A cost-aware logical framework

We present calf , a c ost- a ware l ogical f ramework for studying quantitative aspects of functional programs. Taking inspiration from recent work that reconstructs traditional aspects of programming languages in terms of a modal account of phase distinctions , we argue that the cost structure of programs motivates a phase distinction between intension and extension . Armed with this technology, we contribute a synthetic account of cost structure as a computational effect in which cost-aware programs enjoy an internal noninterference property: input/output behavior cannot depend on cost. As a full-spectrum dependent type theory, calf presents a unified language for programming and specification of both cost and behavior that can be integrated smoothly with existing mathematical libraries available in type theoretic proof assistants. We evaluate calf as a general framework for cost analysis by implementing two fundamental techniques for algorithm analysis: the method of recurrence relations and physicist’s method for amortized analysis . We deploy these techniques on a variety of case studies: we prove a tight, closed bound for Euclid’s algorithm, verify the amortized complexity of batched queues, and derive tight, closed bounds for the sequential and parallel complexity of merge sort, all fully mechanized in the Agda proof assistant. Lastly we substantiate the soundness of quantitative reasoning in calf by means of a model construction.

Input-Output Example-Guided Data Deobfuscation on Binary

Data obfuscation is usually used by malicious software to avoid detection and reverse analysis. When analyzing the malware, such obfuscations have to be removed to restore the program into an easier understandable form (deobfuscation). The deobfuscation based on program synthesis provides a good solution for treating the target program as a black box. Thus, deobfuscation becomes a problem of finding the shortest instruction sequence to synthesize a program with the same input-output behavior as the target program. Existing work has two limitations: assuming that obfuscated code snippets in the target program are known and using a stochastic search algorithm resulting in low efficiency. In this paper, we propose fine-grained obfuscation detection for locating obfuscated code snippets by machine learning. Besides, we also combine the program synthesis and a heuristic search algorithm of Nested Monte Carlo Search. We have applied a prototype implementation of our ideas to data obfuscation in different tools, including OLLVM and Tigress. Our experimental results suggest that this approach is highly effective in locating and deobfuscating the binaries with data obfuscation, with an accuracy of at least 90.34%. Compared with the state-of-the-art deobfuscation technique, our approach’s efficiency has increased by 75%, with the success rate increasing by 5%.

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.

The influence of stimulus and behavioral histories on predictive control of smooth pursuit eye movements

The smooth pursuit system has the ability to perform predictive feedforward control of eye movements. This study attempted to examine how stimulus and behavioral histories of past trials affect the control of predictive pursuit of target motion with randomized velocities. We used sequential ramp stimuli where the rightward velocity was fixed at 16 deg/s while the leftward velocity was either fixed (predictable) at one of seven velocities (4, 8, 12, 16, 20, 24, or 28 deg/s) or randomized (unpredictable). As a result, predictive pursuit responses were observed not only in the predictable condition but also in the unpredictable condition. Linear mixed-effects (LME) models showed that both stimulus and behavioral histories of the previous two or three trials influenced the predictive pursuit responses in the unpredictable condition. Intriguingly, the goodness of fit of the LME model was improved when both historical effects were fitted simultaneously rather than when each type of historical data was fitted alone. Our results suggest that predictive pursuit systems allow us to track randomized target motion using weighted averaging of the information of target velocity (stimulus) and motor output (behavior) in past time sequences.

Place-Related Concepts and Pro-Environmental Behavior in Tourism Research: A Conceptual Framework

The negative impacts of tourism development are increasing environmental problems related to the destruction of the global environment, calling on scholars and practitioners to look more at the relationship between people and place. In this concern, place attachment has attracted wide interest, especially from environmental psychologists, to investigate this relationship. This paper presents different approaches to analyze place attachment and related concepts to determine pro-environmental behavior in tourism consumption. It identifies critical gaps in the literature and extends existing theoretical frameworks by considering additional factors, and the entire process from input (participation), through mediations (attachment, meanings, satisfaction) to output (behavior). It reviews the definition of place attachment and essential place-related concepts and outlines why the suggested framework is necessary to extend the current body of research in people–place relationships. It also broadly evaluates current scales and measurement models of constructs included and the structural model of this framework, thereby making recommendations relevant for subsequent empirical research. Finally, contributions to theory, limitations, and suggestions are discussed.

