Abstract Software development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.

Instrumentation and control systems are nervous systems of nuclear power plant (NPP). These systems interact with several safety-critical components of the NPP, such as actuators, transformers, control valves, sensors, circuit breakers, signal processing units, controllers, and heat exchangers. Therefore, the failure of these systems could result in significant financial loss, harm to human resources, or environmental damage. As a result, these systems need to be highly reliable and accurate. In this article, we suggest a framework, based on the batch deterministic and stochastic Petri nets (BDSPNs) to measure the reliability and performance of safety-critical system. The framework consists of three phases. In the first phase, the system is modeled using the BDSPN to derive the transition rate among the system states. In phase 2, the transition rate matrix is utilized to compute the steady-state probability values, which help to evaluate the response time of the system using Little's law. The third phase uses a transition probability matrix to assess the reliability of the system. The technique is illustrated on shutdown system of NPP and is validated on the operational profile data. The obtained accuracy of 99.9905% in measurement of reliability validates the approach.

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

In this proposal, the impact of dynamic farm environment due to varying vegetation density on the Received Signal Strength (RSS), coverage and energy consumption of an IoT assisted Wireless Sensor Network (IoWSN) is analyzed through measurement campaign. Experimental observations on free-space and tree Path Loss Model (PLM) based sensor node deployment strategies in a cropping period have shown network disconnectivity due to incorrect assessment of excess attenuation caused by dynamically varying vegetation height and density during the monitoring period. To address the challenge, an empirically formulated PLM is proposed to estimate the excess attenuation at different crop development stages of medium grass vegetation. Further, using the formulated PLM, a Non-dominated Sorting Genetic Algorithm (NSGA-III) multi-objective optimization is performed to generate initial node deployment with a heterogeneous transmission range. To address the issue of over-coverage, transmitter output power scheduling is performed with predefined upper limit derived from the NSGA-III optimization. The output power is dynamically scheduled during the monitoring period based on changes in the captured RSS to minimize over-coverage. Improvements in coverage, connectivity, and energy efficiency compared to existing approaches are validated through Proof of Concept.

Precision agriculture, as the future of farming technology, addresses challenges faced by farmers by data mining of information collected through IoT-enabled crop monitoring infrastructures. The identification of crop disease is one of the widely studied challenges. Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. This proposal presents a fuzzy-logic-based pest prediction mechanism assisting in beforehand preparedness for potential pest prevention. The experiments are performed for pests in rice and millet crops. The data mining over samples collected in a cropping cycle revealed the plausible correlation between temperature, relative humidity, and rainfall with pest breeding. The data collected through IoT monitoring infrastructure is analyzed for the ambient breeding condition of the pest. These conditions are then employed to design the knowledge base of the fuzzy system. More specifically, the linguistic variables of the fuzzy membership function are optimized using a genetic algorithm for close prediction of pest breeding in given environmental conditions. The proposal verified that the weather factors have a strong impact on the occurrence of pests and diseases, and the fuzzy-logic-based pest prediction through IoT application development services will help farmers to take precautionary measures beforehand.

This proposal examines the effect of grass vegetation elevation and density on path loss between sensors deployed in an IoT-enabled wireless sensor network (IoWSN) crop monitoring infrastructures. Observations via real-time measurement campaigns at different node heights and vegetation depths revealed that the sensor deployment made using free-space or tree vegetation based path loss model (PLM) experiences network disconnectivity due to variations in vegetation density in a cropping cycle. An empirical PLM is formulated to identify signal strength at different development phases of paddy and sugarcane medium grass vegetation. For this, the 2.4 GHz RF path loss coefficient (PLC) is estimated using data collected through measurement campaign over combinations of sensor height and vegetation density. Further, the formulated PLC is used to train multiple regression model to develop a generic PLM for all medium grass vegetation. Improvements in coverage and connectivity have been validated through proof of concept.

With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.

