Persuasive and Culture-aware Feedback Acquisition

User feedback is an important factor to improve software quality. For example, it can provide information on missing features and clarify user trends and preferences for future improvement. However, gathering user’s feedback is not an easy process since the majority of users lack motivation and interest in providing feedback, especially in a constant and frequent style. In addition, stud- ies have noted that the cultural differences among users also play a role in affect- ing their motivations to feedback acquisition. In this paper, we empirically inves- tigate the role of culture in affecting users’ perception and motivations to give feedback. Our study identifies some key differences between Western and Mid- dle Eastern users on what motivate them to provide feedback and what could have an influence on the quality of the feedback they give. This also makes the case for the need to design a persuasive and culture-aware feedback acquisition

Remote Sensing the Archaeological Traces of Boat Movement in the Marshes of Southern Mesopotamia

This study presents the results of the first remote sensing survey of hollow ways in Southern Mesopotamia between Baghdad and the Persian Gulf, primarily using the imagery in Google Earth. For archaeologists, hollow ways are important trace fossils of past human movement that inform about how people travelled in the past and what considerations were important to them as they moved through the landscape. In this study, remotely sensed hollow ways were ground-truthed and dated by association with both palaeochannels and known archaeological sites. Contextual and morphological evidence of the hollow ways indicate that they are likely the archaeological manifestation of ethnographically attested “water channels” formed through the dense reeds of marshlands in southern Iraq, not formed by traction overland like other known hollow ways. The map itself documents the first known hollow ways preserved underwater and one of the best-preserved landscapes of past human movement in the Near East

Requirements-driven Social Adaptation: Expert Survey

Self-adaptation empowers systems with the capability to meet stakeholders’ requirements in a dynamic environment. Such systems autonomously monitor changes and events which drive adaptation decisions at runtime. Social Adaptation is a recent kind of requirements-driven adaptation which enables users to give a runtime feedback on the success and quality of a system’s configurations in reaching their requirements. The system analyses users’ feedback, infers their collective judgement and then uses it to shape its adaptation decisions. [Question/problem] However, there is still a lack of engineering mechanisms to guarantee a correct conduction of Social Adapta- tion. [Principal ideas/results] In this paper, we conduct a two-phase Expert Sur- vey to identify core benefits, domain areas and challenges for Social Adaptation. [Contribution] Our findings provide practitioners and researchers in adaptive systems engineering with insights on this emerging role of users, or the crowd, and stimulate future research to solve the open problems in this area

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering

ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models

A High-Resolution Wind Farms Suitability Mapping Using GIS and Fuzzy AHP Approach: A National-Level Case Study in Sudan

Wind energy is one of the most attractive sustainable energy resources since it has low operation, maintenance, and production costs and a relatively low impact on the environment. Identifying the optimal sites for installing wind power plants (WPPs) is considered an important challenge of wind energy development which requires careful and combined analyses of numerous criteria. This study introduces a high-resolution wind farms suitability mapping based on Fuzzy Analytical Hierarchy Process (FAHP) and Geographic Information System (GIS) approaches considering technical, environmental, social, and spatial aspects, representing eight different criteria. First, a multi-criteria decision-making analysis based on the FAHP method is employed to assign appropriate weights for the addressed criteria with respect to their relative importance. Since the traditional AHP method, which was found employed in the majority of the relative case-studies, is not efficient in dealing with uncertainty when experts use a basic scale (0 to 1) for their assessments, the FAHP provides more flexible scales through the utilized fuzzy membership functions and the natural linguistic variables. Consequently, this helps to facilitate the assessments made by experts and increases the precision of the obtained results (weights). Next, the high-resolution GIS is used to carry out a spatial analysis and integrate various factors/criteria throughout the proposed index to produce the final suitability map and identify the unsuitable areas. The presented study emphasizes investigating the lightning strike flash rate due to its significant influences on the wind turbine’s safety and operation, yet this crucial factor has been seldomly investigated in previous studies. The obtained findings revealed that the wind speed, the land slope, and the elevation had the highest weighted criteria with 33.1%, 24.8%, and 12.2%, respectively. Besides, the final-developed suitability map revealed that 23.22% and 8.31% of the Sudanese territory are of high and very high suitability, respectively, for wind farms installation which are considered sufficient to cover the electricity needs. The difficulty of acquiring real data and resources for the addressed location was the main challenge of the presented work. The work outlook addresses the suitability mapping of hybrid photovoltaic-wind turbine energy systems, which will require addressing new and significant criteria in the applied methodology

SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia

Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been proposed to identify the causes and occurrences of crime in order to identify ways to reduce crime rates. However, few studies have been conducted in Saudi Arabia technological solutions based on crime analysis. The analysis of crime can help governments identify hotspots of crime and monitor crime distribution. This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years. This research helps law enforcement agencies to effectively utilize available resources in order to reduce crime rates. This paper proposes SARIMA model which focuses on identifying factors that affect crimes in Saudi Arabia, estimating a reasonable crime rate, and identifying the likelihood of crime distribution based on various locations. The dataset used in this study is obtained from Saudi Arabian official government channels. There is detailed information related to time and place along with crime statistics pertaining to different types of crimes. Furthermore, the new proposed method performs better than other traditional classifiers such as Linear Regression, XGB, and Random Forest. Finally, SARIMA model has an MAE score of 0.066559, which is higher than the other models

