Abstract--With the emergence of Web 2.0 and microservices architecture, the number of Web APIs has increased dramatically, further intensifying the demand for efficient Web API recommendation. Existing solutions typically fall into two categories: recommendation-type methods, which treat each API as a label for classification, and match-type methods, which focus on matching mashups through API retrieval. However, three critical challenges persist: 1) the semantic ambiguities in comparing API and mashup descriptions, 2) the lack of detailed comparisons between the individual API and the mashup in recommendation-type methods, and 3) time inefficiencies for API retrieval in match-type methods. T o address these challenges, we propose W ARBERT, a hierarchical BERT -based model for Web API recommendation. W ARBERT leverages dual-component feature fusion and attention comparison to extract precise semantic representations of API and mashup descriptions. W ARBERT consists of two main components: W ARBERT(R) for Recommendation and W ARBERT(M) for Matching. Specifically, W AR-BERT(R) serves as an initial filter, narrowing down the candidate APIs, while W ARBERT(M) refines the matching process by calculating the similarity between candidate APIs and mashup. The final likelihood of a mashup being matched with an API is determined by combining the predictions from W ARBERT(R) and W ARBERT(M). Additionally, W ARBERT(R) incorporates an auxiliary task of mashup category judgment, which enhances its effectiveness in candidate selection. Experimental results on the ProgrammableWeb dataset demonstrate that W ARBERT outperforms most existing solutions and achieves improvements of up to 11.7% compared to the model MTFM (Multi-T ask Fusion Model), delivering significant enhancements in accuracy and efficiency. ITH the emergence of Web 2.0 and microservice architecture, the number of APIs has increased dramatically [1]. Since 2022, there have been more than 24,000 APIs in ProgrammableWeb [2]. The benefits of Web APIs have led to the emergence of a novel method for developing applications, known as Mashup [3]. Mashup enables developers to integrate existing Web API resources to meet complex requirements without starting from scratch [4], [5].

The image above was created via Stable Diffusion with the prompt "lawyers in suits fighting robots with lasers in a futuristic, superhero style." Looks like Matthew Butterick and the Joseph Saveri Law Firm are going to have a busy year! The same folks who filed the class action against GitHub and Microsoft related to Copilot and Codex a couple of months ago, have filed another one against Stability AI, DeviantArt, and Midjourney related to Stable Diffusion. The crux of the complaint is around Stability AI and their Stable Diffusion product, but Midjourney and DeviantArt enter the picture because they have generative AI products that incorporate Stable Diffusion. DeviantArt also has some claims lobbed directly at them via a subclass because they allowed the nonprofit, Large-Scale Artificial Intelligence Open Network's (LAION), to incorporate the art work submitted to their service into a large public dataset of 400 million images and captions.

As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it gets not only time-consuming but also error-prone for developers to find suitable services from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected to the current step as well as its mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of our approach.

As if the internet wasn't already bizarre enough, the deluge of weird AI art created by image generators such as DALL·E 2, MidJourney and Craiyon is making things even stranger. From crossbred cartoon characters to surreal food, apocalyptic selfies muppet fashion shows and – erm – people with tennis balls for heads, DALL·E 2 and others seem to be able to create any weird AI art you can describe in their prompt boxes – with varying degrees of success. Of course DALL·E 2 and other AI art generators don't think of these outlandish things themselves and don't'know' what they're helping users to create. They run text prompts through the databases of millions of images and captions that they've learned. This means the results are only as weird as users' own imaginations. There are plenty of existential concerns about where this could all be going and what it means for human artists, but AI won't be taking over the world and turning us into slaves yet.

What does an "Art Deco Buddhist temple" look like? The phrase is nearly nonsensical; it's hard to imagine a Buddhist temple built in the Art Deco style, the early-twentieth-century Western aesthetic of attenuated architecture and streamlined forms. But this didn't deter the Twitter account @images_ai, which promises "images generated by A.I. machines." When another Twitter user threw out that prompt, in early August, @images_ai responded with a picture that looks something like an Orientalist Disney castle, a mashup of pointy spires and red angled roofs with a patterned stone-gray façade. Or perhaps it resembles the archetypal Chinese Buddhist temple crossed with a McDonald's--a fleeting, half-remembered image from a dream frozen into a permanent JPEG on social media.

