Evaluation of economic loss caused by Indian crested porcupine (Hystrix indica) in agricultural land of district Muzaffarabad, Azad Jammu and Kashmir, Pakistan

The Indian crested porcupine (Hystrix indica) is a vertebrate pest of agricultural lands and forest. The study was aimed to report the damage to local crops by the Indian crested porcupine (Hystrix indica) in the Muzaffarabad District. A survey was conducted to identify the porcupine-affected areas and assess the crop damage to the local farmers in district Muzaffarabad Azad Jammu and Kashmir (AJK) from May 2017 to October 2017. Around 19 villages were surveyed, and a sum of 191 semi-structured questionnaires was distributed among farmers. Crop damage was found highest in village Dhanni where a porcupine destroyed 175 Kg/Kanal of the crops. Regarding the total magnitude of crop loss, village Danna and Koomi kot were the most affected areas. More than half (51.8%) of the respondents in the study area suffered the economic loss within the range of 101-200$, and (29.8%) of the people suffered losses in the range of 201-300$ annually. Among all crops, maize (Zea mays) was found to be the most damaged crop ranging between 1-300 Kg annually. In the study area, porcupine also inflicted a lot of damages to some important vegetables, including spinach (Spinacia oleracea), potato (Solanum tuberosum) and onion (Allium cepa). It was estimated that, on average, 511Kg of vegetables are destroyed by porcupine every year in the agricultural land of Muzaffarabad. It was concluded that the Indian crested porcupine has a devastating effect on agriculture which is an important source of income and food for the local community. Developing an effective pest control strategy with the help of the local government and the Wildlife department could help the farmers to overcome this problem.

A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net) , which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.

Assessment of the Endorsers of e-Business Practices for Food Supply Chain Performance System

Globalisation and changing lifestyle of the people has escalated the demand for the more product customisation, taste preferences and awareness about the usage of quality food commodities. Recent developments in the field of information technology and its integration with the business practices has emerged as a new term named ‘e-business’ (EB). Increasing consumer base of the food supply chains (FSC), has escalated the demand of technological and operation advancements by mediating ‘EB’ activities. Such, practices become extensively crucial when the world is suffering from the pandemic of COVID-19, leading to distressing of FSC linkages causing frequent market closures. To tackle the same presented work, explores the various endorsers (EDR) of the ‘EB’ in FSC, which are contemplated by hybrid combination of multi-criteria decision making techniques. Outcomes of the present work aids managers to formulate the decision policies and develop a robust framework in the direction to cling the ‘EB’ practices with FSC.

Adaptive Model Scheduling for Resource-efficient Data Labeling

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.

Batik in Malaysia and Indonesia: A collaboration for reconciling issues of cultural heritage

Conflict claims for the cultural heritage of batik between Indonesia and Malaysia have created tensions between the people of these two countries. The Indonesian and Malaysian governments have never involved academics and arts education institutions in resolving such conflict claims, yet, these communities can play a significant role in post-conflict reconciliation efforts. This article describes a conflict reconciliation method initiated by academics, artists and art educators through a collaborative art project between art higher education institutions in Malaysia and Indonesia. Ways in which collaborations within and across the art and education communities may address the understanding and reconciliation of issues related to cultural heritage conflict are explored.

A Dynamic Scaling Approach in Hadoop YARN

In Cloud based Big Data applications, Hadoop has been widely adopted for distributed processing large scale data sets. However, the wastage of energy consumption of data centers still constitutes an important axis of research due to overuse of resources and extra overhead costs. As a solution to overcome this challenge, a dynamic scaling of resources in Hadoop YARN Cluster is a practical solution. This paper proposes a dynamic scaling approach in Hadoop YARN (DSHYARN) to add or remove nodes automatically based on workload. It is based on two algorithms (scaling up/down) which are implemented to automate the scaling process in the cluster. This article aims to assure energy efficiency and performance of Hadoop YARN’ clusters. To validate the effectiveness of DSHYARN, a case study with sentiment analysis on tweets about covid-19 vaccine is provided. the goal is to analyze tweets of the people posted on Twitter application. The results showed improvement in CPU utilization, RAM utilization and Job Completion time. In addition, the energy has been reduced of 16% under average workload.

Building Quranic stories ontology using MappingMaster domain-specific language

<p>The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL)technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.</p>

Automatic Facial Mask Rule Violation Detection using Keras & Tensorflow to Limit COVID-19

COVID-19 is a pandemic disease that spread by itself coming in the contact of people. It was initially started from China and now it has been spread all over the world and many casualties have been occurred. Social distancing commonly known as physical distancing is a non-pharmaceutical approach through which it can be reduced. But social distancing only works when people started wearing mask because it can spread by sneezing even having distance among people. So wearing mask is mandatory to stop spreading this virus at its possible extent. In this paper, it has been intended to identify the people who are wearing mask or not. By the help of CCTV camera it can be recognized at the entrance of various public places such as mall, airport, railway station, mart and many more. If facial mask can be recognized effectively with high level of accuracy then it can become mandatory for people who are violating the rules. The proposed system uses Keras and Tensorflow model for identifying whether people are following the rule or not. Tensorflow is a deep learning methodology through which facial mask can be detected with all kind of situations. Proposed system is able to classify whether a person wear a mask or not, it is also able to identify whether people incorrectly wearing mask i.e. partial wearing. It is mandatory to identify whether people are properly using the mask or not. System identify this kind of situation and classified them accordingly. System uses hybrid technique by combining two algorithms i.e. keras and tensorflow. By combining both the systems it can be identified more precisely to identify the rule violations. Keywords: COVID-19, Facial Mask, Convolutional Neural Network, Classifiers, Machine Learning, Image Processing, Pattern Recognition.