A study of the use of abstract types for the representation of engineering units in integration and test applications

Physical quantities using various units of measurement can be well represented in Ada by the use of abstract types. Computation involving these quantities (electric potential, mass, volume) can also automatically invoke the computation and checking of some of the implicitly associable attributes of measurements. Quantities can be held internally in SI units, transparently to the user, with automatic conversion. Through dimensional analysis, the type of the derived quantity resulting from a computation is known, thereby allowing dynamic checks of the equations used. The impact of the possible implementation of these techniques in integration and test applications is discussed. The overhead of computing and transporting measurement attributes is weighed against the advantages gained by their use. The construction of a run time interpreter using physical quantities in equations can be aided by the dynamic equation checks provided by dimensional analysis. The effects of high levels of abstraction on the generation and maintenance of software used in integration and test applications are also discussed

Forward stagewise regression and the monotone lasso

We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron, Hastie, Johnstone & Tibshirani (2004) it is proved that the least angle regression algorithm, with a small modification, solves the lasso regression problem. Here we give an analogous result for incremental forward stagewise regression, showing that it solves a version of the lasso problem that enforces monotonicity. One consequence of this is as follows: while lasso makes optimal progress in terms of reducing the residual sum-of-squares per unit increase in $L_1$-norm of the coefficient $\beta$, forward stage-wise is optimal per unit $L_1$ arc-length traveled along the coefficient path. We also study a condition under which the coefficient paths of the lasso are monotone, and hence the different algorithms coincide. Finally, we compare the lasso and forward stagewise procedures in a simulation study involving a large number of correlated predictors.Comment: Published at http://dx.doi.org/10.1214/07-EJS004 in the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Large language models are not zero-shot communicators

Despite widespread use of LLMs as conversational agents, evaluations of performance fail to capture a crucial aspect of communication: interpreting language in context. Humans interpret language using beliefs and prior knowledge about the world. For example, we intuitively understand the response "I wore gloves" to the question "Did you leave fingerprints?" as meaning "No". To investigate whether LLMs have the ability to make this type of inference, known as an implicature, we design a simple task and evaluate widely used state-of-the-art models. We find that, despite only evaluating on utterances that require a binary inference (yes or no), most perform close to random. Models adapted to be "aligned with human intent" perform much better, but still show a significant gap with human performance. We present our findings as the starting point for further research into evaluating how LLMs interpret language in context and to drive the development of more pragmatic and useful models of human discourse

Analysis of Theoretical and Applied Machine Learning Models for Network Intrusion Detection

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized IDS models that utilize unique algorithms for enhanced intrusion detection. Machine learning models can be trained using a combination of machine learning algorithms, network intrusion datasets, and optimization techniques. This study sought to identify which variation of these parameters yielded the best-performing network intrusion detection models, measured by their accuracy, precision, recall, and F1 score metrics. Additionally, this research aimed to validate theoretical models’ metrics by applying them in a real-world environment to see if they perform as expected. This research utilized a quantitative experimental study design to organize a two-phase approach to train and test a series of machine learning models for network intrusion detection by utilizing Python scripting, the scikit-learn library, and Zeek IDS software. The first phase involved optimizing and training 105 machine learning models by testing a combination of seven machine learning algorithms, five network intrusion datasets, and three optimization methods. These 105 models were then fed into the second phase, where the models were applied in a machine learning IDS pipeline to observe how the models performed in an implemented environment. The results of this study identify which algorithms, datasets, and optimization methods generate the best-performing models for network intrusion detection. This research also showcases the need to utilize various algorithms and datasets since no individual algorithm or dataset consistently achieved high metric scores independent of other training variables. Additionally, this research also indicates that optimization during model development is highly recommended; however, there may not be a need to test for multiple optimization methods since they did not typically impact the yielded models’ overall categorization of v success or failure. Lastly, this study’s results strongly indicate that theoretical machine learning models will most likely perform significantly worse when applied in an implemented IDS ML pipeline environment. This study can be utilized by other industry professionals and research academics in the fields of information security and machine learning to generate better highly-optimized models for their work environments or experimental research