LNCS

We define the model-measuring problem: given a model M and specification φ, what is the maximal distance ρ such that all models M′ within distance ρ from M satisfy (or violate) φ. The model measuring problem presupposes a distance function on models. We concentrate on automatic distance functions, which are defined by weighted automata. The model-measuring problem subsumes several generalizations of the classical model-checking problem, in particular, quantitative model-checking problems that measure the degree of satisfaction of a specification, and robustness problems that measure how much a model can be perturbed without violating the specification. We show that for automatic distance functions, and ω-regular linear-time and branching-time specifications, the model-measuring problem can be solved. We use automata-theoretic model-checking methods for model measuring, replacing the emptiness question for standard word and tree automata by the optimal-weight question for the weighted versions of these automata. We consider weighted automata that accumulate weights by maximizing, summing, discounting, and limit averaging. We give several examples of using the model-measuring problem to compute various notions of robustness and quantitative satisfaction for temporal specifications

IST Austria Technical Report

We define the model-measuring problem: given a model M and specification φ, what is the maximal distance ρ such that all models M'within distance ρ from M satisfy (or violate)φ. The model measuring problem presupposes a distance function on models. We concentrate on automatic distance functions, which are defined by weighted automata. The model-measuring problem subsumes several generalizations of the classical model-checking problem, in particular, quantitative model-checking problems that measure the degree of satisfaction of a specification, and robustness problems that measure how much a model can be perturbed without violating the specification. We show that for automatic distance functions, and ω-regular linear-time and branching-time specifications, the model-measuring problem can be solved. We use automata-theoretic model-checking methods for model measuring, replacing the emptiness question for standard word and tree automata by the optimal-weight question for the weighted versions of these automata. We consider weighted automata that accumulate weights by maximizing, summing, discounting, and limit averaging. We give several examples of using the model-measuring problem to compute various notions of robustness and quantitative satisfaction for temporal specifications

Instrumentation and robotic image processing using top-down model control

A top-down image processing scheme is described. A three-dimensional model of a robotic working environment, with robot manipulators, workpieces, cameras, and on-the-scene visual enhancements is employed to control and direct the image processing, so that rapid, robust algorithms act in an efficient manner to continually update the model. Only the model parameters are communicated, so that savings in bandwidth are achieved. This image compression by modeling is especially important for control of space telerobotics. The background for this scheme lies in an hypothesis of human vision put forward by the senior author and colleagues almost 20 years ago - the Scanpath Theory. Evidence was obtained that repetitive sequences of saccadic eye movements, the scanpath, acted as the checking phase of visual pattern recognition. Further evidence was obtained that the scanpaths were apparently generated by a cognitive model and not directly by the visual image. This top-down theory of human vision was generalized in some sense to the frame in artificial intelligence. Another source of the concept arose from bioengineering instrumentation for measuring the pupil and eye movements with infrared video cameras and special-purpose hardware

Checking behaviours, prospective memory and executive functions

Explanations implicating memory in the causes and severity of checking symptoms have focused primarily on retrospective memory, and relatively little attention has been paid to prospective memory. Limited research has examined the relationship between prospective memory and executive functions. We assessed whether impairments in prospective memory and executive function predict checking symptoms in a sample of 106 adults. Checking symptoms were assessed using the Padua Inventory Washington State University Revision (PI-WSUR). All participants completed the prospective memory questionnaire (PMQ) and four computerised executive function tasks from the CANTAB, measuring inhibition, planning, attention set-shifting and working memory. Prospective memory and inhibition predicted checking symptom severity. Importantly, there were no correlations between internally cued prospective memory and inhibition or between prospective memory aiding strategies and inhibition. These variables appear to have an independent role in checking. The current findings highlight prospective memory and inhibition as key contributors to the checking symptom profile and provide the first evidence that these cognitive processes may independently contribute to checking symptoms. These findings have implications for a model in which memory performance is thought to be secondary to impairments in executive functions

Propagation Path Loss Prediction Model of Multi-Sensor Network in Forest

AbstractDuring the process of carrying on the master plan and design of multi-sensor network in forest, We must consider the coverage of the signal, how to find the best position, through predicting it from launching and checking to accepting the loss value of the electromagnetic wave checked, Can carry on planning and design. Based on the radio wave propagation loss model in free space and the characteristics of radio wave propagation in forest, this paper proposes the generalized predicting model of radio wave propagation loss, To validate the model, a radio propagation measurement campaign was carried out, The modeling results by measuring the parameters of some trees are good agreement with that of the literatur

Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.Comment: Accepted by ICPC-202

Trusted Source Alignment in Large Language Models

Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, claim contextualization, and bias in prompt formulation. Applying the protocol to PaLM-2, we find that as we scale up the model size, the model performance on FactCheckQA improves from near-random to up to 80% balanced accuracy in aligning with trusted sources

Implementasi Metode Rapid Application Development Pada Pengembangan Sistem Kelola Data Kalibrasi di Percetakan Gramedia

The Rapid Application Development (RAD) model is an object-oriented software development model that aims to shorten information system development time. Calibration is an activity of determining the correctness value and checking and adjusting the accuracy of measuring instruments with national standards and/or international standards. As a company engaged in the printing sector, PT Gramedia uses a variety of measuring instruments that need to maintain their level of accuracy, so a calibration process needs to be carried out regularly. The General Affairs (GA) section is responsible for carrying out this calibration activity with the help of outside vendors. Currently, the administrative process for calibration activities is still carried out conventionally, causing various problems that can result in disruptions to production process activities and a decrease in the quality of the printed products produced. The purpose of this research is to develop a web-based information system by applying the RAD development model to speed up the process and the application of the MVC method using the Codeigniter framework in the application coding process. From the test results using the black box method, the resulting calibration administration system can run well in accordance with user expectations

To Be or Not To Be Innovative: An Exercise in Measurement

In this paper, we put forward the idea of an innovation accounting framework and consider two main indicators based on it: expected innovation and innovativeness. The framework is the analogue of the standard framework of economic growth accounting, with innovativeness being a parallel notion to that of (total factor) productivity. We provide an illustration of the idea using data from the European Community Innovation Surveys (CIS1 and CIS2) and measuring innovation by the share of firm innovative sales. We adopt a generalized tobit model of the propensity and intensity of innovation as our accounting framework. We first apply the framework to a comparison of the innovation performance of French manufacturing industries, while also checking the robustness of our estimates to the use of micro- aggregated firm data provided by Eurostat versus the original individual firm data. We also provide an overview of the results of a larger comparison of innovation across seven European countries.