arXiv subjects

Joachim Meyer

Publications and source records attributed to Joachim Meyer.

15 recordsLinked to original sources

arXiv2024

MimIR: An Extensible and Type-Safe Intermediate Representation for the DSL Age

Traditional compilers, designed for optimizing low-level code, fall short when dealing with modern, computation-heavy applications like image processing, machine learning, or numerical simulations. Optimizations should understand the primitive operations of the specific application domain and thus happen on that level. Domain-specific languages (DSLs) fulfill these requirements. However, DSL compilers reinvent the wheel over and over again as standard optimizations, code generators, and general infrastructure & boilerplate code must be reimplemented for each DSL compiler. This paper presents MimIR, an extensible, higher-order intermediate representation. At its core, MimIR is a pure type system and, hence, a form of a typed lambda calculus. Developers can declare the signatures of new (domain-specific) operations, called "axioms". An axiom can be the declaration of a function, a type constructor, or any other entity with a possibly polymorphic, polytypic, and/or dependent type. This way, developers can extend MimIR at any low or high level and bundle them in a "plugin". Plugins extend the compiler and take care of optimizing and lowering the plugins' axioms. We show the expressiveness and effectiveness of MimIR in three case studies: Low-level plugins that operate at the same level of abstraction as LLVM, a regular-expression matching plugin, and plugins for linear algebra and automatic differentiation. We show that in all three studies, MimIR produces code that has state-of-the-art performance.

cs.PL↗

arXiv2024

Input-Gen: Guided Generation of Stateful Inputs for Testing, Tuning, and Training

The size and complexity of software applications is increasing at an accelerating pace. Source code repositories (along with their dependencies) require vast amounts of labor to keep them tested, maintained, and up to date. As the discipline now begins to also incorporate automatically generated programs, automation in testing and tuning is required to keep up with the pace - let alone reduce the present level of complexity. While machine learning has been used to understand and generate code in various contexts, machine learning models themselves are trained almost exclusively on static code without inputs, traces, or other execution time information. This lack of training data limits the ability of these models to understand real-world problems in software. In this work we show that inputs, like code, can be generated automatically at scale. Our generated inputs are stateful, and appear to faithfully reproduce the arbitrary data structures and system calls required to rerun a program function. By building our tool within the compiler, it both can be applied to arbitrary programming languages and architectures and can leverage static analysis and transformations for improved performance. Our approach is able to produce valid inputs, including initial memory states, for 90% of the ComPile dataset modules we explored, for a total of 21.4 million executable functions. Further, we find that a single generated input results in an average block coverage of 37%, whereas guided generation of five inputs improves it to 45%.

cs.SE↗

arXiv2024

Doing AI: Algorithmic decision support as a human activity

Algorithmic decision support (ADS), using Machine-Learning-based AI, is becoming a major part of many processes. Organizations introduce ADS to improve decision-making and use available data, thereby possibly limiting deviations from the normative "homo economicus" and the biases that characterize human decision-making. However, a closer look at the development and use of ADS systems in organizational settings reveals that they necessarily involve a series of largely unspecified human decisions. They begin with deliberations for which decisions to use ADS, continue with choices while developing and deploying the ADS, and end with decisions on how to use the ADS output in an organization's operations. The paper presents an overview of these decisions and some relevant behavioral phenomena. It points out directions for further research, which is essential for correctly assessing the processes and their vulnerabilities. Understanding these behavioral aspects is important for successfully implementing ADS in organizations.

cs.HC↗

arXiv2023

Quantifying Retrospective Human Responsibility in Intelligent Systems

Intelligent systems have become a major part of our lives. Human responsibility for outcomes becomes unclear in the interaction with these systems, as parts of information acquisition, decision-making, and action implementation may be carried out jointly by humans and systems. Determining human causal responsibility with intelligent systems is particularly important in events that end with adverse outcomes. We developed three measures of retrospective human causal responsibility when using intelligent systems. The first measure concerns repetitive human interactions with a system. Using information theory, it quantifies the average human's unique contribution to the outcomes of past events. The second and third measures concern human causal responsibility in a single past interaction with an intelligent system. They quantify, respectively, the unique human contribution in forming the information used for decision-making and the reasonability of the actions that the human carried out. The results show that human retrospective responsibility depends on the combined effects of system design and its reliability, the human's role and authority, and probabilistic factors related to the system and the environment. The new responsibility measures can serve to investigate and analyze past events involving intelligent systems. They may aid the judgment of human responsibility and ethical and legal discussions, providing a novel quantitative perspective.

cs.HC↗

arXiv2023

Operator Responsibility for Outcomes: A Demonstration of the ResQu Model

In systems with advanced automation, human responsibility for outcomes becomes equivocal. We developed the Responsibility Quantification (ResQu) model to compute a measure of operator responsibility (Douer & Meyer, 2020) and compared it to observed and subjective levels of responsibility (Douer & Meyer, 2019). We used the model to calculate operators' objective responsibility in a common fault event in the control room in a dairy factory. We compared the results to the subjective assessments made by different functions in the diary. The capabilities of the automation greatly exceeded those of the human, and the operator should comply with the indications of the automation. Thus, the objective causal human responsibility is 0. Outside observers, such as managers, assigned much higher responsibility to the operator, possibly holding operators responsible for adverse outcomes in situations in which they rightly trusted the automation.

cs.HC↗

arXiv2022

Informing Users: Effects of Notification Properties and User Characteristics on Sharing Attitudes