An increasing number and diversity of services are available, which result in significant challenges to effective reuse service during requirement satisfaction. There have been many service bundle recommendation studies and achieved remarkable results. However, there is still plenty of room for improvement in the performance of these methods. The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements. In this paper, we propose a dynamic representation learning and aligning based model called DySR to tackle these issues. DySR eliminates the representation gap between services and requirements by learning a transformation function and obtains service representations in an evolving social environment through dynamic graph representation learning. Extensive experiments conducted on a real-world dataset from ProgrammableWeb show that DySR outperforms existing state-of-the-art methods in commonly used evaluation metrics, improving $F1@5$ from $36.1\%$ to $69.3\%$.

A music mashup combines audio elements from two or more songs to create a new work. To reduce the time and effort required to make them, researchers have developed algorithms that predict the compatibility of audio elements. Prior work has focused on mixing unaltered excerpts, but advances in source separation enable the creation of mashups from isolated stems (e.g., vocals, drums, bass, etc.). In this work, we take advantage of separated stems not just for creating mashups, but for training a model that predicts the mutual compatibility of groups of excerpts, using self-supervised and semi-supervised methods. Specifically, we first produce a random mashup creation pipeline that combines stem tracks obtained via source separation, with key and tempo automatically adjusted to match, since these are prerequisites for high-quality mashups. To train a model to predict compatibility, we use stem tracks obtained from the same song as positive examples, and random combinations of stems with key and/or tempo unadjusted as negative examples. To improve the model and use more data, we also train on "average" examples: random combinations with matching key and tempo, where we treat them as unlabeled data as their true compatibility is unknown. To determine whether the combined signal or the set of stem signals is more indicative of the quality of the result, we experiment on two model architectures and train them using semi-supervised learning technique. Finally, we conduct objective and subjective evaluations of the system, comparing them to a standard rule-based system.

The Web is ubiquitous, increasingly populated with interconnected data, services, people, and objects. Semantic web technologies (SWT) promote uniformity of data formats, as well as modularization and reuse of specifications (e.g., ontologies), by allowing them to include and refer to information provided by other ontologies. In such a context, multi-agent system (MAS) technologies are the right abstraction for developing decentralized and open Web applications in which agents discover, reason and act on Web resources and cooperate with each other and with people. The aim of the project is to propose an approach to transform "Agent and artifact (A&A) meta-model" into a Web-readable format with ontologies in line with semantic web formats and to reuse already existing ontologies in order to provide uniform access for agents to things.

The College of Humanities and Social Sciences (CHSS) at HBKU aims to deliver innovative programs that meet educational needs in the fields of humanities and social sciences for Qatar and the region. The College of Humanities and Social Sciences (CHSS) at Hamad Bin Khalifa University (HBKU) invites applications for Open Rank positions in the field of Translation Studies. The successful candidate will have long-standing experience in the field of Intercultural and Literary Translation, or Machine Translation, Artificial Intelligence and/or Terminology, a dynamic and innovative research agenda, as evidenced through an internationally recognized, strong record of peer-reviewed publications. The candidate will work closely with other programs in the college, in particular the PhD Program in Humanities and Social Sciences, and with national, regional and international partners and stakeholders. The candidate will be expected to teach graduate courses at MA and PhD level, applying a range of methodologies for teaching and assessment, contribute to all levels of curriculum development in the area(s) of specialty including the development of the interdisciplinary PhD in Humanities and Social Sciences.

Stommel, a co-author of An Urgency of Teachers: The Work of Critical Digital Pedagogy (Hybrid Pedagogy, 2018) and a co-founder of the faculty-development event Digital Pedagogy Lab, recently returned to the classroom full time after several years of running Mary Washington's Division of Teaching and Learning Technologies. He spoke with The Chronicle about how professors bring a "full, complicated self" to the classroom, why he thinks students are marginalized, and whether colleges have really gotten serious about teaching. Ten years ago, the student-success conversation was largely about student affairs and financial aid. Now administrators seem to be talking more about the classroom. So are colleges taking teaching more seriously?