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An open call format of crowdsourcing software development (CSD) is harnessing potential, diverse, and unlimited people. But, several thousand solutions are being submitted at platform against each call. To select and match the submitted task with the appropriate worker and vice versa is still a complicated problem. Focusing the issue, this study proposes a task assignment algorithm (TAA) that will behave as an intermediate facilitator (at platform) between task (from requester) and solution (from worker). The algorithm will divide the tasks' list based on the developer's personality. In this way, we can save the time of both developers and platform by reducing the searching time.

Growing demand for software has attracted the attention of the software development industry. Crowdsourced software development has provided a new method for the software industry to produce quality software based on an open-call format. Selecting an appropriate task to develop (developer-end) or evaluate (platform-end) is one of the primary problems in this type of open-call format. Receiving or assigning an improper task to an improper crowdsource (CS) developer does not only decrease the quality of the software deliverables, but also causes overburden on both the platform and the developers. To solve this problem, sorting the tasks based on the developers' human characteristics may increase task relevancy for developers, which can accelerate efficiency and lessen complexity. Thus, this paper has conducted an empirical experiment to measure the influence of personality on task selection based on the important characteristics of a task: money, time, and type. A total of 83 students from the University of Sindh voluntarily participated in four different short-duration rounds of task development using the developed CS platform. The personality types of the participants were measured based on the Myers-Briggs type indicator. In addition, a complex network technique called weighted degree centrality was applied to identify the most suitable personality for task sorting based on money or complexity attractions (i.e., time or type). Based on the results, it can be observed that personality has a significant relationship with task selection. For instance, developers with intuitive (N) and feeling (F) personality traits are primarily focused on the time duration of a project.

Broadband and highly efficient amplifiers are of great interest in modern wireless communication systems. In this paper, we present a novel two-stage broadband Doherty power amplifier (DPA), which is able to maintain high efficiency at saturated output power and at backoff output power levels over a wide bandwidth. The amplifier uses a novel and simple approach to achieve bandwidth enhancement and at the same time uses a driver amplifier. We employ Klopfenstein taper to deliver wide bandwidth. The measurement results show that the proposed broadband two-stage DPA is able to deliver 31%-35% drain efficiency at 6-dB output power backoff while at the saturation it achieves 40%-55% drain efficiency over the designed bandwidth of 1.5 to 2.6 GHz. The amplifier can deliver above 35-dBm output power with substantial gain of around 20 dB. The proposed DPA is a promising candidate for wideband and high gain requirements in modern wireless systems.

Smartphone based indoor positioning has become a hot topic in pervasive computing, because of the need to improve indoor location-based services. In order to strengthen positioning accuracy, researchers have tried to leverage high-resolution magnetic fingerprint with particle filter and dynamic time warping (DTW). These approaches are computation-hungry, which increases hardware cost for positioning companies. By analyzing magnetic features for pedestrian users, we present a mass-centered weight update scheme to decrease calculation overheads. Finally, the proposed positioning algorithm is tested in a realistic situation, showing high-quality localization capability.

This In the present globalized smart ecosystem, various suggestions of using data as a new tool for the development of the economy are still going on to be presented. Hence, developed countries are trying to pursue different policy measures to develop the big data industry, including promoting big data R&D sector and investment in human resources to retain the pace of this global trend. The government of Pakistan has supported liberal policies to activate the IT its applications such as big data, Internet of Things (IOT) and electronic government (e-government). We used the Analytic Network Process (ANP) model to prioritize policy measures and find out its implications for Pakistan. This study will convey an important lesson for developing countries and particularly for South Asian countries to establish policies for developing big data as a new tool for economic growth in the context of smart ecosystem environment.

The smartphone-based indoor positioning has attracted considerable attention in recent years. In order to implement accurate and infrastructure-free positioning systems, researchers have tried to fuse magnetic field, $Wi-Fi$, and dead reckoning information applying particle filter technique. In fact, magnetic signals have high-resolution and $Wi-Fi$ signals are able to provide coarse-grained global results. However, in order to move particles, the particle filter requires the phone’s orientation aligned with the user moving directions, thus limiting its applications and impairing user experiences. In order to implement an orientation-free and infrastructure-free system, we propose a deep learning based positioning scheme. The proposed system constructs a new kind of rich-information positioning image, then Ieverages convolution neural network to automatically map positioning images to position predictions. We also present a novel extracting and labeling method to generate enough positioning images for training the neural network. Finally, experiments convincingly reveal that the proposed positioning system is orientation-free, infrastructure-free, and achieves good precisions.

Pedometer is an enabling technique for smartphone- based pedestrian positioning systems. Because the sensor drifts, these algorithms can only estimate moving distances from step counts. In order to detect step events, researchers have tried to leverage the peak detection and the periodicity attribute of step acceleration signals. However, many human behaviors are having acceleration peaks and periodic, causing traditional detectors error- prone when the phone is shaken periodically leading state-of-the-art system to high false positive ratio and consequently to big mistake of distance estimations. Based on the acceleration feature analysis of step events, we present a deep convolution neural network based step detection scheme to improve the pedometer robustness. Finally, the proposed step detection algorithm is tested in a realistic situation, showing a high anti periodic negative-step movement capability.

This research paper highlights the scope and fundamental objectives of the Belt and Road strategy, and its impact on social and economic development in China and Pakistan. Two essential elements of the one belt one road (OBOR) initiatives are the silk road economic belt (SREB) and maritime silk route (MSR). The historical background of the Sino-Pak companionship and its bilateral relationships is particularly important in several areas including trade, investment, energy, transport, and defence. The China-Pakistan economic corridor (CPEC) is vital for the participant countries and the region in general, with significant social, cultural, economic, and environmental impacts. Some of the challenges, such as safety and security, are considered, and the Pakistan authorities have already made some reforms to alleviate these challenges. Finally, the authors concluded the work by discussing the present situation in the CPEC and its chance of success in the future.

The profuse use of software has turned the world into global village where everything is accessible at finger tips. The past studies have confirmed the rapid increase in the demand of software whereas its quality supply has drastically decreased to 6%. As high demand and low supply normally generate numerous problems, many researchers, therefore, have raised their concern to develop software affordable, less time consuming and feasible to achieve organisational ends. The findings of the past research studies have determined the fact that besides technical skills, human resources (i.e., personality type for team composition) is of pivotal importance for developing software which has not been seriously addressed. This study has tried to address this prevailing problem by focusing on patterns of personality types of programmer role monitored with team-leader. Additionally, to draw the attention of practitioners, the results are validated with several classification techniques and results appeared with high accuracy. The study has implications on both software developers and researchers having their interest in role of team composition in software development.

Human aspect is considered as one of the success factors for software development. Besides, diversity is labeled as the most effective element for creating effectiveness in team performance. Thus, this study has aimed to find the effects of gender diversity on team performance based on personality types. The personality types' variations were found effective on team performance based on genders. For instance, male-lead groups were found convenient with both genders but female-lead groups looked much comfortable with only female members. Moreover, female-gender looked uneasy in male dominant teams especially when their personality is E (extrovert). Whereas, male-gender was observed as digestible in female-lead teams if the I (introvert) personality is dominating.