Information sharing on social networks is ubiquitous, intuitive, and occasionally accidental. However, people may be unaware of the potential negative consequences of disclosures, such as reputational damages. Yet, people use social networks to disclose information about themselves or others, advised only by their own experiences and the context-invariant informed consent mechanism. In two online experiments (N=515 and N=765), we investigated how to aid informed sharing decisions and associate them with the potential outcomes via notifications. Based on the measurements of sharing attitudes, our results showed that the effectiveness of informing the users via notifications may depend on the timing, content, and layout of the notifications, as well as on the users' curiosity and rational cognitive style, motivating information processing. Furthermore, positive emotions may result in disregard of important information. We discuss the implications for user privacy and self-presentation. We provide recommendations on privacy-supporting system design and suggest directions for further research.

cs.HC↗

arXiv2022

Finding Patterns in Visualized Data by Adding Redundant Visual Information

We present "PATRED", a technique that uses the addition of redundant information to facilitate the detection of specific, generally described patterns in line-charts during the visual exploration of the charts. We compared different versions of this technique, that differed in the way redundancy was added, using nine distance metrics (such as Euclidean, Pearson, Mutual Information and Jaccard) with judgments from data scientists which served as the "ground truth". Results were analyzed with correlations (R2), F1 scores and Mutual Information with the average ranking by the data scientists. Some distance metrics consistently benefit from the addition of redundant information, while others are only enhanced for specific types of data perturbations. The results demonstrate the value of adding redundancy to improve the identification of patterns in time-series data during visual exploration.

stat.CO↗

arXiv2022

Politeness Counts: Perceptions of Peacekeeping Robots

The 'intuitive' trust people feel when encountering robots in public spaces is a key determinant of their willingness to cooperate with these robots. We conducted four experiments to study this topic in the context of peacekeeping robots. Participants viewed scenarios, presented as static images or animations, involving a robot or a human guard performing an access-control task. The guards interacted more or less politely with younger and older male and female people. Our results show strong effects of the guard's politeness. Age and sex of the people interacting with the guard had no significant effect on participants' impressions of its attributes. There were no differences between responses to robot and human guards. This study advances the notion that politeness is a crucial determinant of people's perception of peacekeeping robots.

cs.RO↗

arXiv2020

Maximal benefits and possible detrimental effects of binary decision aids

Binary decision aids, such as alerts, are a simple and widely used form of automation. The formal analysis of a user's task performance with an aid sees the process as the combination of information from two detectors who both receive input about an event and evaluate it. The user's decisions are based on the output of the aid and on the information, the user obtains independently. We present a simple method for computing the maximal benefits a user can derive from a binary aid as a function of the user's and the aid's sensitivities. Combining the user and the aid often adds little to the performance the better detector could achieve alone. Also, if users assign non-optimal weights to the aid, performance may drop dramatically. Thus, the introduction of a valid aid can actually lower detection performance, compared to a more sensitive user working alone. Similarly, adding a user to a system with high sensitivity may lower its performance. System designers need to consider the potential adverse effects of introducing users or aids into systems.

cs.HC↗

arXiv2020

Order of Control and Perceived Control over Personal Information

Focusing on personal information disclosure, we apply control theory and the notion of the Order of Control to study people's understanding of the implications of information disclosure and their tendency to consent to disclosure. We analyzed the relevant literature and conducted a preliminary online study (N = 220) to explore the relationship between the Order of Control and perceived control over personal information. Our analysis of existing research suggests that the notion of the Order of Control can help us understand people's decisions regarding the control over their personal information. We discuss limitations and future directions for research regarding the application of the idea of the Order of Control to online privacy.

cs.HC↗

arXiv2020

Visual Analytics and Human Involvement in Machine Learning

The rapidly developing AI systems and applications still require human involvement in practically all parts of the analytics process. Human decisions are largely based on visualizations, providing data scientists details of data properties and the results of analytical procedures. Different visualizations are used in the different steps of the Machine Learning (ML) process. The decision which visualization to use depends on factors, such as the data domain, the data model and the step in the ML process. In this chapter, we describe the seven steps in the ML process and review different visualization techniques that are relevant for the different steps for different types of data, models and purposes.

cs.LG↗

arXiv2020

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation.

cs.HC↗

arXiv2020

Theoretical, Measured and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. A bias existed only when participants with poor classification capabilities relied less-than-optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems.

cs.HC↗

arXiv2019

Is Privacy Controllable?

One of the major views of privacy associates privacy with the control over information. This gives rise to the question how controllable privacy actually is. In this paper, we adapt certain formal methods of control theory and investigate the implications of a control theoretic analysis of privacy. We look at how control and feedback mechanisms have been studied in the privacy literature. Relying on the control theoretic framework, we develop a simplistic conceptual control model of privacy, formulate privacy controllability issues and suggest directions for possible research.

cs.CY↗

arXiv2016

Single-laser 32.5 Tbit/s Nyquist WDM transmission

We demonstrate 32.5 Tbit/s 16QAM Nyquist WDM transmission over a total length of 227 km of SMF-28 without optical dispersion compensation. A number of 325 optical carriers are derived from a single laser and encoded with dual-polarization 16QAM data using sinc-shaped Nyquist pulses. As we use no guard bands, the carriers have a spacing of 12.5 GHz equal to the Nyquist bandwidth of the data. We achieve a high net spectral efficiency of 6.4 bit/s/Hz using a software-defined transmitter which generates the electrical modulator drive signals in real-time.

cs.NI↗