. The affymetrix data which contained the differential expression values for different cancer were included for the analysis. The analysis includes normalization and filtration and retrieving the up regulated and down regulated genes from the datasets. After retrieving the genes responsible for different cancers common genes list were identified in multiple cancers. This would be helpful for identifying probable biomarkers for ceasing cancer progression. Our further analysis includes clustering using DPClus. The common genes obtained after normalization were clustered and this generated dense cluster from these cluster the gene that could be our probable biomarker were identified and hence could be used for further computation study.
Diagnosis and Prognosis of Alzheimer?s Disease Via 3D CNN

Mild Cognitive Impairment (MCI) is an early symptom of Alzheimer’s disease (AD). The feature extraction and deep learning architecture of the convolutional neural network in 3D brain images is applied to the problem of Alzheimer’s disease. The Structural Magnetic Resonance (sMRI) and Positron Emission Tomography (PET) image of the patient’s brain are classified according to the vigorousness of the disease and is labelled to be either in MCI or in AD or Normal Control (NC) condition. In this paper, we proposed a model and presented the baseline convolutional CNN with four layers viz., Convolutional layer, Leaky Rectified Linear Unit(LReLU), S3Pool layer and Global average pooling. Further, the 3D image data is used to perform the binary and ternary classifications and its performance are examined. The strength of the network has improved interior resource utilization evaluated with medical images, sMRI and PET on hippocampal ROI. The results of our proposed CNN architecture have achieved an accuracy level of 0.945, 0.859 and 0.748 respectively, when compared to the conventional AlexNet based network. The obtained data from the ADNI database shows better performance with our proposed model.
Tax Revenue Reforms and Income Distribution in Developing Countries

We explore the impact of major revenue mobilization episodes on income distribution dynamics using a new “narrative” database of major policy changes in tax and revenue administration systems, covering 45 emerging and low-income countries from 2000 to 2015. Our main finding is that after a tax reform (particularly those affecting the personal income or the operation of the revenue administration), the Gini index falls and the bottom income share rises. This result does not hold for sub-Saharan Africa, calling into question the design of tax reforms implemented in the region (mostly fragile states in the sample). In general, to reduce more rapidly income inequality (and improve the income prospects of the poorest strata of the population), it would be more effective to implement tax reforms when the economy is growing relatively slowly. Finally, the smaller the government and the smaller the tax system, the larger the beneficial impact of tax reforms on income distribution. Our results are robust to a battery of sensitivity and robustness tests.
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis

View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view features to be a global shape descriptor. In this work, we propose a novel view-based Graph Convolutional Neural Network, dubbed as view-GCN, to recognize 3D shape based on graph representation of multiple views in flexible view configurations. We first construct view-graph with multiple views as graph nodes, then design a graph convolutional neural network over view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. The view-GCN is a hierarchical network based on local and non-local graph convolution for feature transform, and selective view-sampling for graph coarsening. Extensive experiments on benchmark datasets show that view-GCN achieves state-of-the-art results for 3D shape classification and retrieval.
A Study of VR Interactive Storytelling for Empathy

본 논문은 VR 인터랙티브 스토리텔링 콘텐츠 개발에서 특히 독자 (또는 플레이어)와 스토리 캐릭터간 공감 형성에 목표를 갖 고, 공감 기반 VR 인터랙티브 스토리 개발을 위한 실질적 지침으로 4가지 주요 요소를 다음과 같이 제안한다. 첫째는 서사 요소로 서 서사학자인 제넷의 초점화 (focalization)를 적용한 서사 이론이고, 둘째는 인터랙티브 스토리텔링에서 서사와 플레이어 제어 사 이의 균형을 고려한 양방향성 (interactivity)이며, 셋째는 인간과 컴퓨터의 상호작용에서 고려하는 가상 현실 요소이며, 넷째는 심 리학과 인지과학 측면에서 고려한 공감 (empathy) 요소이다. 본 논문은 또한 전래 동화인 빨간 망토 이야기를 기반으로 한 VR 인터 랙티브 스토리 시나리오를 소개하고, 언리얼 게임 엔진과 HTC Vive를 활용한 VR 구현을 논의한다.
Raspberry Pi based Home Surveillance and Security using Cloud Server

Internet of Things is playing a crucial role for connecting the things through internet connectivity. At present requirement of home surveillance and security is demanding. In this paper we are proposing internet of things-based home surveillance system using raspberry pi controller and cloud server. With the assistance of PIR sensor, RFID reader, the unusual human activities are recorded and communicates the information to the local home controller via XBee modem. The local home controller sends the received information to the cloud server via Wi-Fi modem. Through cloud server, the user monitors the surveillance and security of the home through UI mobile app.
The Opportunities and Limitations of Human Computer Interaction in Virtual and Augmented Reality

