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| 1 | Framework for Effective Utilization of Distributed Scrum in Software Projects显示文摘There is an emerging interest in using agile methodologies in Global Software Development(GSD)to get the mutual benefits of both methods.Scrum is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated teams.At the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural distances.Due to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in GSD.We conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the challenges.To validate our reviewfindings,we conducted an industrial survey of 305 practitioners.The results of our study are consolidated into a research framework.The framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed Scrum.Results of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges. | Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 2 | A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition显示文摘Human Action Recognition(HAR)is a current research topic in the field of computer vision that is based on an important application known as video surveillance.Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning,but they still face many challenges such as similarity in various actions and redundant features.We proposed a framework for accurate human action recognition(HAR)based on deep learning and an improved features optimization algorithm in this paper.From deep learning feature extraction to feature classification,the proposed framework includes several critical steps.Before training fine-tuned deep learning models–MobileNet-V2 and Darknet53–the original video frames are normalized.For feature extraction,pre-trained deep models are used,which are fused using the canonical correlation approach.Following that,an improved particle swarm optimization(IPSO)-based algorithm is used to select the best features.Following that,the selected features were used to classify actions using various classifiers.The experimental process was performed on six publicly available datasets such as KTH,UT-Interaction,UCF Sports,Hollywood,IXMAS,and UCF YouTube,which attained an accuracy of 98.3%,98.9%,99.8%,99.6%,98.6%,and 100%,respectively.In comparison with existing techniques,it is observed that the proposed framework achieved improved accuracy. | Muhammad Naeem Akbar Farhan Riaz Ahmed Bilal Awan Muhammad Attique Khan Usman Tariq Saad Rehman | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 3 | Population survey and conservation assessment of the globally threatened cheer pheasant(Catreus wallichi) in Jhelum Valley, Azad Kashmir, Pakistan显示文摘The cheer pheasant Catreus wallichi is a globally threatened species that inhabits the western Himalayas. Though it is well established that the species is threatened and its numbers declining, updated definitive estimates are lacking, so in 2011, we conducted a survey to assess the density, population size, and threats to the species in Jhelum valley, Azad Kashmir, which holds the largest known population of cheer pheasants in Pakistan. We conducted dawn call count surveys at 17 points clustered in three survey zones of the valley, 11 of which had earlier been used for a 2002-2003 survey of the birds. Over the course of our survey, 113 birds were recorded. Mean density of cheer pheasant in the valley was estimated at 11.8±6.47 pairs per km2, with significant differences in terms of both counts and estimated density of cheer were significantly different across the three survey zones, with the highest in the Chinari region and the lowest, that is the area with no recorded sightings of the pheasants, in Gari Doppata. The total breeding population of cheer pheasants is estimated to be some 2 490 pairs, though this does not consider the actual area of occupancy in the study area. On the whole, more cheer pheasants were recorded in this survey than from the same points in 2002-2003, indicating some success in population growth. Unfortunately, increasing human settlement, fires, livestock grazing, hunting, and the collection of non-timber forest products continue to threaten the population of cheer in the Jhelum valley. To mitigate these potential impacts, some degree of site protection should be required for the conservation of cheer pheasants in Pakistan, and more effective monitoring of the species is clearly needed. | Muhammad Naeem AWAN Hassan ALI David Charles LEE | 2014 | Zoological Research2014，35，4： | 0 |
| 4 | 巴基斯坦穆扎法拉巴德市Pattika公园鸟类多样性显示文摘2006年6月-2007年5月年对位于巴基斯坦自由克什米尔省穆扎法拉巴德市帕蒂卡(Pattika)休闲公园(EW:73°34′,纬度:NS:34°27′)内的栖息鸟类进行了调查,共有73种,分属10目35科。其中雀型目占73.55%,非雀型目占45%。其分布的多寡随该地区的季节而变化。大量物种出现在迁移繁殖的雨季。该公园处于人口密集之郊外,其间家畜的放养、灌木的砍伐及生境的干扰等人为因素都会对鸟类的分布和数量造成影响。 | Muhammad Naeem Awan Mir Mohammad Saleem | 2007 | Zoological Research2007，28，6： | 0 |
| 5 | 喜马拉雅灰叶猴栖息地利用和食性生物学(英文)显示文摘2006年4月至2007年4月在巴基斯坦克什米尔地区马希亚拉国家公园(Machiara National Park)对喜马拉雅灰叶猴(Semnopithecus entellus ajex)的栖息地利用和食性生物学进行研究。结果表明,冬天,叶猴首选的栖息地多为温暖湿润的针叶林和落叶林混交地区;夏天,它们则迁移至高海拔的亚高山灌木丛林里。喜马拉雅灰叶猴主要以植物的叶子为食,研究期间在该地区共发现49种被采食过的植物(夏季27种,冬季22种)。通过观察它们的所有食物,发现老叶(36.12%)比嫩叶(27.27%)更受欢迎,随后依次为果实17.00%、树根9.45%、树皮6.69%、花2.19%和根茎1.28%。 | Riaz Aziz Minhas Khawaja Basharat Ahmed Muhammad Siddique Awan Naeem Iftikhar Dar | 2010 | Zoological Research2010，31，2： | 0 |
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