Evaluation of the Transient Overvoltages of HVDC Transmission Lines Caused by Lightning Strikes

High-voltage direct-current (HVDC) transmission systems are considered an outstanding solution due to high electrical losses emerging from long-distance transmission. However, HVDC transmission lines (TLs) are vulnerable to lightning strikes. In this work, the Yunnan-Guizhou 500 kV HVDC transmission system is used as a case study to evaluate the impact of lightning strikes on DC-TL overvoltages, as no research studies have been conducted to assess the lightning transient behavior of DC-TLs. A comprehensive investigation of the 500 kV DC-TL transient performance during lightning strikes is performed, taking into account different technical aspects that have not been studied in detail by previous researchers. Additionally, analysis of the back-flashover phenomenon has not been conducted well in previous work, and results on the effect of changing the lightning strike current peak and tower grounding resistance on shielding-failure flashover are quite limited. The distributed-parameter model is used to represent the DC-TL using the electromagnetic transients program (EMTP), considering real parameters of shielding wires and DC towers to study the lightning impact in the case of back-flashover and shielding-failure phenomena. Lightning strike is applied to the shielding wire, and the impact of increasing the peak value of lightning current is investigated on the back-flashover occurrence. Moreover, the influence of tower grounding resistance variation on the transient overvoltages across the tower body and back-flashover phenomenon is evaluated. From the simulation results, increasing the lightning current peak and grounding resistance results in higher overvoltages across the tower body, which increases the probability of back-flashover. Additionally, the shielding failure of the TL is assumed, and the variation impact of the lightning current peak and grounding resistance on shielding-failure flashover is investigated. The results show that the impact of the lightning current peak has a more significant impact than the grounding resistance in the case of shielding-failure flashover

A High-Resolution Wind Farms Suitability Mapping Using GIS and Fuzzy AHP Approach: A National-Level Case Study in Sudan

Wind energy is one of the most attractive sustainable energy resources since it has low operation, maintenance, and production costs and a relatively low impact on the environment. Identifying the optimal sites for installing wind power plants (WPPs) is considered an important challenge of wind energy development which requires careful and combined analyses of numerous criteria. This study introduces a high-resolution wind farms suitability mapping based on Fuzzy Analytical Hierarchy Process (FAHP) and Geographic Information System (GIS) approaches considering technical, environmental, social, and spatial aspects, representing eight different criteria. First, a multi-criteria decision-making analysis based on the FAHP method is employed to assign appropriate weights for the addressed criteria with respect to their relative importance. Since the traditional AHP method, which was found employed in the majority of the relative case-studies, is not efficient in dealing with uncertainty when experts use a basic scale (0 to 1) for their assessments, the FAHP provides more flexible scales through the utilized fuzzy membership functions and the natural linguistic variables. Consequently, this helps to facilitate the assessments made by experts and increases the precision of the obtained results (weights). Next, the high-resolution GIS is used to carry out a spatial analysis and integrate various factors/criteria throughout the proposed index to produce the final suitability map and identify the unsuitable areas. The presented study emphasizes investigating the lightning strike flash rate due to its significant influences on the wind turbine’s safety and operation, yet this crucial factor has been seldomly investigated in previous studies. The obtained findings revealed that the wind speed, the land slope, and the elevation had the highest weighted criteria with 33.1%, 24.8%, and 12.2%, respectively. Besides, the final-developed suitability map revealed that 23.22% and 8.31% of the Sudanese territory are of high and very high suitability, respectively, for wind farms installation which are considered sufficient to cover the electricity needs. The difficulty of acquiring real data and resources for the addressed location was the main challenge of the presented work. The work outlook addresses the suitability mapping of hybrid photovoltaic-wind turbine energy systems, which will require addressing new and significant criteria in the applied methodology

Heat Transfer and Flow Characteristics of Pseudoplastic Nanomaterial Liquid Flowing over the Slender Cylinder with Variable Characteristics

The present article investigates heat transfer and pseudoplastic nanomaterial liquid flow over a vertical thin cylinder. The Buongiorno model is used for this analysis. The problem gains more significance when temperature-dependent variable viscosity is taken into account. Using suitable similarity variables, nonlinear flow equations are first converted into ordinary differential equations. The generating structure is solved by the MATLAB BVP4C algorithm. Newly developed physical parameters are focused. It is observed that the heat transfer rate and the skin friction coefficient is increased remarkably because of mixing nano-particles in the base fluid by considering γb=1, 2, 3, 4 and λ=1, 1.5, 2, 2.5, 3. It is found that the temperature field increases by inclining the values of thermophoresis and Brownian motion parameters. It is also evaluated that the velocity field decreases by increasing the values of the curvature parameter, Weissenberg number and buoyancy ratio characteristics