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Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development. In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security. These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams. This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology. We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developer’s payment requirements, and automatic payment distribution into the digital wallets of development teams. The execution of these smart contracts automatically assign penalties to the customers for late or non-payments and also to the developers for overdue tasks. Moreover, we have also solved the blockchain’s scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage. Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.

The Internet of Things (IoT) is an emerging paradigm that is transforming real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains including healthcare, smart grid, and agriculture. This domain has started revolutionizing the agriculture industry by providing smart solutions for precision farming, greenhouse management, and livestock monitoring. This article aims to present a comprehensive survey on the role of IoT in the Livestock field by categorizing and synthesizing existing research work in this area. To this end, a detailed discussion has been provided on IoT network infrastructure, topologies and platforms employed for livestock management. In addition, a list of communication protocols and connections of IoT-based livestock systems with relevant technologies have also been explored. Furthermore, numerous IoT-based livestock monitoring, controlling, and tracking applications have been discussed. Apart from this, it also analyses distinct security issues in IoT-based livestock field and developed a collaborative security model to detect and minimize the security risk. Lastly, pertinent open research challenges in the domain of IoT-based livestock management have been presented with future research directions.

Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.

There has been significant development in the facial recognition technology during past few decades. This technology has been widely used by different organizations and governments for defense, security, and surveillance projects. Furthermore, it has now been incorporated into our daily usages, such as consumer applications, personal data protection, or cyber-security, particularly while using smartphones. Most of these systems work very efficient, however, there are some challenges related to the accuracy of results of facial recognition systems when tested on images of people with dark skin. As a matter of fact, various studies demonstrate higher accuracy when tested on data set with white skin personnel, while exhibit a much lesser accuracy when the same algorithms are tested on dataset of people with dark skin. This article highlights the variation in accuracy of existing facial recognition algorithms when applied to dark-skinned people. Furthermore, as a principal contribution it presents a hybrid algorithm based on Gaussian and Explicit rule model that improves the accuracy for face-detection for dark skinned people. Thorough experimental evaluation has been conducted with a data set of black faces by first identifying skin and non-skin regions and then applying skin segmentation. The results have been compared with existing face detection algorithms with a clear improvement in the accuracy of 89% for dark skin.

Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentence-level translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.