The influence of stimulus and behavioral histories on predictive control of smooth pursuit eye movements

The pursuit system has the ability to perform predictive control of eye movements. Even when the target motion is unpredictable due to velocity or direction changes, preceding changes in eye velocity are generated based on weighted averaging of past stimulus timing. However, it is still uncertain whether behavioral history influences the control of predictive pursuit. Thus, we attempted to clarify the influences of stimulus and behavioral histories on predictive pursuit to randomized target velocity. We used alternating-ramp stimuli, where the rightward velocity was fixed while the leftward velocity was either fixed (predictable) or randomized (unpredictable). Predictive eye deceleration was observed regardless of whether the target velocity was predictable or not. In particular, the predictable condition showed that the predictive pursuit responses corresponded to future target velocity. The linear mixed-effects model showed that both stimulus and behavioral histories of the previous two or three trials had influences on the predictive pursuit responses to the unpredictable target velocity. Our results suggest that the predictive pursuit system allows to track randomized target motion using the information from previous several trials, and the information of sensory input (stimulus) and motor output (behavior) in the past time sequences have partially different influences on predictive pursuit.

Capacitance pressure sensor with S-type electrode for improved sensitivity

This paper aims at designing a differential pressure sensor. The objective of the work is to design and fabricate the electrodes of a capacitive pressure sensor, so as to measure absolute and differential pressure accurately with improved sensitivity. In place of conventional parallel plate diaphragm, S-type electrodes are proposed in the present work. The work comprises of study of the proposed design in terms of a mathematical model, input-output behavior along with detailed analysis of pressure distribution pattern. Output capacitance obtained for changes in pressure is converted to voltage with the suitable signal conditioning circuit and data acquisition system to acquire the signal on to a PC. A neural network model is designed to compensate the nonlinearities present in the sensor output. Input-output characteristics of the designed sensor shows an improved response as compared with existing pressure sensors.

Machine learning-based performance analysis of two-axial-groove hydrodynamic journal bearings

The precise prediction of a rotor against instability is needed for avoiding the degradation or failure of the system’s performance due to the parametric variabilities of a bearing system. In general, the design of the journal bearing is framed based on the deterministic theoretical analysis. To map the precise prediction of hydrodynamic performance, it is needed to include the uncertain effect of input parameters on the output behavior of the journal bearing. This paper presents the uncertain hydrodynamic analysis of a two-axial-groove journal bearing including randomness in bearing oil viscosity and supply pressure. To simulate the uncertainty in the input parameters, the Monte Carlo simulation is carried out. A support vector machine is employed as a metamodel to increase the computational efficiency. Both individual and compound effects of uncertainties in the input parameters are studied to quantify their effect on the steady-state and dynamic characteristics of the bearing.

Abstract Contract Synthesis and Verification in the Symbolic 𝕂 Framework

In this article, we propose a symbolic technique that can be used for automatically inferring software contracts from programs that are written in a non-trivial fragment of C, called KERNELC, that supports pointer-based structures and heap manipulation. Starting from the semantic definition of KERNELC in the 𝕂 semantic framework, we enrich the symbolic execution facilities recently provided by 𝕂 with novel capabilities for contract synthesis that are based on abstract subsumption. Roughly speaking, we define an abstract symbolic technique that axiomatically explains the execution of any (modifier) C function by using other (observer) routines in the same program. We implemented our technique in the automated tool KINDSPEC 2.1, which generates logical axioms that express pre- and post-condition assertions which define the precise input/output behavior of the C routines. Thanks to the integrated support for symbolic execution and deductive verification provided by 𝕂, some synthesized axioms that cannot be guaranteed to be correct by construction due to abstraction can finally be verified in our setting with little effort.