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Purpose - The purpose of this study is to address the significant challenge faced by startup Online Travel Agencies (OTAs) in building a substantial accommodation inventory amid intense competition and limited brand recognition. The research aims to explore and analyze the various sources of accommodation inventory available to startup OTAs. Methodology/Design/Approach - Data were collected through a series of in-depth interviews with 47 key industry players, spanning four stages, to investigate the process of onboarding new properties onto startup Online Travel Agencies (OTAs) and understand the synchronization of pricing and availability. Findings - The study evaluates various connectivity methods, identifying two main methods including: direct contracting, or third party's connections, encompassing channel manager connection, API integration, affiliation program or white label solutions. In each method there are several sources. The pivotal decision for startup OTAs in selecting inventory sources revolves around factors such as cost, number of listed properties, technology, and reputation. Originality of the research - These findings serve as a practical guide, allowing OTAs to make informed decisions, refine strategies, and optimize connectivity processes for a competitive edge in the dynamic online travel market.

Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first; thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.

Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people's references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information.

Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies.

The design and maintenance of APIs (Application Programming Interfaces) are complex tasks due to the constantly changing requirements of their users. Despite the efforts of their designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), have become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features collected from posts, the experience of users, readability metrics and centrality measures of collaboration network to build a technique, called CAPS, that can classify SO posts concerning API issues. In total, we consider 34 features along eight different dimensions. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We then conduct studies to find important features and also evaluate the performance of the CRF-based technique for classifying issue sentences. Comparison with two other baseline approaches shows that the technique has high potential. We also test the generalizability of CAPS results, evaluate the effectiveness of different classifiers, and identify the impact of different feature sets.

To improve e-government services released by Communication and Information Technology Office of Samarinda City, each system needs to be able to interoperate even when developed by different developers. Interoperation can be achieved by using one data source which is API (Application Programming Interface) for general data objects such as announcements. Given this condition, API built by using microservice can support further enhancement even if API is developed by developers who use different programming languages. The result shows that microservice API can be used to interoperate in relaying data between e-government services and can be developed using more than one programming language and base codes. Further development of this API can be done by adding more data objects, using AWS Cognito as authorization management, adding AWS Elasticsearch to load and filter data, and by showing data objects in real-time on the front end.

Mapping, and more generally geopositioning, has become ubiquitous on the Internet. This democratization of geomatics through the GeoWeb results in the emergence of a new form of mapping based on Web 2.0 technologies. Described as Webmapping 2.0, it is especially characterized by high interactivity and geolocation-based contents generated by users. A series of recent events (hurricanes, earthquakes, pandemics) have urged the development of numerous mapping Web applications intended to provide information to the public, and encourage their contribution to support crisis management. This new way to produce and spread geographic information in times of crisis brings up many questions and new potentials with regard to urgency services, Non Governmental Organisations (NGO), as well as individuals. This paper aims at putting into perspective the development of GeoWeb, both in terms of technologies and applications, against crisis management processes.

Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters.

Open banking reshapes the financial sector by enabling regulated third-party providers to access bank data through APIs, fostering innovation but amplifying operational and financial-crime risks due to increased ecosystem interdependence. To address these challenges, this study proposes an integrated risk-management framework combining System Dynamics, Agent-Based Modelling, and Monte Carlo simulation. This hybrid approach captures feedback effects, heterogeneous agent behaviour, and loss uncertainty within a simulated PSD2-style environment. Simulation experiments, particularly those modelling credential-stuffing waves, demonstrate that stricter onboarding thresholds, tighter API rate limits, and enhanced anomaly detection reduce operational tail losses by approximately 20–30% relative to baseline scenarios. Beyond these specific findings, the proposed framework exhibits significant universality; its modular design facilitates adaptation to broader contexts, including cross-border regulatory variations or emerging BigTech interactions. Ultimately, this multi-method approach translates complex open-banking dynamics into actionable risk metrics, providing a robust basis for targeted resource allocation and supervisory stress testing in evolving financial ecosystems.