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This study explores the role of participation in achieving social sustainability in urban environments. As uncertainties about the future grow, the need for methods that ensure the representation of diverse stakeholders becomes essential. The Participatory A’WOT-TOPSIS Method is introduced as an effective approach for managing multi-actor and multi-decision-making processes. This Multi-Criteria Decision-Making (MCDM) method combines SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis with the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). An empirical application was conducted to assess various urban scenarios through a strategic planning process involving five distinct stakeholder groups. Using an inductive approach, one of three scenarios was selected. Findings demonstrate that the proposed method enhances transparency, ensures objectivity, reduces inconsistencies in stakeholder decision-making, and promotes collaborative representation. However, increasing the number of decision-makers and decisions may lead to greater workload and time demands for those implementing the method. This approach lays the groundwork for future research incorporating elements like representation, belonging, and identity into participatory processes to foster social sustainability in urban areas.

This paper proposes a real-life application of deep reinforcement learning to address the order dispatching problem of a Turkish ultra-fast delivery company, Getir. Before applying off-the-shelf reinforcement learning methods, we define the specific problem at Getir and one of the solutions the company has implemented. We discuss the novel aspects of Getir’s problem compared to the state-of-the-art order dispatching studies and highlight the limitations of Getir’s solution. The overall aim of the company is to deliver to as many customers as possible within 10 minutes. The orders arrive throughout the day, and centralized warehouses in the regions decide whether an incoming order should be served or canceled depending on their couriers’ shifts and status. We use Deep Q-networks to learn the actions of warehouses, i.e., accepting or canceling an order, directly from state dimensions using reinforcement learning. We design the networks with two different rewards. We conduct empirical analyses using real-life data provided by Getir to generate training samples and to assess the models’ performance during a selected 30-day period with a total of 9880 orders. The results indicate that our proposed models are able to generate policies that outperform the rule-based heuristic employed in practice.

In this paper, we investigate the capability of generative adversarial networks, including conditional and conditional convolutional generative adversarial networks, in generating electromagnetic engineered surfaces (EES). Generative models such as generative adversarial networks and their conditional variants can be used to generate different categories of designs based on the current dataset. k-means clustering algorithm is used to obtain the desirable categories of EES designs, including an initial two main categories, followed by six and eight subcategories. Conditional and conditional convolutional generative adversarial networks are proposed and trained on designs with different image dimensions conditioned on different sets of categories. The trained conditional convolutional generative adversarial network models have comparable accuracy with conditional generative adversarial network in low-dimensional designs over two categories. Conditional convolutional generative adversarial networks generate more unique designs for six and eight categories for smaller image dimensions (e.g., 9 × 9 designs) and for two main categories over larger designs. Both generative adversarial network structures are suitable for generating a wide variety of low- and high-pass EES designs. The creation of new datasets can benefit from conditional convolutional generative adversarial networks to provide greater variety in designs.

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most time-consuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In this research, we present a defect prediction model based on an ensemble of classifiers. We have collaborated with an industrial partner from the embedded systems domain. We use our generic defect prediction models with data coming from embedded projects. The embedded systems domain is similar to mission critical software so that the goal is to catch as many defects as possible. Therefore, the expectation from a predictor is to get very high probability of detection (pd) . On the other hand, most embedded systems in practice are commercial products, and companies would like to lower their costs to remain competitive in their market by keeping their false alarm (pf) rates as low as possible and improving their precision rates. In our experiments, we used data collected from our industry partners as well as publicly available data. Our results reveal that ensemble of classifiers significantly decreases pf down to 15% while increasing precision by 43% and hence, keeping balance rates at 74%. The cost-benefit analysis of the proposed model shows that it is enough to inspect 23% of the code on local datasets to detect around 70% of defects.

The thought processes of people have a significant impact on software quality, as software is designed, developed and tested by people. Cognitive biases, which are defined as patterned deviations of human thought from the laws of logic and mathematics, are a likely cause of software defects. However, there is little empirical evidence to date to substantiate this assertion. In this research, we focus on a specific cognitive bias, confirmation bias , which is defined as the tendency of people to seek evidence that verifies a hypothesis rather than seeking evidence to falsify a hypothesis. Due to this confirmation bias, developers tend to perform unit tests to make their program work rather than to break their code. Therefore, confirmation bias is believed to be one of the factors that lead to an increased software defect density. In this research, we present a metric scheme that explores the impact of developers’ confirmation bias on software defect density. In order to estimate the effectiveness of our metric scheme in the quantification of confirmation bias within the context of software development, we performed an empirical study that addressed the prediction of the defective parts of software. In our empirical study, we used confirmation bias metrics on five datasets obtained from two companies. Our results provide empirical evidence that human thought processes and cognitive aspects deserve further investigation to improve decision making in software development for effective process management and resource allocation.

This study proposes a novel contextualized colocation analysis to examine spatial crime patterns within their social contexts. The sample includes all reported MCI crime incidents (i.e., assault, break and enter, robbery, auto theft, and theft over incidents) in the city of Toronto between 2014 and 2019 (n = 178,892). Following a stepwise clustering feature selection, we begin our analysis by regionalizing the city based on the relevant social context indicators through a ward-like hierarchical spatial clustering algorithm. Then, we use a modified colocation miner algorithm with a novel Validity Score (VS) to select significant citywide and regional crime colocation patterns. The results indicate that eating establishments, commercial parking lots, and retail food stores are the most frequent urban facilities in citywide and regional crime colocation patterns. We also note several peculiar crime colocation patterns across disadvantaged neighborhoods. Additionally, the proposed analysis selects the patterns that explain an average of 11% more crime events through the use of VS. Our study offers an alternative method for colocation analysis by effectively identifying crime-specific citywide and regional crime colocation patterns. It also prioritizes the identified colocation patterns by ranking them based on their significance.

Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically kept in 3D space, and generative models suffer from the curse of dimensionality issues in generating such synthetic data. In this paper, we investigate the potential of GANs for generating connected 3D volumes. We propose an improved version of 3D α-GAN by incorporating various architectural enhancements. On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data. We also show that our 3D GAN model can successfully generate high-quality 3D tumor volumes and associated treatment specifications (e.g., isocenter locations). Similar moment invariants to the training data as well as fully connected 3D shapes confirm that improved 3D α-GAN implicitly learns the training data distribution, and generates realistic-looking samples. The capability of improved 3D α-GAN makes it a valuable source for generating synthetic medical image data that can help future research in this domain.

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we propose a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator’s loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.

Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual and methodological details regarding BN construction are reported in the studies. We conduct a systematic literature review on the applications of BNs to predict software quality. We focus on more detailed questions regarding (1) dataset characteristics, (2) techniques used for parameter learning, (3) techniques used for structure learning, (4) use of tools, and (5) model validation techniques. Results on ten primary studies show that BNs are mostly built based on expert knowledge, i.e. structure and prior distributions are defined by experts, whereas authors benefit from BN tools and quantitative data to validate their models. In most of the papers, authors do not clearly explain their justification for choosing a specific technique, and they do not compare their proposed BNs with other machine learning approaches. There is also a lack of consensus on the performance measures to validate the proposed BNs. Compared to other domains, the use of BNs is still very limited and current publications do not report enough details to replicate the studies. We propose a framework that provides a set of guidelines for reporting the essential contextual and methodological details of BNs. We believe such a framework would be useful to replicate and extend the work on BNs.