Software Architecture Descriptions (SADs) are essential for managing the inherent complexity of modern software systems. They enable high-level architectural reasoning, guide design decisions, and facilitate effective communication among diverse stakeholders. However, in practice, SADs are often missing, outdated, or poorly aligned with the system's actual implementation. Consequently, developers are compelled to derive architectural insights directly from source code-a time-intensive process that increases cognitive load, slows new developer onboarding, and contributes to the gradual degradation of clarity over the system's lifetime. To address these issues, we propose a semi-automated generation of SADs from source code by integrating reverse engineering (RE) techniques with a Large Language Model (LLM). Our approach recovers both static and behavioral architectural views by extracting a comprehensive component diagram, filtering architecturally significant elements (core components) via prompt engineering, and generating state machine diagrams to model component behavior based on underlying code logic with few-shots prompting. This resulting views representation offer a scalable and maintainable alternative to traditional manual architectural documentation. This methodology, demonstrated using C++ examples, highlights the potent capability of LLMs to: 1) abstract the component diagram, thereby reducing the reliance on human expert involvement, and 2) accurately represent complex software behaviors, especially when enriched with domain-specific knowledge through few-shot prompting. These findings suggest a viable path toward significantly reducing manual effort while enhancing system understanding and long-term maintainability.

This study proposes a new deep learning method for reconstructing depth images of moving objects within a specific area using Wi-Fi channel state information (CSI). The Wi-Fi-based depth imaging technique has novel applications in domains such as security and elder care. However, reconstructing depth images from CSI is challenging because learning the mapping function between CSI and depth images, both of which are high-dimensional data, is particularly difficult. To address the challenge, we propose a new approach called Wi-Depth. The main idea behind the design of Wi-Depth is that a depth image of a moving object can be decomposed into three core components: the shape, depth, and position of the target. Therefore, in the depth-image reconstruction task, Wi-Depth simultaneously estimates the three core pieces of information as auxiliary tasks in our proposed VAE-based teacher-student architecture, enabling it to output images with the consistency of a correct shape, depth, and position. In addition, the design of Wi-Depth is based on our idea that this decomposition efficiently takes advantage of the fact that shape, depth, and position relate to primitive information inferred from CSI such as angle-of-arrival, time-of-flight, and Doppler frequency shift.

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {\it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {\it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.

Contentgine, the world leader in content-based marketing, today released its latest "Top 5" research ranking the most popular artificial intelligence (AI) content consumed by B2B decision makers and analyzed by its Content Indication Platform (CIP). To determine the category leaders, Contentgine's CIP employed machine learning and AI to examine content consumption across more than 3000 AI case studies, research papers, and eBooks syndicated from the world's largest B2B library. "AI software is not only a category in and of itself, but it is also a core component of other categories," said "Top 5 in 15" Series Host Robert Rose, best-selling author and chief strategy advisor for the Content Marketing Institute. "We're talking about the core component of AI software that may or may not be embedded into other solutions to achieve advanced automation, decision insights, predictive measurement, targeting, personalization, content management, and conversational interfaces. Given the vast interest in this topic today, it's wonderful to see so many well performing assets available to decision makers."

This article is the first in a short series of pieces that will recap each of the day's talks for the benefit of those who weren't able to travel to DC for our first conference. Dr. Sephus came to AWS via a roundabout path, growing up in Mississippi before eventually joining a tech startup called Partpic. When asked, she identified access as the biggest barrier to the greater use of AI/ML--in a lot of ways, it's another wrinkle in the old problem of the digital divide. A core component of being able to utilize most common AI/ML tools is having reliable and fast Internet access, and drawing on experience from her background, Dr. Sephus pointed out that a lack of access to technology in primary schools in poorer areas of the country sets kids on a path away from being able to use the kinds of tools we're talking about. Dr. Sephus said that AWS has been hiring sociologists and psychologists to join its tech teams to figure out ways to tackle the digital divide by meeting people where they are rather than forcing them to come to the technology.

These same abilities humans are now trying to emulate with machines, and they are in fact the core components of Artificial Intelligence (AI), one of the most important technical developments of our era. This technology is transforming knowledge, work, governance and the core of our daily lives, and as the sophistication of these systems increases, especially with the advent of Deep Neural Networks (DNNs), I would argue the human understanding of these systems is decreasing. A need is rising to bring to this field the HCI (Human Computer Interaction) human centered design approach, and within this paper I will suggest the possibilities how art and creative thought together with HCI expertise, can help broaden the current spectrum of AI, it's accessibility and possibly be a joint venture to imagine what AI could become. As humans started developing their ability of self-introspection around 40 thousand years ago, they have used art to communicate, evoke emotions, recall past events and communicate. These cognitive abilities have helped humans survive and evolve as a species, putting into use tools of memory, language, understanding, reasoning, learning, pattern recognition and expression.

The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.

Entity-Relationship (E-R) Modeling is one approach to visualize what story your data is trying to tell. This goal of this predecessor to object modeling (e.g. UML or CRC cards) is to give you a high-level, graphical view of the core components of an enterprise--the E-R diagram. An E-R diagram (sometimes called a Chen diagram, after its creator, Peter Chen) is a conceptual graph that captures meaning rather than implementation [1]. Once you have the diagram, you can convert it to a set of tables.

Klas Government, which makes the world's most powerful, low-SWaP technology for the extreme tactical edge, announced the availability of VoyagerGPU, the market's first tactical GPU that unlocks Artificial Intelligence(AI)/Machine Learning(ML) and video processing/transcoding at the network edge. Embedded graphics-processing units (GPUs) are critical for military systems with heavy processing demands -- such as those required for AI and analyzing moving images in real time. To date, GPUs with this kind of power simply haven't been able to operate at the tactical edge due to Size, Weight and Power (SWaP) and environmental limitations. As a result, Klas unlocks AI/ML breakthroughs in edge environments in ways not previously possible. VoyagerGPU is a core component of Klas' Voyager Tactical Cloud Platform (TCP), which brings the immense analytical power of the cloud to the forward edge and battlefield vehicles operating in tactical edge environments – and at a small form factor. VoyagerGPU also easily configures into the Voyager 6 power chassis, so that AI capabilities can be readily available in military ground vehicles without requiring modifications to the vehicle.

We are living in an era where messaging apps deal with all sorts of our daily activities, and in fact, these apps have already overtaken social networks as can be indicated in the BI Intelligence Report. In addition to this clear point, the consumption of messaging platforms is further expected to grow significantly in the coming years; hence this is a huge opportunity for different businesses to gain attention where people are actively engaged. In this age of instant gratification, consumers expect companies to respond to them quickly without any delay, and this, of course, requires a lot of time and effort for the company to hire and invest in their workforce. Thus, it's now the right time for any organization to think of new ways to stay connected with the end-user. Many organizations undergoing a digital transformation have already started harnessing the power of Artificial Intelligence in the form of AI-assisted Customer Support System, Talent Screening using AI-assisted interviews, etc.