Industrial Control Systems (ICS) are pivotal in critical sectors like energy, transportation, and manufacturing. However, their increasing interconnectivity and complex digital environment have amplified cyber threat risks. Ensuring cyber resilience for ICS requires not just the use of current security management tools to address existing threats, but also a forward-thinking approach to anticipating and mitigating future risks. This paper proposes a novel concept: the Attack-Weakness Embedding Bridge (AWEB), which leverages separate graph structures and textual data from MITRE ATT&CK and CWE to create a unified analytical space. AWEB has the potential to discover previously unrecognized relationships between system weaknesses and attack patterns, significantly enhancing cybersecurity defenses. The model has the flexibility of leveraging fine-tuned or pre-trained LLM embeddings to capture the nuances of the two datasets. The experimental results demonstrate that our proposed model effectively brings attack patterns and common weaknesses in a mathematical space, providing valuable insights for cybersecurity practitioners.

Storytelling involves creating a narrative by sequentially connecting documents, where the chain of documents follows the evolution of events and entities of a specific topic or subject. Early storytelling algorithms consecutively connected pairs of documents with overlapping contents to form a story chain. With the advent of contextual embeddings and distributed attention, the formation of story chains can be much more guided by the flow of concepts to replace mere overlaps of words to generate meaningful stories. This paper introduces a novel storytelling algorithm, DifStoryGen (Diffusion-based Story Generator), that uses a diffusion model and a distributed attention to construct a story from a document collection, such that the story (1) carries a theme from start to end, (2) captures the evolution of an event and associated entities, (3) allows connecting documents without word overlaps but having contextual similarity, and (4) generates hypothetical intermediate documents when required to bridge two documents are temporally distant. Our experimental results show that DifStoryGen outperforms other storytelling methods, producing significantly more meaningful, contextually rich, and coherent narratives.

While the use of the distributional hypothesis has become popular in creating embedding for text corpus, it is rarely used for generating the contextual (distributed) representation of visual objects in video data. In this paper, we present a distributed representation model, vizObj2Vec, that leverages the contexts of visual objects learned from spatiotemporal placements of the objects in the video-frames to construct object-embeddings. The model enables computation of contextual similarity - rather than solely relying on the visual resemblance in similarity computation - between a pair of visual objects. As a result, objects that are contextually connected - because they are in close proximity in a frame or are in nearby frames - appear in a neighborhood in the constructed embedding space. Through a series of extensive experiments, the paper demonstrates (1) the context selection process for visual objects in video data and (2) the potential of the proposed model in distinguishing neighborhoods of contextual objects in the video embedding space.

More than 80% of today's data is unstructured in nature, and these unstructured datasets evolve over time. A large part of these datasets are text documents generated by media outlets, scholarly articles in digital libraries, findings from scientific and professional communities, and social media. Vector space models were developed to analyze text data using data mining and machine learning algorithms. While ample vector space models exist for text data, the evolutionary aspect of ever changing text corpora is still missing in vector-based representations. The advent of word embeddings has enabled us to create a contextual vector space, but the embeddings fail to consider the temporal aspects of the feature space successfully. This paper presents an approach to include temporal aspects in feature spaces. The inclusion of the time aspect in the feature space provides vectors for every natural language element, such as words or entities, at every timestamp. Such temporal word vectors allow us to track how the meaning of a word changes over time, by studying the changes in its neighborhood. Moreover, a time-reflective text representation will pave the way to a new set of text analytic abilities involving time series for text collections.In this paper, we present a time-reflective vector space model for temporal text data that is able to capture short and long-term changes in the meaning of words. We compare our approach with the limited literature on dynamic embeddings. We present qualitative and quantitative evaluations using the tracking of semantic evolution as the target application.