Virtual Reality, Augmented Reality and other such immersive environments have gained popularity with the increase in technological trends in the past decade. As they became widely used, the human computer interface design and the designing criteria emerges as a challenging task. Virtual and Augmented Reality provide a wide range of applications ranging from a primitive level like improving learning, education experiences to complex industrial and medical operations. Virtual reality is a viable alternative that can be focussed on, in the future interface design development because it can remove existing generic and complex physical interfaces and replace them with an alternative sensory relayed input form. It provides a natural and efficient mode of interaction, that the users can work with.Virtual and Augmented reality eradicates the need for development of different acceptable standards for user interfaces as it can provide a whole and generic interface to accommodate the work setting.In this paper, we investigated various prospects of applications for user interaction in Virtual and Augemnted realities and the limitations in the respective domains. The paper provides an outline on how the new era of human computer interaction leading to cognition-based communications, and how Virtual and Augmented realities can tailor the user needs and address the future demands which replaces the need for command-based interaction between the humans and computers.
AN ANALYSIS OF INFORMATION AND COMMUNICATION TECHNOLOGY AND VIRTUAL REALITY TECHNOLOGY IMPLEMENTATION THROUGH A QUANTITATIVE RESEARCH ON USERS’ EXPERIENCES

This study investigated users’ overall experiences with <Klimt Inside> and < Stoclet Frieze>, based on the Technology Acceptance Model (TAM), using the convergence and integrated approach . In terms of <Klimt Inside>, the degree of satisfaction correlated with degree of immersion and Behavioral Intention to Digital Exhibition. The degree of satisfaction and degree of immersion correlated with the detail factors of usability for learning and usability for appreciation. All detail factors of perceived ease of use positively correlated with the degree of satisfaction and degree of immersion. With regard to <Stoclet Friese>, the degree of satisfaction with the VR application significantly correlated with the degree of immersion and Behavioral Intention to Digital Exhibition. However, the degree of satisfaction with the VR application did not affect the degree of satisfaction and degree of immersion with <Klimt Inside> . In the case of <Stoclet Friese>, the detail factors of usability for learning and usability for appreciation were relevant to the degree of satisfaction and degree of immersion. The degree of satisfaction with the VR application significantly correlated with the degree of immersion and Behavioral Intention to the VR Application. The detail factors of perceived ease of use significantly correlated with the degree of satisfaction and influenced the degree of immersion with the VR application. Finally, both <Klimt Inside> and <Stoclet Frieze> did not guarantee the same quality of the original regarding usability for appreciation. Most importantly, these technological reproductions never fully substituted the originals, but their benefits are noteworthy as a means for stimulating intrinsic motivation in learning . As more museums are exploring ICT and VR technology in their exhibition environments, museum professionals and technology specialists should collaboration well to achieve optimal results. Insight on the visitor experience through in-depth and on-going research on the adoption of technologies is required to enrich visitor engagement.
A Model for Accurate Prediction of Child Immunization Data for Knowledge Discovery using Bayesian TAN and Naive Bayes Classifiers

Knowledge Discovery in Databases (KDD) is a splendid methodology of discovering knowledge from gigantic databases by using its various stages viz. Data Selection, Data Preprocessing, Data Transformation, Data Mining and Interpretation/Evaluation. Data Mining is a vital sub-process of KDD methodology that is particularly used to apply the various mining algorithms on the data. In the present research paper, the authors have made an attempt to discover new knowledge by classifying the child immunization data of Jammu and Kashmir State of India. The data for the present work was collected from a web portal named as Health Management Information System (HMIS) facilitated by Ministry of Health and Family Welfare (MoHFW), Government of India. The data consists of diverse health parameters pertaining to the immunization of children and for the present study, the child immunization data of all districts of Jammu and Kashmir State was considered. Two classifiers viz. Bayesian TAN and Naïve Bayes were employed for classifying the districts of Jammu and Kashmir State into High IMR and Low IMR districts based on the available past data from 2014 to 2018. Additionally, various measurement methods have been used to evaluate the performance of the models developed by Bayesian TAN and Naïve Bayes.
MALDROID: ATTRIBUTE SELECTION ANALYSIS FOR MALWARE CLASSIFICATION

Android is the most dominant operating system in the mobile market and the number of Android users is increasing year by year. Malware authors use android market as a hub for malicious apps and spread malware to users with the intention to threaten privacy; and this has remained undetected due to the weakness in signature-based detection. A major problem with malware detection is the existence of numerous features in malware code and the need to look at the relevant features in malware analysis. As a result, applying any security solution in malware analysis is considered inefficient because mobile devices have limited resources in terms of its memory, processor and storage. Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. The experiment evaluated 8000 real data samples and the result showed that InfoGainEval and KNN algorithm are the most selected in attribute selection and classification process.
An empirical study of the effect of social media marketing activities upon customer satisfaction, positive word-of-mouth and commitment in indemnity insurance service

The present study explores the dimensions of social media marketing (SMM) activities and the effect upon customer satisfaction (SAT), positive word-of-mouth (WOM) and commitment in one of high perceived risk services-indemnity insurance service in Japan. An internet based survey was conducted to examine moderation hypotheses, which was analyzed by factor analysis and covariance structure analysis. The results show that the effect of SMM activities can be evaluated by interaction, trendiness, customization and perceived risk. Moreover, the results show that the effect of SMM activities significantly influence SAT, but not positive WOM and commitment. Lastly, this study also finds the linear relationship of SAT→Commitment→Positive WOM in the case of high perceived risk service.
Identification of Premature Ventricular Contraction (PVC) Based on ECG Using Convolutional Neural Network

Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of heartbeats based on ECG signals plays a vital role it the field of cardiac sciences to identify arrhythmias. The use of Artificial Neural Networks (ANN) has proven to be the most effective technique for sole agenda of classification. The use of CNN is simple and more noise immune method in comparison to various other techniques. In this paper, a survey of numerous algorithms and classification techniques along with their performance measures are presented. This paper proposes the identification of PVC on the basis of heart beats by using CNN and the results obtained are compared to other traditional approaches
Detection of Fish Killing Dinoflagellates Cochlodinium polykrikoides and Karlodinium veneficum (Dinophyceae) in the East China Sea by Real-time PCR

The rDNAs of figh-killing dinoflagellates Cochlodinium polykrikoides and Karlodinium veneficum were detected from the East China Sea by species-specific real-time PCR probes. Sequence analysesusing the partial ITS sequences from the real-time PCR products showed identical sequences with C. Polykrikoides and K. veneficum, respectively and low expectation values (E-value) of less than 1e-5 suggesting the presence of these organisms in the East Ching Sea shelf water that flows into the Tsushima Strait and the Yellow Sea.
Dual-T-Type Seven-Level Boost Active-Neutral- Point-Clamped (DTT-7L-BANPC) Inverter

The conventional three-level active-neutral-pointclamped (ANPC) inverter requires high DC-link voltage at least twice the peak of AC output. To reduce the DC-link voltage, a recent topology that enhanced the voltage gain from half to unity has been presented. In this letter, an alternative ANPC topology is established by using two T-type inverters. Two floating capacitors with self-voltage balancing capability are integrated to achieve a voltage-boosting gain of 1.5. In addition, the proposed topology is capable of generating seven voltage levels. Its operation is validated through circuit analysis followed by experimental results of a prototype.
Vul-Mirror: A Few-Shot Learning Method for Discovering Vulnerable Code Clone

It is quite common for reusing code in soft development, which may lead to the wide spread of the vulnerability, so automatic detection of vulnerable code clone is becoming more and more important. However, the existing solutions either cannot automatically extract the characteristics of the vulnerable codes or cannot select different algorithms according to different codes, which results in low detection accuracy. In this paper, we consider the identification of vulnerable code clone as a code recognition task and propose a method named Vul-Mirror based on a few-shot learning model for discovering clone vulnerable codes. It can not only automatically extract features of vulnerabilities, but also use the network to measure similarity. The results of experiments on open-source projects of five operating systems show that the accuracy of Vul-Mirror is 95.7%, and its performance is better than the state-of-the-art methods.
Non albicans Candida species: its isolation pattern, species distribution, virulence factors and antifungal susceptibility profile -

Background: The change in epidemiology and antifungal susceptibility has generated interest of Clinical Microbiologists in identification of Candida up to species level along with antifungal susceptibility pattern. Non-albicans Candida (NAC) has emerged as an important opportunist pathogen. Extracellular hydrolytic enzymes are one of the important virulence attributes of Candida species. Aims & Objective: The present study aimed to determine the species distribution, virulence factors and antifungal susceptibility profile of NAC spp. isolated from various clinical specimens. Material and Methods: Speciation of Candida was done by assessing the germ tube formation, assimilation and fermentation of sugars and colony color on HICHROM Candida agar. In-vitro extracellular hydrolytic enzymes production in NAC spp. was assessed. Antifungal susceptibility testing of the isolates was performed by Hicomb minimum inhibitory concentration (MIC) test. Results: Majority of the isolates were obtained from urine sample (35.6%). C. tropicalis (29.4%) was the major isolate. Maximum extracellular hydrolytic enzymes activity was seen in C. tropicalis. A total of 79 (27.3%) isolates were resistant to fluconazole. Amphotericin B resistance was noted in 17 (5.8%) isolates. Conclusion: NAC spp. cannot be overlooked as mere containment or non-pathogenic commensals as most of them show reduced susceptibility to commonly used antifungal drugs. Extracellular hydrolytic enzymatic activity of NAC Spp. would be an important tool to prove the relation between the infective species of Candida and infection.
RECOGNIZING SENTIMENT PREDICTION ON TWITTER DATA

Escorted by the wide spread of Internet today, people have found a new way of expressing their opinions. It is a platform with a variety of information where an individual can also view the opinions of others. This is continuously growing and becoming an important factor in decision making for various organisations, businesses and even for Politics. In this paper we have chosen the most popular social media platform i.e. Twitter for our Sentiment Analysis. Eventually, Acknowledging the opinions beyond the tweets is of great concern. The fundamental aim of Sentiment Analysis is to reason feelings and ideas of individuals. We have made data analysis with tweets related to a topic and thereby classified their polarity using different machine learning algorithms.