Task Assignment Model for Crowdsourcing Software Development: TAM

Tunio, Muhammad Zahid,Luo, Haiyong,Wang, Cong,Zhao, Fang,Gilal, Abdul Rehman,Shao, Wenhua Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Selection of a suitable task from the extensively available large set of tasks is an intricate job for the developers in crowdsourcing software development (CSD). Besides, it is also a tiring and a time-consuming job for the platform to evaluate thousands of tasks submitted by developers. Previous studies stated that managerial and technical aspects have prime importance in bringing success for software development projects, however, these two aspects can be more effective and conducive if combined with human aspects. The main purpose of this paper is to present a conceptual framework for task assignment model for future research on the basis of personality types, that will provide a basic structure for CSD workers to find suitable tasks and also a platform to assign the task directly. This will also match their personality and task. Because personality is an internal force which whittles the behavior of developers. Consequently, this research presented a Task Assignment Model (TAM) from a developers point of view, moreover, it will also provide an opportunity to the platform to assign a task to CSD workers according to their personality types directly.

Crowdsourcing Software Development: Task Assignment Using PDDL Artificial Intelligence Planning

Tunio, Muhammad Zahid,Luo, Haiyong,Wang, Cong,Zhao, Fang,Shao, Wenhua,Pathan, Zulfiqar Hussain Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

Crowdsourcing Software Development: Task Assignment Using PDDL Artificial Intelligence Planning

( Muhammad Zahid Tunio ),( Haiyong Luo ),( Cong Wang ),( Fang Zhao ),( Wenhua Shao ),( Zulfiqar Hussain Pathan ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

Task Assignment Model for Crowdsourcing Software Development: TAM

Muhammad Zahid Tunio,Haiyong Luo,Cong Wang,Fang Zhao,Abdul Rehman Gilal,Wenhua Shao 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3

Selection of a suitable task from the extensively available large set of tasks is an intricate job for the developersin crowdsourcing software development (CSD). Besides, it is also a tiring and a time-consuming job for theplatform to evaluate thousands of tasks submitted by developers. Previous studies stated that managerial andtechnical aspects have prime importance in bringing success for software development projects, however,these two aspects can be more effective and conducive if combined with human aspects. The main purpose ofthis paper is to present a conceptual framework for task assignment model for future research on the basis ofpersonality types, that will provide a basic structure for CSD workers to find suitable tasks and also a platformto assign the task directly. This will also match their personality and task. Because personality is an internalforce which whittles the behavior of developers. Consequently, this research presented a Task AssignmentModel (TAM) from a developers point of view, moreover, it will also provide an opportunity to the platformto assign a task to CSD workers according to their personality types directly.

Factors Affecting Job Performance: A Case Study of Academic Staff in Pakistan

Fayaz Hussain TUNIO,Amad Nabi AGHA,Faryal SALMAN,Imran ULLAH,Asad NISAR 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.5

This study’s fundamental purpose is to examine the personality factors of business school faculty members in job satisfaction and job performance. Results show the significant impact of multicultural faculty members’ job performance in a diverse environment in the Business schools of Karachi. The data is collected through the multi questionnaires from the various teaching, non-teaching staff, and students of private business schools of Karachi in Pakistan. The data has been tested through the Jamovi-by-medmod, and the regression model is to scrutinize and find the effect dependent variables to mediation. Simultaneously, results are calculated by mediation estimates and path estimates through the medmod technic and regression test from data. It provides a comprehensive insight into various factors such as personality traits, self-efficiency, Psychological diversity climate, self-esteem, and human resource management practices. These are the primary evaluated factors that affect multicultural faculty members’ job satisfaction and job performance. However, results show a positive relationship between diversity climate and job performance, which mediates by job satisfaction. Similarly, personality traits show a positive relationship with job performance that mediates by job satisfaction. Correspondingly, self-esteem spectacles are a positive inter-relationship with job performance which is mediated by job satisfaction.

Financial Distress Prediction Using Adaboost and Bagging in Pakistan Stock Exchange

Fayaz Hussain TUNIO,Yi DING,Amad Nabi AGHA,Kinza AGHA,Hafeez Ur Rehman Zubair PANHWAR 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.1

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies’ bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company’s default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

Enhancement in Isolation among Collinearly Placed Microstrip Patch Antenna Arrays

Irfan Ali, Tunio,Hernan, Dellamaggiora,Umair, Saeed,Ayaz Ahmed, Hoshu,Ghulam, Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1

Strong surface waves among collinearly arranged patch antenna arrays pose unwanted inter element coupling particularly when high permittivity dielectric materials are used. In order to avert those waves, a novel Defected Ground Structure (DGS) is carved out systematically between two E-plane patch antenna elements. The introduced low profile μ shaped structure consequently improves impedance bandwidth and reflection coefficient by suppressing surface waves considerably. Parametric simulation results are analyzed and discussed.

Assessing Efficiency of Handoff Techniques for Acquiring Maximum Throughput into WLAN

Mohsin Shaikha,Irfan Tunio,Baqir Zardari,Abdul Aziz,Ahmed Ali,Muhammad Abrar Khan International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.4

When the mobile device moves from the coverage of one access point to the radio coverage of another access point it needs to maintain its connection with the current access point before it successfully discovers the new access point, this process is known as handoff. During handoff the acceptable delay a voice over IP application can bear is of 50ms whereas the delay on medium access control layer is high enough that goes up to 350-500ms. This research provides a suitable methodology on medium access control layer of the IEEE 802.11 network. The medium access control layer comprises of three phases, namely discovery, reauthentication and re-association. The discovery phase on medium access control layer takes up to 90% of the total handoff latency. The objective is to effectively reduce the delay for discovery phase to ensure a seamless handoff. The research proposes a scheme that reduces the handoff latency effectively by scanning channels prior to the actual handoff process starts and scans only the neighboring access points. Further, the proposed scheme enables the mobile device to scan first the channel on which it is currently operating so that the mobile device has to perform minimum number of channel switches. The results show that the mobile device finds out the new potential access point prior to the handoff execution hence the delay during discovery of a new access point is minimized effectively.

Distributed Incremental Approximate Frequent Itemset Mining Using MapReduce

Mohsin Shaikh,Irfan Ali Tunio,Syed Muhammad Shehram Shah,Fareesa Khan Sohu,Abdul Aziz,Ahmad Ali International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

A Review of Structural Testing Methods for ASIC based AI Accelerators

Umair, Saeed,Irfan Ali, Tunio,Majid, Hussain,Fayaz Ahmed, Memon,Ayaz Ahmed, Hoshu,Ghulam, Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1

Implementing conventional DFT solution for arrays of DNN accelerators having large number of processing elements (PEs), without considering architectural characteristics of PEs may incur overwhelming test overheads. Recent DFT based techniques have utilized the homogeneity and dataflow of arrays at PE-level and Core-level for obtaining reduction in; test pattern volume, test time, test power and ATPG runtime. This paper reviews these contemporary test solutions for ASIC based DNN accelerators. Mainly, the proposed test architectures, pattern application method with their objectives are reviewed. It is observed that exploitation of architectural characteristic such as homogeneity and dataflow of PEs/ arrays results in reduced test overheads.