Recommender System Based on Process Mining

Automation of repetitive tasks can be achieved with Robotic Process Automation (RPA) using scripts that encode fine-grained interactions with software applications on desktops and the web. Automating these processes can be achieved through several applications. It is possible for users to record desktop activity, including metadata, with these tools. The very fine-grained steps in the processes contain details about very small steps that the user takes. Several steps are involved in this process, including clicking on buttons, typing text, selecting the text, and changing the focus. Automating these processes requires connectors connecting them to the appropriate applications. Currently, users choose these connectors manually rather than automatically being linked to processes. In this thesis, we propose a method for recommending the top-k suitable connectors based on event logs for each process. This method indicates that we can use process discovery, create the process models of the train processes with identified connectors, and calculate the conformance checking between the process models and test event logs (unknown connectors). Then we select top-k maximum values of the conformance checking results and observe that we have the suitable connector with 80% accuracy among the top-3 recommended connectors. This solution can be configurable by changing the parameters and the methods of process discovery and conformance checking.Automation of repetitive tasks can be achieved with Robotic Process Automation (RPA) using scripts that encode fine-grained interactions with software applications on desktops and the web. Automating these processes can be achieved through several applications. It is possible for users to record desktop activity, including metadata, with these tools. The very fine-grained steps in the processes contain details about very small steps that the user takes. Several steps are involved in this process, including clicking on buttons, typing text, selecting the text, and changing the focus. Automating these processes requires connectors connecting them to the appropriate applications. Currently, users choose these connectors manually rather than automatically being linked to processes. In this thesis, we propose a method for recommending the top-k suitable connectors based on event logs for each process. This method indicates that we can use process discovery, create the process models of the train processes with identified connectors, and calculate the conformance checking between the process models and test event logs (unknown connectors). Then we select top-k maximum values of the conformance checking results and observe that we have the suitable connector with 80% accuracy among the top-3 recommended connectors. This solution can be configurable by changing the parameters and the methods of process discovery and conformance checking

Towards Automated Circuit Discovery for Mechanistic Interpretability

Recent work in mechanistic interpretability has reverse-engineered nontrivial behaviors of transformer models. These contributions required considerable effort and researcher intuition, which makes it difficult to apply the same methods to understand the complex behavior that current models display. At their core however, the workflow for these discoveries is surprisingly similar. Researchers create a data set and metric that elicit the desired model behavior, subdivide the network into appropriate abstract units, replace activations of those units to identify which are involved in the behavior, and then interpret the functions that these units implement. By varying the data set, metric, and units under investigation, researchers can understand the functionality of each neural network region and the circuits they compose. This work proposes a novel algorithm, Automatic Circuit DisCovery (ACDC), to automate the identification of the important units in the network. Given a model's computational graph, ACDC finds subgraphs that explain a behavior of the model. ACDC was able to reproduce a previously identified circuit for Python docstrings in a small transformer, identifying 6/7 important attention heads that compose up to 3 layers deep, while including 91% fewer the connections

Computer-aided HAZOP of batch processes

The modern batch chemical processing plants have a tendency of increasing technological complexity and flexibility which make it difficult to control the occurrence of accidents. Social and legal pressures have increased the demands for verifying the safety of chemical plants during their design and operation. Complete identification and accurate assessment of the hazard potential in the early design stages is therefore very important so that preventative or protective measures can be integrated into future design without adversely affecting processing and control complexity or capital and operational costs. Hazard and Operability Study (HAZOP) is a method of systematically identifying every conceivable process deviation, its abnormal causes and adverse hazardous consequences in the chemical plants. [Continues.

Post Hoc Explanations of Language Models Can Improve Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of- Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities. However, incorporating such rationales poses challenges in terms of scalability as this requires a high degree of human involvement. In this work, we present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges by automating the process of rationale generation. To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs. Extensive experimentation with real-world datasets demonstrates that our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks, including those where prior approaches which rely on human-annotated rationales such as Chain-of-Thought prompting fall short. Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs. Furthermore, we conduct additional empirical analyses and ablation studies to demonstrate the impact of each of the components of AMPLIFY, which, in turn, lead to critical insights for refining in-context learning

Circuit Component Reuse Across Tasks in Transformer Language Models

Recent work in mechanistic interpretability has shown that behaviors in language models can be successfully reverse-engineered through circuit analysis. A common criticism, however, is that each circuit is task-specific, and thus such analysis cannot contribute to understanding the models at a higher level. In this work, we present evidence that insights (both low-level findings about specific heads and higher-level findings about general algorithms) can indeed generalize across tasks. Specifically, we study the circuit discovered in Wang et al. (2022) for the Indirect Object Identification (IOI) task and 1.) show that it reproduces on a larger GPT2 model, and 2.) that it is mostly reused to solve a seemingly different task: Colored Objects (Ippolito & Callison-Burch, 2023). We provide evidence that the process underlying both tasks is functionally very similar, and contains about a 78% overlap in in-circuit attention heads. We further present a proof-of-concept intervention experiment, in which we adjust four attention heads in middle layers in order to 'repair' the Colored Objects circuit and make it behave like the IOI circuit. In doing so, we boost accuracy from 49.6% to 93.7% on the Colored Objects task and explain most sources of error. The intervention affects downstream attention heads in specific ways predicted by their interactions in the IOI circuit, indicating that this subcircuit behavior is invariant to the different task inputs. Overall, our results provide evidence that it may yet be possible to explain large language models' behavior in terms of a relatively small number of interpretable task-general algorithmic building blocks and computational components

Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla

\emph{Circuit analysis} is a promising technique for understanding the internal mechanisms of language models. However, existing analyses are done in small models far from the state of the art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. In particular, we study multiple-choice question answering, and investigate Chinchilla's capability to identify the correct answer \emph{label} given knowledge of the correct answer \emph{text}. We find that the existing techniques of logit attribution, attention pattern visualization, and activation patching naturally scale to Chinchilla, allowing us to identify and categorize a small set of `output nodes' (attention heads and MLPs). We further study the `correct letter' category of attention heads aiming to understand the semantics of their features, with mixed results. For normal multiple-choice question answers, we significantly compress the query, key and value subspaces of the head without loss of performance when operating on the answer labels for multiple-choice questions, and we show that the query and key subspaces represent an `Nth item in an enumeration' feature to at least some extent. However, when we attempt to use this explanation to understand the heads' behaviour on a more general distribution including randomized answer labels, we find that it is only a partial explanation, suggesting there is more to learn about the operation of `correct letter' heads on multiple choice question answering

Enabling Auditing and Intrusion Detection of Proprietary Controller Area Networks

The goal of this dissertation is to provide automated methods for security researchers to overcome ‘security through obscurity’ used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat\u27 security analysts waste significant time reverse engineering these systems\u27 opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility of these methods using Controller Area Network (CAN) data sampled from passenger vehicles

Discovering Models of Software Processes from Event-Based Data ; CU-CS-819-96

Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be dicult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are rst captured from an on-going process and then used to generate a formal model of the behavior of that process. In this paper we describe a Markov method that we developed specically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study