As the world approaches a state of greater dependence on technology, many products face increasing threats from malicious attackers who are attempting to take advantage of vulnerabilities in software design. Most of the known vulnerabilities are already aggregated, stored in text format, and are readily accessible to the public, making such an aggregated database a prime corpus for analysis using data mining methods. A multitude of research efforts have been deployed analyzing individual aspects of such cyber-security corpora to create taxonomies, assess vulnerability impact, and even predict future vulnerabilities. However, minimal effort has been committed to analyze cyber-security corpora to explore correlations between vulnerabilities and study the evolution of a vulnerability from its genesis. In this paper, we propose an integrated data mining framework to automatically lay out how vulnerabilities develop over time and detect the evolution of a specific cyber-security threat. We use (1) a Supervised Topical Evolution Model (STEM), which discovers temporal themes from a text corpus and (2) a diffusion-based storytelling technique that sifts through past vulnerability reports to describe how a current threat evolved. The STEM gives a holistic evolution structure of the vulnerabilities, while diffusion-based storytelling provides the precise genealogy of a specific threat. A considerable series of experiments demonstrate that the proposed framework can discover evolutionary patterns in today's most pressing vulnerabilities with a high degree of precision. As case studies, we explore the development of vulnerabilities in certain products, providing a unique insight into the correspondence between seemingly unrelated vulnerabilities and the impact of that correspondence on overall software security.

In this era of big data, many domains on the web naturally have massive amount of labeled text data that are growing over time, for example, digital publication archives, social media posts, and question-answer forums. Probabilistic graphical models have shown great potential for mining such text corpora in recent years. Some of these algorithms utilize explicit annotations and labels associated with documents to guide the probabilistic model to find hidden themes. A few techniques attempt to utilize the timestamps associated with documents to model the evolution of those latent topics. However, no effort has been devoted to utilize these two different dimensions of information together — timestamps and labels or annotations — to discover evolution of labeled themes. In this paper, we present a new topical model called the Supervised Topical Evolution Model (STEM), which is a monolithic graphical model capable of using annotations, timestamps, and textual contents to discover interpretable and evolving themes from big text datasets. STEM simultaneously learns latent themes and their changes over time using a stochastic process that is driven by labels or annotations. In addition, we provide an asynchronously distributed inference process for STEM that results in significant speedup in learning time, making the model scalable for large datasets. Extensive experiments demonstrate that our proposed model is able to infer highly interpretable topics that reflect temporal patterns, in much less time than other comparable topic modeling methods.

With increasing public consciousness regarding sustainability, companies are ever more eager to introduce eco-friendly products and services. Assessing environmental footprints and designing sustainable products are challenging tasks since they require analysis of each component of a product through their life cycle. To achieve sustainable design of products, companies need to evaluate the environmental impact of their system, identify the major contributors to the footprint, and select the design alternative with the lowest environmental footprint. In this article, we formulate sustainable design as a series of clustering and classification problems, and propose a framework called AutoLCA that simplifies the effort of estimating the environmental footprint of a product bill of materials by more than an order of magnitude over current methods, which are mostly labor intensive. We apply AutoLCA to real data from a large computer manufacturer. We conduct a case study on bill of materials of four different products, perform a “hotspot” assessment analysis to identify major contributors to carbon footprint, and determine design alternatives that can reduce the carbon footprint from 1% to 36%.

Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

Intelligence analysts grapple with many challenges, chief among them is the need for software support in storytelling, i.e., automatically 'connecting the dots' between disparate entities (e.g., people, organizations) in an effort to form hypotheses and suggest non-obvious relationships. We present a system to automatically construct stories in entity networks that can help form directed chains of relationships, with support for co-referencing, evidence marshaling, and imposing syntactic constraints on the story generation process. A novel optimization technique based on concept lattice mining enables us to rapidly construct stories on massive datasets. Using several public domain datasets, we illustrate how our approach overcomes many limitations of current systems and enables the analyst to efficiently narrow down to hypotheses of interest and reason about alternative explanations.

Modern data mining settings involve a combination of attribute-valued descriptors over entities as well as specified relationships between these entities. We present an approach to cluster such non-homogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints. Unlike prior work that views constraints as boolean criteria, we present a formulation that allows constraints to be satisfied or violated in a smooth manner. This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function. We present results on both synthetic data as well as several real-world datasets.