Inference has been central problem for understanding and reasoning in artificial intelligence. Especially, Natural Language Inference is an interesting problem that has attracted the attention of many researchers. Natural language inference intends to predict whether a hypothesis sentence can be inferred from the premise sentence. Most prior works rely on a simplistic association between the premise and hypothesis sentence pairs, which is not sufficient for learning complex relationships between them. The strategy also fails to exploit local context information fully. Long Short Term Memory (LSTM) or gated recurrent units networks (GRU) are not effective in modeling long-term dependencies, and their schemes are far more complex as compared to Convolutional Neural Networks (CNN). To address this problem of long-term dependency, and to involve context for modeling better representation of a sentence, in this article, a general Self-Attentive Convolution Neural Network (SACNN) is presented for natural language inference and sentence pair modeling tasks. The proposed model uses CNNs to integrate mutual interactions between sentences, and each sentence with their counterparts is taken into consideration for the formulation of their representation. Moreover, the self-attention mechanism helps fully exploit the context semantics and long-term dependencies within a sentence. Experimental results proved that SACNN was able to outperform strong baselines and achieved an accuracy of 89.7% on the stanford natural language inference (SNLI) dataset.

Abstract Software organizations are increasingly combining agile methodologies and distributed software development (DSD) for efficiently and effectively built software products. There are numerous challenges associated with scaling agile methods in a distributed environment. Our study is intended to explore and prioritize the challenges for scaling agile practices in the DSD environment. This study was divided into 3 stages. In the first stage, 22 challenges were identified from literature review and grouped into 4 categories: management, team, technology, and process based on discussion with experts. In the second stage, an online questionnaire study was conducted to validate the identified challenges. Finally, analytic hierarchy process method was used to prioritize challenges and their categories based on their relative importance. The results highlighted that management is a most significant category as compared with the other categories. Similarly, lack of management commitments, lack of effective communication, lack of knowledge sharing, etc are identified as the most significant challenges that need to be focused by the organizations for scaling agile methodologies. On the basis of the research findings, we could conclude that the identified challenges along with their categories provide a robust framework to scale agile methodologies in the DSD environment.

There are so many IP-based mobility management schemes proposed and standardized but mobility management is still a bigger challenge today. The notion of Mobile IPv4 (MIPv4) has been standardized as a macro-mobility management protocol. Hierarchical Mobility Management scheme for MIPv4 networks (HMIPv4), Dynamic Hierarchical Mobile IP (DHMIP) have been suggested as micro-mobility management protocols. A network-assisted protocol called Proxy MIPv4 (PMIPv4) has been proposed to reduce the wireless overhead of mobile users (MUs) induced by location registration. But none of these schemes consider the fixed mobility pattern of MUs. In urban cities, many MUs have a fixed mobility pattern on a daily basis and there is scope of further reduction in registration cost and packet delivery cost. In this paper, we propose a hierarchical mobility anchoring point (HMAP) for IPv4 networks. The proposed scheme has reduced the location update cost and the packet delivery cost when compared with the existing MIPv4, DHMIP, HMIPv4 and PMIPv4 schemes.

The current era of mobile networks allow users to communicate while they are moving. These network facilities are dealt by the location management (LM). In this article, the authors are proposing a nature-inspired LM technique based on ant system. The bottom line of proposed scheme is to follow the route of ahead entities and perform the LM based on it. The performance evaluation is carried out and is compared with existing LM scheme. It is observed that the signalling cost, network traffic and overheads are reduced, as compared to traditional LM scheme, in the communication network.

In mobile networks, when the mobile terminal (MT) has an active call while crossing the boundary of two systems, it is required that the existing connection should not be lost. To meet this demand, the signalling protocol of mobility management must be designed for supporting location registration and call delivery for roaming users who move beyond their home network. The objective of this study is to provide a modified boundary location register (MBLR) signalling protocol for inter-system roaming. The idea of generating routing number in advance plays a key role for the proposal. The location registration and call delivery process of the new protocol are proposed which tries to reduce the signalling cost and latency. An analytical model is developed for comparative performance analysis of the boundary location register and MBLR protocol. However, this will result in increased processing requirements at the database server. Thus, trade-off between signalling, database access costs and processing overheads must be recognised.

A partially overlapping super location area (POSLA) Scheme is suggested to overcome the varying updation problem of Overlapping Super Location Area (OSLA) Scheme. In this scheme, the LA is partially overlapped by its surrounding LAs and the location updates will be done based on the minimum distance from the center cell. The suggested scheme, POSLA, shows the reduction in the location update cost as compared to OSLA scheme.